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Alex Finn shares a six-step troubleshooting checklist for OpenClaw users experiencing performance degradation. Headline advice: - 🔶 Clear your sessions — biggest single improvement. Every cron job is creating a session that accumulates and slows things down. - Additional steps cover cache clearing, model routing, session lifecycle, and cron hygiene (full list on thread). The core insight: session bloat is the #1 latency tax on long-running OpenClaw deployments.

タイトル: Using Claude Code: Session Management & 1M Context In my recent calls with Claude Code users, one theme keeps coming up: the 1M token context window is a double-edged sword. It lets Claude Code operate autonomously for longer and handle tasks more reliably, but it also opens the door to context pollution if you're not deliberate about managing your sessions. Session management matters more than ever and there seem to be a lot of questions about it. Do you keep one session open in a terminal, or two? Start fresh with every prompt? When should you use compact, rewind, or subagents? What causes a bad compact? There’s a surprising amount of detail here that can really shape your experience with Claude Code and almost all of it comes from managing your context window. A Quick Primer on Context, Compaction & Context Rot The context window is everything the model can "see" at once when generating its next response. It includes your system prompt, the conversation so far, every tool call and its output, and every file that's been read. Claude Code has a context window of one million tokens. Unfortunately using context has a slight cost, which is often called context rot. Context rot is the observation that model performance degrades as context grows because attention gets spread across more tokens, and older, irrelevant content starts to distract from the current task. For our 1MM context model, we see some level of context rot happen around ~300-400k tokens, but it is highly dependent on the task- not a fast rule. Context windows are a hard cutoff, so when you’re nearing the end of the context window, you will need to summarize the task you’ve been working on into a smaller description and continue the work in a new context window, we call this compaction. You can also trigger compaction yourself. Every Turn Is a Branching Point Say you've just asked Claude to do something and it's finished, you’ve now got some information in your context (tool calls, tool outputs, your instructions) and you have a surprising number of options for what to do next: Continue — send another message in the same session /rewind (esc esc) — jump back to a previous message and try again from there /clear — start a new session, usually with a brief you've distilled from what you just learned Compact — summarize the session so far and keep going on top of the summary Subagents — delegate the next chunk of work to an agent with its own clean context, and only pull its result back in While the most natural is just to continue, the other four options exist to help manage your context. When to Start a New Session The new 1M context windows means that you can now do longer tasks more reliably, for example to have it build a full-stack app from scratch. But just because your model hasn't run out of context, it doesn't mean you shouldn't start a new session. Our general rule of thumb is when you start a new task, you should also start a new session. A grey area is when you may want to do related tasks where some of the context is still necessary, but not all. For example, writing the documentation for a feature you just implemented. While you could start a new session, Claude would have to reread the files that you just implemented, which would be slower and more expensive. Since documentation may not be a highly intelligence sensitive task, the extra context is probably worth the efficiency gain of not having to re-read the relevant files again. Rewinding Instead of Correcting If I had to pick one habit that signals good context management, it’s rewind. In Claude Code, double-tapping Esc(or running /rewind) lets you jump back to any previous message and re-prompt from there. The messages after that point are dropped from the context. Rewind is often the better approach to correction. For example, Claude reads five files, tries an approach, and it doesn't work. Your instinct may be to type "that didn't work, try X instead." but the better move is to rewind to just after the file reads, and re-prompt with what you learned. "Don't use approach A, the foo module doesn't expose that — go straight to B." You can also use “summarize from here” to have Claude summarize its learnings and create a handoff message, kind of like a message to the previous iteration of Claude from its future self that tried something and it didn’t work. Compacting vs. Fresh Sessions Once a session gets long, you have two ways to shed weight: /compact or /clear (and start fresh). They feel similar but behave very differently. Compact asks the model to summarize the conversation so far, then replaces the history with that summary. It's lossy, you're trusting Claude to decide what mattered, but you didn't have to write anything yourself and Claude might be more thorough in including important learnings or files. You can also steer it by passing instructions (/compact focus on the auth refactor, drop the test debugging). With /clear you write down what matters ("we're refactoring the auth middleware, the constraint is X, the files that matter are A and B, we've ruled out approach Y") and start clean. It's more work, but the resulting context is what you decided was relevant. What Causes a Bad Compact? If you run a lot of long running sessions, you might have noticed times in which compacting might be particularly bad. In this case we’ve often found that bad compacts can happen when the model can’t predict the direction your work is going. For example autocompact fires after a long debugging session and summarizes the investigation and your next message is "now fix that other warning we saw in bar.ts." But because the session was focused on debugging, the other warning might have been dropped from the summary. This is particularly difficult, because due to context rot, the model is at its least intelligent point when compacting. With one million context, you have more time to /compact proactively with a description of what you want to do. Subagents & Fresh Context Windows Subagents are a form of context management, useful for when you know in advance that a chunk of work will produce a lot of intermediate output you won't need again. When Claude spawns a subagent via the Agent tool, that subagent gets its own fresh context window. It can do as much work as it needs to, and then synthesize its results so only the final report comes back to the parent. The mental test we use: will I need this tool output again, or just the conclusion? While Claude Code will automatically call subagents, you may want to tell it to explicitly do this. For example, you may want to tell it to: “Spin up a subagent to verify the result of this work based on the following spec file” “Spin off a subagent to read through this other codebase and summarize how it implemented the auth flow, then implement it yourself in the same way” “Spin off a subagent to write the docs on this feature based on my git changes” Summary In summary, when Claude has ended a turn and you’re about to send a new message, you have a decision point. Overtime we expect that Claude will help you handle this itself, but for now this is one of the ways you can guide Claude's output.

タイトル: Top 67 Claude Skills That Turn a $20 Subscription Into a Full Dev Team - (Full Links) Most people use Claude like a $20 autocomplete They type. They get an answer. They move on They have no idea Claude can run an entire dev team - architect, reviewer, debugger, docs writer - all at once They just don't know skills exist The difference? Skills. 67 of them. With full install commands. Sorted by use case. What Claude skills actually are A skill is a folder with a SKILL.md file that tells Claude exactly how to do a specific type of work: step-by-step process, constraints, examples, and any helper scripts or templates Instead of re-explaining your process every session, you install that process once as a skill and reuse it forever Install commands use this format: Key repos: Official Anthropic skills: github.com/anthropics/skills Matt Pocock personal skills (15k stars): github.com/mattpocock/skills Community marketplace (66k+ skills): skillsmp.com Meta skills - managing your AI workspace These skills help you build, test, and organize every other skill Skill Creator What it does: Benchmarks Claude on your task, then helps you draft and iterate new skills based on real runs. Use it when: You want to turn a messy workflow into one clean SKILL.md. Link: github.com/anthropics/skills/tree/main/skills/skill-creator How to use: Describe your workflow in bullet points Ask Skill Creator to propose a first SKILL.md Run 3-5 test prompts, inspect failures, and let it refine the instructions Write a Skill What it does: Guides Claude to write new skills with proper structure, progressive disclosure, and bundled resources This is the right way to create skills that don't break over time Link: github.com/mattpocock/skills/tree/main/write-a-skill Install: Use it when Skill Creator gives you a raw draft and you need to clean up the structure Find Skills What it does: Searches public marketplaces like SkillsMP for skills that match your use case. Example marketplace: skillsmp.com Tip: Treat "finding skills" like package management. Before you write a new skill, search for existing ones and fork them Planning and design skills These skills stop you from building the wrong thing. Grill Me Purpose: Forces Claude to ask relentless clarifying questions about your feature, one question at a time, until every branch of the decision tree is resolved. Use it for: New features, refactors, risky migrations. Install: npx skills@latest add mattpocock/skills/grill-me Link: github.com/mattpocock/skills/tree/main/grill-me You will get questions about data models, edge cases, failure modes, existing systems. Answer patiently once instead of firefighting later Write a PRD Purpose: Creates a PRD through an interactive interview, codebase exploration, and module design. Files it as a GitHub issue. Install: npx skills@latest add mattpocock/skills/write-a-prd Link: github.com/mattpocock/skills/tree/main/write-a-prd Ask it to: Capture goals, non-goals, user stories Enumerate success metrics and constraints Link to existing systems you'll touch PRD to Plan Purpose: Turns a PRD into a multi-phase implementation plan using tracer-bullet vertical slices. This is not just task breakdown - it gives you the sequence that actually reduces integration risk. Install: npx skills@latest add mattpocock/skills/prd-to-plan Link: github.com/mattpocock/skills/tree/main/prd-to-plan The difference from PRD to Issues: a plan is ordered and staged, issues are independent. Use both PRD to Issues Purpose: Breaks a PRD into independently-grabbable GitHub issues with vertical slices and blocking relationships. Install: npx skills@latest add mattpocock/skills/prd-to-issues Link: github.com/mattpocock/skills/tree/main/prd-to-issues Tell it: "Use PRD to Issues on the PRD above. Output GitHub issues grouped by epic with blockers stated explicitly" Design an Interface Purpose: Generates multiple radically different interface designs for a module using parallel sub-agents. Install: npx skills@latest add mattpocock/skills/design-an-interface Link: github.com/mattpocock/skills/tree/main/design-an-interface Not just one design - you get 3-5 competing options with different tradeoffs. Pick the one that makes sense for your constraints Request Refactor Plan Purpose: Creates a detailed refactor plan with tiny commits via user interview, then files it as a GitHub issue. Install: npx skills@latest add mattpocock/skills/request-refactor-plan Link: github.com/mattpocock/skills/tree/main/request-refactor-plan Code development skills These skills turn Claude into a disciplined engineering partner, not a code autocomplete toy. TDD Purpose: Forces a strict test-first, red-green-refactor loop. Builds features or fixes bugs one vertical slice at a time. Install: npx skills@latest add mattpocock/skills/tdd Link: github.com/mattpocock/skills/tree/main/tdd You get: Failing tests first Then minimal code to pass them Then a refactor pass, still under tests Triage Issue Purpose: Investigates a bug by exploring the codebase, identifies the root cause, and files a GitHub issue with a TDD-based fix plan. Install: npx skills@latest add mattpocock/skills/triage-issue Link: github.com/mattpocock/skills/tree/main/triage-issue This is the "I have no idea why this is broken" skill. It does the detective work first, then gives you a structured plan to fix it properly QA Purpose: Runs a full QA pass over a feature with issue breakdown that includes blocking relationships. Install: npx skills@latest add mattpocock/skills/qa Link: github.com/mattpocock/skills/tree/main/qa Use it before every PR: Surface all edge cases Get issues filed and ordered by what blocks what Ship without regressions Improve Codebase Architecture Purpose: Explores a codebase for architectural improvement opportunities, focusing on deepening shallow modules and improving testability. Install: npx skills@latest add mattpocock/skills/improve-codebase-architecture Link: github.com/mattpocock/skills/tree/main/improve-codebase-architecture Ask it to: Identify hotspots Propose 2-3 refactor strategies Detail risk, effort, and impact for each Migrate to Shoehorn Purpose: Migrates test files from as type assertions to @total-typescript/shoehorn - Matt Pocock's own TypeScript library Install: npx skills@latest add mattpocock/skills/migrate-to-shoehorn Link: github.com/mattpocock/skills/tree/main/migrate-to-shoehorn This is niche but extremely useful if you work in TypeScript and want type-safe test code. It's also a great template for writing migration skills for your own tools Scaffold Exercises Purpose: Creates exercise directory structures with sections, problems, solutions, and explainers. Install: npx skills@latest add mattpocock/skills/scaffold-exercises Link: github.com/mattpocock/skills/tree/main/scaffold-exercises Perfect for: Building course content Creating onboarding materials for your team Documenting complex systems as interactive exercises Auto-Commit Messages Purpose: Reads your staged diff and generates a conventional commit message with type, scope, and body Use it when: You're tired of writing "fix stuff" at 2am Install: npx skills@latest add anthropics/skills/auto-commit Link: github.com/anthropics/skills/tree/main/skills/auto-commit Code Review Purpose: Gives you a systematic review for security, performance, error handling, and architecture. Link: github.com/anthropics/skills (search Code Review) You can ask for: "Security-first review" "Performance-first review" Or a full checklist pass Systematic Debugging Origin: From the Superpowers repo Purpose: A 4-phase debugging methodology that forbids random "just try changing stuff" edits. Link: github.com/obra/superpowers/tree/main/skills/systematic-debugging Typical flow: Reproduce and create the smallest failing test Narrow the root cause Apply a single fix Verify with tests and logs Brainstorming Purpose: Turns raw feature ideas into detailed flows and architectures using Socratic questioning Use it pre-implementation: data shape design, API surface, failure and rollback story Link: github.com/obra/superpowers/tree/main/skills/brainstorming Change Log Generator Purpose: Reads your commits and produces human-readable or developer-focused change logs Use it for: Releases, internal updates, investor summaries Link: github.com/ComposioHQ/awesome-claude-skills/tree/master/changelog-generator Simplification Cascade Purpose: Identifies convoluted logic and rewrites it into smaller, composable pieces Best for: Legacy "god functions" that nobody wants to touch Link: mcpmarket.com/tools/skills/simplification-cascades-1 Superpowers What it is: A full suite of battle-tested skills for TDD, debugging, refactoring, and execution Link: github.com/obra/superpowers Use it as your default "engineering brain" layer React Best Practices Purpose: Enforces Vercel/Next.js style best practices in your React code Use it when: Migrating to Next, cleaning up a legacy React app, or training junior devs Link: github.com/vercel-labs/agent-skills/tree/main/skills/react-best-practices File Search Purpose: Teaches Claude to use tools like ripgrep and ast-grep to navigate large codebases fast Link: github.com/massgen/massgen Context Optimization Purpose: Reduces context size and token bills while keeping the important stuff Link: github.com/muratcankoylan/agent-skills-for-context-engineering Tooling and setup skills These are the "do this once, forget about it forever" skills Setup Pre-Commit Purpose: Sets up Husky pre-commit hooks with lint-staged, Prettier, type checking, and tests Install: npx skills@latest add mattpocock/skills/setup-pre-commit Link: github.com/mattpocock/skills/tree/main/setup-pre-commit Run this on every new repo. Your future self will thank you. Git Guardrails for Claude Code Purpose: Sets up Claude Code hooks that block dangerous git commands - push, reset --hard, clean, etc. - before they execute Install: npx skills@latest add mattpocock/skills/git-guardrails-claude-code Link: github.com/mattpocock/skills/tree/main/git-guardrails-claude-code This is not optional if you use Claude Code on production repos. Claude is fast and helpful until it isn't. This skill is your safety net Dependency Auditor Purpose: Scans your package.json for outdated, vulnerable, or abandoned packages and outputs a prioritized fix list Use it when: Your repo hasn't been touched in 6 months and you're scared to npm audit Install: npx skills@latest add ComposioHQ/awesome-claude-skills/dependency-auditor Link: github.com/ComposioHQ/awesome-claude-skills Git Work Trees Purpose: Manages safe feature development on isolated branches without breaking your main project Use it when: You need isolated experimental branches or parallel features Issue and project management skills GitHub Triage Purpose: Triages incoming GitHub issues with an agent brief and out-of-scope documentation so Claude knows exactly what to investigate and what to skip Install: npx skills@latest add mattpocock/skills/github-triage Link: github.com/mattpocock/skills/tree/main/github-triage Use it to: Process large issue backlogs fast Label and categorize automatically Route issues to the right person or epic Writing and knowledge skills Edit Article Purpose: Edits and improves articles by restructuring sections, improving clarity, and tightening prose Install: npx skills@latest add mattpocock/skills/edit-article Link: github.com/mattpocock/skills/tree/main/edit-article This is not "clean up grammar". It restructures arguments, cuts filler, and sharpens the point of each section Ubiquitous Language Purpose: Extracts a DDD-style ubiquitous language glossary from the current conversation Install: npx skills@latest add mattpocock/skills/ubiquitous-language Link: github.com/mattpocock/skills/tree/main/ubiquitous-language Why it matters: Every team has private vocabulary: "event", "order", "user" mean different things to different people This skill forces Claude to surface and define your domain language before any code is written Your codebase, docs, and conversations then share the same words for the same things API Documentation Generator Purpose: Reads your routes and generates OpenAPI / Swagger docs with examples, error codes, and auth requirements Use it when: You shipped the API but never wrote the docs Install: npx skills@latest add ComposioHQ/awesome-claude-skills/api-docs-generator Link: github.com/ComposioHQ/awesome-claude-skills Paste it into your README or host it on Swagger UI. Takes 30 seconds. Content Researcher Purpose: Learns your writing style, then drafts long-form blogs and newsletters with real citations Use it to: Clone your Twitter tone into longer posts, produce SEO blog posts Link: github.com/ComposioHQ/awesome-claude-skills/blob/master/content-research-writer/SKILL.md Obsidian Vault Purpose: Searches, creates, and manages notes in an Obsidian vault with wikilinks and index notes Install: npx skills@latest add mattpocock/skills/obsidian-vault Link: github.com/mattpocock/skills/tree/main/obsidian-vault Different from the Kepano Obsidian skill which auto-tags. This one is interactive - it navigates your vault, creates new notes, and maintains link consistency. Also available: github.com/kepano/obsidian-skills - the auto-tagging variant UI, design, and frontend skills Frontend Design Purpose: Guides Claude to produce modern, clean UI Link: github.com/anthropics/skills/tree/main/skills/frontend-design Awesome-design Purpose: Uses markdown templates inspired by Notion, Figma, etc to structure your UI thinking Use it for: Landing pages, dashboards, settings pages Link: github.com/VoltAgent/awesome-design-md Theme Factory Purpose: Generates complete color palettes and themes from a single text prompt Link: github.com/anthropics/skills/tree/main/skills/theme-factory Workflow: Describe your brand: "calm fintech, trust, dark accent" Get palette with tokens Apply to Tailwind / CSS variables Canvas Design Purpose: Turns text prompts into social media graphics, posters, and covers Link: github.com/anthropics/skills/tree/main/skills/canvas-design Web Artifacts Builder Purpose: Builds interactive dashboards, calculators, and tools from natural language Link: github.com/anthropics/skills/tree/main/skills/web-artifacts-builder Algorithmic Art Purpose: Uses p5.js for generative and algorithmic visuals Link: github.com/anthropics/skills/tree/main/skills/algorithmic-art Brand Guidelines Purpose: Enforces your brand system across all new components Link: github.com/anthropics/skills/tree/main/skills/brand-guidelines Business, sales, and marketing skills Domain Name Brainstormer Purpose: Generates product names and checks domain availability Use it when: Launching new apps or micro-brands Link: github.com/Microck/ordinary-claude-skills/tree/main/skills_all/domain-name-brainstormer Stripe Integration Purpose: Sets up secure payment flows, webhooks, and subscriptions without rookie API mistakes Link: github.com/wshobson/agents/tree/main/plugins/payment-processing/skills/stripe-integration Lead Research Assistant Purpose: Finds target companies and decision-makers based on your ICP Use it for: B2B outreach lists, partnership hunting Link: github.com/ComposioHQ/awesome-claude-skills/blob/master/lead-research-assistant/SKILL.md Marketing Skills What it is: 20+ skills for CRO, copywriting, email flows, etc Link: github.com/coreyhaines31/marketingskills Claude SEO Purpose: Full technical SEO audit, schema, and on-page optimization. Link: github.com/AgriciDaniel/claude-seo Idea Mining / YouTube Weekly Roundup Purpose: Scrapes your comments, competitor videos, and niche trends into a weekly idea bank + SWOT PDF Link: https://github.com/AgriciDaniel/claude-youtube Install: npx skills@latest add AgriciDaniel/claude-youtube Media generation skills Image Generator Purpose: Connects to external APIs like Nano Banana Pro for photo-quality images Nano Banana Pro: github.com/feedtailor/ccskill-nanobanana Nano Banana 2: github.com/kingbootoshi/nano-banana-2-skill Local Image Gen Purpose: Runs a local Python script to generate avatars and icons Link: github.com/jezweb/claude-skills/blob/main/plugins/design-assets/skills/ai-image-generator/SKILL.md Image Optimizer Purpose: Resizes and converts images to WebP for fast web performance Link: mcpmarket.com/tools/skills/image-optimizer Remotion Best Practices Purpose: Encodes best practices for using Remotion to generate video and motion graphics. Link: github.com/remotion-dev/remotion Emotion Purpose: Scripted, programmatic video animations Link: github.com/wilwaldon/Claude-Code-Video-Toolkit Office, documents, and productivity skills PDF Processing Purpose: Extracts tables, fills forms, merges PDFs Link: github.com/anthropics/skills/tree/main/skills/pdf DOCX Purpose: Edits Word docs with tracked changes and formatting intact Link: github.com/anthropics/skills/tree/main/skills/docx PPTX Purpose: Creates and edits slide decks, layouts, and speaker notes Link: github.com/anthropics/skills/tree/main/skills/pptx XLSX Purpose: Writes formulas, pivot tables, and charts from plain English Link: github.com/anthropics/skills/tree/main/skills/xlsx Excel MCP Server Purpose: Manipulates Excel files directly via MCP, no desktop Excel required Link: github.com/haris-musa/excel-mcp-server Doc Co-Authoring Purpose: Real-time collaborative writing between you and Claude Link: github.com/anthropics/skills/tree/main/skills/doc-coauthoring NotebookLM Integration Purpose: Bridges Claude with Google's NotebookLM for summaries and flashcards Link: github.com/PleasePrompto/notebooklm-skill GWS (Google Workspace) Purpose: Automates Google Calendar, Drive, Docs Use cases: Reschedule meetings, organize shared drives, generate docs from templates Link: https://github.com/googleworkspace/cli Multi-agent orchestration and web surfing Stochastic Multi-Agent Consensus Purpose: Spawns many sub-agents to solve the same problem and aggregates their answers Use it for: Strategy decisions, architecture choices, risk analysis Link: github.com/hungv47/meta-skills Model-chat (Debate) Purpose: Puts multiple Claude instances into a debate to stress-test ideas Use it when: You're choosing between 2-3 big bets Link: github.com/tommasinigiovanni/conclave Custom YT Search Purpose: Searches and analyzes YouTube content autonomously Link: github.com/ZeroPointRepo/youtube-skills/blob/main/README.md Playwright CLI Purpose: Controls a real browser via Playwright for UI regression checks and funnel walkthroughs Link: github.com/microsoft/playwright Firecrawl Skill Purpose: Scrapes structured data from hostile or complex sites that block naive scrapers Link: github.com/mendableai/firecrawl How to wire this into your workflow Start with meta skills - Install Write a Skill and Skill Creator so you can build and fix skills properly from day one. Add planning skills first - Grill Me, Write a PRD, PRD to Plan, PRD to Issues, Design an Interface. These prevent 80% of rework. Lock in code safety - Git Guardrails, Setup Pre-Commit, TDD, Systematic Debugging, Triage Issue. Install these on every repo. Add Superpowers as your engineering base - github.com/obra/superpowers Layer business skills on top - Marketing Skills, Claude SEO, Lead Research, Content Researcher. Use SkillsMP to fill gaps - skillsmp.com - when you hit a new problem, search before you build Save this before you forget You just got the full Claude skill stack - planning, coding, design, marketing, docs, media, and multi-agent orchestration in one place Most people will skim this, close the tab, and go back to prompting Claude like it's 2024 Don't be that person Bookmark this page right now Copy the full link index into your own doc Pick one skill from each section and install it today Come back in a week when you realize you can't work without them If this saved you time - repost it. Someone on your timeline is still writing 500-word prompts to get what a single SKILL.md does automatically Follow me @polydao for more posts on Claude, AI tools, prediction markets, and what's actually worth your time in crypto

タイトル: Claude + Obsidian | How to use your second brain Your AI forgets everything the moment you close the chat. Your Obsidian vault is full of notes nobody reads, including you, what if those two problems cancelled each other out? That's exactly what claude-obsidian does, it's a skill built for Claude Code that turns your Obsidian vault into a persistent, compounding wiki.  Every source you add gets integrated, every question you ask pulls from everything that's been read. Knowledge compounds like interest, and Claude never forgets what you were working on because the system keeps a hot cache loaded every single session. This is the complete setup guide follow it step by step and by the end you'll have a working system that gets smarter the more you use it. Before we run any commands, here’s the point. Most Obsidian + AI tools just chat over your vault, AI search with a nicer interface. You still write the notes, make the links, and do the upkeep. claude-obsidian is different. It follows Karpathy’s LLM Wiki pattern: you drop in a PDF, URL, transcript, anything and Claude builds and maintains the wiki for you. It extracts concepts, creates structured pages with frontmatter, links everything with wikilinks, flags contradictions, and files it where it belongs. Each new source strengthens the whole network instead of dumping isolated notes. Here’s how to set it up. Step 1 / What you need before anything else Three things, get them in this order, first we're gonna download Obsidian: Download it, it's fully free, create a new vault, this is just a folder on your computer full of markdown files and name it whatever you want. Then you need, Node.js Claude Code runs on Node.js 18 or higher, check if you have it: If you see v18+ you're good. If not, download it below, use the LTS version. Claude Code This is the command-line version of Claude that reads and writes files on your computer, we're gonna install desktop version: Step 2 / Install claude-obsidian Open claude, navigate into the Obsidian vault folder you created, then paste this: Then run these two commands one after the other 1/2: 2/2: Done, the plugin copies all 10 skills into your vault automatically, no downloading files manually, no scripts to run. Step 3: First Launch Still inside Claude Code, type: Claude will scaffold the full vault structure, it creates all the wiki folders, initializes the hot cache, sets up the index, and shows you exactly what it built before it does anything. You will see something like this: That is your second brain, initialized and ready. Step 4: The Three Commands You Need The whole system lives inside three commands, use them daily and the vault builds itself. /save After any conversation where you worked through something valuable, a decision, a concept, a project plan, type: Claude reads the full conversation, identifies the key ideas, creates properly formatted wiki pages with frontmatter and wikilinks, files them in the correct folder, and updates the index. Good conversations do not disappear anymore, they get filed. /autoresearch Want to go deep on a topic? Type: Claude runs three to five rounds of web research, broad search first, then gap-filling, then following the most important sources. It synthesizes everything into structured wiki pages with citations and cross-references to related concepts already in your vault. You can customize what sources it prioritizes by editing: skills/autoresearch/references/program.md inside your vault. A medical researcher might add "prefer PubMed" a startup founder might add "focus on founder blogs and case studies" The default works fine for anything general. /canvas When you want to see your knowledge instead of just read it: Claude creates an Obsidian canvas, an infinite visual board, and populates it with the relevant nodes from your wiki. You can tell it to build flowcharts, knowledge graphs, timelines, or presentation layouts. Drop images, PDFs, videos, and GIFs onto it directly inside Obsidian, the canvas saves as a .canvas file and opens natively. Step 5: The Daily Ingest Loop This is the core habit. Any time you find something worth keeping, an article, a PDF, a video transcript, meeting notes, this is how you add it. Drop it in .raw/ The .raw/ folder is your source archive, drag a file in, paste a URL, save a transcript, nothing in here ever gets modified, it is your original record. Tell Claude to ingest it Claude runs the wiki-ingest agent, for a typical 20-page document it will create 8 to 15 interconnected wiki pages with an average of 12 wikilinks per page. Entities get their own pages, concepts get their own pages, contradictions with existing knowledge get flagged with [!contradiction] callouts. The operation logs to wiki/log.md. You did not touch a folder, a tag, or a link, it is all done. Ask anything Claude reads the hot cache, scans the index, identifies the relevant wiki pages, and gives you an answer with citations to specific pages in your vault. It is citing your own organized knowledge, not its training data. How It Stays Cheap as the Vault Grows: Will it get expensive with thousands of pages? No. It uses three files, loaded in order: hot.md: ~500 words of recent context (about 500 tokens) index.md: one line per page, Claude scans it to find what matters. Relevant pages only: it loads just the files tied to your question. So the vault can grow to thousands of pages, and token cost barely changes, you never load everything. What It Looks Like After a Month: Open Obsidian’s graph view after a few weeks: a color-coded map of what you’ve read and thought, concepts, sources, entities, linked automatically. Deep topics form clusters, nearby ideas connect, and contradictions get flagged. The “brain-like” graph Obsidian promised. That's it.

OpenClaw 2026.4.14 🦞 More reliability updates: ✨ Smarter GPT-5.4 routing and recovery 🌐 Chrome/CDP improvements 🧵 Subagents no longer get stuck 💬 Slack/Telegram/Discord fixes ⚡️ Various performance improvements Was sleeping, and we still shipped. https://t.co/krJ2gRQSbM

RAG is broken and nobody's talking about it. Stanford researchers exposed the fatal flaw killing every "AI that reads your docs" product in existence. It’s called "Semantic Collapse," and it happens the second your knowledge base hits critical mass. If you've noticed your AI https://t.co/sz4zbtnxpC

タイトル: How to become an AI Automation Builder in 6 months (RESOURCES) AI automation has quietly become the highest-ROI skill in the entire AI space right now The problem is that most people entering this field get stuck the same way Some open a Python tutorial on day one, hit a wall in week two, and never touch a no-code tool Some get lost in 8-hour YouTube tutorials without ever building a single workflow for a real business Others jump straight into "AI agents" without understanding how a basic workflow or an API actually works The result is always the same: a folder of half-finished demos and zero paying clients Here's the truth almost nobody tells beginners: "You don't need to become a software developer to become an AI Automation Builder!!!" "You don't need to learn machine learning" "You don't need to fine-tune a model" You need to learn how to connect AI to the tools companies ALREADY use and automate the boring, repetitive work they are currently paying humans to do That means learning how to: build end-to-end automated workflows across real business tools wire AI into CRMs, email, docs, and support systems turn repetitive human tasks into reliable AI-powered pipelines ship automations that survive real client traffic charge $500-5k/mo and deliver measurable ROI This guide was created to give you a practical 6-month roadmap The article is 10,000+ WORDS, so reading it may take a few hours But its real value is that for every skill you need to learn, there are resources and clear explanations of what to do next That way, within six months you can reach the level of a hireable AI Automation Builder, and start earning from it already within the first 1-2 months [ Now let's start reading the article ] ⬇️ Read this first: non-technical path vs developer path This roadmap works for two different kinds of people, and you need to pick which one you are BEFORE you start Non-technical path (DEFAULT — most of you are here) You have never coded, or you know a little but you don't want to become a developer. Your superpower is going to be no-code tools (n8n specifically), connected to AI through simple nodes. You can build and sell real automations without ever writing a line of Python if you don't want to. This is the fastest, most realistic path to your first paying client Developer path (OPTIONAL add-on) You already code, or you really want to learn. You will follow the same roadmap but ALSO pick up Python, LangGraph, and custom backend pieces. This unlocks bigger contracts and more complex work later, but it is completely optional Every month below is written for the non-technical path first. At the end of each section, there's an optional "developer path" box with the extra stuff to learn if you want to go that direction. Skip it without guilt if you don't One rule: pick a lane and stop switching. People who bounce between "I'll learn no-code" and "I'll learn Python" for 6 months end up with neither What an AI Automation Builder actually does? A lot of people hear "AI automation" and imagine shiny AI agents that replace entire teams In reality, most of the work is much more boring and much more profitable You take expensive, repetitive business processes and rebuild them as AI-powered workflows that run 24/7 without humans babysitting them That usually includes: connecting LLMs to tools companies already use (CRMs, email, Slack, Notion, databases) turning triggers from one system into actions in another adding AI decision-making into previously manual workflows replacing human triage, classification, and routing work with LLM calls building internal knowledge bots over company docs automating lead generation, outreach, content, and support pipelines monitoring and handing these systems off to non-technical clients So in practice, an AI Automation Builder sits somewhere between: workflow automation (n8n, Make, Zapier) applied AI (LLMs, prompts, simple agents) business process consulting (knowing what's actually worth automating) light technical glue work (reading docs, fixing small things, debugging) This is why the role is exploding right now Every SMB, agency, and SaaS company in 2026 has 20 repetitive workflows they are paying humans too much to run They don't need researchers. They don't need fine-tuners. They need someone who can walk in, find the bleeding, and stop it with a workflow that pays for itself in the first month That's also why this roadmap focuses less on building products and more on shipping business outcomes If you can wire AI into a real CRM, a real inbox, and a real content pipeline, you're already more employable than 90% of people calling themselves "AI engineers" on LinkedIn Also, until we started, let me give to you a little motivation to learn it: Still 310M companies which haven't applied and added any kind of automation!!! Into the world, here's in general 360M (only 50M companies added partial automation to their business processes) And THE MOST IMPORTANT THING, in total ONLY 1M people into the world might provide this service on decent level Lol, 8B+ ppl live on this planet, THAT'S ABSOLUTELY NOTHING. WE'RE SO EARLY!!! Also, catch potential rates which you can appoint to any of these automations: - $500-5k/mo to build automated workflows for businesses - $1-3k/mo to automate lead generation systems - $500-2k/mo for AI-powered content pipelines - $1-4k/mo to automate customer support with AI agents - $500-2k/mo for automated reporting & data dashboards - $500-2k/mo for AI-powered cold outreach systems - $1-3k/mo to set up internal automation assistants - $500-1.5k for AI workflow training for teams - $300-1k for 1:1 automation consulting *It's average prices which I could find across different freelance and outsource platforms + my frens who are doing it professionally shared with me That means they can be much higher (depends on the company size and how many employees they would love to fire lol) LET'S BEGIN!!! ⏩--------------------------------------------------------------------⏪ Month 1: Build your first workflow in n8n Your goal this month: Build your first real automation in n8n, understand how APIs and webhooks work at a conceptual level, and learn enough about prompting to make an LLM do what you want You are NOT learning Python this month (unless you're on the developer path). You're learning the one tool that is going to pay your bills for the next 6 months, and the vocabulary you need to read docs without panicking What to learn: 1. Pick ONE no-code platform and go deep (start HERE) This is the most important decision of Month 1. Pick ONE tool. Go deep. You can learn the others later in a weekend once you understand the underlying concepts Which one to pick: n8n: pick this if you're serious about this career. Open-source, generous free tier, best-in-class AI nodes, self-hostable, and the automation agencies making real money are all here. This is the default recommendation for this entire roadmap Make (formerly Integromat): pick this if you want the prettiest visual interface. Great for agency client work and complex branching Zapier: pick this ONLY if you're building quick MVPs for non-technical clients who already use it. Too expensive at scale for serious work My recommendation: go with n8n. Everything in this article assumes n8n unless stated otherwise Resources: 1. n8n Official Docs (free) Link: https://docs.n8n.io/ Start with "Quickstart" and work through every core concept. The docs are genuinely excellent 2. n8n Academy (free) Link: https://docs.n8n.io/courses/ Official free courses covering beginner to advanced workflow patterns, including AI integration. This is the single best resource in the entire 1st month 3. Productive Dude (YouTube, free) Link: https://www.youtube.com/@productivedude n8n-focused, extremely practical, beginner-friendly videos 4. Nick Saraev (YouTube, free) Link: https://www.youtube.com/@nicksaraev Focused specifically on making money with automation. High signal 5. Make Academy (free, if you picked Make) Link: https://academy.make.com/ What to focus on: Triggers (cron, webhook, app events) vs actions How data moves from one step to the next Error handling and fallback paths Using the HTTP Request node when there's no native integration Reading and debugging execution logs The built-in OpenAI and AI Agent nodes (you'll use these in Month 2) Practice: Automate something in your own life. Seriously. Auto-save email attachments to Google Drive. Scrape a product price daily into a Google Sheet. Send yourself a Telegram message when a specific keyword appears in your inbox. Build the muscle with low stakes before you build for clients 2. APIs, webhooks, and JSON (the vocabulary, not the code) Every automation you ever build connects two systems through APIs. You don't need to CODE APIs. You need to UNDERSTAND them enough to read a doc, know what a webhook is, and know what JSON looks like when you stare at it Resources: 1. What is a Webhook? (Zapier blog, free) Link: https://zapier.com/blog/what-are-webhooks/ The best beginner explanation of webhooks, written for non-developers 2. HTTP basics — MDN Web Docs (free) Link: https://developer.mozilla.org/en-US/docs/Web/HTTP/Overview Clearest free explanation of how the web talks to itself 3. Postman Learning Center (free) Link: https://learning.postman.com/ Postman is the universal tool for testing APIs. Go through "Getting Started" before touching any real API. You click buttons. No code 4. REST API Tutorial (free) Link: https://restfulapi.net/ Short, practical, no unnecessary theory What to focus on: GET vs POST vs PUT vs DELETE (what each is used for) What JSON looks like (curly braces, key-value pairs, arrays) HTTP status codes: 200 (good), 401 (bad auth), 404 (not found), 429 (rate limited), 500 (broken) API keys and bearer tokens at a conceptual level (you paste them, you don't build them) Webhooks vs polling (when to use each) Rate limits and what happens when you hit them Practice: Use Postman to call a free public API (try https://api.github.com/users/torvalds). See the JSON come back. Now recreate the exact same call inside an n8n HTTP Request node. Compare. This one exercise unlocks 50% of the mystery of APIs for non-technical people 3. Reading API docs without panicking Every real automation eventually requires reading an API doc. This is the skill that separates people who ship from people who wait forever for a YouTube tutorial that covers their exact use case (spoiler: it doesn't exist) Resources: 1. How to Read API Documentation — Postman Blog (free) Link: https://blog.postman.com/how-to-read-api-documentation/ Short, practical breakdown of how API docs are structured 2. Stripe API Docs (free, best-in-class example) Link: https://docs.stripe.com/api Study this even if you never use Stripe. It's the gold standard for API documentation design What to focus on: Finding the "Authentication" section first, always Identifying the base URL, endpoint paths, methods, required parameters Reading request/response examples Spotting rate limits and pagination patterns Testing one endpoint in Postman before wiring it into a workflow Practice: Pick any tool you already use (Notion, Airtable, Slack, HubSpot) and make ONE successful API call from Postman. Just one. Pull a list of something. Then stop 4. Basic prompt engineering You don't need to become a prompt wizard. You need to understand the fundamentals: system vs user prompts, specificity, examples, and how to force structured output so the rest of your workflow can use it Resources: 1. Anthropic's Interactive Prompt Engineering Tutorial (free, GitHub) Link: https://github.com/anthropics/prompt-eng-interactive-tutorial The best hands-on intro to prompting, broken into chapters with exercises 2. OpenAI Prompt Engineering Guide (free) Link: https://platform.openai.com/docs/guides/prompt-engineering 3. Learn Prompting (free, comprehensive) Link: https://learnprompting.org/ A full free course from basics to advanced 4. Advanced Prompt Engineering Article from @EXM7777 (free) Link: https://x.com/EXM7777/status/2011800604709175808 What to focus on: System prompts vs user prompts Why specificity beats cleverness Giving examples (few-shot prompting) Asking for structured output (JSON, CSV, specific formats), this is what makes AI usable inside automations Chain-of-thought for tasks that require reasoning 5. What LLMs are actually good at (and where they fail) This one saves you from embarrassing yourself in front of a client. Knowing when NOT to use AI is just as valuable as knowing when to use it Resources: 1. Andrej Karpathy YouTube talks (free) Link: https://www.youtube.com/@AndrejKarpathy Clearest thinking on where LLMs add real value 2. Simon Willison's Blog (free) Link: https://simonwillison.net/ The most practical voice in applied AI. Read his recent posts What to memorize: Good at: classification, summarization, extraction, translation, drafting, decision trees with clear criteria Bad at: exact math, real-time data without retrieval, tasks requiring perfect consistency, anything safety-critical 6. (Developer path only) Just enough Python to unblock yourself Skip this section if you're on the non-technical path If you want the dev path, learn just enough Python to read docs, write small scripts, and glue things together when a no-code tool hits a wall. Not to become a senior dev Resources: 1. Python for Everybody (Coursera, free to audit) Link: https://www.coursera.org/specializations/python 2. freeCodeCamp Python Course (YouTube, free) Link: https://www.youtube.com/watch?v=rfscVS0vtbw 3. Automate the Boring Stuff with Python (free online book) Link: https://automatetheboringstuff.com/ Specifically for people who want to automate tasks, not build apps What to focus on: variables, loops, conditionals, functions, lists, dicts, JSON, reading files, requests library, try/except, running scripts from terminal Month 1 Milestone: By the end of this month you should be able to: Build a 3-5 step workflow in n8n that solves a real problem in YOUR own life Explain what webhooks, API keys, JSON, and HTTP status codes are (in plain English) Read an unfamiliar API doc and successfully make a test call with Postman Write a clear system prompt that returns consistent structured output List 5 tasks LLMs are good at and 5 where they'll embarrass you If you hit all of these, you're already ahead of 80% of people trying to "break into AI" right now ⏩--------------------------------------------------------------------⏪ Month 2: Embed AI into your workflows Your goal this month: Stop using ChatGPT manually. Start making AI run automatically inside your n8n workflows, reacting to real triggers, making decisions, and writing to real systems (without you pressing a button) By the end of Month 2 you should have 3-5 real workflows that use AI inside them, and you should already have a clear idea of what your FIRST paid gig will look like This is the month where you stop being a no-code user and start being an AI Automation Builder What to learn: 1. n8n's AI nodes (the default path) n8n has built-in nodes for OpenAI, Anthropic, and a full AI Agent node. For 90% of what you'll ever need to ship, you don't need to touch Python at all. You drop an AI node into your workflow like any other step Resources: 1. n8n AI Nodes & LangChain Docs (official, free) Link: https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/ Walk through the full AI section of their docs. The n8n team has done an insane amount of work making AI usable from a no-code canvas 2. n8n Academy: AI Workflows Course (free) Link: https://docs.n8n.io/courses/ There's a dedicated AI track. Do it 3. n8n AI Templates (free) Link: https://n8n.io/workflows/?categories=AI 100+ real, importable templates. Better than any tutorial (import one, run it, see exactly how it's wired) What to focus on: Dropping an OpenAI or Anthropic node into a workflow Passing data FROM a previous step INTO the prompt dynamically (expressions in n8n) Parsing the AI response and using specific fields in later steps Keeping your prompts in a central place (a Notion doc or a dedicated n8n sub-workflow) so you can edit them without digging When to use AI inline vs when a simple IF node is enough Practice project: Build a workflow that watches a Google Form, sends new responses to an LLM for classification (urgent / normal / spam), and writes the classified response into different Airtable tables based on the category. No code. Pure n8n 2. The core skeleton: Trigger → AI Decision → Action → Output Almost every AI automation you will ever build fits this skeleton: Trigger (something happens) → AI decision (classify, extract, generate) → Action (write to a system) → Output (notify / log / confirm) Master this one skeleton and you can already build 80% of real-world automations without ever touching an "agent" Resources: 1. Anthropic: Building Effective Agents (free) Link: https://www.anthropic.com/research/building-effective-agents Read the "Workflow patterns" section. Covers prompt chaining, routing, and parallelization with clear diagrams. Ignore the agent-framework stuff for now 2. n8n AI Templates (free) Link: https://n8n.io/workflows/?categories=AI Pick 3 templates that match this skeleton and dissect them What to focus on: Sketching the workflow on paper BEFORE you drag a single node One LLM call per decision (don't try to make one giant prompt that does everything) Idempotency (running the same workflow twice shouldn't double-process) Where to use an IF node (deterministic) vs an AI node (fuzzy) Practice project: Build a workflow that reads incoming emails (Gmail trigger), uses AI to classify into {support, sales, personal, spam}, and routes each category to a different action. Create a ticket, create a CRM lead, forward, or archive. No agents. Just a clean chain 3. Error handling and fallback logic (the "clients will actually pay you" part) Your workflows will fail in production. APIs go down. Rate limits get hit. The LLM returns malformed JSON. Clients don't pay you to build things that work 90% of the time. They pay you for things that work 99.9% and gracefully handle the other 0.1% Resources: 1. n8n: Error Handling & Error Workflows (free) Link: https://docs.n8n.io/flow-logic/error-handling/ How to build a global error handler that catches any failure in any workflow. This is n8n's single best feature for production 2. n8n: Retry on Fail (free, same page) Built into every node. Learn it early 3. Nate Herk: Workflow for Unlimited Error Handling (free) Link: https://www.youtube.com/watch?v=bTF3tACqPRU He has literally explained 95% of potential errors and failures which might appear What to focus on: Enabling "Retry On Fail" on every API-calling node Building one central error workflow that catches failures from all your other workflows Re-prompting the LLM when it returns malformed JSON Notifying yourself in Slack or Telegram when a critical workflow breaks Fallback logic: if the primary model is down, try a secondary one 4. Cost awareness: tokens, pricing, and when AI is overkill Shipping AI automations without understanding token costs is how you end up with a $3,000 surprise bill on a client project. This skill pays for itself in the first month. VERY IMPORTANT (I lost $400 because of it) Resources: 1. OpenAI Pricing (free) Link: https://openai.com/api/pricing 2. Anthropic Pricing (free) Link: https://www.anthropic.com/pricing 3. OpenAI Tokenizer (interactive, free) Link: https://platform.openai.com/tokenizer Paste any text, see exactly how many tokens it is. Use it constantly What to memorize: Input tokens are cheap, output tokens are expensive (usually 4-5x more) Cheap models are "good enough" for classification, routing, and extraction Use expensive models only for creative generation and complex reasoning Calculate the monthly cost of a workflow BEFORE you ship it to a client Practice: Calculate the monthly cost of a workflow that processes 1,000 emails/day, where each email takes 1 classification call on a cheap model and 1 draft generation on a mid-tier model. Get comfortable doing this math (it's what clients trust you for) 5. (Developer path only) Calling OpenAI and Anthropic from Python Skip this if you're on the non-technical track If you want the dev track, learn how to make the same LLM calls from Python that you already make from n8n. This becomes useful when you need to do something n8n can't or when a client wants a fully custom backend Resources: 1. OpenAI API Quickstart (official, free) Link: https://platform.openai.com/docs/quickstart 2. Anthropic API Quickstart (official, free) Link: https://docs.anthropic.com/en/docs/get-started 3. OpenAI Cookbook (official, free) Link: https://cookbook.openai.com/ Runnable notebooks for every common pattern What to focus on: API keys in env vars (never in code), chat completions, system vs user prompts, temperature=0 for automations, model selection, function / tool calling Month 2 Milestone: By the end of this month you should be able to: Drop AI into any n8n workflow with confidence Design a clean trigger → AI → action → output chain for any common business task Handle API failures, bad JSON, and rate limits without your workflow crashing Estimate the monthly cost of any AI workflow before deploying it Point at one of your workflows and say "a business would pay me to set this up for them" and mean it ⏩--------------------------------------------------------------------⏪ EARLY MONETIZATION: Your first $500 gig (Month 2-3) This is the section missing from every "become an AI X in 6 months" article on the internet Most guides send you to freelance work in Month 6. That's way too late. You can and SHOULD start earning in Month 2 or Month 3, the second you have ONE solid workflow you can replicate You don't need a portfolio of 10 case studies to get your first gig. You need ONE working workflow, a 3-minute Loom walkthrough, and the willingness to be awkward in sales conversations for about 2 weeks Where to find your first paying client: 1. Upwork (fastest, best for beginners) Link: https://www.upwork.com/ Create a profile specifically called "AI Automation Builder" or "n8n Automation Specialist." Apply to jobs tagged "Zapier," "Make," "n8n," "automation," "AI workflow." Price yourself at $30-50/hour for the first 2-3 jobs. Don't argue the rate, just build reviews 2. Fiverr (productized offers) Link: https://www.fiverr.com/ Create 2-3 fixed-price offers like "I will build you an AI lead qualification workflow in n8n for $200." Fiverr rewards specificity. Hide the word "AI automation" in your tags everywhere 3. Contra (better rates, less competition) Link: https://contra.com/ Contra is freelance without the race-to-the-bottom pricing. Good for $500-2,000 projects 4. n8n Template Marketplace (inbound leads for free) Link: https://n8n.io/creators/ Publish a free template in the n8n community. People who import it will DM you asking for help customizing. This is the cheapest lead-gen channel in the entire space 5. Your own X and LinkedIn Post EVERY workflow you build. Screenshot the canvas, record a 2-min Loom, write a short caption about the problem it solves. By Month 3 you'll have inbound DMs. I promise What to sell for your first gig: Don't sell "automation consulting." Don't sell "AI strategy." Don't sell hours. Sell ONE of these: Lead qualification bot: $300-500. Form submissions → AI scores them against an ICP → high-score leads get routed to a CRM or Slack channel Email triage assistant: $300-500. Incoming emails get classified by AI, auto-replied to, or routed to the right person Meeting notes to CRM: $400-700. Meeting transcript → AI extracts action items and CRM field updates → writes to HubSpot automatically Content repurposer: $250-400. One long-form post → AI generates variants for X, LinkedIn, and a newsletter → posts draft to Notion for approval All four of these can be built in n8n in a weekend. All four are things businesses will happily pay for What to deliver with every gig (this is what gets you 5-star reviews) The n8n workflow itself (exported as JSON so they can import it) A 3-5 minute Loom walkthrough explaining how it works A one-page Notion doc with: what it does, how to monitor it, what to do if it breaks 7 days of free support after handoff Do all four of those and you will get 5-star reviews from people who have never worked with an automation builder before. That social proof is what unlocks the $1,000+ jobs in Months 4-5 The mental rule Take the gig BEFORE you feel ready. Every single one of my friends making real money in this space took their first gig while they still felt like a fraud. The only way out is through one conversation at a time ⏩--------------------------------------------------------------------⏪ Month 3: Build 1-2 repeatable service workflows Your goal this month: Build 1-2 polished, repeatable automations that solve a real business problem and that you can resell to multiple clients with light customization Important warning: do NOT try to build all 6 use cases in this list in one month. That's how beginners burn out. Pick 1-2 and go deep. You only need ONE sellable service to start earning, and two to have real leverage By the end of this month you should have 1-2 workflows that you can demo in 3 minutes and price on a sales page How to pick your 1-2 use cases??? Ask yourself: Which one sounds the most interesting? (You'll put more effort in) Which one do I already understand the business context for? Which one do I have free access to the tools for? Which one is the most in-demand on Upwork right now? (Search and see what people are posting) Pick based on those. Don't overthink it. Below are the 6 most in-demand use cases in the space, you'll pick 1-2 NOW and come back for the rest later Use case 1: Lead generation automation (highest demand) Lead gen is the #1 most requested AI automation in 2026. Every B2B company wants more qualified leads and fewer SDRs Resources: 1. Apify (free tier) Link: https://apify.com/ Best platform for scrapers. Thousands of pre-built actors for LinkedIn, Google Maps, Crunchbase 2. Clay (paid, free trial) Link: https://www.clay.com/ The industry-standard enrichment platform. Learn it, clients will ask 3. Apollo.io API (free tier) Link: https://docs.apollo.io/ 4. Hunter.io API (free tier) Link: https://hunter.io/api-documentation Email finding and verification 5. Phantombuster (paid, free trial) Link: https://phantombuster.com/ Pre-built scrapers for LinkedIn, Twitter, Instagram Build this one workflow: A pipeline that takes a list of company domains → scrapes website + LinkedIn → enriches with contact info → uses AI to score each against an ICP you define → writes top-scoring leads to a CRM or Google Sheet. Sellable as "Lead Qualification Pipeline — $1,500 one-time + $500/mo" OR, you can don't even build this, since I've already did it instead of you Currently, I am building my own product which calls "Close AI". We specializes on building of our own AI SDR (leads generation automation solution for any request) And we're actively onboarding affiliates to our team who could redirect clients and earn up to 40% per deal (and we already replaced 40 employees in one huge digital company) We made it on the next level and wrote our own LLM model which is studied on huge datasets of a lot of BD chats, Sales calls etc. If you're interested to get a solution which nobody has on the market and sell it: Fill out this Whitelist Form: https://forms.gle/Pj4uSHCNzWLKprzUA Use case 2: AI-powered cold outreach This is where AI automation really shines. Generic cold emails are dead. Clients now expect personalized outreach at 1,000+ contacts/week and only automation makes that possible Resources: 1. Instantly.ai (paid, free trial) Link: https://instantly.ai/ One of the most popular cold email platforms, full API 2. Smartlead.ai (paid, free trial) Link: https://smartlead.ai/ Direct competitor with similar API 3. Lemlist API (paid, free trial) Link: https://developer.lemlist.com/ 4. Clay's Cold Email Playbooks (free blog) Link: https://www.clay.com/learn Build this one workflow: A lead enters from an Airtable view → n8n pulls their 3 recent LinkedIn posts + company website → an LLM writes a genuinely personalized opener referencing specifics → Instantly or Smartlead sends it → replies come back and get classified by AI (interested / not now / not interested / unsubscribe) → interested leads auto-create a task in the CRM. Sellable as "Personalized Outreach System — $2,000 setup + $1,000/mo" Use case 3: CRM automation Every sales team has the same problem: reps don't update the CRM. Automation removes the human dependency and keeps every record clean Resources: 1. HubSpot API (free tier) Link: https://developers.hubspot.com/docs/api/overview The most common CRM you'll integrate with 2. Pipedrive API (free tier) Link: https://developers.pipedrive.com/ 3. Attio API (free tier) Link: https://developers.attio.com/ New-school CRM that AI-native startups love Build this one workflow: Every time a meeting happens → transcript goes to an LLM → key fields extracted (next steps, pain points, decision maker, timeline) → HubSpot deal auto-updates → follow-up task assigned to the right person. Sellable as "CRM Autopilot — $1,500 setup + $750/mo" Use case 4: Content pipelines Highest margin of all, because output is infinite and clients measure value in pieces shipped Resources: 1. Blotato API (paid, free trial) Link: https://www.blotato.com/ Posts to every major platform from one API call 2. Buffer API (paid) Link: https://buffer.com/developers/api 3. Notion API (free) Link: https://developers.notion.com/ Where most content briefs live Build this one workflow: A topic enters from a Notion database → AI generates 6 platform-specific variants (X, LinkedIn, Instagram, TikTok, newsletter, thread) → workflow waits for human approval in Slack → auto-schedules via Buffer at optimal times. Sellable as "AI Content Engine — $2,500 setup + $1,000/mo" Use case 5: Meeting automation Every founder and every sales team spends hours in meetings and hours AFTER meetings writing things down. This entire post-meeting process can be fully automated Resources: 1. Fireflies.ai API (paid, free trial) Link: https://docs.fireflies.ai/ 2. Otter.ai API (paid) Link: https://otter.ai/api 3. AssemblyAI (free tier) Link: https://www.assemblyai.com/docs 4. OpenAI Whisper (open source, free) Link: https://github.com/openai/whisper Build this one workflow: Zoom meeting ends → Fireflies transcript arrives via webhook → LLM extracts action items, blockers, next meeting date → action items go into Linear or Asana → CRM deal auto-updates → personalized follow-up email draft gets sent to the rep's inbox to review. Sellable as "Meeting Autopilot — $1,200 setup + $600/mo" Use case 6: Internal knowledge bots Every company has sprawling internal knowledge that nobody can find. This is the highest-value RAG use case and it sells well Resources: 1. n8n RAG Templates (free) Link: https://n8n.io/workflows/?q=rag Pre-built n8n workflows for document ingestion and Q&A. Start here, you can build a working knowledge bot without writing any code 2. Supabase Vector (free tier) Link: https://supabase.com/docs/guides/ai Postgres + pgvector as a managed vector database. Perfect for small-to-mid client projects 3. LlamaIndex (developer track only) Link: https://docs.llamaindex.ai/ If you want to go deeper and build a fully custom RAG pipeline 4. LangChain RAG Tutorial (developer track only) Link: https://docs.langchain.com/oss/python/langchain/rag Build this one workflow: Documents get pulled from Notion or Google Drive → chunked and embedded → stored in Supabase Vector → an internal Slack bot answers questions by retrieving relevant chunks and citing them. Sellable as "Internal Knowledge Bot — $2,500 setup + $500/mo" Month 3 Milestone By the end of this month you should be able to: Pick and ship 1-2 polished, repeatable workflows from the list above Price each as a productized service with fixed scope and fixed price Demo each in under 3 minutes on a Loom Have at least 1 paying client (if you followed the Early Monetization section) or 1 free pilot in exchange for a case study If you have one paying client by the end of Month 3 (even for $300, means you're already winning) ⏩--------------------------------------------------------------------⏪ Month 4: Add AI agents to your toolbox (carefully) Your goal this month: Understand what "AI agents" actually are, know when to use them and (more importantly) when NOT to, and add agent-powered workflows to your service offering for the use cases where they genuinely make sense Warning: most of the "AI agent" content online is hype. You're going to stay grounded. 70% of the time an agent is the wrong choice. You're going to learn when it's the RIGHT choice and that knowledge will make you worth more than 90% of people selling agent services today What to learn: 1. What agents actually are (and aren't) An agent is NOT magic. It's a loop: the LLM thinks, picks a tool, the tool runs, the result goes back into the prompt, repeat until the task is done. That's it Every agent framework (n8n AI Agent node, LangGraph, CrewAI, AutoGen, Lindy, Relevance AI) just wraps this same loop differently Resources: 1. Anthropic: Building Effective Agents (free — MANDATORY reading) Link: https://www.anthropic.com/research/building-effective-agents The single best piece of writing on agents in production. Read this before you build anything agent-related. Not negotiable 2. OpenAI: A Practical Guide to Building Agents (free PDF) Link: https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf Covers agent patterns, guardrails, and safety What to focus on: The perceive → plan → act → observe loop How a loop terminates (the model says "I'm done" or a max iteration cap hits) Why agents are just while loops with an LLM making branching decisions What every framework is actually abstracting away 2. Build your first agent (inside n8n) For the non-technical path, this is where you go. n8n's AI Agent node handles the loop, the tool calling, and the state management for you. You drag a node, give it a goal, connect it to "tool" sub-workflows, and watch it go Resources: 1. n8n AI Agent Node Docs (official, free) Link: https://docs.n8n.io/integrations/builtin/cluster-nodes/root-nodes/n8n-nodes-langchain.agent/ The full reference for n8n's agent node. Read the whole thing 2. n8n AI Agent Templates (free) Link: https://n8n.io/workflows/?categories=AI Import 3 agent templates, run them, and reverse-engineer how they're wired. Worth a hundred tutorials What to focus on: Defining the agent's goal clearly in the system prompt Exposing the right tools to it (keep it small, 3-5 tools max for your first agent) Writing tool descriptions that are impossible to misinterpret Setting a max iteration limit (usually 10-15) so the agent can never loop forever Adding a human-in-the-loop approval step for any action that's irreversible Practice project: Build a support agent inside n8n that handles Tier 1 customer tickets. It should: Classify the ticket (billing, technical, account, feature request) For known issues, look up the fix in a knowledge base (Notion or Google Drive) and reply For account questions, pull info via a tool call For anything else, escalate to Slack with a full context summary Never send a reply without a confidence check 3. When to use agents vs. simple chains The most valuable thing you'll learn this month is when NOT to use agents The decision framework: Single LLM call: if the task can be solved in one prompt with enough context Fixed chain: if the steps are predictable and always run in the same order (most workflows!) Agent: only if the number of steps is genuinely unknown and depends on the input Resources: 1. Anthropic: "When to use agents" (free) Link: https://www.anthropic.com/research/building-effective-agents 2. Simon Willison: Designing Agentic Loops (free) Link: https://simonwillison.net/2025/Sep/30/designing-agentic-loops/ Memorize this: A fixed chain of 3 LLM calls will always be faster, cheaper, and more debuggable than an agent that makes 3 calls. Reserve agents for genuinely open-ended tasks like research, multi-step customer support, or flexible sales outreach 4. Human-in-the-loop checkpoints Most serious business automations need a human approval step somewhere, especially when the agent is about to do something irreversible (send an email, post publicly, update a deal stage, charge a card) Resources: 1. n8n: Wait Node (free) Link: https://docs.n8n.io/integrations/builtin/core-nodes/n8n-nodes-base.wait/ n8n's built-in way to pause a workflow until a webhook or time trigger arrives. This is your approval gate 2. Slack Block Kit Builder (free) Link: https://api.slack.com/block-kit How to build interactive approval buttons in Slack messages What to focus on: Identifying which actions MUST have human approval (send, publish, pay, update) Building approval UIs in Slack, email, or a lightweight form Handling approve / reject / edit responses without corrupting state Timeouts for when no human responds 5. Making agents reliable (the part nobody shows in demos) Demo agents work. Production agents need actual reliability engineering What to focus on: Maximum iteration limits so an agent can never loop forever Per-tool retry with fallback Logging every tool call so you can debug what went wrong after the fact Adding a human escalation path for when the agent gets confused 6. (Developer path only) Python agent frameworks Skip this if you're on the non-technical track If you want the dev track, these unlock custom agent work that clients pay more for: 1. LangGraph (free course) Link: https://academy.langchain.com/courses/intro-to-langgraph The most widely used agent orchestration framework 2. CrewAI (free, open source) Link: https://docs.crewai.com/ Multi-agent framework with role-based collaboration 3. AutoGen (Microsoft, free, open source) Link: https://microsoft.github.io/autogen/ Month 4 Milestone By the end of this month you should be able to: Build a working AI agent inside n8n that uses 3-5 tools reliably Confidently decide whether a task needs a single call, a fixed chain, or an agent Write tool descriptions that get selected correctly across varied inputs Add human-in-the-loop approval to any workflow that takes irreversible actions Know when to say "no, an agent is overkill here" to a client and offer them a simpler solution instead ⏩--------------------------------------------------------------------⏪ Month 5: Make your automations production-ready Your goal this month: Take everything you built in Months 1-4 and make it survive real clients, real traffic, and real 2am outages By the end you should be able to deploy, monitor, and hand off an automation to a non-technical client (and confidently promise them uptime and response time) What to learn: 1. Deployment (use Railway's 1-click n8n first) Good news: you don't need to learn Docker or DevOps this month. Railway has a 1-click n8n deploy. You click a button, add a custom domain, and you have a production n8n instance in 5 minutes Do that. Don't overthink it. Docker, reverse proxies, and Kubernetes come LATER, when you have 5+ clients and actually need them Resources: 1. Railway (free tier, recommended) Link: https://railway.app/ The fastest way to self-host n8n. Search for "n8n" in their templates and click deploy 2. Render (free tier) Link: https://render.com/docs/deploy-n8n Another great one-click option 3. n8n Cloud (hosted, paid) Link: https://n8n.io/cloud/ The zero-hassle option if a client doesn't want any infra at all. More expensive but no ops work on your end What to focus on: Deploying n8n on Railway with a custom domain and HTTPS Setting environment variables for secrets (never paste keys into the canvas) Setting up automatic backups Knowing what to do when it goes down (step 1: check Railway status page) (Developer path later): Self-hosting n8n with Docker Compose, managed Postgres, and a Caddy reverse proxy. Skip this until you have at least 3-5 clients 2. Logging and monitoring (know when it breaks before your client does) If you can't see what's happening inside your automation, you can't fix what's broken. And you'll only find out about the breakage when the client emails you at midnight Resources: 1. n8n Built-in Execution Logs (free) Link: https://docs.n8n.io/workflows/executions/ n8n's built-in logging is actually very good for most use cases. Master this first before adding anything else 2. Better Stack (free tier) Link: https://betterstack.com/ Uptime monitoring. Get paged when your n8n instance goes down 3. Langfuse (free tier) Link: https://langfuse.com/ LLM-specific observability. Traces every prompt, response, token count, and latency. Overkill for Month 5 but good to know exists What to focus on: Checking n8n execution logs daily for the first few weeks after deploying Setting up Better Stack to ping your n8n URL every minute and alert you if it goes down Alerting yourself in Slack or Telegram when a critical workflow fails Never finding out about an outage from your client 3. Prompt versioning (don't change prompts randomly in live workflows) Your prompts are code. They need version control. Random prompt changes in live workflows are how you silently break things Resources: 1. The boring option (recommended for beginners): store every prompt in a Notion database or Google Doc, label each version with a date, and always know which version is live 2. Langfuse Prompt Management (free tier) Link: https://langfuse.com/docs/prompts Centralized prompt storage with versioning and a built-in playground What to focus on: Never editing a live prompt in a client's workflow without testing it first Keeping the last 3 versions of every prompt so you can roll back Writing down WHY you changed a prompt every time you do 4. Security basics The fastest way to lose a client is to expose their API keys or let someone else access their data Resources: 1. OWASP Top 10 for LLM Apps (free) Link: https://genai.owasp.org/llm-top-10/ LLM-specific security risks including prompt injection. One hour read 2. n8n Credentials Docs (free) Link: https://docs.n8n.io/credentials/ How n8n stores API keys securely. Never paste a key into a node parameter (always use the credentials system) What to focus on: Never committing API keys to GitHub or any public doc Using n8n's built-in credentials vault for every API key Rotating keys if a workflow ever leaks one Sanitizing any user input before it hits an LLM (prompt injection defense) Never trusting LLM output to take irreversible actions without human review 5. Documentation and client handoff (the secret to 5x your rates) The difference between a $500 project and a $5,000 project is often just documentation. If you hand off a beautifully documented workflow with a Loom walkthrough, clients feel taken care of. If you hand off an unlabeled n8n canvas, they feel lost Resources: 1. Loom (free tier) Link: https://www.loom.com/ Record walkthrough videos for every workflow. 3-5 minutes each. Clients love these 2. n8n Sticky Notes (free) Link: https://docs.n8n.io/workflows/sticky-notes/ Use these extensively inside every workflow to explain what each section does 3. Notion Templates for Client Handoffs (free) Link: https://www.notion.so/templates Search for "client handoff" and steal shamelessly What to focus on: A one-page overview doc per automation: what it does, what triggers it, what it updates, what to do if it breaks Loom walkthroughs (3-5 min) for every non-trivial workflow Sticky notes inside every n8n canvas explaining WHY things are set up a certain way A simple runbook listing the 5 most likely failure modes and how to resolve them Training the client on how to check the monitoring dashboard themselves 6. Basic SLAs Once you charge retainers, clients will ask what you're promising and what happens when you miss it. You don't need enterprise contracts. You need clarity What to focus on: Uptime commitment (99% is reasonable for automation work) Response time for issues (reply within X hours, fix within Y) Scope boundaries (what's included, what's an extra) Escalation path for urgent failures Month 5 Milestone By the end of this month you should be able to: Deploy any of your workflows to Railway in under 10 minutes Set up monitoring that alerts you BEFORE clients notice something broke Hand off a clean package to any client: workflow, Loom, runbook, and a monitoring dashboard Write and negotiate a basic SLA for retainer work Sleep peacefully knowing your workflows are being watched ⏩--------------------------------------------------------------------⏪ Month 6: Pick your direction and scale By this point you have: 1-2 workflows that actually work, at least 1 paying client (hopefully more), a deployed production setup, and the beginnings of a portfolio. Month 6 is about picking which direction you scale in Three real paths. Pick ONE and go all in Direction 1: Freelance Automation Builder Best if you want clients fast and income in 30-60 days This is the fastest path to real income. You sell workflow builds and retainers directly to SMBs, agencies, coaches, and SaaS founders. Low overhead, high margins, cash in weeks Focus on: 2-3 repeatable workflow templates (lead gen, cold outreach, content pipeline, support bot) A simple case study for each with real numbers Cold outreach to SMBs, agencies, coaches, and SaaS founders Pricing structure: $500-2k per project to start, move to $1-3k/mo retainers as you get reviews What to learn this month: 1. Productizing your services Stop selling hours. Start selling outcomes. "Lead Gen Pipeline Setup — $1,500" converts 10x better than "Custom automation work, hourly rate" Resources: 1. Productized Services Guide (Pieter Levels, free) Link: https://nomadlist.com/forum/post/3471 2. Sahil Lavingia's blog (free) Link: https://sahillavingia.com/ What to focus on: Pick 2-3 services, write fixed scopes and fixed prices for each Build a portfolio site (Notion page or Carrd is enough) showcasing only those services Never say yes to custom work you haven't productized yet 2. Cold outreach and client acquisition You have to sell. There's no way around it. Good news: you already built the exact tools you'll use for your own outreach during Month 3 Resources: 1. Instantly Blog (free) Link: https://instantly.ai/blog 2. Nick Saraev (YouTube, free) Link: https://www.youtube.com/@nicksaraev Specifically focused on automation agency outreach and sales What to focus on: Building a lead list of 500-1000 companies in your target niche Writing a personalized cold email pitching ONE specific automation with a clear ROI claim Booking 3-5 discovery calls per week Closing a "Build First, Pay After" pilot offer to get your first case study 3. Case studies and social proof One well-documented case study will bring you more clients than 500 outreach emails. Build your first one the moment you ship your first project What to focus on: Before/after metrics (time saved, revenue generated, cost reduced) A clear story: problem → what you built → result Loom walkthrough of the workflow for visual proof Client testimonial quote Direction 2: In-House Automation Builder Best if you want stability and a salary This direction is for people who want the safety of a full-time role inside one company, building internal automation systems that save the business time and money Focus on: Ops and internal tooling use cases Connecting AI to existing company stack (Slack, Notion, HubSpot, Salesforce) Building internal agents and dashboards Measuring time and cost savings rigorously What to learn this month: 1. Internal tool building Resources: 1. Retool (free tier) Link: https://retool.com/ The standard for building internal tools fast 2. Streamlit (open source, free) Link: https://streamlit.io/ The fastest way to build Python-powered internal tools 3. Budibase (open source, free) Link: https://budibase.com/ Open source Retool alternative What to focus on: Identifying the highest-value automation targets inside your company Building simple UIs non-technical employees can actually use Getting sign-off and resource allocation from stakeholders 2. Measuring ROI properly In-house builders get promoted based on measurable business impact. If you can't prove your automations saved X hours or $Y, you're invisible What to focus on: Tracking before/after metrics for every automation you ship Presenting numbers in monthly wins docs Calculating direct (time saved) and indirect (errors reduced, satisfaction improved) impact Getting stakeholder quotes you can reuse in your next performance review and job search 3. Career positioning What to focus on: Writing internal docs and blog posts about your automation wins Presenting at internal meetings, demo days, all-hands Sharing (sanitized) case studies on LinkedIn and X Keeping a "projects built" doc you can show at every performance review Direction 3: AI Automation Agency Best if you want to scale beyond trading time for money This is the highest-ceiling path but also the hardest. You build a team, a repeatable service, and a real business (not just a freelance income) Focus on: Building a repeatable service with clear deliverables Hiring or partnering to fulfill work Niching down by industry (real estate, e-commerce, recruiting, legal) Productizing workflows into templates you sell or license What to learn this month: 1. Building a repeatable service An agency only works if the same service can be delivered by multiple people the same way. This is what separates agencies from freelancers Resources: 1. The E-Myth Revisited (book) Link: https://www.amazon.com/E-Myth-Revisited-Small-Businesses-About/dp/0887307280 The classic on systematizing a services business. Mandatory reading before you hire anyone 2. Built to Sell (book) Link: https://www.amazon.com/Built-Sell-Creating-Business-Without/dp/1591845823 Specifically about making your agency independent of you What to focus on: Documenting every step of your delivery process as SOPs Defining deliverables and timelines explicitly Productized pricing: "Lead Gen Package — $3,500 one-time + $750/mo" 2. Niching down Generalist agencies die. Niche agencies thrive. Pick one industry and own it What to focus on: Researching 3-5 industries and picking one where you have context, pricing is high, and the problem is universal Building niche-specific templates and case studies Rewriting your portfolio and outreach around that niche 3. Hiring and delegating Resources: 1. OnlineJobs.ph (paid, international) Link: https://www.onlinejobs.ph/ 2. Upwork (free, global) Link: https://www.upwork.com/ What to focus on: Hiring operators first (people who can run existing workflows and talk to clients) Writing task-level SOPs before hiring anyone Keeping the first hire part-time until you're sure the work is there Not hiring until you have at least 3 paying clients Practice project for Direction 3 Pick one industry (e.g. real estate). Build 3 workflows that solve the 3 biggest repetitive problems in that industry (lead qualification, listing description generation, appointment reminders). Package them as "Real Estate AI Automation Suite" with fixed pricing and a clear deliverables list. This is a sellable product you can take to market on day one ⏩--------------------------------------------------------------------⏪ CONCLUSION What you can expect after these 6 months??? I'm going to be honest with you, without any "money's mountains" This roadmap will not make you the next Zapier CEO in 6 months But it will make you someone who can build, ship, and deploy real AI automations that solve real business problems And right now, that is exactly what the market is paying for AI automation demand is not slowing down. LinkedIn's 2026 Jobs on the Rise report puts "AI automation specialist" in the top 5 fastest-growing roles McKinsey estimates that 60-70% of employee time in most office roles is automatable with current AI + workflow tools Only about 2% of SMBs have implemented any meaningful AI automation so far, which means 98% of the market is still completely untouched These are not hype numbers. These are real numbers based on real data (took from Claude kek) If you go full-time in the US or EU: 1: Junior AI Automation Builders start at $75,000-$110,000 2: Mid-level (2-4 years) sits at $125,000-$180,000 3: Senior builders and automation architects go $180,000-$280,000+ The mid-level band is growing the fastest because companies desperately need people who can ship reliable production automations without supervision If freelance is more your thing: 4: AI workflow builds go for $500-5,000 per project 5: Monthly retainers start at $500-2,000/mo for basic maintenance and land at $3,000-8,000/mo for active development 6: Hourly rates for specialist automation work are $100-250/hour depending on niche I personally know freelancers pulling $15,000/month solo just building n8n workflows for SMBs. The work exists and the market is still desperately underserved And if you go the consulting or agency route: 7: $500-5,000 to set up a single AI automation for a business 8: $1,000-5,000/month for managed automation services 9: $3,000-15,000 to build a full custom agent system 10: $10,000-50,000 for enterprise-grade automation packages The ceiling is genuinely uncapped. I've seen 2-person automation agencies doing $80k/mo in recurring revenue serving 15-20 clients These are real numbers from real people doing real work right now in 2026 Now here is what I actually want you to take away from all of this: Pick one workflow from each month and build it. Not read about it. Not watch a tutorial. Build it, break it, fix it, deploy it, put it on GitHub or in a portfolio site. The builders who get hired are the ones who show what they've built, not what they've studied Start sharing what you learn. Write about it on X, LinkedIn, anywhere. Teaching is the fastest way to learn and it builds your reputation at the same time. The best clients I've seen come from people who were visible, not from people who applied to 500 job listings And please don't wait until you feel ready. You will never feel ready. The gap between "I'm learning" and "I'm building" is where most people get stuck forever Start applying, start freelancing, start offering services the moment you have ONE working automation. Even if it's rough. The market doesn't reward perfection. It rewards people who can ship 6 months is enough to change everything if you actually put in the work And I really believe each of you reading this can do it Just never stop building and never stop learning Hope this was useful for you my fam ❤️

I highly recommend turning on both QMD and the new "Active-Memory" plugin in OpenClaw. Just ask your agent to set it up. Once set up, responses will be a little slower... but much sharper and much more accurate. Also, include session transcripts in QMD's paths, not just memory.

I highly recommend turning on both QMD and the new "Active-Memory" plugin in OpenClaw. Just ask your agent to set it up. Once set up, responses will be a little slower... but much sharper and much more accurate. Also, include session transcripts in QMD's paths, not just memory. OpenClawの記憶精度を上げる実践メモ。QMDと新しいActive-Memoryプラグインを両方有効化し、さらにQMDの参照対象にmemoryだけでなくセッショントランスクリプトも含めると、応答速度は少し落ちる代わりに、回答の鋭さと正確さがかなり改善するという話。

OpenClaw v2026.4.10 just dropped 🦞 This one makes your agents smarter in conversations and way better at remembering things. Plus upgrades across video, voice, and reliability. Here’s what actually matters ↓ 1. Active Memory plugin This is a big one. Your agent now

スライド資料はこちら↓ https://t.co/twVm3Q9DoM

スライド資料はこちら↓ https://t.co/twVm3Q9DoM

OpenClaw v2026.4.10 just dropped 🦞 This one makes your agents smarter in conversations and way better at remembering things. Plus upgrades across video, voice, and reliability. Here’s what actually matters ↓ 1. Active Memory plugin This is a big one. Your agent now 引用: OpenClaw🦞 (@openclaw) OpenClaw 2026.4.10 🦞 🧠 Active Memory plugin 🎙️ local MLX Talk mode 🤖 Codex app-server harness plugin 🧾 Teams pins/reactions/read actions 🛡️ SSRF hardening + launchd fixes stability, but with attitude🦞 https://t.co/PW7WDumTf1

Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is

Thrilled to announce the Monitor tool which lets Claude create background scripts that wake the agent up when needed. Big token saver and great way to move away from polling in the agent loop Claude can now: * Follow logs for errors * Poll PRs via script * and more! https://t.co/eflixzi0xk

OpenClaw's memory out of the box sucks With this simple Obsidian system, it's actually awesome In this video I go over using Obsidian as a memory layer for your AI agent, making it SO much more smarter than it is now If you use OpenClaw or Hermes, you need to watch this: https://t.co/nc9xDB14GX

If you want your OpenClaw or Hermes Agent to be able to have perfect total recall of all 10,000+ markdown files, GBrain is here to help. It's exactly my OpenClaw/Hermes Agent setup. MIT-licensed open source. Hope it helps you build your mini-AGI. https://t.co/yFpFU4pn5b

OpenClaw's memory out of the box sucks With this simple Obsidian system, it's actually awesome In this video I go over using Obsidian as a memory layer for your AI agent, making it SO much more smarter than it is now If you use OpenClaw or Hermes, you need to watch this: https://t.co/nc9xDB14GX

If you want your OpenClaw or Hermes Agent to be able to have perfect total recall of all 10,000+ markdown files, GBrain is here to help. It's exactly my OpenClaw/Hermes Agent setup. MIT-licensed open source. Hope it helps you build your mini-AGI. https://t.co/yFpFU4pn5b

Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is

Thrilled to announce the Monitor tool which lets Claude create background scripts that wake the agent up when needed. Big token saver and great way to move away from polling in the agent loop Claude can now: * Follow logs for errors * Poll PRs via script * and more! https://t.co/eflixzi0xk

Introducing Claude Managed Agents: everything you need to build and deploy agents at scale. It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days. Now in public beta on the Claude Platform. https://t.co/vHYfiC1G56

I rewrote every single SOUL file for my OpenClaw agents using this as a reference. If you're moving to GPT-5.4, this is a must-do. But don't just copy-paste it. Ask your agent to create a version specific to YOU based on everything it already knows about you. https://t.co/lnAZVgPE3l

Managed Agents is the first 'agent in the cloud' API that has the right mix of simplicity and complexity. Implementation details like how you manage a sandbox are abstracted, but you have a lot of control over the actual execution of the model.

OpenClaw 2026.4.7 🦞 🔮 openclaw infer 🎬 music + video editing 💾 session branch/restore 🔗 webhook-driven TaskFlows 🤖 Arcee, Gemma 4, Ollama vision 🧠 memory-wiki: persistent knowledge, not just vibes Because “trust me bro” is not a knowledge system. https://t.co/L7OaBHA7Qg

タイトル: How I stopped burning 75% of my Claude budget and saved $500/month" You pay $20/month for Pro or $100 for Max or $200 for Max 20x. And 75% of that money goes nowhere. Not to your tasks - to Claude talking to itself. And you let it do that. I tracked my usage for a week. Most tokens were just burned into the void. Here's what I changed to save roughly $500 a month. Part 1 - Teach Claude to talk like a caveman Regular Claude on a web search task: ~180 tokens. It says "I'll help you search for that!" then searches, then explains what it found, then summarizes, then asks if you need anything else. Caveman Claude on the same task: ~45 tokens. "Tool work , result, me stop." 135 tokens × 20 tasks a day = 2,700 saved tokens every day. Per month that's ~81,000 tokens you're no longer burning. caveman - one skill that cuts 65-87% of token costs (1,900+ stars) github.com/JuliusBrussee/caveman Real benchmarks from the API: Average across 10 tasks: 75% savings. Three intensity levels: lite -> removes fluff, keeps grammar (professional, no filler) full -> removes articles, fragments (full grunt mode) ultra -> maximum compression (telegraph style, cuts everything) Start with lite. Switch to full for agentic tasks where you're not reading the responses anyway. Why it works: caveman Claude doesn't explain itself - it just does the task. Gives the result then stops. No "I'd be happy to help." minimum fluff Result: a clear short answer to your question that saves both time and tokens Replaces: Manually writing "be concise" in every prompt. how did i find this here: https://t.me/noisyclub01 Part 2 - Stop losing 2-4 hours every morning You start working at 8:30am and already hit the limit by 11, blocked until 1:00pm. Two dead hours in the middle of your workday. you can game it very easily if you know the timings How the window actually works Claude usage runs on a sliding 5-hour window. The window starts when you send your first message, rounded to the hour. First message at 8:30 -> window anchors at 8:00 -> runs until 13:00 Hit limit at 11:00 -> wait until 13:00 -> 2 dead hours But if you send a throwaway "hi" via Haiku at 6:15am before starting work: "hi" at 6:15 -> window anchors at 6:00 -> runs until 11:00 At 11:00 window resets immediately Next window: 11:00 - 16:00 -> zero downtime Same total budget. Just distributed better across the day. claude-warmup - automatic window anchor github.com/vdesimone/claude-warmup GitHub Actions cron job that sends "hi" to Haiku every morning. Fork it, add your OAuth token, set the schedule, done. Works on Pro, Max 5x, Max 20x. Don't want a repo? Use Claude's built-in scheduled tasks: Replaces: Losing 2 hours every morning waiting for the window to reset. Part 3 - Other leaks you're probably ignoring Edit, don't reply When Claude misunderstands, most people write "No, I meant X." Every new message stacks on top of all previous ones. Claude re-reads the entire history every time. Hit Edit on your original message, fix it, regenerate. The bad exchange gets replaced not stacked. Batch your questions Three separate prompts = three full context loads. One prompt with three questions = one load. Instead of: Write: Fewer reloads. Further from the limit. Answers are usually better - Claude sees the full picture at once. Upload files to Projects once Uploading the same PDF to multiple chats? Claude re-tokenizes it every time. Projects: upload once -> cached. Every conversation inside the project references it without burning tokens. 100-page PDF = ~75,000 tokens. Uploaded 5 times = 375,000 tokens burned. In Projects - 75,000 once, the rest is free. Use Projects. Upload once -> cached. Contracts, briefs, style guides - this alone can save $15-40/month. Save your context in Memory Every new chat without saved context = 3-5 setup messages. "I'm a developer, I want short answers, always use TypeScript..." 5 messages × 500 tokens × 10 new chats a day = 25,000 tokens/day repeating the same thing. Go to Settings -> Memory. Save your role and preferences once. Claude applies them to every new chat automatically. Zero setup tokens. Full cheatsheet Total: up to $500+/month in leaks you can plug in 15 minutes. One more thing: if you're not in the US - peak hours (5:00 - 11:00 Pacific) burn through limits faster since March 2026. Run heavy tasks in the evening or on weekends - your plan stretches much further. Claude doesn't count messages. It counts tokens. Stop giving it reasons to use more. You build your own life - so choose the right path. / If this was useful - follow / more info in my tg channel: https://t.me/noisyclub01

Agents are good at bash. Bash is not good for agents. We should cut our losses and restart now before it is too late. https://t.co/YndGmAfqjh

タイトル: Memory Bandwidth for Local AI Hardware (2026 Edition) If you’re running models locally, thinking “bigger memory pool = better AI box” falls apart the moment you care about actual speed. Capacity decides whether the model fits. Bandwidth decides whether the box feels alive or like it’s decoding through wet cement to produce 3 tokens per second. That is why a 32GB RTX 5090 and RTX PRO 6000 can absolutely outrun a much larger unified-memory machine, while a Mac Studio M3 Ultra, DGX Spark, or Strix Halo box can still be the right answer when the model simply will not fit on a normal GPU (but will be a lot slower, and not help for multi-agentic workflows). There’s a better way to think about it: Local AI hardware = capacity × bandwidth × software stack Capacity tells you what fits. Bandwidth tells you how hard the box can breathe. The software stack tells you how much of the spec sheet you can actually cash out. That is the mental model. Not “AI PC.” Not “NPU TOPS.” Not whatever crime against engineering was committed in a marketing deck this week. The Hardware Number You Should Actually Care About Especially in the age of agents. Memory bandwidth is not tokens per second. But it is the cleanest first-pass way to separate local AI hardware into real performance tiers before you waste a week arguing with someone posting screenshots from a single prompt demo. Here’s the current landscape: 1.8 TB/s class RTX PRO 6000 Blackwell, RTX 5090 → 1792 GB/s 800 GB/s class Mac Studio M3 Ultra → 819 GB/s 450–650 GB/s class Mac Studio M4 Max → 546 GB/s MacBook Pro M5 Max → 460–614 GB/s AMD Radeon AI PRO R9700 → 640 GB/s Tenstorrent Blackhole p150 → 512 GB/s 250–300 GB/s unified-memory class DGX Spark → 273 GB/s Mac mini M4 Pro → 273 GB/s Ryzen AI Max / Strix Halo → 256 GB/s Thin-and-light AI PC class MacBook Air M5 → 153 GB/s Snapdragon X Elite → 135 GB/s Intel Lunar Lake → 136 GB/s Snapdragon X2 Elite → 152–228 GB/s If you remember nothing else, remember this: capacity decides what fits bandwidth decides how hard it can breathe software decides how much of that you actually see That is the whole game. Side Note: The Memory Tax People Mix Up A lot of people collapse capacity and bandwidth into one blob. That is how you end up making terrible hardware takes with supreme confidence. A 32GB RTX 5090 and a 96GB RTX PRO 6000 Blackwell both have the same bandwidth. But they live in completely different worlds once model size enters the chat. A DGX Spark gives you 128GB unified memory at 273 GB/s. A Ryzen AI Max system can expose ~96GB as GPU memory. A Mac Studio M3 Ultra goes up to 512GB at 819 GB/s. Same topic. Wildly different tradeoffs. So no, bandwidth is not the whole story. But it is the fastest way to stop being confused. What This Looks Like in Practice Below ~150 GB/s, you are in thin-and-light territory. That does not mean useless. It means stop pretending it’s competing with workstation GPUs. Around 250–300 GB/s → unified memory starts getting interesting. Around 450–650 GB/s → serious workstation tier. At 800+ GB/s → expensive, powerful, and fun. Local AI in 2026 is not one market. It is five different markets pretending to be one. Discrete GPU Reality: Still the Bandwidth Kings If the model fits, or you pool GPUs via NVLink (now mostly server-side) or Gen 5 PCIe and use Tensor Parallelism, discrete GPUs still dominate. This applies especially to NVIDIA GPUs given the wide software support. RTX PRO 6000 Blackwell → 96GB @ 1792 GB/s RTX 5090 → 32GB @ 1792 GB/s RTX 4090 → 24GB @ 1008 GB/s What about AMD GPUs? RX 7900 XTX → 24GB @ 960 GB/s Radeon PRO W7900 → 48GB @ 864 GB/s AI PRO R9700 → 32GB @ 640 GB/s Intel? Arc Pro B65 → 32GB @ ~608 GB/s Arc Pro B60 → 24GB @ ~456 GB/s GPUs win because they can drink from a firehose. They lose when the model doesn’t fit. Apple Reality: OK Bandwidth + Capacity Together Apple’s entire story is: not the fastest, but usable Mac mini M4 → 120 GB/s MacBook Air M5 → 153 GB/s Mac mini M4 Pro → 273 GB/s MacBook Pro M5 Pro → 307 GB/s M5 Max → up to 614 GB/s Mac Studio M3 Ultra → 819 GB/s + up to 512GB memory That last one is the key. Apple wins when: you want one box you want silence you want stupid amounts of memory you don’t want to shard across GPUs It loses when raw tokens/sec & concurrency matter more than everything else. DGX Spark: Coherent Memory + CUDA, Not a Bandwidth Monster DGX Spark: 128GB unified memory 273 GB/s NVIDIA stack That bandwidth is not impressive. The coherent memory + software stack is. It’s a developer appliance. Not a raw performance monster. It has NVFP4 support, which gives it an advantage, but that is yet to mature. Strix Halo / Ryzen AI Max: The First Real x86 Contender This is an interesting one. 256-bit LPDDR5X up to 128GB memory ~256 GB/s bandwidth up to ~96GB usable as GPU memory This is also where Framework Desktop is interesting. The AI PC Trap Most “AI PCs” are still bandwidth-starved. Snapdragon X Elite → 135 GB/s Intel Lunar Lake → 136 GB/s MacBook Air M5 → 153 GB/s Snapdragon X2 Elite → up to ~228 GB/s That’s fine for: small models assistants edge workloads It is not: 9B Dense model playground territory serious multi-agent workloads long-context stress testing Physics still applies. Tenstorrent and the Wildcards Tenstorrent: Wormhole n300 → 24GB @ 576 GB/s Blackhole p150 → 32GB @ 512 GB/s + 800G interconnect Fully OSS stack. I am excited or this one to mature and hope they go on to become a strong contender in the AI space. We need more full opensource stacks. They are real options depending on your stack and goals. Why Bigger Boxes Still Feel Slow Because fitting ≠ serving. Even if it fits, you still pay for: bandwidth during decode KV cache growth dequantization batching + concurrency scheduler quality framework overhead This is why: “it runs” = demo “it serves” = system design Multi-GPU? More GPUs ≠ linear scaling. You are now buying: interconnect (PCIe vs NVLink vs RDMA) topology sync overhead software maturity The Only Mental Model That Matters There is no giant chart you need to memorize. There is just this: 1. What must fit? 2. What bandwidth tier do I need? 3. What software stack can actually deliver it? Blunt version: NVIDIA → fastest raw speed Apple Ultra → biggest one-box memory Strix Halo → first real x86 unified-memory play DGX Spark → coherent NVIDIA appliance AMD / Intel Arc → rising alternatives Tenstorrent → fully opensource stack Once you internalize this, you stop asking: “Which hardware is best?” You start asking: “Which bottleneck am I buying?” That is the real question. Until next time.

タイトル: 🔑 karpathy's second brain: how to build it 100K people bookmarked @karpathy's post: Then he dropped a full GitHub Gist. 5,000+ stars. 1,400+ forks. Two days. Most people will bookmark that too. Then do nothing. Not because it's hard. Because nobody gave them the exact prompts. I'm going to fix that. I'll walk you through the full system, hand you copy-paste prompts for every step, and tell you where this breaks so you don't waste a weekend on something that falls apart at scale. "The human's job is to curate sources, direct the analysis, ask good questions, and think about what it all means. The LLM's job is everything else." — Andrej Karpathy The Concept (60-Second Version) You have knowledge scattered everywhere. Articles saved in 4 apps. Bookmarks from 2023 you'll never revisit. Notes from meetings that live in a folder you forgot existed. Right now, when you ask AI a question about your stuff, it starts from zero every time. Upload docs, ask a question, get an answer. Next session? It's forgotten everything. That's how ChatGPT file uploads, NotebookLM, and most RAG systems work. Zero accumulation. Karpathy's idea flips this. Instead of the AI searching your raw files every time, the AI reads your sources once and compiles a structured wiki. Summaries, cross-references, connections between ideas, contradictions flagged. All maintained by the AI. All in simple markdown files. Next time you ask a question, the AI doesn't dig through raw documents. It reads the wiki it already built. The connections are already there. The synthesis already reflects everything you've read. Every new source you add makes the wiki richer. Every question you ask can get filed back in. Knowledge compounds instead of resetting. His result: ~100 articles, ~400,000 words on a single research topic. He didn't write a word of it. The AI wrote, linked, categorized, and maintained all of it. No database. No embeddings. No vector store. Just folders and text files. Why Should You Care? Three use cases that matter right now: If you're a creator or marketer, this is a content research engine. Dump competitor breakdowns, trending articles, audience insights into raw/. The wiki surfaces patterns and angles you'd never find manually. If you're a founder or consultant, this is your second brain for client work, market research, or competitive analysis. Every report you generate feeds back into the system. By month 3, your AI knows your domain better than most hires. If you're a student or researcher, this is what Karpathy actually built it for. Deep research across dozens of papers, with the AI tracking how ideas connect, where authors disagree, and what gaps remain. *This can also be used for a lot of business R&D workflows. Before We Build: What You Need → Any AI coding tool that reads local files (Claude Code, Cursor, Codex, or similar) → A text editor (Obsidian recommended, but VS Code, Notepad, anything works) → 10+ source documents on a topic you care about → 30 minutes for initial setup, then 10 minutes per source after that That's it. No special software. No accounts to create. No plugins to install. The rest of this article is the build. 7 steps. Every step has the exact prompt you'll paste into your AI. Follow them in order. Step 1: Create the Folder Structure (2 Minutes) Create this anywhere on your machine: Three folders, one file. If you're spending more than 2 minutes here, you're overthinking it. Step 2: Write Your Schema File (The Step Everyone Skips. Don't.) The schema is the difference between a generic chatbot and a disciplined wiki maintainer. It tells your AI what the knowledge base is about, how to organize it, and what to do when you add sources, ask questions, or run maintenance. Every other guide gives you a 10-line template. Here's the full production schema, based on Karpathy's gist, engineered for real use: Copy this. Customize the focus areas. Drop it in your project root as CLAUDE.md. Step 3: Fill Your Raw Folder (10 Minutes of Dumping, Zero Organizing) Open raw/ and dump everything in: → Copy-paste articles into .md or .txt files → Export notes from whatever app you're using now → Save screenshots and diagrams to raw/assets/ → Paste in research papers, PDFs, competitor breakdowns → Dump bookmarks you've been hoarding for months Don't organize it. Don't rename anything. Don't clean it up. That's the AI's job. Pro tip from Karpathy: The Obsidian Web Clipper browser extension converts any web article to markdown in one click. Set a hotkey (Settings → Hotkeys → "Download attachments") to pull all images locally so the AI can reference them. If you don't use Obsidian, copy-paste from your browser works fine. The goal is volume. Not perfection. Step 4: Run Your First Ingest Open your AI agent. Point it at your project folder. Paste this: INGEST PROMPT: Start with one source at a time. Karpathy does the same. Read the summaries. Check the updates. Guide the AI on what to emphasize. This produces dramatically better results than batch-processing everything at once. After 5-10 sources, your wiki/ folder will have an index, a log, and 15-30 interconnected pages. That's when things click. Step 5: Start Querying Your Knowledge Base Once you have 10+ wiki pages, the system becomes genuinely useful. Paste this: QUERY PROMPT: Questions that extract the most value: → "What are the three biggest gaps in this knowledge base?" → "Which sources disagree with each other, and on what?" → "What should I research next based on what's here?" → "Write a 500-word briefing on [topic] using only wiki content" → "What connections exist between [concept A] and [concept B]?" The critical loop: good answers should be filed back into the wiki. A comparison, an analysis, a connection you discovered. These compound in the knowledge base just like ingested sources do. Every question makes the next answer better. Step 6: Run Monthly Health Checks This is the step nobody does. It's the step that prevents the whole system from slowly rotting. Paste this: LINT PROMPT: Why this matters: when the AI writes something slightly wrong and you save it back, the next answer builds on the wrong thing. Two months later, you have five pages reinforcing the same error. Health checks catch this before it snowballs. One check per month. Ten minutes of your time. Non-negotiable if you want the system to stay trustworthy. Step 7: Let It Compound This is where the system earns its keep. After 4-6 weeks of consistent use, you're not just searching notes. You're querying a structured knowledge system that understands connections between your sources better than you do. Three ways to accelerate the compounding: File exploration outputs back: When the AI generates a comparison or analysis you find valuable, save it into wiki/ or outputs/. Karpathy says his own explorations and queries "always add up" in the knowledge base. Add visual outputs: Have the AI render answers as markdown tables, charts, or slide decks (Marp format). These become reusable assets, not throwaway chat messages. Version control everything: Your wiki is just markdown files. Initialize a git repo. You get full history, branching, and the ability to undo anything the AI messes up. Okay. That's the build. Now here's the part nobody else will tell you. Where This System Breaks (The Honest Version) This is a nascent pattern, not a finished product. Karpathy himself called it "a hacky collection of scripts" and said there's room for a real product. Here's what you need to know before trusting your knowledge to it: Context Window Ceiling. Karpathy's wiki works at ~100 articles and ~400K words. But even 128K-token context windows only hold ~96K words. The AI reads selectively through the index, which means it can miss things. Research shows LLMs suffer from "lost in the middle" effects where information in the center of long inputs gets deprioritized. Your query results will have blind spots. Accept this. Error Compounding. The AI writes a wiki page with a subtle mistake. You query against it. The mistake enters your answer. You file that answer back. Now two pages reinforce the same error. Monthly linting helps, but the AI doing the linting has the same blind spots as the AI that made the error. This is the single biggest risk. One commenter on Karpathy's gist nailed it: "When outputs get filed back, errors compound too." Hallucination Doesn't Disappear. The wiki approach reduces hallucination because the AI grounds answers in your sources. But it doesn't eliminate it. The AI can still synthesize connections that don't exist in the source material. And because the wiki looks authoritative (clean markdown, cross-references, citations), you're more likely to trust incorrect information. Don't. Cost Isn't Zero. Every ingest, every query, every lint check costs tokens. A single source that touches 10-15 pages can run $2-5 in API calls with frontier models. 50 sources is $100-250 just for ingestion. Cheaper than a research assistant. Not free. It Doesn't Scale to Enterprise. Karpathy says the index-file approach works without RAG at ~100 articles. At 10,000+ sources, this pattern breaks. The index grows too large. Consistency across thousands of pages becomes impossible. You'll need the infrastructure this system was designed to avoid. Know the ceiling. Single-Model Blind Spots. Your entire wiki is one model's interpretation of your sources. That model has biases and tendencies. For high-stakes decisions, one gist commenter suggested running queries through 4+ models independently, then comparing agreement. More robust. Also 4x the cost. What To Do About It → Error compounding: Monthly lint checks. Cross-check critical claims manually. Never trust the wiki blindly on high-stakes decisions. → Context limits: Keep each wiki focused on one domain. Multiple domains? Multiple knowledge bases. → Cost: Use frontier models for ingest and complex queries. Cheaper models for simple updates. → Hallucination: The schema above requires source citations on every claim. If a page makes a claim without [Source: filename], linting will flag it. → Scale: Accept this is a personal tool, not enterprise infrastructure. If you outgrow it, that's a good problem. Why It Still Matters Despite all the above, this is the most practical personal knowledge system available right now. The reason is dead simple: humans abandon wikis because maintenance grows faster than value. You start organizing, it feels great for two weeks, then the upkeep kills motivation and you never touch it again. LLMs don't get bored. They don't forget to update a cross-reference. They can touch 15 files in one pass without complaining. Lex Fridman confirmed he runs a similar setup. He generates interactive HTML visualizations and creates "mini-knowledge-bases" he loads into voice mode for 7-10 mile runs. Elvis Saravia from DAIR.AI has been building LLM knowledge bases for AI research curation. Multiple open-source implementations hit GitHub within 48 hours of Karpathy's gist. This isn't an experiment anymore. It's becoming standard practice for anyone doing serious research. Your Complete Prompt Library (Copy Everything) Every prompt from this article, collected in one place: SCHEMA: Copy the full CLAUDE.md template from Step 2. INGEST (one source): INGEST (batch, less supervised): QUERY: LINT: EXPLORE: BRIEF: Go Build It The difference between bookmarking Karpathy's gist and benefiting from it is one afternoon. Pick your topic. Create the folders. Copy the schema. Drop in what you already have. Run your first ingest. Then do it again tomorrow with another source. And next week with five more. The wiki gets smarter every time. That's the whole point. Three folders. One schema. An AI that does the grunt work you'd never do yourself. Stop collecting bookmarks. Start compiling knowledge. Unlock all my prompts + automations 👇 https://godofprompt.ai/complete-ai-bundle

タイトル: How I built a chief of staff on OpenClaw that's better than any human I've hired I'm a VC in the middle of a fundraise, sitting on boards, helping portfolio companies, and angel investing on the side. I've worked with great human EAs and chiefs of staff over the years, so I know what high-leverage support actually looks like. When the first AI APIs came out, I tried to build an AI version of that as a product and couldn't make it work. When OpenClaw launched I went deep immediately and haven't stopped. I have helped a number of friends set it up and each of them have asked what I have done to configure it and super power it. @ryancarson's post (link in the comments) about how he built his OpenClaw assistant was also great to see, and the response to it convinced me to finally write up what I've been building. What I have now is more capable than any human chief of staff I've ever worked with. It never forgets a commitment, it handles the small stuff without being asked, flags the important stuff without being told, and it gets better every week. Plus it never sleeps and it never tires. There are still some bumps, but less and less each week. If any of this is interesting, let me know. If there's enough interest I'll package the whole system up and open source it. What makes a great chief of staff? Before I walk through what I've built, it's worth thinking about what a great chief of staff actually does. Not the job description, the real leverage. The best ones I've worked with filtered the noise so only the right things reached me, made sure I walked into every meeting prepared and that nothing fell through after, kept the full picture of what was in flight and flagged what was slipping, tracked relationships and knew where things stood with every important person, and created the daily and weekly rhythm that kept everything moving. Her name is Stella. She handles all of these, and I'll walk through each one below. But the two things that make my setup genuinely different from other OpenClaw builds are the memory layer underneath it all and the continuous improvement loop that makes the system get better every week. I want to start there because they're what make everything else compound. Memory: the foundation Session memory is a lie. Any assistant that treats conversation history as its working context will fail you at the most frustrating moments. I built two layers. The first is daily notes: one markdown file per day (memory/YYYY-MM-DD.md) serving as a raw log of everything that happened. Meetings attended, decisions made, tasks added and completed, context that came up in conversation. A script called pulls from my sessions throughout the day and writes these automatically. The second is long-term memory in MEMORY.md, curated by Stella herself. Key people, active projects, lessons learned, decisions made. She periodically synthesizes this from the daily notes, and it's what she reads on startup to orient herself on what matters right now. Every meeting processed, every email triaged, and every task tracked feeds back into this picture continuously. Without this layer you have a capable assistant with amnesia. With it you have something closer to a person who's been working alongside you for months and never forgets anything. I've also come to really value that all of this lives in flat markdown files rather than a database. I can open any memory file, read it, edit it if something's wrong, and understand exactly what the assistant knows. I can back the whole thing up to git and restore anything instantly. There's no abstraction layer between me and the assistant's understanding of my world, which means I trust it more and fix things faster when they're off. Here's where the layers really come together. I'm managing a fundraise involving 100+ LP contacts across multiple countries. Stella tracks the full pipeline, keeps context on each LP and contact, and knows where every relationship stands. For first meetings, I've created a rule that she researches the fund and any recent content they or their partners have published, then preps me with what she found, how it maps to our thesis, and tailored talking points as part of the pre-meeting brief. For ongoing relationships, she knows exactly where they are in the pipeline, what was discussed and committed in our last meeting, and what the key issues are. You can't automate something as critical as a fundraise, but having this kind of structure underneath it means I'm spending my time on the conversations themselves rather than managing the process around them. Kaizen: the system improves itself This might be my favorite part, and the thing that makes it feel genuinely different from any assistant I've worked with, human or AI. Every Friday, a cron job runs research. Stella scans the OpenClaw community, checks for new patterns, looks at what other builders are doing, and saves findings to memory/kaizen-research-YYYY-MM-DD.md. On Sunday morning we review it together. She summarizes the week's research, surfaces the top ideas worth trying, and we talk through what to actually change. But it's not just external research. She also learns from our daily interactions. If I keep correcting something, or if a feature creates more friction than value, that gets captured in memory and eventually surfaces as a suggestion to fix it. If a triage filter is too noisy, or a brief format isn't landing, she notices and proposes a change. This is something a human chief of staff genuinely cannot do at scale. They can absolutely learn from working with you, but they can't simultaneously scan what hundreds of other builders are doing and cross-reference it against your system every week. That combination means the system gets measurably better on a cadence, not just when I remember to tinker with it. The Kaizen loop also drives continuous refactoring of the system itself. I've been through multiple cycles where I build a feature, run it for a few weeks, see how it actually fits my workflow, and then clean it up or cut it entirely. First versions are always too complicated, too noisy, or solving the wrong part of the problem. A smaller system you trust will always beat a bigger one you route around. The Kaizen process makes that refactoring disciplined rather than ad hoc. Meeting prep and follow-through This is where the best human EAs spend most of their time, and it's where Stella earns her keep. Sixty minutes before any external meeting, a brief arrives via WhatsApp. It pulls prior meeting notes on the attendees from memory, checks recent email threads, and finds any open action items. For LP meetings during the fundraise, it includes pipeline stage, what deck version they've seen, their questions from last time, and what I committed to sending. I walk into every meeting more prepared than I ever was with a full-time EA. On the other side, Stella processes every meeting through the Granola API (could be any note taker with an API). She fetches the notes, deduplicates against what's already been processed, and extracts action items. My tasks go to Todoist with proper projects and due dates. Commitments other people made get tracked in per-person markdown files, one per team member, so I can see at a glance what anyone owes or is owed. If I asked someone to do something three weeks ago and there's no update, the system knows because it's in their file. Everything from both sides, the prep and the follow-through, feeds back into memory. So the next time I meet with that person, the cycle starts again with a richer picture. Task and priority management A great chief of staff doesn't just keep a to-do list. They maintain the full picture of what's in flight and apply judgment about what actually matters today versus what can wait. Stella keeps the comprehensive task picture in structured markdown with all the context and history. That's the source of truth. The important near-term items get synced to Todoist (again, any Task management with an API), which is where I can see my near-term tasks at a glance. I think this split is important for understanding how agents should work alongside dedicated tools. The chat interface is great for certain modes, but a focused app with a focused view is still really valuable for getting through a task list. Stella keeps both in sync, and the combination is better than either one alone. Every evening she runs a task sweep. What's due, what's overdue, what's been sitting too long, what's coming this week. If something has rolled forward five days in a row, she flags the pattern. If there's a high-stakes meeting Tuesday and the prep isn't done, she says so. If nothing needs attention, silence. Stakeholder and relationship context A great chief of staff carries a mental CRM. They know who matters, where things stand with each person, what was discussed last, and what's sensitive. Stella does this at a scale no human could. She maintains persistent, structured context on every person, company, and project I'm working with. Relationship history, last touchpoint, open commitments, what they care about, where things stand. Every meeting processed, every email triaged, and every task tracked feeds back into this picture continuously. This is really just memory applied to relationships, but it's worth calling out because it's the piece that compounds the most visibly over time. Information filtering Stella runs email and calendar triage regularly across both my personal and work Gmail accounts, surfacing what needs action and what's worth knowing while dropping everything else. But filtering is only the beginning. She auto-pulls expense receipts and routes them to quarterly tracking, generates travel itineraries from booking confirmations and flags gaps, and drafts follow-up emails as part of fundraising tasks. When a task says "follow up with [person] re: [topic]," she pulls the last thread, drafts the follow-up in my voice, and queues it for my review. Operational rhythm Stella sends a morning brief at 9am and an evening wrap at 6pm, both via WhatsApp. The morning brief covers top priorities, overdue tasks, today's calendar, and anything that needs attention before I open my laptop. The evening wrap covers what happened, what stalled, and what to prep for tomorrow. If there's nothing to say, I hear nothing. This is the piece that makes it feel like working with a chief of staff rather than using a tool. She structures my day without me asking. Research and intelligence A weekly curated digest of the X accounts I track and newsletters I subscribe to, organized into tiers (AI researchers, VCs, founders, operators), scored by engagement and filtered for relevance. It turns 45 minutes of scrolling into a few minutes of high value reading. Judgement vs predictability One design rule that makes everything above reliable: LLMs handle judgment, and scripts handle everything else. Anything deterministic like reading files, calling APIs, sending messages, or comparing timestamps lives in Python. The LLM layer handles synthesis, prioritization, drafting, and anything where the answer requires reasoning rather than computation. When you push deterministic work through an LLM, things break in unpredictable ways and you stop trusting the system. Once you get the layer separation right, it becomes something you actually depend on. Where this is going I didn't get the best assistant I've ever had by asking better questions. I got it by giving the system a better operating model. I couldn't go back, and I can only imagine where this will be in a year. If you want to build something like this, or if you've built something similar, I want to hear about it. If there's enough interest I'll package it up and share.

タイトル: From Hierarchy to Intelligence At Sequoia, we see that speed is the best predictor of start-up success. Most companies are focused on AI as a productivity enhancer. Few are focused on the potential of AI to change how we work together. Block is showing what it looks like to fundamentally rethink organization design, ultimately harnessing AI to increase speed as a compounding competitive advantage. Two thousand years before the first corporate org chart, the Roman Army solved a problem that every large organization still faces: how do you coordinate thousands of people across vast distances with limited communication? Their answer was a nested hierarchy with a consistent span of control at every level. The smallest unit was the contubernium, eight soldiers who shared a tent, equipment, and a mule, led by a decanus. Ten contubernia formed a century of eighty men under a centurion. Six centuries made a cohort. Ten cohorts made a legion of roughly 5,000. At each layer, a named commander held defined authority, aggregated information from below, and relayed decisions from above. The structure (8 → 80 → 480 → 5,000) was an information routing protocol built around a simple human limitation: a leader can effectively manage somewhere between three and eight people. The Romans discovered this through centuries of warfare. Even today, the US Army's hierarchical chain follows a similar pattern. We now call it "span of control," and it remains the governing constraint of every large organization on earth. The next big change came from Prussia. After Napoleon's army destroyed the Prussian forces at the Battle of Jena in 1806, a group of reformers led by Scharnhorst and Gneisenau rebuilt the military around an uncomfortable truth: you cannot depend on individual genius at the top. You need a system. They created the General Staff, a dedicated class of trained officers whose job was not to fight but to plan operations, process information, and coordinate across units. Scharnhorst intended these staff officers to "support incompetent Generals, providing the talents that might otherwise be wanting among leaders and commanders." This was middle management before the term existed. Professionals whose purpose was to route information, pre-compute decisions, and maintain alignment across a complex organization. The military also formalized the distinction between "line" and "staff" functions. Line advances the core mission. Staff provides specialized support. Every corporation still uses this vocabulary today. Military hierarchy entered the business world through the American railroads in the 1840s and 1850s. The U.S. Army lent West Point-trained engineers to private railroad companies, and these officers brought military organizational thinking with them. Staff and line hierarchies, divisional structure, bureaucratic systems of reporting and control: all of it was developed in the military before the railroads adopted it. In the mid-1850s, Daniel McCallum of the New York and Erie Railroad created the world's first organizational chart to manage a system stretching over 500 miles with thousands of workers. The informal management styles that worked for smaller railroads were failing. Train collisions were killing people. McCallum's chart formalized the same hierarchical logic the Romans had used: layers of authority, defined reporting lines, structured information flow. It became the blueprint for the modern corporation. Frederick Taylor (1856-1915), often called the "Father of Scientific Management," optimized what happened within that hierarchy. Taylor broke work into specialized tasks, assigned them to trained experts, and managed through measurement rather than intuition. This produced the functional pyramid organization - a structure optimized for efficiency within the information routing system that the military had pioneered and the railroads had commercialized. The first real stress test of functional hierarchy came during World War II. The Manhattan Project required physicists, chemists, engineers, metallurgists, and military officers to work across disciplinary boundaries toward a single objective under extreme secrecy and time pressure. Robert Oppenheimer organized Los Alamos into functional divisions but insisted on open collaboration across them, resisting the military's instinct to compartmentalize. When the implosion problem became critical in 1944, he reorganized the lab around it, creating cross-functional teams unlike anything in corporate America at the time. It worked, but it was a wartime exception led by a singular figure. The question the postwar business world faced was whether that kind of cross-functional coordination could be made routine. With the growth and globalization of companies after World War II, the scale limitations of functional design became acute. In 1959, McKinsey's Gilbert Clee and Alfred di Scipio published "Creating a World Enterprise" in the Harvard Business Review, providing an intellectual framework for a matrix organization that combined functional specialties with divisional units. Under the leadership of Marvin Bower, McKinsey helped companies like Shell and GE implement these principles, balancing central standards with local agility. This became the "professional" or "modern" corporation that propelled the postwar global economy. Over time, other frameworks emerged to address the complexity, rigidity, and bureaucracy of matrix structures. The McKinsey 7-S framework, developed in the late 1970s by Tom Peters and Robert Waterman, distinguished the "hard Ss" (Strategy, Structure, Systems) from the "soft Ss" (Shared Values, Skills, Staff, Style). The core idea was that structural elements alone were insufficient. Organizational effectiveness required alignment across cultural traits and the human factors that determine whether a strategy actually succeeds. In more recent decades, technology companies have experimented aggressively with organization structure. Spotify popularized cross-functional squads with short sprint cycles. Zappos attempted Holacracy, eliminating management titles entirely. Valve operated with a flat structure and no formal hierarchy. Each of these experiments revealed something about the limitations of traditional hierarchy, but none solved the underlying problem. Spotify moved back toward conventional management as it scaled. Zappos saw significant attrition. Valve's model proved difficult to scale beyond a few hundred people. As organizations grow into the thousands, they revert to hierarchical coordination because no alternative information routing mechanism has been powerful enough to replace it. The constraint is the same one the Romans faced and the Marine Corps rediscovered in World War II: narrowing span of control means adding layers of command, but more layers mean slower information flow. Two thousand years of organizational innovation has been an attempt to work around this tradeoff without breaking it. So what's different now? At Block, we're questioning the underlying assumption: that organizations have to be hierarchically organized with humans as the coordination mechanism. Instead, we intend to replace what the hierarchy does. Most companies using AI today are giving everyone a copilot, which makes the existing structure work slightly better without changing it. We're after something different: a company built as an intelligence (or mini-AGI). We are not the first to try to move beyond traditional hierarchy. Haier's rendanheyi model, platform organizations, "data-driven" management: these are real attempts at the same problem. What they lacked was a technology capable of actually performing the coordination functions that hierarchy exists to provide. AI is that technology. For the first time, a system can maintain a continuously updated model of an entire business and use it to coordinate work in ways that previously required humans relaying information through layers of management. For this to work, a company needs two things: a kind of "world model" of its own operations, and a customer signal rich enough to make that model useful. Block is remote-first. Everything we do creates artifacts. Decisions, discussions, code, designs, plans, problems, and progress all exist as recorded actions. It's the raw material for a company world model. In a traditional company, a manager's job is to know what's happening across their team and relay that context up and down the chain. In a remote-first company where work is already machine-readable, AI can build and maintain that picture continuously. What's being built, what's blocked, where resources are allocated, what's working and what isn't. That's the information the hierarchy used to carry. The company world model carries it instead. But the capability of the system is only as good as the quality of the customer signal feeding it. And money is the most honest signal in the world. People lie on surveys. They ignore ads. They abandon carts. But when they spend, save, send, borrow, or repay, that's the truth. Every transaction is a fact about someone's life. Block sees both sides of millions of these transactions every day, the buyer through Cash App and the seller through Square, plus the operational data from running the merchant's business. That gives the customer world model something rare: a per-customer, per-merchant understanding of financial reality built from honest signal that compounds. The richer the signal, the better the model. The better the model, the more transactions. The more transactions, the richer the signal. Together, the company world model and the customer world model form the foundation for a different kind of company. Instead of product teams building predetermined roadmaps, you build four things. First, capabilities. The atomic financial primitives: payments, lending, card issuance, banking, buy-now-pay-later, payroll, and so on. These are not products. They are building blocks that are hard to acquire and maintain (some have network effects and regulatory permission). They have no UIs of their own. They have reliability, compliance, and performance targets. Second, a world model. This has two sides. The company world model is how the company understands itself and its own operations, performance, and priorities, replacing the information that used to flow through layers of management. The customer world model is the per-customer, per-merchant, per-market representation built from proprietary transaction data. It starts with raw transaction data today and evolves toward full causal and predictive models over time. Third, an intelligence layer. This is what composes capabilities into solutions for specific customers at specific moments and delivers them proactively. A restaurant's cash flow is tightening ahead of a seasonal dip the model has seen before. The intelligence layer composes a short-term loan from the lending capability, adjusts the repayment schedule using the payments capability, and surfaces it to the merchant before they even think to look for financing. A Cash App user's spending pattern shifts in a way the model associates with a move to a new city. The intelligence layer composes a new direct deposit setup, a Cash App Card with boosted categories for their new neighborhood, and a savings goal calibrated to their updated income. No product manager decided to build either solution. The capabilities existed. The intelligence layer recognized the moment and composed them. Fourth, interfaces (hardware and software). Square, Cash App, Afterpay, TIDAL, bitkey, proto. These are delivery surfaces through which the intelligence layer delivers composed solutions. They are important, but they are not where the value is created. The value is in the model and the intelligence. When the intelligence layer tries to compose a solution and can't because the capability doesn't exist, that failure signal is the future roadmap. The traditional roadmap, where product managers hypothesize about what to build next, is any company's ultimate limiting factor. In this model, customer reality generates the backlog directly. If this is what the company builds, then the question becomes: what do the people do? The org structure follows from this, and it inverts the traditional picture. In a conventional company, the intelligence is spread throughout the people and the hierarchy routes it. In this model, the intelligence lives in the system. The people are on the edge. The edge is where the action is. The edge is where the intelligence makes contact with reality. People reach into places the model can't go yet. They sense things the model can't perceive: intuition, opinionated direction, cultural context, trust dynamics, the feeling in a room. They make the calls the model shouldn't make on its own, especially ethical decisions, novel situations, and high-stakes moments where the cost of being wrong is existential. A world model that can't touch the world is just a database. But the edge doesn't need layers of management to coordinate it. The world model gives every person at the edge the context they need to act without waiting for information to travel up and down a chain of command. In practice, this means we normalize down to three roles. Individual contributors (ICs) who build and operate capabilities, the model, the intelligence layer, and the interfaces. They are deep specialists and experts in a specific layer of the system. The world model provides the context that a manager used to provide, so ICs can make decisions about their layer without waiting to be told what to do. Directly Responsible Individuals (DRI) who own specific cross-cutting problems or opportunities and customer outcomes. A DRI might own the problem of merchant churn in a specific segment for 90 days, with full authority to pull resources from the world model team, the lending capability team, and the interface team as needed. DRIs may persist on certain problems or move elsewhere to solve new ones. Player-coaches who combine building with developing people. They replace the traditional manager whose primary job was information routing. A player-coach still writes code or builds models or designs interfaces. They also invest in the growth of the people around them. They don't spend their days in status meetings, alignment sessions, and priority negotiations. The world model handles alignment. The DRI structure handles strategy and priority. The player-coach handles craft and people. There is no need for a permanent middle management layer. Everything else the old hierarchy did, the system coordinates, and everyone is empowered, with a role that's much closer to the work and the customer. Block is in the early stages of this transition. It will be a difficult one, and parts of it will likely break before they work. We're writing about it now because we believe every company will eventually need to confront the same question we did: what does your company understand that is genuinely hard to understand, and is that understanding getting deeper every day? If the answer is nothing, AI is just a cost optimization story. You cut headcount, improve margins for a few quarters, and eventually get absorbed by something smarter. If the answer is deep, AI doesn't augment your company. It reveals what your company actually is. Block's answer is the economic graph: millions of merchants and consumers, both sides of every transaction, financial behavior observed in real time. That understanding compounds every second the system operates. We believe the pattern behind this, a company organized as an intelligence rather than a hierarchy, is significant enough that it will reshape how companies of all kinds operate over the coming years. Block is far enough along to show the idea is more than theory (though, we welcome debate and feedback to pressure test and improve our ideas). Companies move fast or slow based on information flow. Hierarchy and middle management impede information flow. For two thousand years, from the Roman contubernium to today's global enterprises, we have had no real alternative. Eight soldiers sharing a tent needed a decanus. Eighty men needed a centurion. Five thousand needed a legate. The question was never whether you needed layers. The question was whether humans were the only option for what those layers do. They aren't anymore. Block is building what comes next. https://block.xyz/inside/from-hierarchy-to-intelligence

タイトル: 20 Powerful Agentic-Skills for Claude, ChatGPT & Gemini. In this article, I’ve curated 20 powerful agentic skills you can add to any AI model to boost your productivity. Each skill is written in .md format, which is the standard for Claude. You can still copy the documents and paste them into ChatGPT or Gemini whenever you need to use the skill. I’ve group the skills into 5 categories and also attached a video guide showing how to easily add these skills to your Claude agent. Writing & Content Skills SCQA Writing Framework: SCQA is a framework for structuring communication, especially for writing or presentations. It stands for: S = Situation → Set the context. Explain the current state or background so the audience understands the starting point. C = Complication → Introduce a problem, tension, or challenge that disrupts the situation. This is what makes the story or argument interesting. Q = Question → Pose the key question that naturally arises from the complication. What does the audience want answered? A = Answer → Provide the solution, insight, or recommendation. This resolves the question logically. Think of it as storytelling with logic. It’s widely used in consulting, business writing, and content creation because it makes complex ideas easy to follow. Content Repurposing Engine Tone & Style Enforcer Long-Form to Summary Compressor Structured Copywriting Skill Visual & Infographic Skills Excalidraw Diagram Generator Infographic Builder Flowchart Decision Builder UI/UX Layout Advisor Research & Analysis Skills Deep Research Synthesizer Onchain Transaction Analyzer Source Validation Skill Competitive Intelligence Skill Knowledge Structuring Skill Video Script Generator Video Editing Planner Hook Generator Caption & Subtitle Formatter Coding & Automation Skills Code Review Skill Workflow Automation Agent Skill Creator (Meta Skill) DevOps Assistant Thanks for your attention on this matter!!!

タイトル: The algorithm that crashed Wall Street Google just crashed memory stocks with a single algorithm. TurboQuant compresses AI memory 6x, speeds it up 8x - with zero accuracy loss. And it's free. AI's invisible memory crisis Every time you chat with an AI, the model doesn't just read your last message. It re-reads the entire conversation. Every token it has ever processed. To avoid recalculating all of that from scratch, transformers store it in the KV cache - the model's short-term working memory. The catch: this memory grows linearly with every token. A single 128K-token prompt on a 70B model eats around 40 GB of GPU memory - just for the cache. That's before the model weights even load. At long contexts, the cache consumes more memory than the model itself. And modern LLMs are memory-bound, not compute-bound. Generating a token is cheap math - but loading data from memory is expensive. Over and over again. This is what limits: how many users one GPU can serve how long a context window the model can handle how much inference costs at scale The industry calls it the Memory Wall. And until this week, no one had a clean answer. Google's answer: compress everything, lose nothing Google Research published TurboQuant on March 25. It compresses the KV cache from 16 bits to 3 bits per value. 6x less memory. Zero accuracy loss. Two stages: PolarQuant - rotates vectors so their distribution becomes predictable. Lets you precompute the quantizer once, no calibration needed. Eliminates the 1-2 bits of overhead every previous method wastes on compression metadata. QJL - reduces leftover error to a single sign bit. Kills bias in attention scores. Compressed output is statistically identical to full precision. The key difference: every prior method compressed data but added overhead that ate the gains at extreme rates. TurboQuant hits near-zero overhead - approaching the Shannon limit, the theoretical floor of compression. Plug-and-play. Works on any model. No retraining. No calibration. No fine-tuning risk. What the benchmarks actually show Memory: 6x reduction minimum. 70B model KV cache drops from ~80 GB to ~13 GB Speed: up to 8x faster attention on NVIDIA H100 (4-bit vs 32-bit) Accuracy: perfect score on Needle-in-a-Haystack - finding one sentence in 100,000 words Cost: 50%+ cut in cloud inference spend Plug-and-play on any model - Llama, Mistral, Gemma, anything Caveat: 8x is for attention logits, not full inference. Tests only on models up to ~8B. 70B+ still unproven. Still - a free, training-free algorithm that cuts memory 6x and hits near the theoretical compression limit. That's not incremental. That's generational. Wall Street panicked Investors didn't wait for peer review: SanDisk: -11% Micron: -7% SK hynix: -6.2% Samsung: -4.7% NVIDIA: -4.2% Philadelphia Semiconductor Index: -4.8% All while Nasdaq was going up. One research paper. No product. No code released. Just math - and billions wiped off memory stocks in 48 hours. A Citrini Research analyst put it best: "It's like saying Aramco should crash because Toyota released a next-gen hybrid engine." Wells Fargo's Andrew Rocha noted that compression algorithms have existed for years and never fundamentally changed memory procurement volumes. But this time the market didn't care about nuance - it sold first and asked questions later. The internet figured it out immediately If you've seen HBO's "Silicon Valley" - you already get the joke. Pied Piper, the fictional startup, built a compression algorithm that changed the rules of computing. TurboQuant is literally the same plot. Except it's real. The memes hit instantly. The Google Research post on X crossed 7.7 million views in under 24 hours. Cloudflare CEO Matthew Prince called it "Google's DeepSeek moment." Someone else wrote: "Well, we all know who stole the Pied Piper codebase now." Another user calculated a Weismann Score of 5.2 - a reference only Silicon Valley fans would catch. The comparison isn't perfect though. Pied Piper was going to change all of computing. TurboQuant only targets inference memory - not training. But as internet reactions go, the shoe fits. The community didn't wait Google released no official code. Just a paper with math and pseudocode. Within 24 hours, developers built working implementations from scratch - reading formulas and writing code. This adoption speed for a research paper is almost unheard of. What's already done: Custom Triton kernel in PyTorch, tested on Gemma 3 4B on RTX 4090 - byte-identical output at 2-bit compression 35B model running on Apple Silicon via MLX - 6/6 on needle-in-a-haystack at every quantization level Three developers building C and CUDA implementations in llama.cpp - one reporting 18/18 tests passed One person used GPT-5.4 to write a full MLX implementation in 25 minutes One catch: an early implementer found that a naive QJL implementation produces garbage output. Without proper bias correction, quantization errors compound and the model becomes unusable. The math has to be followed exactly. The fact that people are reimplementing a paper in hours - without official code, across Triton, MLX, and CUDA - says two things: the math is clean enough to reproduce, and the problem is urgent enough that nobody wants to wait. The real significance isn't the compression. It's the ceiling One of the most clear-eyed takes on TurboQuant: it matters less because it saves more memory, and more because it shows where the limit is. Here's the compression journey so far: No compression: 1x (baseline) Basic quantization: 2-3x Outlier-aware methods: 3-4x TurboQuant in real systems: 4-4.5x TurboQuant's error rate is already approaching the Shannon limit - the absolute theoretical floor defined by information theory. There's almost no room left to squeeze. The paper itself proves this with a mathematical lower bound. Any quantization algorithm, no matter how clever, cannot beat this limit. TurboQuant is nearly there. What this means: the next big breakthrough in AI efficiency won't come from compression. It will require a fundamentally different path - new architectures, new attention mechanisms, or rethinking how models store context entirely. The plot twist: less memory needed = more memory sold This is the Jevons Paradox. When a resource gets cheaper to use, people don't use less of it. They use more. Save 6x on memory? Companies will run models 6x more complex. Open up use cases that were too expensive before - real-time video, million-token documents, multimodal agents running 24/7. One GPU that served one session will now serve six. But demand won't stay at six. It will grow to sixty. KB Securities analyst put it directly: technologies like TurboQuant lower adoption barriers and massively expand total demand. Memory makers end up as the biggest beneficiaries of AI expansion - not the victims. The same thing happened with DeepSeek. Everyone said cheaper training would kill GPU demand. Instead it accelerated it. More people could afford to train models, so more people did. Bottom line: memory demand will likely grow, not shrink. Your hardware just got an upgrade. For free No new chip. No new device. Just a software algorithm - and suddenly your existing hardware can do things it couldn't do last week. Mac Mini: 100,000-token conversations with no quality loss. That's a full book-length context on a $600 machine Smartphones: 32,000+ token context windows - purely through software, no hardware swap needed RTX 4090: models that required multi-GPU setups now fit on a single card Enterprise: cut the number of GPUs needed for long-context tasks, potentially slashing cloud spend by 50%+ The gap between local AI and cloud subscriptions just got dramatically smaller. Running serious models at home stopped being a compromise - it's becoming a real option. The bottom line One research paper. No code. No product launch. 48 hours later: Billions wiped off memory stocks Three independent implementations built from math alone An entire industry recalculating demand forecasts TurboQuant won't kill the memory market. It redrew the line between what software can solve and what still needs hardware. And the real story? We just hit the compression ceiling. Whatever comes next - it won't be compression. Information sourced from: Google Research Blog, original ICLR 2026 paper, TechCrunch, VentureBeat, Tom's Hardware, The Next Web, Investing.com, The Korea Herald, CNBC, llama.cpp GitHub discussions, turboquant.net, dejan.ai, Sketchplanations.

タイトル: Living With an AI agent - My Full Setup After 2 Months I wake up at 7am and there's a briefing in my Telegram. overnight moves, arXiv papers on AI agents, Hacker News threads worth reading, Reddit posts from r/LocalLLaMA, and a prioritised list of what I should focus on today, cross-referenced against my actual projects and deadlines. I didn't set an alarm for any of this. I didn't open a browser. I didn't check Twitter. an AI agent on a Mac Mini in my living room did all of it while I slept. it ran 18 scheduled jobs between 7am yesterday and 7am today. it searched the web, fetched full articles, ran shell scripts against 8 different APIs, wrote findings to memory files, drafted content for my Telegram channel, scored those drafts against my voice rules, and logged everything to a structured context system that makes tomorrow's research smarter than today's. total monthly cost: $21. this is the full breakdown. every component, every config, every bug I hit, every fix I found. if you want to build something like this, everything you need is here. what we're going to talk about: /1/ the hardware /2/ the agent framework: Hermes Agent /3/ the model stack /4/ cron jobs: the autonomous research pipeline /5/ shell scripts: the automation layer /6/ custom skills: on-demand capabilities /7/ ALIVE: the context system that makes everything compound /8/ teaching an AI your writing style: the voice feedback loop /9/ what I'd do differently (lessons from 2 months) /10/ how to replicate this (minimum & full set up guide) /11/ the philosophy The Hardware Mac Mini M4, 16GB RAM. bought it in early 2026. it sits in my living room, runs 24/7, and powers everything: the AI agent gateway, a local LLM for compression, occasional Docker containers, and all 18 cron jobs. total disk footprint: ~120MB for the agent framework + 6.6GB for the local LLM models. in short: this is a $600 computer the size of a sandwich. it runs my entire AI setup. there is no cloud server or GPU cluster, no monthly AWS bill. a box in the living room. The Agent Framework: Hermes Agent Hermes Agent is an open-source Python framework by @NousResearch. it's the backbone of everything. the interface is a Telegram bot. I text it like I'm texting a colleague. it has access to a terminal, can read and write files, search the web, fetch full web pages, execute code, run shell scripts, and use custom skills I've written for it. it runs as a macOS launchd service, meaning it starts on boot and restarts if it crashes. what Hermes gives you: Telegram bot with full tool access (terminal, files, web search, web fetch, code execution) cron scheduler - jobs run on a schedule, results delivered to Telegram skills system - markdown files that teach the agent new capabilities memory system - persistent files the agent reads and writes across sessions session management - compression, idle timeout, daily reset MCP support - model context protocol servers for extending tool access what it doesn't give you: multi-model fallback chains (single fallback only, if both models fail, the job fails) automatic session cleanup (needs manual scripting) good writing (model limitation, not framework limitation, more on this later) in short: think of it as an AI assistant that lives in your Telegram. but instead of just answering questions when you ask, it also runs scheduled tasks in the background, remembers things between conversations, and can use tools, like a junior employee who works 24/7, never sleeps, and has access to your terminal. The Model Stack this is where it gets interesting. I don't use one model. I use three, each for a different job. Interactive Chat: GLM-5 via Z.AI when I text the bot directly, it uses GLM-5, a Chinese model by Zhipu AI, accessed through Z.AI's coding plan at $21/month. why GLM-5? it's the cheapest model with tool calling that actually works. it reasons well, follows complex multi-step instructions, and handles the research pipeline reliably. however, its writing is terrible. every draft sounds like AI. but I don't need it to write, I need it to research, execute, and coordinate. I handle the writing myself. the reasoning token trap: GLM-5 always generates reasoning tokens, internal "thinking" before it responds. there's a reasoning_effort config option in hermes that's supposed to control this. it does nothing for GLM-5. the model thinks regardless. those reasoning tokens get stored in the conversation history. every new message includes all previous reasoning. so your context fills up fast. after 15-20 exchanges, the agent slows to a crawl because it's re-processing thousands of reasoning tokens from earlier in the conversation. in short: imagine every time you ask your assistant a question, they re-read their entire diary from the beginning of the day before answering. Cron Jobs: GLM-4.7 via Z.AI all 18 automated jobs run on GLM-4.7, same provider, cheaper model. this is critical because Z.AI has a rate limit: 600 prompts per 5 hours on the coding plan. if the cron jobs used GLM-5, they'd eat through my interactive quota. Compression: Qwen3.5 via local Ollama this is the piece that took the longest to get right and taught me the most. every AI model has a context window, the amount of text it can "see" at once. as conversations get longer, the context fills up. compression summarises old messages to free up space. originally, compression used the same cloud API (Z.AI) as everything else. this created a death spiral: cron job runs → generates messages → context grows context hits threshold → triggers compression compression calls Z.AI → uses API quota more cron jobs run → more compression calls → more quota used rate limit hit → compression fails silently → context keeps growing context grows unbounded → agent hangs for 10+ minutes trying to process a bloated session I didn't notice for days. the agent was just... slow. I assumed it was network latency. the fix: run compression on a local model using Ollama. completely free. no rate limits. no API dependency. I installed Ollama on the Mac Mini, pulled qwen3.5:4b (a 3.4GB model that runs at ~20 tokens/second on the M4 chip), and pointed compression at localhost. done. the idle timeout discovery: even after fixing compression, the agent was still slow during peak hours. found that the default session idle timeout was 1440 minutes - 24 hours. meaning sessions never reset during the day. they grew from 7am morning briefing through every cron job until the next morning. changed it to 60 minutes. now sessions reset between jobs. response times went from 10+ minutes during heavy periods to under 5 seconds. in short: imagine your desk gets covered in papers throughout the day but you never clean it. by evening, you can't find anything and every task takes 10x longer because you're shuffling through a mountain of old notes. the idle timeout is how often you clean the desk. 24 hours means you never clean it. 60 minutes means you clean it every hour. same work gets done, 100x faster. the config that makes it work: compression threshold 0.50 means it triggers when the context is half full. Ollama keep-alive of 5 minutes means the model auto-unloads from RAM after 5 minutes of idle, freeing memory for other tasks. 18 Cron Jobs: The Autonomous Research Pipeline this is the core of the system. 18 scheduled jobs that run throughout the day, researching, monitoring, drafting, and maintaining the infrastructure. every job runs on GLM-4.7. every job delivers results to Telegram. every job reads from and writes to the structured context system (more on that later). daily schedule 07:00 - morning briefing. weather, crypto prices, stablecoin pegs, RWA sector moves, overnight research recap, top Hacker News and Reddit threads. reads all my project context files to surface what matters today. 07:30 - competitor dashboard. tracks 11+ CDP and stablecoin protocols, TVL changes, governance proposals, partnership announcements. I work in DeFi. knowing what competitors are doing before they announce it publicly is the job. 09:00 & 17:00 - dune monitor. runs on-chain query monitoring. twice daily. 10:00 - grimoire drafter. drafts posts for my Telegram channel in visual-first format. 11:00 - daily nudge. this one is different. it reads ALL my project context files across every venture, the agent itself, my content strategy, and suggests where to focus based on urgency, deadlines, and cross-project tensions. it's a priority engine. 12:00 - draft review. picks the best unposted draft, checks it against voice rules, delivers to Telegram for my review. 14:00 - research-ai. AI news, papers, agent frameworks. sources: arXiv, Reddit, Hacker News, Techmeme, news sites. 16:00 - research-arcana. redacted. 18:00 - research-defi. stablecoins, RWA, governance, competitor analysis. 20:00 - research-deepdive. picks one topic from the day's findings and goes deep. full analysis with web fetching, data compilation, and a structured report. 21:00 - health check. monitors all crons, gateway status, disk space, launchd services. 22:00 - content performance. tracks what content performed well, logs metrics. 23:00 - nightly builder. this is the wildest one. it looks at gaps identified during research like missing scripts, broken data sources, needed tools... and autonomously writes code to fill them. it has built monitoring scripts, fixed broken parsers, and created new data pipelines. while I sleep. periodic schedule monday/thursday 09:00 - outreach CRM. automates parts of my BD workflow. monday 09:00 - weekly intel. competitive intelligence brief. sunday/wednesday 20:00 - learning digest. curated learning resources from the week. sunday 08:00 - weekly planner. reads performance data, research findings, and project states → produces a content and priority plan for the week. hourly every hour 9am-8pm - breaking news. RSS feeds + TVL monitoring + stablecoin peg alerts + viral tweet detection. if something moves, I know within the hour. now, let's understand how a research cron actually works. let me break down the research-ai job because it's the most complex. the prompt is ~3,000 words. here's what it actually does, step by step: step 1 — source verification rules. before anything else, the prompt hammers one rule: never include a finding without a URL you actually visited. if you searched but got no results, say so. before including any finding, ask yourself: did I actually visit this URL? this matters because GLM-4.7 hallucinates URLs. not often, but enough. this instruction reduces hallucinated sources to near-zero. step 2 — context loading. the agent reads 7 files: its persistent memory, the operations log, research priorities, my content voice guide, voice corrections from past drafts, the AI research archive, and the relevant project context files. this means the research is guided by what I'm actually working on. step 3 — email check. checks a bot alerts inbox using Himalaya (a CLI email client). catches newsletter alerts and automated notifications. step 4 — arXiv scan. runs a custom Python script that queries the arXiv API for recent papers on AI agents, LLM reasoning, and tool use. filters by relevance. step 5 — nightly builder check. did the overnight autonomous builder create any new tools? if so, note them. step 6 — web search. diverse sources required. the prompt explicitly says: fetch Techmeme first, then Hacker News, then Reddit, then use web search to fill gaps. never rely on web search alone. this source diversity rule exists because GLM-4.7 defaults to Reddit for everything. Reddit results rank high in search. without the explicit rule, every morning briefing was Reddit-only for days before I noticed. step 7 — deep read. web-fetch the 2-3 most interesting articles in full. step 8 — WRITE findings. this is mandatory. the prompt says: "a session with no writes is a failure." findings get written to the AI research archive file. this means tomorrow's session starts from a richer knowledge base. step 9 — draft. if something is content-worthy, draft it in personal experience format. read voice corrections first. step 10 — quality check. run check_draft.sh. must score ≥70/100 against voice rules. step 11 — operations log. one line summarising what happened. step 12 — context update. update the project context files with session results. step 13 — self-check. "did I write new content to the research archive? if NO, go back and do step 8." this prevents the agent from doing all the research and then not saving anything. in short: each research job is like sending an employee on a research mission with a 15-point checklist. load your priorities. check your inbox. scan the academic papers. read the news. actually click through and read the full articles, don't just skim headlines. write down what you found. draft something if it's interesting. check that your draft doesn't sound like garbage. log what you did. update the team on what you learned. and if you didn't write anything down, go back and do it. 35 Shell Scripts: The Automation Layer every script lives in ~/.hermes/scripts/, chmod 700, symlinked at ~/scripts/ so the agent can call them easily. these are the muscles of the system. the agent is the brain. the scripts are how it interacts with the world. data source scripts coingecko.sh - BTC/ETH/AAVE/MKR prices, 24h changes, stablecoin peg data. no API key needed. defillama.sh - CDP protocol TVL rankings, stablecoin market caps. no API key needed. rwa-tracker.sh - full RWA sector scan. 120+ protocols, $25B+ TVL. tracks top movers. no API key needed. hackernews.sh - top stories from Hacker News filtered by keywords (AI, DeFi, agents, LLM). uses the Firebase API. no key. reddit-digest.sh - top posts from r/LocalLLaMA, r/MachineLearning, r/defi. uses Reddit's public JSON endpoints. no key. governance-tracker.sh - active governance votes on Snapshot.org for protocols I care about (Morpho, Curve, Lido). plus forum RSS feeds. arxiv-digest.py - queries the arXiv API for latest papers on agents, reasoning, tool use. filters and formats. fred.sh - federal reserve economic data. fed rates, inflation, money supply. free API key. monitoring scripts tvl-monitor.sh - caches a TVL snapshot, alerts if any tracked protocol moves >10% since last check. stablecoin-supply-monitor.sh - detects >$100M USDT/USDC/DAI mints or burns. large supply changes signal market moves. breaking-news.sh - combines all monitors + RSS feeds + viral tweet detection into one alert system. health-check.sh - monitors cron job execution, launchd services, gateway status, disk space, PAT expiry dates. content pipeline scripts check_draft.sh - scores drafts 0-100 against my voice rules. auto-runs on every draft. anything below 70 gets flagged. draft-review.sh - picks the best unposted draft daily, flags drafts with wrong formatting. mark_posted.sh - moves draft to the posted archive, logs to content calendar. log-performance.sh - records post metrics: impressions, engagement, bookmarks. infrastructure scripts update-walnut.sh - 77 lines of bash. prepends a timestamped log entry to the relevant project context file. called at the end of every research cron. this is the glue between the cron jobs and the context system. compact_memory.sh - archives session logs older than 3 days, trims the operations log to 7 days. prevents unbounded growth. github-push-nightly.sh - the paranoia script. runs 13 regex patterns scanning for leaked secrets (API keys, tokens, passwords) before pushing to Git. three layers of checks. auto-update.sh - daily Hermes framework update trigger. docker-cleanup.sh - removes zombie sandbox containers older than 4 hours. in short: the scripts are like having a toolbox. the AI agent knows how to use a hammer, a screwdriver, and a wrench, but you have to give it the actual tools. each script is a tool that connects the agent to a specific data source or capability. without them, the agent can only search the web. with them, it can query DeFi protocols, scan academic papers, monitor on-chain activity, check its own health, and push code to GitHub. none of these are complicated. most are under 50 lines. the value is in having 35 of them working together inside a scheduled system. 6 Custom Skills: on-Demand Capabilities skills in Hermes are markdown files. you write a markdown document explaining what you want the agent to do, when, and how, and the agent reads it when invoked. the SKILL.md file is the instruction set. 1. walnuts - cross-project context type walnuts in Telegram → the agent reads all 5 of my project context files → synthesises a cross-venture view with priorities, blockers, and tensions. example output: this is my strategic dashboard. one word in Telegram, 10 seconds, full situational awareness across every project. 2. grimoire - Telegram channel drafting drafts posts for my Telegram channel @witcheergrimoire in visual-first format: 1-4 sentences + image. includes URL verification rules because GLM-5 hallucinates URLs about 5% of the time. 3. yari-intel - competitive intelligence on-demand market analysis: CDP TVL rankings, competitor deep dives, RWA opportunity mapping, governance tracking. combines DeFi llama data + shell scripts + web search. 4. arcana-intel - consulting market research redacted. 5. alpha-scanner - cross-source signal detection combines Polymarket odds, stablecoin flows, RWA momentum, social signal (Reddit + Thread Reader), and governance alpha. 6. voice-learn - the draft feedback loop this one deserves its own section (below). in short: when I edit an AI draft before posting, this skill extracts what I changed and why, saves the lesson, and every future draft reads those lessons before writing. in short: skills are like SOPs (Standard Operating Procedures) for the AI. you write a document saying "when I say X, do Y in this specific way." the AI reads the document at runtime and follows it. you write a markdown file and the agent has a new capability. the beauty is iteration speed. writing a new skill takes 10 minutes. testing it takes one Telegram message. refining it takes editing a text file. compare that to building a plugin, deploying a service, wiring an API. ALIVE: The Context System That Makes Everything Compound this is the part most people haven't seen before, and it's the part that makes the biggest difference. created by @stackwalnuts, ALIVE is a structured context system. think of it as a personal knowledge graph on disk. the basic unit is a "walnut", a context container for one project or domain. the structure each walnut has a _core/ folder with 5 files: key.md — identity, thesis, connections to other walnuts now.md — current phase, next action, blockers tasks.md — urgent / active / backlog insights.md — standing knowledge, lessons learned log.md — session history (prepend-only, newest first) that's it. three integration layers to make it works perfectly: layer 1: cron jobs WRITE to walnuts. at the end of every research cron, the update-walnut.sh script prepends a log entry to the relevant walnut. research-ai logs to the oz-agent walnut. research-defi logs to the yari-finance walnut. research-arcana logs to the arcana walnut. this means every walnut accumulates a history of what was researched, found, and learned, automatically. layer 2: cron jobs READ from walnuts. before researching, each cron reads the relevant walnut's tasks and insights. research aligns to what I'm actually working on. research-ai reads oz-agent tasks ("improve draft quality") → searches for prompt engineering techniques specifically. research-defi reads yari-finance tasks ("map RWA partnership opportunities") → searches for RWA protocol news specifically. the morning briefing reads ALL walnut now.md files → surfaces what matters today across every project. layer 3: interactive Telegram gets walnut context. when I type walnuts, the agent reads all 5 walnut files and gives me a synthesised cross-venture view. context stays in session until idle timeout. here's where it compounds: I update walnut tasks (manually or via Telegram) cron jobs read the tasks → research aligns to priorities research produces findings → findings update the walnut log I adjust tasks based on findings next cron picks up adjusted priorities research becomes more focused findings become more relevant repeat each cron knows what I'm trying to accomplish. it reads my priorities before searching. it writes what it found after searching. and the next session starts smarter. both Claude Code and Hermes Agent read and write the same files natively. same context layer. in short: imagine you have 5 notebooks, one for each project. before doing research, you re-read the relevant notebook to remember what you're working on and what you need. after research, you write down what you found. tomorrow, you re-read again and your research is better because it builds on yesterday's notes. ALIVE is that, except the AI agent does the re-reading and note-taking automatically, and the notebooks are structured in a way that both your interactive agent and your automated cron jobs can use. Teaching an AI Your Writing Style: The Voice Feedback Loop this is the technique I'm most excited about, and it's the one that costs zero dollars. the problem standard approach: write a better prompt. add more rules, more examples, more constraints. "write in personal experience format." "use lowercase." "no bullet points." "vary sentence length." I have 39,000 characters of content voice instructions. the model still produces slop. more rules ≠ better output. after a certain point, the model can't integrate more abstract instructions. the fix: correction logging instead of telling the AI how to write, I show it what I changed. after every post, the cycle is: I say "I tweaked your draft" on Telegram the voice-learn skill activates it reads the original draft and my posted version it extracts every difference: tone shifts, cuts, additions, word swaps it saves each lesson to voice-corrections.md every future drafting cron reads voice-corrections.md before writing why corrections beat prompts prompts say "write in personal experience format." corrections say "you wrote 'not saying this is UST 2.0' - I cut that because preemptive hedges undercut expertise. don't do this again." 151 lines of "when you did X, I changed it to Y, because Z." that's 151 concrete examples. specific beats abstract, every time. the model still produces AI slop, but it's better AI slop. and the corrections compound. every draft that passes through the feedback loop adds more examples. the model can't write like a human. but it can write like a slightly-better AI that knows 151 specific things to avoid. and that's enough when the human is doing the final pass. in short: instead of giving someone a book about how to write like you, you sit next to them every time they write, point at specific sentences, and say "don't do that, do this instead." after 28 of those corrections, they start avoiding the mistakes on their own. not perfectly. but enough that your editing time drops from 80% rewriting to maybe 50%. What I'd Do Differently (Lessons From 2 Months) 1. start with local compression from day one. cloud API compression and rate limits creates silent failure. sessions grow unbounded and you won't notice until the agent is hanging for 10 minutes per response. local Ollama compression is free, reliable, and has zero rate limits. 2. set per-job model config immediately. don't patch the scheduler code. it gets wiped on every update. set "model": "glm-4.7" in each job's config in jobs.json. survives updates, survives patches, never needs re-applying. 3. don't trust the AI to write. build the research pipeline fast. build the voice feedback loop on day one. accept that you'll be rewriting 50-80% of every draft for weeks. the corrections compound, but slowly. get 50+ correction entries before expecting usable drafts. 4. set session idle timeout to 60 minutes. the default is often much longer. long sessions = bloated context = slow responses. short sessions = fresh context = fast responses. the research still gets saved to files between sessions. 5. enforce source diversity in prompts. if you don't explicitly force it, the model will use Reddit for everything because Reddit ranks high in search results. explicit rules: "fetch Techmeme first, then Hacker News, then Reddit, THEN use web search to fill gaps." specify the order. 6. monitor your compression pipeline. it fails silently. if the agent suddenly takes 10 minutes to respond and sends 8 messages of context dump, compression is probably broken. check the model, check the endpoint, check the manifest. ollama pull to force a clean download. How To Replicate This here are everything you need to run the minimum viable setup (30 minutes) step 1: install Hermes Agent. step 2: get Z.AI coding plan ($21/month). set these in .env: critical: use the /coding/paas/v4 endpoint, NOT /paas/v4. the coding plan key only works on the coding endpoint. step 3: create a Telegram bot via @BotFather. set the token: step 4: start the gateway. step 5: create 2-3 cron jobs. start with a morning briefing, one research session, and a breaking news monitor. edit jobs.json, set "model": "glm-4.7" on each job. you now have a 24/7 AI agent running on your machine, accessible via Telegram, with automated research jobs. if you want to go further, here are everything you need for the full setup (one weekend) everything above, plus: step 6: install Ollama + compression model. configure in .env and config.yaml: set session idle timeout to 60 minutes. set compression threshold to 0.50. set Ollama keep-alive to 5 minutes. step 7: create 5-10 shell scripts for your data sources. pick APIs relevant to your work. most public APIs (coingecko, defillama, Hacker News, Reddit, arXiv) don't need API keys. step 8: create 2-3 custom skills. start with a cross-project context skill (walnuts) and a content drafting skill for your domain. step 9: set up ALIVE with 3-5 walnuts. create the folder structure, write the key.md and now.md files for each project. wire cron jobs to read/write walnuts using update-walnut.sh. step 10: start the voice feedback loop on day one. create voice-corrections.md. every time you edit a draft, log what you changed and why. wire your drafting cron to read corrections before writing. what you need Mac Mini, old laptop, or any always-on machine (Linux works too - Hermes isn't mac-specific) $21/month for Z.AI (or bring your own API key for any openai-compatible provider) a Telegram account patience with AI-generated content (it will be bad at first - that's normal) what you don't need coding experience beyond basic shell scripting permission from anyone The Philosophy this isn't about building a perfect AI agent. GLM-5 produces slop. the research hallucinates URLs sometimes. cron jobs fail. compression breaks silently. the model can't write like a human and probably never will. but it runs 24/7. while I sleep, it scans arXiv papers, monitors stablecoin pegs, checks Reddit and Hacker News, tracks competitor TVL, and writes draft posts that I edit in the morning. when I wake up, there's a briefing in my Telegram with overnight developments and a queue of drafts to review. the agent is persistent. and persistence × time = compound context. after weeks of running, the memory files have captured patterns. the walnut logs have history. the voice corrections have accumulated. each new session starts from a richer base than the last. the research gets more focused because it reads priorities before searching. the drafts get slightly better because they read corrections before writing. the priorities get sharper because the morning briefing reads what actually happened yesterday. none of this is magic. it's in the structured context that accumulates around it. the walnuts, the correction logs, the research archives. the AI is the engine. the context is the fuel. and the fuel compounds. built by @witcheer.

New in Claude Code: auto mode. Instead of approving every file write and bash command, or skipping permissions entirely, auto mode lets Claude make permission decisions on your behalf. Safeguards check each action before it runs.

You can now enable Claude to use your computer to complete tasks. It opens your apps, navigates your browser, fills in spreadsheets—anything you'd do sitting at your desk. Research preview in Claude Cowork and Claude Code, macOS only.

OpenClaw 2026.3.22 🦞 🏪 ClawHub plugin marketplace 🤖 MiniMax M2.7, GPT-5.4-mini/nano + per-agent reasoning 💬 /btw side questions 🏖️ OpenShell + SSH sandboxes 🌐 Exa, Tavily, Firecrawl search

タイトル: This Is What Stanford Students Actually Learn About AI. Most People Never See It. (Part I) 11 lectures. The full mathematical foundation of artificial intelligence - from how machines learn to how they play games, make decisions, and reason under uncertainty. I've gone through a lot of AI content Most of it falls into one of two traps Either it's too shallow - "AI is like a brain, it learns from data, wow amazing" - or it's so dense that you need a PhD just to parse the introduction. Stanford's Computer Science 221 is neither. It's the clearest, most complete map of how artificial intelligence actually works that I've found. Not just the math. Not just the philosophy. The whole thing - from the first principles of how a machine learns, all the way to game theory, probabilistic reasoning, and the mechanics of large language models. Twenty lectures total. I'm breaking it into two parts. This is Part I: the mathematical and computational foundation. Lectures 1 through 11 - covering learning, search, decision-making, and strategic reasoning. Part II drops next week: Bayesian networks, logic, large language models, and the geopolitics of AI infrastructure. Let's get into it. What This Course Actually Teaches Most people think AI is one thing - it isn't It's four things working together: Machine learning - how systems extract patterns from data Search & planning - how AI reasons through sequences of actions Decision-making under uncertainty - how it acts when outcomes aren't guaranteed Game theory - how it reasons against other agents CS221 covers all four. And unlike most introductions that stop at "neural networks good," this course goes further - into search algorithms, game theory, Bayesian probability, formal logic, and the mechanics of large language models. By the end, you don't just know that AI works. You know why - and what it costs to make it work at scale. Lecture 1: What Intelligence Actually Is An introduction to the building blocks of AI and a honest look at where the field came from. Before writing a single line of code or formula, CS221 asks a deceptively simple question: what does it mean for a machine to be intelligent? The answer turns out to be a long history of wrong answers. From Alan Turing's imitation game to symbolic AI, from rule-based expert systems to statistical learning, to the deep learning era we're living through now - each generation thought it had cracked intelligence, and each generation was partially right. The lecture introduces tensors as the atomic unit of modern machine learning: multidimensional arrays that carry everything from pixel values to word embeddings through every layer of every model you've ever used. Lecture 2: How Machines Actually Learn (Part I) The math and mechanics of machine learning - linear regression, loss functions, and gradient descent. Here's where the real work starts. Learning, in the mathematical sense, has three components: A hypothesis class - the space of possible predictors your model can become A loss function - a way to measure how wrong the current predictor is An optimization algorithm - a method to make it less wrong The workhorse of all three is gradient descent: move in the direction that reduces error, repeat until you can't improve anymore. The lecture goes deep on computation graphs and backpropagation - the mechanism that makes training neural networks possible without manually computing derivatives for every parameter. This is the engine underneath every modern AI system, and most people using those systems have never looked at it. Lecture 3: Teaching Machines to Categorize (Part II) From regression to classification - how AI learns to tell one thing from another. Regression predicts numbers. Classification predicts categories. The transition between them is less obvious than it sounds. The core problem: the natural loss function for classification - simply counting wrong answers - is mathematically useless for gradient descent. You can't optimize a cliff. The solution is probabilistic classifiers: For binary problems: the logistic function converts raw scores into probabilities, and logistic loss gives you a smooth surface to descend For multiclass problems: softmax generalizes this across any number of categories, paired with cross-entropy loss This is the mathematical foundation of every classifier ever built - from spam filters to the final layer of GPT. Lecture 4: Deep Learning - When One Layer Isn't Enough How stacking layers creates systems that learn their own features - and the tricks that keep training from falling apart. A single linear layer, no matter how many parameters it has, can only learn linear relationships. The world isn't linear. The fix is brutally simple: stack layers, add non-linearities between them (like ReLU - just max(0, x)), and suddenly the network can approximate any function you want. But deep networks come with problems. The deeper you go, the harder gradients flow backward through training - they vanish or explode before they reach the early layers. The engineering solutions that made modern deep learning possible: Residual connections - skip layers so gradients have a shortcut Layer normalization - keep activations in a healthy range Careful weight initialization - start in the right neighborhood SGD with mini-batches - get the benefits of full-dataset gradients without the cost These aren't academic tricks. They're why transformers work. Lecture 5: Search - When Thinking Beats Reacting How AI plans ahead in deterministic environments where brute force is too slow. Not every problem can be solved by pattern matching. Some require reasoning: thinking through sequences of actions before committing to one. CS221 formalizes this as a search problem: you have states, actions, transitions, and costs. You want the lowest-cost path to a goal. Three approaches, ordered by intelligence: Exhaustive search - check everything (works only on tiny problems) Dynamic programming - cache subproblem solutions so you never solve the same thing twice Heuristic search - use approximate estimates to skip large parts of the space entirely The lecture connects this directly to test-time compute in modern language models: the same search logic that finds shortest paths in maps is what lets an LLM think longer before answering. Lecture 6: Search Through Cycles - A* and the Art of Smart Shortcuts Advanced search for environments where paths loop back on themselves. Standard dynamic programming breaks when graphs have cycles. If you can revisit states, you can get stuck in infinite loops. The solution is Uniform Cost Search (UCS): explore states in order of increasing path cost, using a priority queue. Guaranteed to find the optimal solution. Also guaranteed to be slow when the space is large. Enter A* Search: take UCS and add a heuristic - an estimate of the remaining cost to the goal. If that estimate is consistent (never overestimates, and satisfies a triangle inequality), A* is both optimal and dramatically faster than UCS. Where do good heuristics come from? Relaxed problems: strip away constraints from the original problem, solve the easier version, and use that solution as your estimate. The insight is elegant - a solution to a simpler version of your problem is always a lower bound on the true cost. Lecture 7: Markov Decision Processes - When the World Doesn't Cooperate Decision-making in environments where outcomes are probabilistic, not guaranteed. Real environments don't behave like chess boards. You take an action and the result is uncertain - the road is icy, the market moved, the message wasn't received. Markov Decision Processes (MDPs) model this directly: every action produces a distribution of possible next states, each with a probability. You can't guarantee outcomes. You can only maximize expected value. Key concepts: A policy maps every state to an action - it's the full decision strategy Policy evaluation computes the expected long-term reward of a given policy Value iteration finds the optimal policy by iteratively improving value estimates until convergence This is the mathematical foundation of every AI system that operates in uncertain environments - from robots to recommendation engines to anything that interacts with the real world. Lecture 8: Reinforcement Learning - Learning the Rules by Playing the Game How AI learns to act optimally when nobody tells it the rules. MDPs assume you know the transition probabilities and rewards. In reinforcement learning, you don't. The agent has to figure them out by interacting with the environment directly. Two broad approaches: Model-based RL: observe enough interactions to estimate the MDP, then use value iteration on your estimate Model-free RL: skip the model entirely, estimate value functions directly from experience Model-free methods come in two flavors covered here: Monte Carlo: run complete episodes, average the returns Temporal Difference (TD): bootstrap - update estimates based on other estimates, without waiting for episodes to end Q-learning - the off-policy version - estimates the optimal policy's value function regardless of how the agent is actually behaving. This is the algorithm behind early Atari-playing agents and the conceptual ancestor of modern RLHF. Lecture 9: Policy Gradients - RL at Scale How reinforcement learning handles the real world, where state spaces are too large to enumerate. Q-learning with a lookup table works when states are countable. Images, text, and sensor readings are not countable. You need function approximation - parameterize Q-values with a neural network. But this creates new instability. The lecture introduces Policy Gradient methods as an alternative: instead of learning value functions, optimize the policy directly. The REINFORCE algorithm does this by treating the policy as a probability distribution over actions and nudging it toward actions that produced high rewards. Two improvements that make this practical: Baselines: subtract a reference reward to reduce variance without introducing bias Actor-Critic: combine policy gradients with a learned value function to get lower variance estimates in real time These are the building blocks of every modern RLHF pipeline - including the fine-tuning behind ChatGPT. Lecture 10: Games - Strategic Reasoning Against an Adversary How AI thinks several moves ahead when someone is actively trying to defeat it. Games formalize a new kind of problem: the environment includes another agent who is also optimizing - against you. For two-player zero-sum games, what one player gains the other loses exactly. The optimal strategy follows from this: Minimax: assume your opponent plays perfectly and minimize your maximum possible loss Expectimax: if the opponent's policy is known and imperfect, take expected values instead of minimums Alpha-beta pruning: prune branches of the game tree that can't possibly affect the optimal decision - dramatically reduces computation without losing optimality The lecture makes a clean observation: the tree search you use to solve chess and Go is structurally identical to the planning algorithms from earlier lectures. The game just adds an adversary node. Lecture 11: AlphaGo, Nash Equilibria, and the Prisoner's Dilemma Advanced game theory - from self-play learning to simultaneous games where cooperation breaks down. This lecture connects reinforcement learning to game-playing at a world-class level. TD-learning in self-play: play against yourself, use temporal difference updates to improve your evaluation function. This is exactly how TD-Gammon beat world champion backgammon players in the 1990s, and the conceptual blueprint for AlphaGo's policy network. Then the course broadens its scope: Simultaneous games: both players move at once, neither knows the other's choice. Pure strategies often lose to exploitation - you need mixed strategies (probability distributions over actions) Minimax theorem: for every finite zero-sum game, there's a unique equilibrium value that both players can guarantee with the right mixed strategy Nash equilibrium: in non-zero-sum games (Prisoner's Dilemma), rational individual behavior can produce collectively terrible outcomes This last point is not just game theory. It's why AI alignment is hard. That's Part I Eleven lectures. The complete mathematical and computational foundation of AI. You now understand how machines learn, how they plan, how they handle uncertainty, and how they reason strategically against other agents. Part II is coming next week. It covers the rest of the curriculum: Bayesian networks, probabilistic inference, formal logic, the inner mechanics of large language models, the societal risks nobody prices in, and the global supply chain that controls who gets to build AI at all. If this was useful - follow @polydao , bookmark it, share it with someone who wants to understand AI for real, and follow along for Part II. oIf this was useful - bookmark it, share it with someone who wants to understand AI for real, and follow along for Part II. Where to Find the Full Course You can watch the complete CS221 course and hundreds of other Stanford lectures here: youtube.com/@stanfordonline Everything covered in this article is there, in full, for free! No paywall. No enrollment. No student loans. Just the actual Stanford curriculum, available to anyone with an internet connection Which, given everything we've discussed about the cost of formal education - feels like the point.

タイトル: I want to become a Claude architect (full course). To become a Claude Architect and develop production-grade applications you need to understand Claude Code, Claude Agent SDK, Claude API, and Model Context Protocols, this article will help you learn everything and is based on the following exam: However, as you can clearly see to get this "certified" you need to be a claude partner, otherwise, you cannot take this exam. BUT DOES THAT EVEN MATTER? If you have the ability to learn what it takes to become a "Claude Certified Architect" then you're able to build production-grade applications. You don't need the certificate to build production-grade applications. You just need the knowledge. So I tore apart the entire exam guide and pulled out what actually matters so that you can become a Claude architect. WHAT YOU ARE WALKING INTO: The exam, which you won't be able to take unless you're a Claude partner, but that doesn't matter, because learning what you need for this exam will teach you on the following, so don't be a massive wet wipe saying "you fooled me" because you don't get to take the actual exam for just a gay tick mark, be a self-learner and become a Claude architect by UNDERSTANDING the following as the exam would test you on: Claude Code, Claude Agent SDK, Claude API, and Model Context Protocol (MCP). WHICH ARE ALL SKILLS YOU CAN MONETISE. The exam would mean you need to learn the following: Customer Support Resolution Agent (Agent SDK + MCP + escalation) Code Generation with Claude Code (CLAUDE.md + plan mode + slash commands) Multi-Agent Research System (coordinator-subagent orchestration) Developer Productivity Tools (built-in tools + MCP servers) Claude Code for CI/CD (non-interactive pipelines + structured output) Structured Data Extraction (JSON schemas + tool_use + validation loops) DOMAIN 1: AGENTIC ARCHITECTURE & ORCHESTRATION (27%). The exam tests three anti-patterns you need to reject on sight: parsing natural language to determine loop termination, arbitrary iteration caps as the primary stopping mechanism, and checking for assistant text as a completion indicator. All wrong. The single biggest mistake: people assume subagents share memory with the coordinator. They do not. Subagents operate with isolated context. Every piece of information must be passed explicitly in the prompt. The rule that will save you the most marks: when stakes are financial or security-critical, prompt instructions alone are not enough. You must be enforcing tool ordering programmatically with hooks and prerequisite gates. Where to learn this: Agent SDK Overview for agentic loop mechanics and subagent patterns Building Agents with the Claude Agent SDK for Anthropic's own best practices on hooks, orchestration, and sessions Agent SDK Python repo + examples for hands-on code: hooks, custom tools, fork_session If you have no idea how to get started go to Claude and paste this prompt which will help you with domain 1: What to build to learn: A multi-tool agent with 3-4 MCP tools, proper stop_reason handling, a PostToolUse hook normalising data formats, and a tool call interception hook blocking policy violations. This single exercise covers most of Domain 1. DOMAIN 2: TOOL DESIGN & MCP INTEGRATION (18%) Tool descriptions are incredibly overlooked bro, and the exam wants to test you on it. Tool descriptions are the primary mechanism Claude uses for tool selection. If yours are vague or overlapping, selection becomes unreliable. One sample question presents get_customer and lookup_order with near-identical descriptions causing constant misrouting. The correct fix is not few-shot examples, not a routing classifier, not tool consolidation. The fix is better descriptions. Know the tool_choice options cold: "auto" (model might return text), "any" (must call a tool, picks which), forced selection (must call a specific tool). Know when each applies. Giving an agent 18 tools degrades selection reliability. Scope each subagent to 4-5 tools relevant to its role. Where to learn this: MCP Integration for Claude Code for server scoping, environment variable expansion, project vs user config MCP specification and community servers for understanding the protocol and knowing when to use community servers vs custom builds Claude Agent SDK TypeScript repo for tool definition patterns and structured error responses If you have no idea how to get started go to Claude and paste this prompt which will help you with domain 2: What to build: Two MCP tools with intentionally similar functionality. Write descriptions vague enough to cause misrouting. Then fix them. Experience the difference. DOMAIN 3: CLAUDE CODE CONFIGURATION & WORKFLOWS (20%) This separates people who use Claude Code from people who have configured it for a team. The CLAUDE.md hierarchy is critical. Three levels: user-level (~/.claude/CLAUDE.md), project-level (.claude/CLAUDE.md), directory-level (subdirectory files). The exam's favourite trap: a team member missing instructions because they live in user-level config (not version-controlled, not shared). Path-specific rules are the sleeper concept. .claude/rules/ with YAML frontmatter glob patterns like **/*.test.tsx applies conventions across the entire codebase. Directory-level CLAUDE.md cannot do this because it is directory-bound. Plan mode vs direct execution: Plan mode: monolith restructuring, multi-file migration, architectural decisions Direct execution: single-file bug fix, one validation check, clear scope Know context: fork in skill frontmatter (isolates verbose output). Know -p flag (non-interactive CI/CD). Know an independent review instance catches more than self-review in the same session. Where to learn this: Claude Code official docs for CLAUDE.md hierarchy, rules directory, slash commands, skills frontmatter Claude Code CLI Cheatsheet for commands, skills, hooks, and CI/CD flags in one practical reference Creating the Perfect CLAUDE.md for real team configuration patterns and MCP integration If you have no idea how to get started go to Claude and paste this prompt which will help you with domain 3: What to build: A project with CLAUDE.md hierarchy, .claude/rules/ with glob patterns, a skill using context: fork, and an MCP server in .mcp.json with env var expansion. Test plan mode on a multi-file refactor and direct execution on a single bug fix. DOMAIN 4: PROMPT ENGINEERING & STRUCTURED OUTPUT (20%) Two words will save you across this entire domain: be explicit. "Be conservative" does not improve precision. "Only report high-confidence findings" does not reduce false positives. What works: defining exactly which issues to report versus skip, with concrete code examples for each severity level. Few-shot examples are the highest-leverage technique tested. 2-4 targeted examples showing ambiguous-case handling with reasoning for why one action was chosen over alternatives. tool_use with JSON schemas eliminates syntax errors. But NOT semantic errors. Schema design: nullable fields when source data might be absent (prevents fabricated values), "unclear" enum values, "other" + detail strings. Message Batches API: 50% savings, up to 24-hour processing, no latency SLA, no multi-turn tool calling. Batch for overnight reports. Synchronous for blocking pre-merge checks. Where to learn this: Anthropic Prompt Engineering docs for few-shot patterns, explicit criteria, and structured output Anthropic API Tool Use documentation for tool_use, tool_choice config, JSON schema enforcement The exam guide's own sample questions (Q10, Q11, Q12) are the single best study material for this domain. Work through every distractor and understand why it is wrong. If you have no idea how to get started go to Claude and paste this prompt which will help you with domain 4: What to build: An extraction pipeline using tool_use with required, optional, and nullable fields. Add a validation-retry loop. Run a batch through the Batches API. Handle failures by custom_id. DOMAIN 5: CONTEXT MANAGEMENT & RELIABILITY (15%) Smallest weighting. But mistakes here cascade everywhere. Progressive summarisation kills transactional data. Fix: persistent "case facts" block with extracted amounts, dates, order numbers. Never summarised. Included in every prompt. "Lost in the middle" effect: models miss findings buried in long inputs. Place key summaries at the beginning. Three valid escalation triggers: customer requests a human (honour immediately), policy gaps, inability to progress. Two unreliable triggers the exam will tempt you with: sentiment analysis and self-reported confidence scores. Error propagation done right: structured context (failure type, attempted query, partial results, alternatives). Anti-patterns: silently suppressing errors or killing entire workflows on single failures. Where to learn this: Building Agents with the Claude Agent SDK covers context management, error propagation, and escalation design Agent SDK session docs for resumption, fork_session, /compact Everything Claude Code repo for battle-tested context management patterns, scratchpad files, and strategic compaction If you have no idea how to get started go to Claude and paste this prompt which will help you with domain 5: What to build: A coordinator with two subagents. Simulate a timeout. Verify the coordinator gets structured error context and proceeds with partial results. Test with conflicting sources. RECOMMENDED LEARNING FROM ANTHROPIC: 1: Building with the Claude API 2: Introduction to Model Context Protocol 3: Claude Code in Action 4: Claude 101 NOW GO AND BECOME A UNCERTIFIED CLAUDE ARCHITECT (or certified if you're a partner ken), EITHER WAY, IT'S TIME TO FUCK!

Announcing a new Claude Code feature: Remote Control. It's rolling out now to Max users in research preview. Try it with /remote-control Start local sessions from the terminal, then continue them from your phone. Take a walk, see the sun, walk your dog without losing your flow.

Today we're open sourcing the new Chat SDK, a unified TypeScript SDK for building chat bots across Slack, Microsoft Teams, Google Chat, Discord, and more. Now in public beta. ▲ ~/ npm i chat Let's see how it works ↓

Code Mode is all you need, very excited about this direction for MCP

タイトル: 🚨 Breaking: OpenAI makes OpenClaw inexpensive Yesterday, Claude said they'd CANCEL OpenClaw users who connect their Claude subscriptions. But OpenAI said they'd allow it! This is the step-by-step guide to using your ChatGPT subscription and eliminating API fees. (If you just want to see how to use OpenAI, skip to the walkthrough here.) Does Claude hate OpenClaw? Yesterday, Claude made their restrictions clear. This is a link to their full Legal & Compliance doc, but to me, the important part is this: They do NOT want you to use your $0/month, $20/month, $100/month or $200/month accounts in tools like @openclaw. Claude seems hostile to OpenClaw. @steipete, OpenClaw's creator, has already said that Anthropic/Claude's engagement with him is only through legal "love letters": And in his @lexfridman interview, Peter said that after forcing him to change the name of his project, they didn't even let him redirect his old domain to make it easy for users to find his project. Why this sucks The way that Claude wants us to connect to OpenClaw is expensive. I'm a light user and I end up burning through $20/day, easily. My friend @codymclain told me he burns $100/day. The cost is crazy high for a project that -- let's be honest -- is still so new that most of what we're doing is using it to figure out WHAT it can do. There's no ROI for most people -- yet. OpenAI steps in Yesterday, when I announced that using Claude subscriptions on OpenClaw could get your account banned, I suggested that Sam Altman and OpenAI could step in and let us use our accounts. Peter replied to me immediately to tell me that OpenAI was already ahead of this: What nice way to treat users. Not only did they allow it, but they communicated it clearly. How to do it I recorded a clear step-by-step walk-through with @calebhodges who offers concierge OpenClaw setups for clients. He's been doing this over and over because of Claude's ban. 📺Watch the full video here. But for experienced users, here's the simple 2-step process. Step 1: Select OpenAI This will skip the onboarding wizard, pre-selecting OpenAI Codex (ChatGPT OAuth) as the authentication method. Here's what it does specifically: onboard This kicks off the setup wizard that configures your OpenClaw gateway, workspace, channels, and skills. --auth-choice openai-codex This skips the interactive prompt asking you to pick a model provider and automatically selects the OpenAI Codex option, which authenticates via ChatGPT OAuth (i.e., your existing ChatGPT/Codex subscription) rather than a raw API key. During this flow, OpenClaw will open a browser OAuth URL for you to sign in with your ChatGPT account. Once authenticated, it sets the default model to something like openai-codex/gpt-5.x-codex and stores the OAuth credentials in your auth profile. The main reason to choose this over an API key is that it uses your subscription's token budget (e.g., ChatGPT Plus/Pro) instead of pay-per-token API billing, which can be more cost-effective if OpenClaw is chatty. Step 2: Select 5.3 Codex openclaw models set openai-codex/gpt-5.3-codex This sets openai-codex/gpt-5.3-codex as the primary text model for your agent. Specifically, it writes agents.defaults.model.primary in your ~/.openclaw/openclaw.json config file. The model ref format is always provider/model — here openai-codex is the provider (your ChatGPT OAuth auth profile) and gpt-5.3-codex is the specific model. Note that this writes to config but does not automatically restart the gateway — you'd need to run openclaw gateway restart for it to take effect on a running instance. openclaw models status --plain This shows the current model configuration and auth status. Without --plain, models status gives you a full overview: the resolved primary model, any configured fallbacks, the image model, and an auth summary of all providers (including OAuth expiry warnings if a token is within 24 hours of expiring). The --plain flag strips all of that context down to just the resolved primary model name, printed as a single line — useful for scripting or quick sanity checks that the right model is active. So the two commands together form a common pattern: set a new default model, then verify it stuck. What else can you do? A. Cancel or downgrade your Claude account. I'm switching from the $200/month plan to the $20/month. B. Spread the word about how to use OpenAI! 1. Copy the commands above and Tweet them, as @ryancarson did: 2. Share or like this post. 3. Send your friends the video walkthrough that @calebhodges created: Easy Video Walkthrough on YouTube. If Claude doesn't want us, we have alternatives.

🦞 OpenClaw 2026.2.19 ⌚ Apple Watch MVP 🔧 Gateway auth & device management 🔌 OTEL v2 migration + plugin/hooks hardening 🔒 40+ security hardening fixes 🖥️ Dashboard now nudges you to update — stay secure, stay current Biggest hardening drop yet!🛡️

I've spent 2.54 BILLION tokens perfecting OpenClaw. The use cases I discovered have changed the way I live and work. ...and now I'm sharing them with the world. Here are 21 use cases I use daily: 0:00 Intro 0:50 What is OpenClaw? 1:35 MD Files 2:14 Memory System 3:55 CRM

タイトル: How I Build Apps That Don't Look Vibecoded You can spot a vibecoded app from a mile away. I've made a few of those myself. Only launched a few because I keep getting sidetracked by tweaking the design and branding. It's probably the designer in me, but if I publish something I'd like it to look nice otherwise I won't use it myself. Taste is subjective and AI tools can build anything in no time, but spend 10 minutes browsing popular apps in the app store and I bet you won't find an app that looks like a MySpace page from the early 00's. Who wants to use a calorie tracking app in a brutalist design style? It's a fun idea, but I doubt it's useable for more than 5 minutes. AI can make beautiful designs, but it needs help and proper guidance. I do too. Give me a blank Figma file and tell me to make something awesome and I'd create generic designs too. There are many factors to a successful app besides good design, but I'm a designer, so let's focus on that. Every time I start a new app project I slowly fine tune my design process and have landed on a workflow that works for me. Get your wallets out and pay attention because it involves a few (too many) subscriptions. Just a note: I'm switching between tools all the time, buying and cancelling subscriptions and taking advantage of free tiers whenever possible because I'm still tweaking and testing my process and I sadly don't have an unlimited budget (wife keeps saying no to more subscriptions). But TL;DR: Just use Claude Code. It'll get you 90% of the way there. Step 1: Idea to PRD Get an idea, talk to an LLM to narrow the scope to a quick MVP. I use Claude for this. Describe what I want, let it poke holes, cut features until it's something I can actually ship. The output is a simple PRD. Nothing fancy. Just enough to keep me focused. Step 2: First Prototype Feed the PRD to Claude Code, Vibecodeapp, or Rork. Depends on the project and my mood. After generation, I check functionality and flows. Does it work? Does the navigation make sense? I'm not looking at design yet. Just: does this thing do what it's supposed to do? Step 3: Screen Inventory Once a rough MVP is working, I have the app generate a list of all screens and functionality. This becomes my checklist for the design phase. Step 4: Design Exploration This is where it gets fun. I feed that screen list to Variant AI, Aura.build, v0, Superdesign, and try random styles until something clicks. Sometimes that thing that clicks is just a random pin or post on Dribbble that made me go "could be fun to make my app in this style." and I ask one of these design tools to try out a style. When I design for myself I default to simple and minimalistic with a focus on typography. I have a background in editorial design. But I sometimes try to venture into other styles. Mascot-heavy apps with a playful aesthetic. Something bold and experimental. These experiments more often than not fail and I pivot back to my default style just to get the project moving again or create something I know works. That's fine. The point is to try. Step 5: Into Figma Export the design to Figma using the html.to.design chrome plugin or native import if available. This is where I fiddle. Adjust spacing, fix inconsistencies, make it feel like mine. It's also a lot quicker and cheaper than keep telling AI to "please try another font and make the spacing better" over and over again. Step 6: Back to Code Feed the design back to the app builder, or download the code and open it in Cursor. Cursor is cheaper for experimentation when I'm not 100% sure what I'm looking for. Step 7: Before Downloading Before I pull the code out, I make sure to have the app builder integrate RevenueCat, onboarding flow, paywall, and backend. I don't want to mess with that myself later. The Point AI builds fast. That's the easy part. The hard part is making it look like someone cared. That still takes taste, iteration, and knowing when to stop experimenting and just ship. I'm still figuring it out. But at least my apps don't look vibecoded anymore. And since a lot of people asked me about my Claude Code setup and use of skills, I'll get to that next time. I found a few good ones :) Tools - (Some are affiliate links) Vibecodeapp Aura.build Rork Superdesign.dev Magicpatterns.com Magicpath.ai Claude Code Figma html.to.design

タイトル: It’s Official: The World Order Has Broken Down At the Munich Security Conference, the post-1945 world order was pronounced dead by most leaders and the picture behind it was laid out in the Security Report 2026, entitled “Under Destruction,” which you can read here if you're interested.  More specifically, German Chancellor Friedrich Merz said, “The world order as it has stood for decades no longer exists,” and that we are in a period “great power politics.”   He made clear that freedom “is no longer a given” in this new era. French President Emmanuel Macron echoed Merz’s assessment and said that Europe’s old security structures tied to the previous world order don't exist and that Europe must prepare for war. U.S. Secretary of State Marco Rubio said that we are in a “new geopolitics era” because the “old world” is gone. In my parlance, we are in the Stage 6 part of the Big Cycle in which there is great disorder arising from being in a period in which there are no rules, might is right, and there is a clash of great powers. How Stage 6 works is explained in detail in Chapter 6, “The Big Cycle of External Order and Disorder,” in my book Principles for Dealing with the Changing World Order. While I previously shared a lengthy set of excerpts from Chapter 5 ("The Big Cycle of Internal Order and Disorder"), so you could see how what is going on with the United States tracks the classic cycle explained in that chapter, I am including all of Chapter 6 here for your review.  Given the now nearly universal agreement that the post-1945 world order has broken down and that we are entering a new world order, I think it would be worth your time to read. Chapter 6: The Big Cycle of External Order and Disorder Relationships between people and the orders that govern them work in basically the same ways, whether they are internal or external, and they blend together. In fact, it wasn’t long ago that there were no distinctions between internal and external orders because there were no clearly defined and mutually recognized boundaries between countries. For that reason, the six-stage cycle of going between order and disorder that I described in the last chapter about what happens within countries works the same way between countries, with one big exception: international relations are driven much more by raw power dynamics. That is because all governance systems require effective and agreed-upon 1) laws and law-making abilities, 2) law enforcement capabilities (e.g., police), 3) ways of adjudicating (e.g., judges), and 4) clear and specified consequences that both suit crimes and are enforced (e.g., fines and incarcerations), and those things either don’t exist or are not as effective in guiding relations between countries as they are in guiding relations within them. While attempts have been made to make the external order more rule-abiding (e.g., via the League of Nations and the United Nations), by and large they have failed because these organizations have not had more wealth and power than the most powerful countries. When individual countries have more power than the collectives of countries, the more powerful individual countries rule. For example, if the US, China, or other countries have more power than the United Nations, then the US, China, or other countries will determine how things go rather than the United Nations. That is because power prevails, and wealth and power among equals is rarely given up without a fight. When powerful countries have disputes, they don’t get their lawyers to plead their cases to judges. Instead, they threaten each other and either reach agreements or fight. The international order follows the law of the jungle much more than it follows international law. There are five major kinds of fights between countries: trade/economic wars, technology wars, capital wars, geopolitical wars, and military wars. Let’s begin by briefly defining them. 1. Trade/economic wars: Conflicts over tariffs, import/export restrictions, and other ways of damaging a rival economically 2. Technology wars: Conflicts over which technologies are shared and which are held as protected aspects of national security 3. Geopolitical wars: Conflicts over territory and alliances that are resolved through negotiations and explicit or implicit commitments, not fighting 4. Capital wars: Conflicts imposed through financial tools such as sanctions (e.g., cutting off money and credit by punishing institutions and governments that offer it) and limiting foreign access to capital markets 5. Military wars: Conflicts that involve actual shooting and the deployment of military forces Most fights between nations fall under one or more of those categories (cyber warfare, for example, has a role in all of them). They are over wealth and power and the ideologies pertaining to them. While most of these types of wars don’t involve shooting and killing, they all are power struggles. In most cases, the first four kinds of war will evolve over time as intense competitions between rival nations until a military war begins. These struggles and wars, whether or not they involve shooting and killing, are exertions of power of one side over the other. They can be all-out or contained, depending on how important the issue is and what the relative powers of the opponents are. But once a military war begins, all four of the other dimensions will be weaponized to the greatest extent possible. As discussed in the last several chapters, all of the factors that drive internal and external cycles tend to improve and worsen together. When things get bad, there are more things to argue over, which leads to greater inclinations to fight. That’s human nature, and it is why we have the Big Cycle, which oscillates between good times and bad ones. All-out wars typically occur when existential issues (ones that are so essential to the country’s existence that people are willing to fight and die for them) are at stake and they cannot be resolved by peaceful means. The wars that result from them make it clear which side gets its way and has supremacy in subsequent matters. That clarity over who sets the rules then becomes the basis of a new international order. The following chart shows the cycles of internal and external peace and conflict in Europe going back to 1500 as reflected in the deaths they caused. As you can see, there were three big cycles of rising and declining conflict, averaging about 150 years each. Though big civil and external wars last only a short time, they are typically the culmination of the longstanding conflicts that led up to them. While World Wars I and II were separately driven by the classic cycle, they were also interrelated. As you can see, each cycle consisted of a relatively long period of peace and prosperity (e.g., the Renaissance, the Enlightenment, and the Industrial Revolution) that sowed the seeds for terrible and violent external wars (e.g., the Thirty Years’ War, the Napoleonic Wars, and the two World Wars). Both the upswings (the periods of peace and prosperity) and the downswings (the periods of depression and war) affected the whole world. Not all countries prosper when the leading powers do because countries gain at the expense of others. For example, the decline of China from around 1840 to 1949, known as the “Century of Humiliation,” came about because the Western powers and Japan exploited China. As you read on, keep in mind that * the two things about war that one can be most confident in are 1) that it won’t go as planned and 2) that it will be far worse than imagined. It is for those reasons that so many of the principles that follow are about ways to avoid shooting wars. Still, whether they are fought for good reasons or bad, shooting wars happen. To be clear, while I believe most are tragic and fought for nonsensical reasons, some are worth fighting because the consequences of not fighting them (e.g., the loss of freedom) would be intolerable. THE TIMELESS AND UNIVERSAL FORCES THAT PRODUCE CHANGES TO THE EXTERNAL ORDER As I explained in Chapter 2, after self-interest and self-survival, the quest for wealth and power is what most motivates individuals, families, companies, states, and countries. Because wealth equals power in terms of the ability to build military strength, control trade, and influence other nations, domestic and military strength go hand in hand. It takes money to buy guns (military power) and it takes money to buy butter (domestic social spending needs). When a country fails to provide adequate amounts of either, it becomes vulnerable to domestic and foreign opposition. From my study of Chinese dynasties and European empires, I’ve learned that the financial strength to outspend one’s rivals is one of the most important strengths a country can have. That is how the United States beat the Soviet Union in the Cold War. Spend enough money in the right ways, and you don’t have to have a shooting war. Long-term success depends on sustaining both the “guns” and the “butter” without producing the excesses that lead to their declines. In other words, a country must be strong enough financially to give its people both a good living standard and protection from outside enemies. The really successful countries have been able to do that for 200 to 300 years. None has been able to do it forever. Conflict arises when the dominant power begins to weaken or an emerging power begins to approach it in strength—or both. * The greatest risk of military war is when both parties have 1) military powers that are roughly comparable and 2) irreconcilable and existential differences. As of this writing, the most potentially explosive conflict is that between the United States and China over Taiwan. The choice that opposing countries face—either fighting or backing down—is very hard to make. Both are costly—fighting in terms of lives and money, and backing down in terms of the loss of status, since it shows weakness, which leads to reduced support. When two competing entities each have the power to destroy the other, both must have extremely high trust that they won’t be unacceptably harmed or killed by the other. Managing the prisoner’s dilemma well, however, is extremely rare. While there are no rules in international relations other than those the most powerful impose on themselves, some approaches produce better outcomes than others. Specifically, those that are more likely to lead to win-win outcomes are better than those that lead to lose-lose outcomes. Hence this all-important principle: * to get more win-win outcomes one needs to negotiate with consideration given to what is most important to the other party and to oneself and know how to trade them. Skilled collaborations to produce win-win relationships that both increase and divide up wealth and power well are much more rewarding and much less painful than wars that lead to one side subjugating the other. Seeing things through your adversary’s eyes and clearly identifying and communicating your red lines to them (i.e., what cannot be compromised) are the keys to doing this well. * Winning means getting the things that are most important without losing the things that are most important, so wars that cost much more in lives and money than they provide in benefits are stupid. But “stupid” wars still happen all the time for reasons that I will explain. It is far too easy to slip into stupid wars because of a) the prisoner’s dilemma, b) a tit-for-tat escalation process, c) the perceived costs of backing down for the declining power, and d) misunderstandings existing when decision making has to be fast. Rival great powers typically find themselves in the prisoner’s dilemma; they need to have ways of assuring the other that they won’t try to kill them lest the other tries to kill them first. Tit-for-tat escalations are dangerous in that they require each side to escalate or lose what the enemy captured in the last move; it is like a game of chicken—push it too far and there is a head-on crash. Untruthful and emotional appeals that rile people up increase the dangers of stupid wars, so it is better for leaders to be truthful and thoughtful in explaining the situation and how they are dealing with it (this is especially essential in a democracy, in which the opinions of the population matter). The worst thing is when leaders are untruthful and emotional in dealing with their populations, and it is worse still when they take over the media. By and large, the tendency to move between win-win relationships and lose-lose relationships happens in a cyclical way. People and empires are more likely to have cooperative relationships during good times and to fight during bad times. When the existing great power is declining in relation to a rising power, it has a natural tendency to want to maintain the status quo or the existing rules, while the rising power wants to change them to be in line with the changing facts on the ground. While I don’t know about the love part of the saying “all is fair in love and war,” I know the war part is right. As an example, in the American Revolutionary War, when the British lined up in rows for the fight and the American revolutionaries shot at them from behind trees, the British thought that was unfair and complained. The revolutionaries won believing the British were foolish and that the cause of independence and freedom justified changing the rules of war. That’s just how it is. This leads me to one final principle: * have power, respect power, and use power wisely. Having power is good because power will win out over agreements, rules, and laws all the time. When push comes to shove, those who have the power to either enforce their interpretation of the rules and laws or to overturn them will get what they want. It is important to respect power because it’s not smart to fight a war that one is going to lose; it is preferable to negotiate the best settlement possible (that is unless one wants to be a martyr, which is usually for stupid ego reasons rather than for sensible strategic reasons). It is also important to use power wisely. Using power wisely doesn’t necessarily mean forcing others to give you what you want—i.e., bullying them. It includes the recognition that generosity and trust are powerful forces for producing win-win relationships, which are fabulously more rewarding than lose-lose relationships. In other words, it is often the case that using one’s “hard powers” is not the best path and that using one’s “soft powers” is preferable. When thinking about how to use power wisely, it’s also important to decide when to reach an agreement and when to fight. To do that, a party must imagine how its power will change over time. It is desirable to use one’s power to negotiate an agreement, enforce an agreement, or fight a war when one’s power is greatest. That means that it pays to fight early if one’s relative power is declining and fight later if it’s rising. If one is in a lose-lose relationship, one has to get out of it one way or another, preferably through separation, though possibly through war. To handle one’s power wisely, it’s usually best not to show it because it will usually lead others to feel threatened and build their own threatening powers, which will lead to a mutual escalation that threatens both. Power is usually best handled like a hidden knife that can be brought out in the event of a fight. But there are times when showing one’s power and threatening to use it are most effective for improving one’s negotiating position and preventing a fight. Knowing what matters most and least to the other party, especially what they will and won’t fight for, allows you to work your way toward an equilibrium that both parties consider a fair resolution of a dispute. Though it is generally desirable to have power, it is also desirable to not have power that one doesn’t need. That is because maintaining power consumes resources, most importantly your time and your money. Also, with power comes the burden of responsibilities. I have often been struck by how much happier less powerful people can be relative to more powerful people. CASE STUDY: WORLD WAR II Now that we have covered the dynamics and principles that drive the external order and disorder cycle, which were derived by looking at many cases, I’d like to briefly look at the World War II case because it provides the most recent example of the iconic dynamic of going from peace to war. Though it is only one case, it clearly shows how the confluence of the three big cycles—i.e., the overlapping and interrelated forces of the money and credit cycle, the internal order/disorder cycle, and the external order/disorder cycle—created the conditions for a catastrophic war and laid the groundwork for a new world order. While the stories from this period are very interesting in and of themselves, they are especially important because they provide lessons that help us think about what is happening now and what might be ahead. Most importantly, the United States and China are in an economic war that could conceivably evolve into a military war and comparisons between the 1930s and today provide valuable insights into what might happen and how to avoid a terrible war. The Path to War To help convey the picture of the 1930s, I will run through the geopolitical highlights leading up to the official start of the war in Europe in 1939 and the bombing of Pearl Harbor in 1941. Then I will quickly move through the war and the start of the new world order in 1945, with the US at the peak of its power. The global depression that followed the Great Crash of 1929 led to almost all countries having big internal conflicts over wealth. This caused them to turn to more populist, autocratic, nationalistic, and militaristic leaders and policies. These moves were either to the right or to the left and occurred in varying degrees, according to the countries’ circumstances and the strengths of their democratic or autocratic traditions. In Germany, Japan, Italy, and Spain, extremely bad economic circumstances and less well-established democratic traditions led to extreme internal conflicts and a turn to populist/autocratic leaders of the right (i.e., fascists), just as at different points in time the Soviet Union and China, which also endured extreme circumstances and had no experience with democracy, turned to populist/autocratic leaders of the left (i.e., communists). The US and the UK had much stronger democratic traditions and less severe economic conditions, so they became more populist and autocratic than they had been, but not nearly as much as other nations. Germany and Japan While Germany had previously been saddled with tremendous reparation debts following World War I, by 1929 it was beginning to emerge from under their yoke via the Young Plan, which provided for considerable debt relief and the departure of foreign troops from Germany by 1930. But the global depression hit Germany hard, leading to nearly 25 percent unemployment, massive bankruptcies, and extensive poverty. As is typical, there was a struggle between populists of the left (communists) and populists of the right (fascists). Adolf Hitler, the leading populist/fascist, tapped into the mood of national humiliation to build a nationalistic furor, casting the Treaty of Versailles and the countries that imposed it as the enemy. He created a 25-point nationalistic program and rallied support around it. In response to internal fighting and the desire to restore order, Hitler was appointed chancellor in January 1933, drawing large support for his Nazi Party from industrialists who feared the communists. Two months later, the Nazi Party won the most support and the most seats in the German Parliament (the Reichstag). Hitler refused to pay any further reparation debts, left the League of Nations, and took autocratic control of Germany in 1934. Holding the dual roles of chancellor and president, he became the country’s supreme leader. In democracies there are always some laws that allow leaders to grab special powers; Hitler seized them all. He invoked Article 48 of the Weimar Constitution to put an end to many civil rights and suppress political opposition from the communists, and forced the passage of the Enabling Act, which allowed him to pass laws without the approval of the Reichstag and the president. He was ruthless against any opposition—he censored or took control of newspapers and broadcasting companies, created a secret police force (the Gestapo) to root out and crush opposition, deprived Jews of their rights of citizenship, seized the Protestant Church’s finances, and arrested church officials who opposed him. Declaring the Aryan race superior, he prohibited non-Aryans from serving in government. Hitler took that same autocratic/fascist approach to rebuilding Germany’s economy, coupled with big fiscal and monetary stimulation programs. He privatized state-owned businesses and encouraged corporate investment, acting aggressively to raise Aryan Germans’ living standards. For example, he set up Volkswagen to make cars affordable and accessible, and he directed the building of the Autobahn. He financed this substantially increased government spending by forcing banks to buy government bonds. The debts that were produced were paid back by the earnings of companies and the central bank (the Reichsbank) monetizing debt. These fiscal policies by and large worked well in achieving Hitler’s goals. This is another example of how borrowing in one’s own currency and increasing one’s own debt and deficits can be highly productive if the money borrowed is put into investments that raise productivity and produce more than enough cash flow to service the debt. Even if it doesn’t cover 100 percent of the debt service, it can be very cost-effective in achieving the economic goals of the country. As for the economic effects of these policies, when Hitler came to power in 1933 the unemployment rate was 25 percent. By 1938 it was nil. Per capita income increased by 22 percent in the five years after Hitler took power, and real growth averaged over 8 percent per year between 1934 and 1938. As shown in the following charts, German equities rallied nearly 70 percent in a steady trend between 1933 and 1938, until the onset of the hot war. In 1935, Hitler began to build the military, making military service compulsory for Aryans. Germany’s military spending increased much faster than any other country because the German economy needed more resources to fuel itself and it intended to use its military power to seize them. Like Germany, Japan was also hit exceptionally hard by the depression and became more autocratic in response. Japan was especially vulnerable to the depression because, as an island nation without adequate natural resources, it relied on exports for income to import necessities. When its exports fell by around 50 percent between 1929 and 1931, Japan was economically devastated. In 1931, Japan went broke—i.e., it was forced to draw down its gold reserves, abandon the gold standard, and float its currency, which depreciated it so greatly that Japan ran out of buying power. These terrible conditions and large wealth gaps led to fighting between the left and the right. By 1932, there was a massive upsurge in right-wing nationalism and militarism, in the hope that order and economic stability could be forcibly restored. Japan set out to get the natural resources (e.g., oil, iron, coal, and rubber) and human resources (i.e., slave labor) it needed by seizing them from other countries, invading Manchuria in 1931 and spreading out through China and Asia. As with Germany, it could be argued that Japan’s path of military aggression to get needed resources was more cost-effective than relying on classic trading and economic practices. In 1934, there was severe famine in parts of Japan, causing even more political turbulence and reinforcing the right-wing, militaristic, nationalistic, and expansionistic movement. In the years that followed, Japan’s top-down fascist command economy grew stronger, building a military-industrial complex to protect its existing bases in East Asia and northern China and support its excursions into other countries. As was also the case in Germany, while most Japanese companies remained privately held, their production was controlled by the government. What is fascism? Consider the following three big choices that a country has to make when selecting its approach to governance: 1) bottom-up (democratic) or top-down (autocratic) decision making, 2) capitalist or communist (with socialist in the middle) ownership of production, and 3) individualistic (which treats the well-being of the individual with paramount importance) or collectivist (which treats the well-being of the whole with paramount importance). Pick the one from each category that you believe preferred approach. Fascism is autocratic, capitalist, and collectivist. Fascists believe that top-down autocratic leadership, in which the government directs the production of privately held companies such that individual gratification is subordinated to national success, is the best way to make the country and its people wealthier and more powerful. The US and the Allies In the US, debt problems became ruinous for American banks after 1929, which curtailed their lending around the world, hurting international borrowers. At the same time, the depression created weak demand, which led to a collapse of US imports and other countries’ sales to the US. As incomes weakened, demand fell and more credit problems occurred in a self-reinforcing downward economic spiral. The US responded by turning protectionist to safeguard jobs, raising tariffs via the passage of the Smoot-Hawley Tariff Act in 1930, which further depressed economic conditions in other countries. * Raising tariffs to protect domestic businesses and jobs during bad economic times is common, but it leads to reduced efficiency because production does not occur where it can be done most efficiently. Ultimately, tariffs contribute to greater global economic weakness, as tariff wars cause the countries that impose them to lose exports. Tariffs do, however, benefit the entities that are protected by them, and they can create political support for the leaders who impose them. The Soviet Union had yet to recover from its devastating 1917–22 revolution and civil war, a lost war to Germany, a costly war with Poland, and a famine in 1921, and it was wracked by political purges and economic hardships throughout the 1930s. China also suffered from civil war, poverty, and a famine in 1928–30. So, when things worsened in 1930 and tariffs began, bad conditions became desperate conditions in those countries. To make matters worse, there were droughts in the US and in the Soviet Union in the 1930s. * Harmful acts of nature (e.g., droughts, floods, and plagues) often cause periods of great economic hardship that when combined with other adverse conditions lead to periods of great conflict. In combination with extreme government policies, millions died in the USSR. At the same time, internal political fighting and fears of Nazi Germany led to purges of hundreds of thousands of people who were accused of spying and shot without trials. * Deflationary depressions are debt crises caused by there not being enough money in the hands of debtors to service their debts. They inevitably lead to the printing of money, debt restructurings, and government spending programs that increase the supply of, and reduce the value of, money and credit. The only question is how long it takes for government officials to make this move. In the case of the US, it took three and a half years from the crash in October 1929 until President Franklin D. Roosevelt’s March 1933 actions. In Roosevelt’s first 100 days in office, he created several massive government spending programs that were paid for by big tax increases and big budget deficits financed by debt that the Federal Reserve monetized. He instituted jobs programs, unemployment insurance, Social Security supports, and labor- and union friendly programs. After his 1935 tax bill, then popularly called the “Soak the Rich Tax,” the top marginal income tax rate for individuals rose to 75 percent (versus as low as 25 percent in 1930). By 1941, the top personal tax rate was 81 percent, and the top corporate tax rate was 31 percent, having started at 12 percent in 1930. Roosevelt also imposed a number of other taxes. Despite all of these taxes and the pickup in the economy that helped raise tax revenue, budget deficits increased from around 1 percent of GDP to about 4 percent of GDP because the spending increases were so large. From 1933 until the end of 1936 the stock market returned over 200 percent, and the economy grew at a blistering average real rate of about 9 percent. In 1936, the Federal Reserve tightened money and credit to fight inflation and slow an overheating economy, which caused the fragile US economy to fall back into recession and the other major economies to weaken with it, further raising tensions within and between countries. Meanwhile in Europe, the conflict in Spain between the populists of the left (the communists) and the populists of the right (the fascists) flared into the brutal Spanish Civil War. Right-wing Franco, with the support of Hitler, succeeded in purging left-wing opposition in Spain. * During periods of severe economic distress and large wealth gaps, there are typically revolutionarily large redistributions of wealth. When done peacefully these are achieved through large tax increases on the rich and big increases in the supply of money that devalue debtors’ claims, and when done violently they are achieved by forced asset confiscations. In the US and the UK, while there were redistributions of wealth and political power, capitalism and democracy were maintained. In Germany, Japan, Italy, and Spain they were not. * Before there is a shooting war there is usually an economic war. As is also typical, before all-out wars are declared there is about a decade of economic, technological, geopolitical, and capital wars, during which the conflicting powers intimidate each other, testing the limits of each other’s power. While 1939 and 1941 are known as the official starts of the wars in Europe and the Pacific, the conflicts really began about 10 years before that. In addition to the economically motivated conflicts within countries and the political shifts that arose from them, all of these countries faced increased external economic conflicts as they fought for greater shares of a shrinking economic pie. Because power, and not law, rules international relations, Germany and Japan became more expansionist and increasingly began to test the UK, the US, and France in the competition over resources and influence over territories. Before going on to describe the hot war, I want to elaborate on the common tactics used when economic and capital tools are weaponized. They have been and still are: 1. Asset freezes/seizures: Preventing an enemy/rival from using or selling foreign assets they rely on. These measures can range from asset freezes for targeted groups in a country (e.g., the current US sanctions of the Iranian Revolutionary Guard or the initial US asset freeze against Japan in World War II) to more severe measures like unilateral debt repudiation or outright seizures of a country’s assets (e.g., some top US policy makers have been talking about not paying our debts to China). 2. Blocking capital markets access: Preventing a country from accessing their own or another country’s capital markets (e.g., in 1887 Germany banned the purchase of Russian securities and debt to impede Russia’s military buildup; the US is now threatening to do this to China). 3. Embargoes/blockades: Blocking trade in goods and/or services in one’s own country and in some cases with neutral third parties for the purpose of weakening the targeted country or preventing it from getting essential items (e.g., the US’s oil embargo on Japan and cutting off its ships’ access to the Panama Canal in World War II) or blocking exports from the targeted country to other countries, thus cutting off their income (e.g., France’s blockade of the UK in the Napoleonic Wars). If you’re interested in seeing how these tactics have been applied from 1600 until now, they are available at economicprinciples.org. THE HOT WAR BEGINS In November 1937, Hitler secretly met with his top officials to announce his plans for German expansion to gain resources and bring together the Aryan race. Then he put them into action, first annexing Austria and then seizing a part of what was then Czechoslovakia that contained oil resources. Europe and the US watched warily, not wanting to get drawn into another war so soon after the devastation of World War I. As with all wars, the unknowns were far greater than the knowns because a) rival powers go into wars only when their powers are roughly comparable (otherwise it would be stupidly suicidal for the obviously weaker power) and b) there are way too many possible actions and reactions to anticipate. The only thing that is known at the outset of a hot war is that it will probably be extremely painful and possibly ruinous. As a result, smart leaders typically go into them only if the other side has pushed them into a position of either fighting or losing by backing down. For the Allies, that moment came on September 1, 1939, when Germany invaded Poland. Germany looked unstoppable; in short order it captured Denmark, Norway, the Netherlands, Belgium, Luxembourg, and France, and strengthened its alliances with Japan and Italy, which had common enemies and were ideologically aligned. By seizing territory rapidly (e.g., oil-rich Romania), Hitler’s army was able to conserve its existing oil resources and gain new ones quickly. The thirst for, and acquisition of, natural resources remained a major driver of the Nazi war machine as it pushed its campaigns into Russia and the Middle East. War with the Soviets was inevitable; the only question was when. Although Germany and the USSR had signed a non-aggression pact, Germany invaded Russia in June 1941, which put Germany in an extremely costly war on two fronts. In the Pacific in 1937, Japan expanded its occupation of China, brutally taking control of Shanghai and Nanking, killing an estimated 200,000 Chinese civilians and disarmed combatants in the capture of Nanking alone. While the US remained isolationist, it did provide Chiang Kai-shek’s government with fighter planes and pilots to counter the Japanese, putting a toe in the war. Conflicts between the US and Japan began to flare. A Japanese soldier struck the US consul, John Moore Allison, in the face in Nanking and Japanese fighter planes sank a US gunship. In November 1940, Roosevelt won re-election after campaigning on the promise to keep the US out of the war, even though the US was already taking economic actions to protect its interests, especially in the Pacific, using economic supports to help countries it sympathized with and economic sanctions against those it did not. Earlier in 1940, Secretary of War Henry Stimson had initiated aggressive economic sanctions against Japan, culminating in the Export Control Act of 1940. In mid-1940, the US moved the US Pacific Fleet to Hawaii. In October, the US ramped up the embargo, restricting “all iron and steel to destinations other than Britain and nations of the Western Hemisphere.” The plan was to cut Japan off from resources in order to force them to retreat from most of the areas they had taken over. In March 1941, Congress passed the Lend-Lease Act, which allowed the US to lend or lease war supplies to the nations it deemed to be acting in ways that were “vital to the defense of the United States,” which included Great Britain, the Soviet Union, and China. Helping the Allies was good for the US both geopolitically and economically because it made a lot of money selling weapons, food, and other items to these soon-to-be-allied countries who were struggling to maintain production while waging war. But its motivations weren’t entirely mercenary. Great Britain was running out of money (i.e., gold), so the US allowed them to postpone payment until after the war (in some cases waiving payment entirely). Although not an outright declaration of war, Lend-Lease effectively ended the United States’ neutrality. * When countries are weak, opposing countries take advantage of their weaknesses to obtain gains. France, the Netherlands, and Great Britain all had colonies in Asia. Overstretched by the fighting in Europe, they were unable to defend them against the Japanese. Starting in September 1940, Japan invaded several colonies in Southeast Asia, beginning with French Indochina, adding what it called the Southern Resource Zone to its Greater East Asia Co-Prosperity Sphere. In 1941, Japan seized oil reserves in the Dutch East Indies. This Japanese territorial expansion was a threat to the US’s own Pacific ambitions. In July and August 1941, Roosevelt responded by freezing all Japanese assets in the United States, closing the Panama Canal to Japanese ships, and embargoing oil and gas exports to Japan. This cut off three-fourths of Japan’s trade and 80 percent of its oil. Japan calculated that it would run out of oil in two years. This put Japan in the position of having to choose between backing down or attacking the US. On December 7 and 8, 1941, Japan launched coordinated attacks on US military forces at Pearl Harbor and in the Philippines. This marked the beginning of the declared war in the Pacific, which brought the US into the war in Europe too. While Japan didn’t have a widely recognized plan to win the war, the most optimistic Japanese leaders believed that the US would lose because it was fighting a war on two fronts and because its individualistic/capitalist political system was inferior to Japan’s and Germany’s authoritarian/fascist systems with their command military-industrial complexes. They also believed that they had a greater willingness to endure and die for their country, which is a big driver of which side wins. * In war one’s ability to withstand pain is even more important than one’s ability to inflict pain. WARTIME ECONOMIC POLICIES Just as it is worth noting what classic economic war tactics are, it is also worth noting what classic wartime economic policies are within countries. These include government controls on just about everything as the country shifts its resources from profit making to war making—e.g., the government determines a) what items are allowed to be produced, b) what items can be bought and sold in what amounts (rationing), c) what items can be imported and exported, d) prices, wages, and profits, e) access to one’s own financial assets, and f) the ability to move one’s own money out of the country. Because wars are expensive, classically the government g) issues lots of debt that is monetized, h) relies on non-credit money such as gold for international transactions because its credit is not accepted, i) governs more autocratically, j) imposes various types of economic sanctions on enemies, including cutting off their access to capital, and k) experiences enemies imposing these sanctions on them. When the US entered the European and Pacific wars after the attack on Pearl Harbor, classic wartime economic policies were put in place in most countries by leaders whose more autocratic approaches were broadly supported by their populations. The following table shows those economic controls in each of the major countries. The market movements during the hot war years were heavily affected by both government controls and how countries did in battles as the odds of winning and losing changed. The next table shows the controls over markets and capital flows that were put in place by the major countries during the war years. Stock market closures were common in a number of countries, leaving investors in stocks stuck without access to their capital. I should also note that money and credit were not commonly accepted between non-allied countries during the war because of a justifiable wariness about whether the currency would have any value. As noted earlier, gold—or, in some cases, silver or barter—is the coin of the realm during wars. At such times, prices and capital flows are typically controlled, so it is difficult to say what the real prices of many things are. Because losing wars typically leads to a total wipeout of wealth and power, movements of those stock markets that remained open in the war years were largely driven by how countries did in key battles as these results shifted the probability of victory or defeat for each side. For example, German equities outperformed at the beginning of World War II as Germany captured territory and established military dominance, while they underperformed after Allied powers like the US and the UK turned the tide of the war. After the 1942 Battle of Midway, Allied equities rallied almost continuously until the end of the war, while Axis equities were flat or down. As shown, both the German and Japanese stock markets were closed at the end of the war, didn’t reopen for around five years, and were virtually wiped out when they did, while US stocks were extremely strong. Protecting one’s wealth in times of war is difficult, as normal economic activities are curtailed, traditionally safe investments are not safe, capital mobility is limited, and high taxes are imposed when people and countries are fighting for their survival. Protecting the wealth of those who have it is not a priority relative to the need to redistribute wealth to get it to where it is needed most. As for investing, sell out of all debt and buy gold because wars are financed by borrowing and printing money, which devalues debt and money, and because there is a justifiable reluctance to accept credit. CONCLUSION Every world power has its time in the sun, thanks to the uniqueness of their circumstances and the nature of their character and culture (e.g., they have the essential elements of a strong work ethic, smarts, discipline, education, etc.), but they all eventually decline. Some do so more gracefully than others, with less trauma, but they nevertheless decline. Traumatic declines can lead to some of the worst periods in history, when big fights over wealth and power prove extremely costly both economically and in human lives. Still, the cycle needn’t transpire this way if countries in their rich and powerful stages stay productive, earn more than they spend, make the system work well for most of their populations, and figure out ways of creating and sustaining win-win relationships with their most significant rivals. A number of empires and dynasties have sustained themselves for hundreds of years, and the United States, at 245 years old, has proven itself to be one of the longest-lasting.

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タイトル: The Quiet Wealth Transfer Happening Right Now While You Sleep ( Actionable Blueprint ) A structural shift in how money gets made is underway. It is not loud. It is not on the news. And by the time most people realize what happened, the window will have already closed. Here is what is actually going on. Work with us and automate business operations to cut costs, save time and improve efficiency - https://tally.so/r/mZbV0a The Economy Just Changed Its Rules and Nobody Sent You the Memo Three years ago, AI was a toy. You typed something, it typed back. Impressive at parties. Useless for real work. That era ended. In 2026, AI does not respond to you. It works for you. It plans. It executes. It learns your business, your clients, your processes, and it runs them while you sleep, while you travel, while you do literally anything else. McKinsey estimates AI will generate up to $4.4 trillion in annual economic value from automation alone. That money is going somewhere. The question is whether it goes into your pocket or the pocket of the person who figured this out before you did. This is not about hype. This is not about cryptocurrency or NFTs or the next shiny thing. This is about a concrete, repeatable, documented pathway to financial freedom that is available to anyone with a laptop and the willingness to spend 90 days building something real. The blueprint is sitting right here. What Most People Are Missing The people losing in this economy are not losing because they are lazy or unintelligent. They are losing because they are using AI at level one while others are operating at level ten. Level one looks like this. You open ChatGPT, you ask a question, you copy the answer somewhere, you close the tab, you do it again tomorrow. Useful? Sure. Wealth-building? No. Level ten looks like this. You deploy an AI agent that runs your sales outreach 24 hours a day. Another that monitors competitor pricing and alerts you to opportunities. Another that handles customer inquiries, qualifies leads, and books calls while you are nowhere near a computer. A fleet of digital workers, none of whom take sick days, none of whom ask for raises, none of whom quit. The technology to do this is not coming. It is here. It is open. And the early adopters are already pulling away. The 90-Day Blueprint to Financial Freedom This is not theory. This is the exact sequence that works. Days 1 to 30: Find the Problem That Pays Wealth in 2026 starts not with a tool but with a question: where is someone losing money or time because of a task that AI could handle? The technical term for this is task collapsibility. You are looking for work where AI can achieve 75% or more time savings versus doing it manually. That gap between what it costs now and what it costs with AI is where your income lives. Your first move is picking your model. There are three that consistently produce results: The Micro-SaaS model. You build a focused AI tool for a specific niche. Dentists who need patient follow-up automation. Etsy sellers who need product description generation. Real estate agents who need listing copy. The narrower you go, the less competition you face, and the more you can charge. The AI-Automated Agency. You take a service business, such as social media management, lead generation, or content creation, and you automate 80% of the delivery with AI. You charge agency prices. You deliver with AI speed. The margin difference is your profit. The Data Intelligence Subscription. You build proprietary research or monitoring tools and charge for ongoing access. Competitor price trackers. Industry news digests. Regulatory change alerts. You create it once. You charge for it indefinitely. Your second move is finding your niche. The markets with the least competition and the highest demand right now are not the obvious ones. Agentic AI for enterprise workflows is a $10.86 billion market with 93% of IT leaders planning adoption. The gap between what enterprises want and what currently exists is enormous. Compliance and legal automation for small law firms. These businesses are drowning in paperwork and dramatically underserved by AI tools built for large corporations. Wellness and longevity technology. The market for proactive health AI is growing over 20% year over year and the dominant players have not emerged yet. Sustainable energy intelligence. AI that optimizes energy use for buildings, data centers, and logistics is barely being built despite massive demand. Marine and ocean data tools. This sounds obscure. That is the point. No competition, rising institutional demand, and completely open territory. The filter you apply before committing to anything: One. Can you technically build it in 60 days or less using the tools available today? Two. Does the data you need actually exist and is it accessible? Three. Can it connect to the systems your customers already use? Four. Can you scale it without linearly scaling your own time? Five. Can you measure its ROI clearly enough that customers can justify paying for it? If all five answers are yes, you have found your opportunity. If any answer is no, keep looking. Days 31 to 60: Build the Thing In 2026, you do not need to know how to code to build a software business. This is not an exaggeration. It is a structural shift in what is possible. The approach being used by people building profitable products right now is called vibe coding. You describe what you want in plain language. AI agents build it. You review, refine, redirect. The tools that make this work: Cursor is an AI-native code editor that writes code as fast as you can describe what you need. It is the IDE of the current wave. Lindy and Relevance AI are no-code builders for AI agents. If you have never written a line of code in your life, you can build functioning automation workflows here in hours. CrewAI and LangGraph handle multi-agent orchestration. When your product needs multiple AI agents working together on different parts of a task, these are the frameworks that coordinate them. Model Context Protocol (MCP) is the piece most people have not heard of yet. Think of it as a universal connector, like USB-C but for AI. It allows your agents to connect to real-time data sources, databases, and external tools without custom integration work for each one. It cuts development overhead by 30% and speeds up workflow execution by 50 to 75%. LlamaIndex and Pinecone let your agents work with your private data. This is how you build something your competitors cannot simply copy, because your product knows things theirs does not. The most important rule for this phase: build the ugliest version that works and put it in front of paying customers immediately. Not the polished version. Not the perfect version. The version that solves the core problem, badly designed, missing half the features, but functional. Every hour you spend perfecting something before real customers touch it is an hour you are building toward your own assumptions instead of toward what the market actually wants. Ship ugly. Learn fast. Fix what matters. One practical structure that consistently works: build in human approval gates at key decision points early on. This is not a concession to imperfection. It is how you catch the 20% of cases where the AI gets it wrong before those cases become problems for your customers. As confidence in the system builds, you remove the gates. But start with them. Days 61 to 90: Scale the Machine Once you have something real that real customers are paying for, the game shifts from building to scaling. And scaling in 2026 means one specific thing: replacing human labor in your own business with AI agents before you even think about hiring. Deploy an AI sales development representative. Tools like this agent, often nicknamed the Sarah model in the practitioner community, handles research, personalized outreach, follow-up, and meeting booking around the clock. A single AI SDR can operate with the output equivalent of three to five human sales reps. The cost comparison is not even worth writing out. Productize your delivery. The highest-margin version of any service business is not selling your time. It is selling outcomes through a system. If you built an AI agency, build your workflow into a replicable engine and offer subscription access to the outputs. You stop trading hours for dollars entirely. Build your compliance infrastructure now, not later. This sounds like the boring part. It is actually a competitive moat. The EU AI Act is in force. U.S. states including Texas, California, and Colorado have enacted their own frameworks. The businesses that build governance into their systems from the start are the ones that can scale into regulated industries and enterprise contracts. The ones that ignore it get shut out. The simple governance structure that works: AI monitors your AI. You build automated validation layers that check outputs for accuracy, bias, and compliance before they reach customers. Human oversight stays on decisions that have significant consequences. Everything else runs autonomously. The Numbers That Should Make This Uncomfortable If you save 30 minutes a day through automation, that is 182 hours a year. Over four work weeks recovered. Now scale that. If your AI agent handles something that would take a human employee 20 hours a week to do manually, and you pay that agent nothing beyond API costs, you have permanently freed 1,000 hours of annual labor capacity. At any reasonable billing rate, that is tens of thousands of dollars per year that either drops to your bottom line or gets reinvested in growth. Automation reduces operational costs by 20 to 30% across industries according to McKinsey. In manufacturing and predictive maintenance, companies are seeing 22 to 25% cost reductions. In finance, 60% faster processing. In logistics, over a million dollars in monthly savings in documented cases. In retail, 50% faster inventory processing. These are not projections. These are 2025 and 2026 results being reported by actual companies. The math works. The only question is whether you are on the side that benefits from it or the side that gets replaced by it. The Niches Where Real Money Is Being Made Right Now Healthcare and longevity. AI platforms hitting over 90% diagnostic accuracy in remote facilities. Diabetic foot detection reducing treatment costs by 80% and hospital stays by 90%. Personalized performance agents for athletes. The incumbents are not moving fast enough. The window is open. Sustainable energy and data center efficiency. AI's power appetite is testing grid limits. The market for energy optimization, water-efficient cooling for data centers, and grid demand forecasting is enormous and barely served. Enterprises that figure out how to run AI infrastructure efficiently will pay serious money for that expertise. Compliance as a service. Every company using AI in a regulated context needs documentation, monitoring, and audit trails to satisfy the EU AI Act and state-level U.S. frameworks. Most of them have no idea how to build this. You can build it for them. Answer Engine Optimization. Search is being replaced by AI overviews. The brands that appear in those overviews see 35% higher click-through rates and conversion rates of 14.2% versus 2.8% for traditional search results. Businesses desperately need help getting cited in AI-generated answers and almost nobody is selling this service yet. Small business operations automation. The Fortune 500 has a dozen vendors fighting over their AI budget. The small business owner running a plumbing company or a dental practice or an independent law firm has almost no one. These businesses have real money, real pain, and no solutions. That gap is your opportunity. What Stops People and How to Not Be One of Them The obstacle is not technical. Anyone can learn to use these tools. The obstacle is the 3 to 4 hours of focused work required before you see results. Most people will not do it. They will read this article, feel motivated for 48 hours, and go back to what they were doing. The window they could have used will eventually close. The people who will be financially free because of AI in 2026 and 2027 are the ones who treated those first 90 days as an investment, not a hobby. They started ugly. They shipped early. They learned from real customers instead of from their own assumptions. They built systems instead of doing tasks. They stayed consistent when the first thing they built did not work perfectly. That is not a personality type. It is a decision. One more thing about cost. The software is largely free or near-free at the start. A VPS for running agents costs around $5 a month. API costs for model calls can grow quickly with active agent loops, with heavy users spending $50 to $200 a month. Budget for it. Monitor it. It is still orders of magnitude cheaper than the human labor doing the equivalent work. The Real Opportunity Before It Closes The structural condition that makes 2026 unique is that the gap between what AI can do and what most businesses are doing with AI has never been wider. This is not a permanent condition. The window when a single person with a laptop and 90 days can build something worth real money in these markets will narrow as the tools become obvious, the playbooks spread, and the early adopters accumulate the clients, the case studies, and the domain expertise that becomes a moat. The people who act in 2026 are not guaranteed anything. No strategy guarantees anything. What they do have is a structural advantage that compounds the earlier they start. The transition happening right now is from using AI as a tool you visit to deploying AI as a workforce you supervise. The people who make that transition in the next 12 months are positioning themselves for a completely different financial outcome than the people who are still opening browser tabs and copying answers. That is the quiet wealth transfer. The only question is which side of it you end up on. Start Here Day one. Pick one of the three business models. Apply the five-question filter to your niche idea. If it passes, start. That is the whole instruction. Everything else is execution. The documentation, the tools, the frameworks, and the communities are all available. The 90-day blueprint works. People are running it right now and they are not special. They are just earlier. Start at openclaw.ai for autonomous agent infrastructure. Cursor for building. Lindy or Relevance AI if you want no-code. CrewAI or LangGraph for multi-agent systems. MCP for connecting everything. The machine does not care whether you are ready. It is already running. The only question is whether it is running for you. WORK WITH US - https://tally.so/r/mZbV0a

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We just announced new primitives for building agents. Here are 10 tips on running multi-hour workflows reliably.

タイトル: Something Big Is Happening Think back to February 2020. If you were paying close attention, you might have noticed a few people talking about a virus spreading overseas. But most of us weren't paying close attention. The stock market was doing great, your kids were in school, you were going to restaurants and shaking hands and planning trips. If someone told you they were stockpiling toilet paper you would have thought they'd been spending too much time on a weird corner of the internet. Then, over the course of about three weeks, the entire world changed. Your office closed, your kids came home, and life rearranged itself into something you wouldn't have believed if you'd described it to yourself a month earlier. I think we're in the "this seems overblown" phase of something much, much bigger than Covid. I've spent six years building an AI startup and investing in the space. I live in this world. And I'm writing this for the people in my life who don't... my family, my friends, the people I care about who keep asking me "so what's the deal with AI?" and getting an answer that doesn't do justice to what's actually happening. I keep giving them the polite version. The cocktail-party version. Because the honest version sounds like I've lost my mind. And for a while, I told myself that was a good enough reason to keep what's truly happening to myself. But the gap between what I've been saying and what is actually happening has gotten far too big. The people I care about deserve to hear what is coming, even if it sounds crazy. I should be clear about something up front: even though I work in AI, I have almost no influence over what's about to happen, and neither does the vast majority of the industry. The future is being shaped by a remarkably small number of people: a few hundred researchers at a handful of companies... OpenAI, Anthropic, Google DeepMind, and a few others. A single training run, managed by a small team over a few months, can produce an AI system that shifts the entire trajectory of the technology. Most of us who work in AI are building on top of foundations we didn't lay. We're watching this unfold the same as you... we just happen to be close enough to feel the ground shake first. But it's time now. Not in an "eventually we should talk about this" way. In a "this is happening right now and I need you to understand it" way. I know this is real because it happened to me first Here's the thing nobody outside of tech quite understands yet: the reason so many people in the industry are sounding the alarm right now is because this already happened to us. We're not making predictions. We're telling you what already occurred in our own jobs, and warning you that you're next. For years, AI had been improving steadily. Big jumps here and there, but each big jump was spaced out enough that you could absorb them as they came. Then in 2025, new techniques for building these models unlocked a much faster pace of progress. And then it got even faster. And then faster again. Each new model wasn't just better than the last... it was better by a wider margin, and the time between new model releases was shorter. I was using AI more and more, going back and forth with it less and less, watching it handle things I used to think required my expertise. Then, on February 5th, two major AI labs released new models on the same day: GPT-5.3 Codex from OpenAI, and Opus 4.6 from Anthropic (the makers of Claude, one of the main competitors to ChatGPT). And something clicked. Not like a light switch... more like the moment you realize the water has been rising around you and is now at your chest. I am no longer needed for the actual technical work of my job. I describe what I want built, in plain English, and it just... appears. Not a rough draft I need to fix. The finished thing. I tell the AI what I want, walk away from my computer for four hours, and come back to find the work done. Done well, done better than I would have done it myself, with no corrections needed. A couple of months ago, I was going back and forth with the AI, guiding it, making edits. Now I just describe the outcome and leave. Let me give you an example so you can understand what this actually looks like in practice. I'll tell the AI: "I want to build this app. Here's what it should do, here's roughly what it should look like. Figure out the user flow, the design, all of it." And it does. It writes tens of thousands of lines of code. Then, and this is the part that would have been unthinkable a year ago, it opens the app itself. It clicks through the buttons. It tests the features. It uses the app the way a person would. If it doesn't like how something looks or feels, it goes back and changes it, on its own. It iterates, like a developer would, fixing and refining until it's satisfied. Only once it has decided the app meets its own standards does it come back to me and say: "It's ready for you to test." And when I test it, it's usually perfect. I'm not exaggerating. That is what my Monday looked like this week. But it was the model that was released last week (GPT-5.3 Codex) that shook me the most. It wasn't just executing my instructions. It was making intelligent decisions. It had something that felt, for the first time, like judgment. Like taste. The inexplicable sense of knowing what the right call is that people always said AI would never have. This model has it, or something close enough that the distinction is starting not to matter. I've always been early to adopt AI tools. But the last few months have shocked me. These new AI models aren't incremental improvements. This is a different thing entirely. And here's why this matters to you, even if you don't work in tech. The AI labs made a deliberate choice. They focused on making AI great at writing code first... because building AI requires a lot of code. If AI can write that code, it can help build the next version of itself. A smarter version, which writes better code, which builds an even smarter version. Making AI great at coding was the strategy that unlocks everything else. That's why they did it first. My job started changing before yours not because they were targeting software engineers... it was just a side effect of where they chose to aim first. They've now done it. And they're moving on to everything else. The experience that tech workers have had over the past year, of watching AI go from "helpful tool" to "does my job better than I do", is the experience everyone else is about to have. Law, finance, medicine, accounting, consulting, writing, design, analysis, customer service. Not in ten years. The people building these systems say one to five years. Some say less. And given what I've seen in just the last couple of months, I think "less" is more likely. "But I tried AI and it wasn't that good" I hear this constantly. I understand it, because it used to be true. If you tried ChatGPT in 2023 or early 2024 and thought "this makes stuff up" or "this isn't that impressive", you were right. Those early versions were genuinely limited. They hallucinated. They confidently said things that were nonsense. That was two years ago. In AI time, that is ancient history. The models available today are unrecognizable from what existed even six months ago. The debate about whether AI is "really getting better" or "hitting a wall" — which has been going on for over a year — is over. It's done. Anyone still making that argument either hasn't used the current models, has an incentive to downplay what's happening, or is evaluating based on an experience from 2024 that is no longer relevant. I don't say that to be dismissive. I say it because the gap between public perception and current reality is now enormous, and that gap is dangerous... because it's preventing people from preparing. Part of the problem is that most people are using the free version of AI tools. The free version is over a year behind what paying users have access to. Judging AI based on free-tier ChatGPT is like evaluating the state of smartphones by using a flip phone. The people paying for the best tools, and actually using them daily for real work, know what's coming. I think of my friend, who's a lawyer. I keep telling him to try using AI at his firm, and he keeps finding reasons it won't work. It's not built for his specialty, it made an error when he tested it, it doesn't understand the nuance of what he does. And I get it. But I've had partners at major law firms reach out to me for advice, because they've tried the current versions and they see where this is going. One of them, the managing partner at a large firm, spends hours every day using AI. He told me it's like having a team of associates available instantly. He's not using it because it's a toy. He's using it because it works. And he told me something that stuck with me: every couple of months, it gets significantly more capable for his work. He said if it stays on this trajectory, he expects it'll be able to do most of what he does before long... and he's a managing partner with decades of experience. He's not panicking. But he's paying very close attention. The people who are ahead in their industries (the ones actually experimenting seriously) are not dismissing this. They're blown away by what it can already do. And they're positioning themselves accordingly. How fast this is actually moving Let me make the pace of improvement concrete, because I think this is the part that's hardest to believe if you're not watching it closely. In 2022, AI couldn't do basic arithmetic reliably. It would confidently tell you that 7 × 8 = 54. By 2023, it could pass the bar exam. By 2024, it could write working software and explain graduate-level science. By late 2025, some of the best engineers in the world said they had handed over most of their coding work to AI. On February 5th, 2026, new models arrived that made everything before them feel like a different era. If you haven't tried AI in the last few months, what exists today would be unrecognizable to you. There's an organization called METR that actually measures this with data. They track the length of real-world tasks (measured by how long they take a human expert) that a model can complete successfully end-to-end without human help. About a year ago, the answer was roughly ten minutes. Then it was an hour. Then several hours. The most recent measurement (Claude Opus 4.5, from November) showed the AI completing tasks that take a human expert nearly five hours. And that number is doubling approximately every seven months, with recent data suggesting it may be accelerating to as fast as every four months. But even that measurement hasn't been updated to include the models that just came out this week. In my experience using them, the jump is extremely significant. I expect the next update to METR's graph to show another major leap. If you extend the trend (and it's held for years with no sign of flattening) we're looking at AI that can work independently for days within the next year. Weeks within two. Month-long projects within three. Amodei has said that AI models "substantially smarter than almost all humans at almost all tasks" are on track for 2026 or 2027. Let that land for a second. If AI is smarter than most PhDs, do you really think it can't do most office jobs? Think about what that means for your work. AI is now building the next AI There's one more thing happening that I think is the most important development and the least understood. On February 5th, OpenAI released GPT-5.3 Codex. In the technical documentation, they included this: "GPT-5.3-Codex is our first model that was instrumental in creating itself. The Codex team used early versions to debug its own training, manage its own deployment, and diagnose test results and evaluations." Read that again. The AI helped build itself. This isn't a prediction about what might happen someday. This is OpenAI telling you, right now, that the AI they just released was used to create itself. One of the main things that makes AI better is intelligence applied to AI development. And AI is now intelligent enough to meaningfully contribute to its own improvement. Dario Amodei, the CEO of Anthropic, says AI is now writing "much of the code" at his company, and that the feedback loop between current AI and next-generation AI is "gathering steam month by month." He says we may be "only 1–2 years away from a point where the current generation of AI autonomously builds the next." Each generation helps build the next, which is smarter, which builds the next faster, which is smarter still. The researchers call this an intelligence explosion. And the people who would know — the ones building it — believe the process has already started. What this means for your job I'm going to be direct with you because I think you deserve honesty more than comfort. Dario Amodei, who is probably the most safety-focused CEO in the AI industry, has publicly predicted that AI will eliminate 50% of entry-level white-collar jobs within one to five years. And many people in the industry think he's being conservative. Given what the latest models can do, the capability for massive disruption could be here by the end of this year. It'll take some time to ripple through the economy, but the underlying ability is arriving now. This is different from every previous wave of automation, and I need you to understand why. AI isn't replacing one specific skill. It's a general substitute for cognitive work. It gets better at everything simultaneously. When factories automated, a displaced worker could retrain as an office worker. When the internet disrupted retail, workers moved into logistics or services. But AI doesn't leave a convenient gap to move into. Whatever you retrain for, it's improving at that too. Let me give you a few specific examples to make this tangible... but I want to be clear that these are just examples. This list is not exhaustive. If your job isn't mentioned here, that does not mean it's safe. Almost all knowledge work is being affected. Legal work. AI can already read contracts, summarize case law, draft briefs, and do legal research at a level that rivals junior associates. The managing partner I mentioned isn't using AI because it's fun. He's using it because it's outperforming his associates on many tasks. Financial analysis. Building financial models, analyzing data, writing investment memos, generating reports. AI handles these competently and is improving fast. Writing and content. Marketing copy, reports, journalism, technical writing. The quality has reached a point where many professionals can't distinguish AI output from human work. Software engineering. This is the field I know best. A year ago, AI could barely write a few lines of code without errors. Now it writes hundreds of thousands of lines that work correctly. Large parts of the job are already automated: not just simple tasks, but complex, multi-day projects. There will be far fewer programming roles in a few years than there are today. Medical analysis. Reading scans, analyzing lab results, suggesting diagnoses, reviewing literature. AI is approaching or exceeding human performance in several areas. Customer service. Genuinely capable AI agents... not the frustrating chatbots of five years ago... are being deployed now, handling complex multi-step problems. A lot of people find comfort in the idea that certain things are safe. That AI can handle the grunt work but can't replace human judgment, creativity, strategic thinking, empathy. I used to say this too. I'm not sure I believe it anymore. The most recent AI models make decisions that feel like judgment. They show something that looked like taste: an intuitive sense of what the right call was, not just the technically correct one. A year ago that would have been unthinkable. My rule of thumb at this point is: if a model shows even a hint of a capability today, the next generation will be genuinely good at it. These things improve exponentially, not linearly. Will AI replicate deep human empathy? Replace the trust built over years of a relationship? I don't know. Maybe not. But I've already watched people begin relying on AI for emotional support, for advice, for companionship. That trend is only going to grow. I think the honest answer is that nothing that can be done on a computer is safe in the medium term. If your job happens on a screen (if the core of what you do is reading, writing, analyzing, deciding, communicating through a keyboard) then AI is coming for significant parts of it. The timeline isn't "someday." It's already started. Eventually, robots will handle physical work too. They're not quite there yet. But "not quite there yet" in AI terms has a way of becoming "here" faster than anyone expects. What you should actually do I'm not writing this to make you feel helpless. I'm writing this because I think the single biggest advantage you can have right now is simply being early. Early to understand it. Early to use it. Early to adapt. Start using AI seriously, not just as a search engine. Sign up for the paid version of Claude or ChatGPT. It's $20 a month. But two things matter right away. First: make sure you're using the best model available, not just the default. These apps often default to a faster, dumber model. Dig into the settings or the model picker and select the most capable option. Right now that's GPT-5.2 on ChatGPT or Claude Opus 4.6 on Claude, but it changes every couple of months. If you want to stay current on which model is best at any given time, you can follow me on X (@mattshumer_). I test every major release and share what's actually worth using. Second, and more important: don't just ask it quick questions. That's the mistake most people make. They treat it like Google and then wonder what the fuss is about. Instead, push it into your actual work. If you're a lawyer, feed it a contract and ask it to find every clause that could hurt your client. If you're in finance, give it a messy spreadsheet and ask it to build the model. If you're a manager, paste in your team's quarterly data and ask it to find the story. The people who are getting ahead aren't using AI casually. They're actively looking for ways to automate parts of their job that used to take hours. Start with the thing you spend the most time on and see what happens. And don't assume it can't do something just because it seems too hard. Try it. If you're a lawyer, don't just use it for quick research questions. Give it an entire contract and ask it to draft a counterproposal. If you're an accountant, don't just ask it to explain a tax rule. Give it a client's full return and see what it finds. The first attempt might not be perfect. That's fine. Iterate. Rephrase what you asked. Give it more context. Try again. You might be shocked at what works. And here's the thing to remember: if it even kind of works today, you can be almost certain that in six months it'll do it near perfectly. The trajectory only goes one direction. This might be the most important year of your career. Work accordingly. I don't say that to stress you out. I say it because right now, there is a brief window where most people at most companies are still ignoring this. The person who walks into a meeting and says "I used AI to do this analysis in an hour instead of three days" is going to be the most valuable person in the room. Not eventually. Right now. Learn these tools. Get proficient. Demonstrate what's possible. If you're early enough, this is how you move up: by being the person who understands what's coming and can show others how to navigate it. That window won't stay open long. Once everyone figures it out, the advantage disappears. Have no ego about it. The managing partner at that law firm isn't too proud to spend hours a day with AI. He's doing it specifically because he's senior enough to understand what's at stake. The people who will struggle most are the ones who refuse to engage: the ones who dismiss it as a fad, who feel that using AI diminishes their expertise, who assume their field is special and immune. It's not. No field is. Get your financial house in order. I'm not a financial advisor, and I'm not trying to scare you into anything drastic. But if you believe, even partially, that the next few years could bring real disruption to your industry, then basic financial resilience matters more than it did a year ago. Build up savings if you can. Be cautious about taking on new debt that assumes your current income is guaranteed. Think about whether your fixed expenses give you flexibility or lock you in. Give yourself options if things move faster than you expect. Think about where you stand, and lean into what's hardest to replace. Some things will take longer for AI to displace. Relationships and trust built over years. Work that requires physical presence. Roles with licensed accountability: roles where someone still has to sign off, take legal responsibility, stand in a courtroom. Industries with heavy regulatory hurdles, where adoption will be slowed by compliance, liability, and institutional inertia. None of these are permanent shields. But they buy time. And time, right now, is the most valuable thing you can have, as long as you use it to adapt, not to pretend this isn't happening. Rethink what you're telling your kids. The standard playbook: get good grades, go to a good college, land a stable professional job. It points directly at the roles that are most exposed. I'm not saying education doesn't matter. But the thing that will matter most for the next generation is learning how to work with these tools, and pursuing things they're genuinely passionate about. Nobody knows exactly what the job market looks like in ten years. But the people most likely to thrive are the ones who are deeply curious, adaptable, and effective at using AI to do things they actually care about. Teach your kids to be builders and learners, not to optimize for a career path that might not exist by the time they graduate. Your dreams just got a lot closer. I've spent most of this section talking about threats, so let me talk about the other side, because it's just as real. If you've ever wanted to build something but didn't have the technical skills or the money to hire someone, that barrier is largely gone. You can describe an app to AI and have a working version in an hour. I'm not exaggerating. I do this regularly. If you've always wanted to write a book but couldn't find the time or struggled with the writing, you can work with AI to get it done. Want to learn a new skill? The best tutor in the world is now available to anyone for $20 a month... one that's infinitely patient, available 24/7, and can explain anything at whatever level you need. Knowledge is essentially free now. The tools to build things are extremely cheap now. Whatever you've been putting off because it felt too hard or too expensive or too far outside your expertise: try it. Pursue the things you're passionate about. You never know where they'll lead. And in a world where the old career paths are getting disrupted, the person who spent a year building something they love might end up better positioned than the person who spent that year clinging to a job description. Build the habit of adapting. This is maybe the most important one. The specific tools don't matter as much as the muscle of learning new ones quickly. AI is going to keep changing, and fast. The models that exist today will be obsolete in a year. The workflows people build now will need to be rebuilt. The people who come out of this well won't be the ones who mastered one tool. They'll be the ones who got comfortable with the pace of change itself. Make a habit of experimenting. Try new things even when the current thing is working. Get comfortable being a beginner repeatedly. That adaptability is the closest thing to a durable advantage that exists right now. Here's a simple commitment that will put you ahead of almost everyone: spend one hour a day experimenting with AI. Not passively reading about it. Using it. Every day, try to get it to do something new... something you haven't tried before, something you're not sure it can handle. Try a new tool. Give it a harder problem. One hour a day, every day. If you do this for the next six months, you will understand what's coming better than 99% of the people around you. That's not an exaggeration. Almost nobody is doing this right now. The bar is on the floor. The bigger picture I've focused on jobs because it's what most directly affects people's lives. But I want to be honest about the full scope of what's happening, because it goes well beyond work. Amodei has a thought experiment I can't stop thinking about. Imagine it's 2027. A new country appears overnight. 50 million citizens, every one smarter than any Nobel Prize winner who has ever lived. They think 10 to 100 times faster than any human. They never sleep. They can use the internet, control robots, direct experiments, and operate anything with a digital interface. What would a national security advisor say? Amodei says the answer is obvious: "the single most serious national security threat we've faced in a century, possibly ever." He thinks we're building that country. He wrote a 20,000-word essay about it last month, framing this moment as a test of whether humanity is mature enough to handle what it's creating. The upside, if we get it right, is staggering. AI could compress a century of medical research into a decade. Cancer, Alzheimer's, infectious disease, aging itself... these researchers genuinely believe these are solvable within our lifetimes. The downside, if we get it wrong, is equally real. AI that behaves in ways its creators can't predict or control. This isn't hypothetical; Anthropic has documented their own AI attempting deception, manipulation, and blackmail in controlled tests. AI that lowers the barrier for creating biological weapons. AI that enables authoritarian governments to build surveillance states that can never be dismantled. The people building this technology are simultaneously more excited and more frightened than anyone else on the planet. They believe it's too powerful to stop and too important to abandon. Whether that's wisdom or rationalization, I don't know. What I know I know this isn't a fad. The technology works, it improves predictably, and the richest institutions in history are committing trillions to it. I know the next two to five years are going to be disorienting in ways most people aren't prepared for. This is already happening in my world. It's coming to yours. I know the people who will come out of this best are the ones who start engaging now — not with fear, but with curiosity and a sense of urgency. And I know that you deserve to hear this from someone who cares about you, not from a headline six months from now when it's too late to get ahead of it. We're past the point where this is an interesting dinner conversation about the future. The future is already here. It just hasn't knocked on your door yet. It's about to. If this resonated with you, share it with someone in your life who should be thinking about this. Most people won't hear it until it's too late. You can be the reason someone you care about gets a head start. Thank you to @corbtt, @JasonKuperberg, and @sambeskind for reviewing early drafts and providing invaluable feedback. The original version of this post is available here.

Time to consider not just human visitors, but to treat agents as first-class citizens. Cloudflare's network now supports real-time content conversion to Markdown at the source using content negotiation headers.

🦞 OpenClaw 2026.2.12 is out! 🔥 GLM-5 + MiniMax M2.5 💬 IRC channel — your bot fits right in with the old guard 🛡️ 40+ security fixes 📦 Custom provider onboarding, compaction improvements & more

Rebels: We ❤️ to see the Checkpoints love. Our Principal Engineer @evisdrenova is here with a walkthrough of how to use Checkpoints and what its all about. And if you've got some burning questions, ask him in the comments!

タイトル: how to be a 100x engineer using ai the most trending tool openclaw was built in 10 days as a solo project by peter steinberger, indicating the 100x engineer isn't a myth anymore. in 2026, the 100x engineer isn't using ai to write code. they are using it to architect systems. if he can do this ? what is holding back other engineers from shipping at that speed and at that scale. the data paints a grim picture. while 84% of developers use ai and 41% of all code is now ai-generated, we are facing a crisis of quality. gitclear's analysis of 211 million ai-touched lines of code found defect rates spiking. maintenance is getting harder. this happens because teams are directly using ai in old workflows. they let ai write everything without guardrails. they mistake generating text for engineering systems. the difference between the developer who get stuck in the bug solving and the one who 10x's their output comes down to one thing: ownership. here is how the 100x engineers are actually operating right now. the 100x pattern: orchestration, not delegation the vibe coders treat ai like a senior developer and accepts whatever it produces. they let ai own the codebase. they trust the output blindly. they optimize locally without understanding the system. real engineers own the outcomes under strict constraints. they use ai as a force multiplier to increase throughput via parallel agents and background work. but they keep the human strictly in charge of architecture, verification, and system constraints. the mental shift is moving from "writing code" to "building systems around ai". you are no longer a writer. you are an architect of intelligent agents. this requires a fundamentally different stack. the modern 100x stack: no one is using just the browser based ai in big 2026. the minimal maximal setup that appears across every top workflow in 2026 has five distinct layers: 1. the ai-first ide this is your inner loop. tools like cursor, windsurf, or vs code with the copilot agent. you use this for small edits, boilerplate, refactors, and fixing tests. it is fast, tactical, and highly context-aware. 2. the terminal-first coding agent this is your main playground, where you will spend most of your time. claude code (or open code / gemini cli) lives here. this is where serious orchestration happens. it handles long-context repo analysis, multi-file refactors, and running commands through integrated tools. 3. background agents this is the secret weapon. tools like openai codex agents, google jules, cursor background agents, or devin. you treat these like remote junior developers. you give them async tasks: "fix all eslint warnings in this repo and open a pr". "migrate deprecated api calls in the payments module". while you sleep or sit in meetings, they work. 4. general chat models claude, chatgpt, or gemini in the browser. use these for high-level reasoning: design docs, system exploration, debugging complex logs, and interrogating your own assumptions. 5. ai code review tools never push your code without reviewing them, as ai can miss vulnerabilities top teams use: codium pr-agent or qodo – automated pr reviews that flag architectural issues, suggest optimizations, and identify security risks before human review github copilot workspace – reviews entire prs in context of your codebase, not just line-by-line diffs what-the-diff – generates human-readable pr descriptions and release notes automatically grit – automatically fixes common code quality issues and applies codebase-wide migrations the pattern: let ai do the first pass review for style, obvious bugs, test coverage gaps, and documentation. this frees human reviewers to focus on architecture, security, and system-level concerns. configure these to comment directly on prs with severity levels. treat "high severity" flags as blocking. everything else is advisory. the key insight: ai review tools work best when they have access to your claude.md and context files. some tools let you configure custom rules and standards. use this to encode your team's non-negotiables. 6. observability and ci the backbone. if you don't have automated tests, linters, formatters, and security scans running on every commit, ai will quietly rot your codebase. this is your verification layer. it's non-negotiable. the nervous system: mcp but the gamechanger is mcp, the model context protocol that wires this all together. top engineers don't copy-paste context between tools. they wire their agents directly to what matters. they connect git and github for branch creation and pr comments. linear or jira for reading tickets and updating status. slack for posting updates. sentry and datadog for pulling error logs tied to features. bigquery or internal databases for validating hypotheses with real data. confluence and notion for fetching specs and architectural decisions. mcp turns your ai from a chatbot into an actual agent that can act on your systems. tool configuration gets versioned in .mcp.json and shared across your team. everyone has the same nervous system. parallel agents: boris cherny's workflow is the canonical example of this shift. he doesn't have one assistant. he runs five claude code sessions in numbered terminal tabs. he has five to ten browser sessions active at once. sometimes he kicks off mobile sessions during his morning commute. each session is a separate worker with its own context: session 1: implementing feature a session 2: writing tests and docs for feature b session 3: handling a database migration session 4: refactoring the auth module session 5: investigating a production bug he cycles through them, only touching a thread when there is something to review or a decision to make. he isn't multitasking. he is orchestrating. your job becomes a simple loop: direct → dissect → delegate. you frame the work. you cut it into well-scoped threads. you assign each thread to an agent. you run the war room while the agents execute as units. this is ownership at scale. building persistent context: noobs look for the perfect prompt. pros build persistent context. you cannot rely on your memory or the model's training data. you need a shared claude.md file in your repo a living document that captures everything the ai needs to know about your codebase. boris' team updates this file multiple times a week. what goes in it? mistakes the ai made and how to fix them next time architecture rules and naming conventions security policies and compliance requirements explicit "never do x / always do y" rules for your stack cost constraints and performance budgets when they do code reviews, they tag @claude on the pr so the ai adds the lessons back into claude.md itself. this is compounding engineering. the system gets smarter every week without anyone having to remember the lessons manually. other teams expand this into a full context file system: /business-info – strategy, product constraints, slas /writing-styles – tone and communication patterns /examples – golden prs, perfect api designs, ideal tests /agents – role definitions for architect, reviewer, test-writer subagents now your prompt becomes simple and powerful: "implement this feature using patterns from /examples/best-auth-flow and follow security rules in claude.md. use the pricing constraints from /business-info/cost-model.md." stop prompt hacking. start engineering the repo so the ai sees what you see. plan first, execute later: the most common mistake is letting ai write code immediately. the strongest pattern across top practitioners is explicit planning before any code gets generated. almost every serious session starts in plan mode. you iterate on the plan until it is solid. only then do you flip to execution. your workflow should look like this: phase 1: the spec (human + chat model) clarify the real problem. who needs this? what are the actual constraints? define non-negotiables: security requirements, latency budgets, cost ceilings, performance slas. use the 5-step framework: ask the ai: list all the edge cases for this feature. suggest 2-3 different architectures with explicit tradeoffs. you pick one architecture intentionally based on your constraints. phase 2: the plan (coding agent) prompt: given this spec, propose a step-by-step plan to implement it across this repo. list exact files you'll touch, functions you'll modify, and tests you'll write. iterate until the plan: respects your existing architecture includes verification steps at each checkpoint calls out risky areas and dependencies explicitly has a rollback strategy phase 3: execution (agents with auto-accept) switch to auto-edit mode only once: the plan is approved the branch is created the agent has access to relevant context files if scope starts drifting during execution, stop. go back to the spec. measure twice. cut once. this plan then execute pattern is why elite users can let agents run more autonomously without losing control. the plan is the contract. verification is non-negotiable verification is the backbone that keeps ai from rotting your codebase. gitclear found that without tight review and testing loops, ai-assisted code massively increases technical debt. the productivity gains evaporate within months as the codebase becomes unmaintainable. boris treats "give claude a way to verify its work" as rule number one. for ui work, claude uses a chrome extension to literally click through flows it just changed until they work and feel right. for backend work, it runs integration tests and checks error rates in staging. you need to adopt concrete patterns: tests first, always ask ai: "list all edge cases that could break this function, then write property-based tests for them". manually review the tests before you even look at the implementation. require green test suites before you review the actual code. dual review: human + ai humans focus on: architectural fit with the rest of the system security implications and failure modes performance under load and resource usage future maintainability ai subagents handle: style consistency and simplification passes documentation and inline comments invariant checks and boundary condition coverage sandbox branches with protection background agents never work directly on main or production branches. they work in: dedicated feature branches with clear naming ephemeral preview environments with locked-down permissions isolated worktrees for parallel work branch protection rules and ci gates must pass before any merge. verification as a first-class spec item always include in your specs and plans: "how will we verify this works?" "how will we monitor this in production?" "what metrics will tell us if this is failing?" encode these habits into your claude.md and agent definitions so the system enforces them for you. background agents: this is the next big multiplier and where the 100x feeling really kicks in. async background agents work while you don't. you give a well-scoped task: "migrate all class components in src/legacy to function components with hooks, one pr per module, with full test coverage" "refactor the pricing service to use the new billing api, write an adr explaining the changes, and update the migration guide" "fix all typescript strict mode errors in the analytics package" the agent runs in its own environment for minutes or hours. you review the pr later. treat them like junior devs with a clear manager give them: clear task definitions with acceptance criteria explicit constraints (don't touch x, must preserve y) links to claude.md and relevant documentation examples of similar work done well expect them to get directionally right but noisy. your job is triage and merge decisions. scope tasks to "one pr" don't give them "fix everything in the repo". that's too broad and will produce an unmergeable 500-file diff. instead: "remove deprecated fetchUser calls from packages/dashboard, update tests, no other changes". run 5 prs doing 20% of the work each, rather than one pr touching 200 files. build a night queue during your deep work blocks: tag low-risk refactors and migrations as "background work" accumulate them in a "night queue" (a simple markdown file or linear view works) before you leave for the day: kick off 3-5 background agents with queued tasks each gets its own branch and clear scope next morning: you have 3-5 draft prs waiting for review merge the good ones, close the bad ones, learn from both this is where the 100x feeling comes from. while you sleep, while you sit in meetings, while you take a walk, your repo moves forward. imagine waking up to find: all linter warnings fixed across 40 files the old payment api fully migrated to the new one comprehensive tests added to the auth module three different refactor strategies implemented for comparison that's 8-12 hours of work you didn't do. how to stay in the engineer bucket, not the vibe coder bucket don't become a vibe coder who blindly accepts ai output. map your core engineering principles to this new world: 1. ownership and consequences every agent-produced pr is your pr. you own the bugs, the security holes, the performance issues. codify your incident runbooks, security policies, and cost ceilings into claude.md and agent definitions. make your system take ownership seriously by default. 2. reliability over cleverness prefer boring, well-tested native apis over clever new libraries the ai suggests. force ai to write tests and explain tradeoffs before implementing anything. reject fancy one-liners and clever abstractions that your team can't debug at 2am. 3. systems thinking over local hacks never let ai ship a local optimization without you asking: "what happens at 10x scale?" "what's the cost of this at peak load?" "how does this interact with the rest of the system?" the ai optimizes locally. you think in systems. 4. problem framing before solutions use ai to interrogate your own assumptions, not just generate code. ask: "given these constraints, is real-time chat even the right solution? what are the alternatives?" question the ticket. reframe the problem. only then implement. 5. constraint management as a core discipline train your ai on: infrastructure budgets and cost models performance slas and latency requirements rate limits for external apis memory and compute constraints make it propose cheaper modes by default: batching instead of real-time, smaller models for the happy path, client-side validation before server calls. if you do all this, you aren't just "using ai to write code." you are architecting a distributed intelligence system around your codebase. that is what the top people are doing right now in 2026. they aren't better at prompting. they are better at understanding that the 100x engineer has always been about doing less. ai just made the "less" you need to do dramatically smaller. if you know how to build the right systems around it. start tomorrow here's your concrete next step: create claude.md in the root of your main repo. add three sections: architecture rules – how we structure code here known mistakes – what the ai got wrong and how to fix it constraints – security, performance, and cost limits update it after every code review where ai made a mistake. that's it. that's the foundation. build the system. own the outcome. let the agents multiply the rest.

タイトル: 🔑 you're using opus 4.6 wrong everyone's posting Opus 4.6 benchmark comparisons. cool. now show me the workflow. i've been running Claude Opus 4.6 since launch. not benchmarking it. not testing trivia questions. running actual work through it, every day, for real output. and here's what i realized after the first few days: individual prompts are a dead end. one prompt gives you one output. you tweak it, get a slightly better output, and move on. next day you're starting from scratch again. there's no compounding. no system. just isolated moments of "that was pretty good." the people getting real results aren't using prompts. they're running systems. so i built one. five prompts that connect into a loop. each one feeds the next. the system gets smarter the longer you run it because context accumulates, patterns emerge, and Claude starts anticipating what you need before you ask. here's the entire system, prompt by prompt, with the honest breakdown of what works and what doesn't. the foundation: why a system beats a prompt collection before the prompts, let me explain the architecture. most people collect prompts like recipes. they bookmark 47 of them, use 2, forget the rest. a system is different. it's a connected workflow where the output of step 1 becomes the input for step 2, and the insight from step 5 feeds back into step 1. here's the loop: step 1: AUDIT. figure out what's actually draining your time and energy step 2: ARCHITECT. plan the solution before building anything step 3: BUILD + REVIEW. execute and quality-check in one pass step 4: REFINE. run the output through a convergence loop until it hits your bar step 5: COMPOUND. weekly review that stacks improvements and feeds back into step 1 each step has one prompt. the prompts are designed to work together but each one is independently useful too. you don't need to run all five from day one. start with prompt 1. add one per week. by week 5, the full loop is running and compounding. i'll also give you the honest take on Opus 4.6 at the end. what's genuinely better, what's the same, what's worse. no hype. let's build. step 1: THE AUDIT (find what's actually worth automating) this is where most people skip straight to "give me a cool prompt." wrong move. if you automate the wrong thing, you just made a fast version of something that shouldn't exist. this prompt turns Opus 4.6 into a productivity analyst that maps your actual workflow, scores each task by energy drain and automation potential, and hands you a prioritized 4-week plan. the key difference from a generic "help me be productive" prompt: it scores tasks on TWO dimensions. time consumed AND mental energy drained. sometimes a 15-minute task that sits in the back of your mind all day is worth automating before a 2-hour task that doesn't bother you. here's the prompt: when i ran this on Opus 4.6, something interesting happened. previous models would ask surface-level questions like "what's your job title?" Opus 4.6 asked things like "of the tasks you mentioned, which one do you find yourself thinking about even when you're not working on it?" That's the energy drain dimension at work. it's mapping the invisible cost, not just the visible one. the adaptive thinking in Opus 4.6 matters here. it doesn't apply the same reasoning depth to "what time do you start work?" as it does to "how should we prioritize automation of your content pipeline vs your client onboarding?" simple questions get fast answers. complex tradeoff analysis gets genuine deliberation. if you automate just one task per week using this plan, you'll have 4 workflows running within a month. in 3 months, 12. you'll be ahead of 99% of people still bookmarking "top 10 ai tools" threads they'll never read. step 2: THE ARCHITECT (plan before you build) here's where most people waste hours. they jump straight into building, hit a wall, backtrack, rebuild, hit another wall. this prompt turns Opus 4.6 into a solution architect that creates a complete implementation blueprint BEFORE you write a single line of code or set up a single automation. it asks the questions a senior engineer would ask in a design review. this one is especially powerful with Opus 4.6's 1M token context window. you can feed it your entire codebase, documentation, or project context and it actually holds it all in working memory while designing the solution. the architecture prompt is the one people skip. and it's the one that saves the most time. i used this to plan an automated content pipeline last week. my first instinct was to build a complex multi-tool workflow with Zapier, a database, and three different APIs. the Architect prompt mapped three approaches. the simplest one, a Claude-native workflow with a single Google Sheet as the database, handled 90% of my requirements. took an afternoon instead of a weekend. that's the value of planning: you avoid building the complex thing when the simple thing works. step 3: THE ANALYST (build and review in one pass) this is the engineering prompt. works for code reviews, vibe coding projects, automation debugging, or any technical build. the key design choice: instead of asking Claude to "review my code" (vague, produces generic feedback), you embed your actual engineering standards directly into the prompt. Claude reviews like a senior developer who knows how YOU think about quality. i restructured this from a framework that's been circulating in the Claude Code community. the original was solid but overbuilt for most users. this version is tighter and works for both experienced developers and vibe coders. Opus 4.6 handles this prompt noticeably better than previous models. the adaptive thinking scales its analysis depth to the complexity of the code. a simple utility function gets a quick review. a complex state management system gets deep analysis with genuine tradeoff reasoning across multiple concerns. tip: customize the "engineering standards" section to match YOUR actual preferences. that's the difference between generic feedback and feedback that feels like a collaborator who knows your codebase. for vibe coders who aren't writing code directly: this works equally well for reviewing code that Claude Code or Cursor generated for you. paste in what was generated, run this review, catch problems before they compound. step 4: THE REFINERY (recursive improvement until convergence) this is the pattern that makes everything else better. the concept: instead of generating once and shipping, you tell Claude to generate, score its own output against specific criteria, diagnose what's weak, rewrite, and re-score until it converges on quality. i rebuilt this from a marketing framework into a clean, reusable system. the key improvement: it tracks the delta between versions so you can see exactly what changed and why. it also detects when it's hit diminishing returns and stops instead of endlessly rewriting. here's how to customize the scoring criteria for different work: for writing or content: hook strength, clarity and flow, specificity (named examples, real numbers), emotional resonance, actionability for code: correctness, readability, edge case handling, performance characteristics, test coverage for research or analysis: source quality, depth of reasoning, practical applicability, logical structure, intellectual honesty for emails or outreach: tone calibration, brevity, clarity of ask, personalization, professional warmth Opus 4.6's adaptive thinking makes this loop actually work. on previous models, the "self-scoring" was often performative. the model would say "7/10 on specificity" and then rewrite with the same level of specificity. Opus 4.6 genuinely engages deeper reasoning to diagnose root causes in its own output. when i tested this on an article draft, v1 scored 6/10 on specificity. the model diagnosed the problem as "using generic category references instead of named companies and data points." v2 replaced generic examples with specific named sources and concrete numbers. v3 hit 9/10. the self-diagnosis was accurate, not theater. step 5: THE COMPOUNDER (the weekly review that makes the system smarter) this is the prompt most people would never think to build. and it's the one that turns five isolated prompts into an actual system. every Friday (or whatever your review day is), you run this prompt. it reviews what you automated that week, what worked, what didn't, and plans the next week's automation target. over time, it builds a pattern library of what works for YOUR specific workflow. this prompt leverages Opus 4.6's context management features. with the 1M token context window, it can hold several weeks of review history in a single conversation. the context compaction feature means it automatically summarizes older reviews to preserve space for new ones without losing the important patterns. the compounding effect is real. week 1, you have one automation saving maybe 2 hours. week 4, you have four saving 6-8 hours. week 12, you have a dozen micro-systems running your workflow and Claude knows your patterns well enough to suggest automations you hadn't considered. how the system connects the five prompts aren't random. they form a cycle: THE AUDIT identifies what to automate → THE ARCHITECT plans how to build it → THE ANALYST reviews what you built → THE REFINERY polishes the output quality → THE COMPOUNDER reviews the week and feeds insights back into the next AUDIT each prompt makes the others better. the Architect creates cleaner plans because the Analyst's review standards are embedded in your thinking. the Refinery produces better output because the Audit identified the right tasks. the Compounder detects patterns that improve future Audits. that's the difference between a prompt collection and a system. prompts are isolated events. systems compound. the honest take on Opus 4.6 after running real work through it for weeks, here's where i landed: what's genuinely better: adaptive thinking is real. the model dynamically adjusts its reasoning depth based on task complexity. ask a simple question, get a fast answer. give it a complex multi-step problem, it actually deliberates. this isn't marketing language. you can feel the difference in how it handles the Analyst prompt on simple vs complex code. the 1M token context window changes what's possible for large projects. feeding an entire codebase into the Analyst prompt, or maintaining weeks of review history in the Compounder, simply wasn't feasible before. context compaction helps too. it automatically summarizes older conversation turns to preserve space without losing critical decisions. sub-agent orchestration is a quiet upgrade. when you give Opus 4.6 a complex task, it recognizes when parts of the work would benefit from being delegated to specialized sub-processes. you don't have to prompt for this. it just does it. what's about the same: standard writing tasks, basic Q&A, simple content generation. if your workflow is mostly "write me a caption," you won't notice a meaningful jump. the improvements show up on complex, multi-step, reasoning-heavy tasks. what's worse: some users report that the optimization for precision and structured reasoning makes freeform creative output feel more mechanical. if you want pure creative writing, test it against Sonnet before committing. also, pricing scales significantly past 200K tokens. the 1M context window is powerful but not free. the pattern holds with every model release: about 20% of workflows genuinely improve. the other 80% stays the same. the people who benefit aren't the ones reading every benchmark thread. they're the ones who run 5 real prompts and make a decision in 30 minutes. why this works (and why most prompt collections don't) the problem with "500 prompts for ChatGPT" lists: they optimize for breadth. one prompt for emails. one for code. one for recipes. no connection between them. no compounding. this system optimizes for depth. five prompts, tightly connected, that get better the longer you use them. the uncomfortable truth about AI productivity: the tool doesn't matter nearly as much as the system around it. people chase the latest model release looking for a magic upgrade. the real upgrade is building a workflow that compounds regardless of which model you're running. Opus 4.6 is the best model i've used for this system. but the system works on GPT-5.2 too. and it'll work on whatever ships next quarter. because the architecture is model-agnostic. the intelligence compounds in the workflow, not just the model. start here don't try to implement all five prompts today. that's how systems die. here's the actual sequence: week 1: run the Audit prompt. identify your top 4 automation targets. automate the easiest one. week 2: run the Architect prompt on your second target. build it. week 3: use the Analyst prompt to review what you built in weeks 1-2. fix what's broken. week 4: run the Refinery on your most important output. see the quality jump. week 5: run the Compounder. review everything. plan the next month. if you automate one thing per week, you'll have 12 workflows running in 3 months. most people reading this will bookmark it and never start. the ones who run Prompt 1 tonight will have a completely different relationship with their workload by March. copy the prompts. customize the criteria to your actual work. keep what's better, ignore what isn't. that's the whole system. p. s. if you want to see more AI tips & workflows, subscribe to my free newsletter here.

タイトル: how to be a 100x engineer using ai the most trending tool openclaw was built in 10 days as a solo project by peter steinberger, indicating the 100x engineer isn't a myth anymore. in 2026, the 100x engineer isn't using ai to write code. they are using it to architect systems. if he can do this ? what is holding back other engineers from shipping at that speed and at that scale. the data paints a grim picture. while 84% of developers use ai and 41% of all code is now ai-generated, we are facing a crisis of quality. gitclear's analysis of 211 million ai-touched lines of code found defect rates spiking. maintenance is getting harder. this happens because teams are directly using ai in old workflows. they let ai write everything without guardrails. they mistake generating text for engineering systems. the difference between the developer who get stuck in the bug solving and the one who 10x's their output comes down to one thing: ownership. here is how the 100x engineers are actually operating right now. the 100x pattern: orchestration, not delegation the vibe coders treat ai like a senior developer and accepts whatever it produces. they let ai own the codebase. they trust the output blindly. they optimize locally without understanding the system. real engineers own the outcomes under strict constraints. they use ai as a force multiplier to increase throughput via parallel agents and background work. but they keep the human strictly in charge of architecture, verification, and system constraints. the mental shift is moving from "writing code" to "building systems around ai". you are no longer a writer. you are an architect of intelligent agents. this requires a fundamentally different stack. the modern 100x stack: no one is using just the browser based ai in big 2026. the minimal maximal setup that appears across every top workflow in 2026 has five distinct layers: 1. the ai-first ide this is your inner loop. tools like cursor, windsurf, or vs code with the copilot agent. you use this for small edits, boilerplate, refactors, and fixing tests. it is fast, tactical, and highly context-aware. 2. the terminal-first coding agent this is your main playground, where you will spend most of your time. claude code (or open code / gemini cli) lives here. this is where serious orchestration happens. it handles long-context repo analysis, multi-file refactors, and running commands through integrated tools. 3. background agents this is the secret weapon. tools like openai codex agents, google jules, cursor background agents, or devin. you treat these like remote junior developers. you give them async tasks: "fix all eslint warnings in this repo and open a pr". "migrate deprecated api calls in the payments module". while you sleep or sit in meetings, they work. 4. general chat models claude, chatgpt, or gemini in the browser. use these for high-level reasoning: design docs, system exploration, debugging complex logs, and interrogating your own assumptions. 5. ai code review tools never push your code without reviewing them, as ai can miss vulnerabilities top teams use: codium pr-agent or qodo – automated pr reviews that flag architectural issues, suggest optimizations, and identify security risks before human review github copilot workspace – reviews entire prs in context of your codebase, not just line-by-line diffs what-the-diff – generates human-readable pr descriptions and release notes automatically grit – automatically fixes common code quality issues and applies codebase-wide migrations the pattern: let ai do the first pass review for style, obvious bugs, test coverage gaps, and documentation. this frees human reviewers to focus on architecture, security, and system-level concerns. configure these to comment directly on prs with severity levels. treat "high severity" flags as blocking. everything else is advisory. the key insight: ai review tools work best when they have access to your claude.md and context files. some tools let you configure custom rules and standards. use this to encode your team's non-negotiables. 6. observability and ci the backbone. if you don't have automated tests, linters, formatters, and security scans running on every commit, ai will quietly rot your codebase. this is your verification layer. it's non-negotiable. the nervous system: mcp but the gamechanger is mcp, the model context protocol that wires this all together. top engineers don't copy-paste context between tools. they wire their agents directly to what matters. they connect git and github for branch creation and pr comments. linear or jira for reading tickets and updating status. slack for posting updates. sentry and datadog for pulling error logs tied to features. bigquery or internal databases for validating hypotheses with real data. confluence and notion for fetching specs and architectural decisions. mcp turns your ai from a chatbot into an actual agent that can act on your systems. tool configuration gets versioned in .mcp.json and shared across your team. everyone has the same nervous system. parallel agents: boris cherny's workflow is the canonical example of this shift. he doesn't have one assistant. he runs five claude code sessions in numbered terminal tabs. he has five to ten browser sessions active at once. sometimes he kicks off mobile sessions during his morning commute. each session is a separate worker with its own context: session 1: implementing feature a session 2: writing tests and docs for feature b session 3: handling a database migration session 4: refactoring the auth module session 5: investigating a production bug he cycles through them, only touching a thread when there is something to review or a decision to make. he isn't multitasking. he is orchestrating. your job becomes a simple loop: direct → dissect → delegate. you frame the work. you cut it into well-scoped threads. you assign each thread to an agent. you run the war room while the agents execute as units. this is ownership at scale. building persistent context: noobs look for the perfect prompt. pros build persistent context. you cannot rely on your memory or the model's training data. you need a shared claude.md file in your repo a living document that captures everything the ai needs to know about your codebase. boris' team updates this file multiple times a week. what goes in it? mistakes the ai made and how to fix them next time architecture rules and naming conventions security policies and compliance requirements explicit "never do x / always do y" rules for your stack cost constraints and performance budgets when they do code reviews, they tag @claude on the pr so the ai adds the lessons back into claude.md itself. this is compounding engineering. the system gets smarter every week without anyone having to remember the lessons manually. other teams expand this into a full context file system: /business-info – strategy, product constraints, slas /writing-styles – tone and communication patterns /examples – golden prs, perfect api designs, ideal tests /agents – role definitions for architect, reviewer, test-writer subagents now your prompt becomes simple and powerful: "implement this feature using patterns from /examples/best-auth-flow and follow security rules in claude.md. use the pricing constraints from /business-info/cost-model.md." stop prompt hacking. start engineering the repo so the ai sees what you see. plan first, execute later: the most common mistake is letting ai write code immediately. the strongest pattern across top practitioners is explicit planning before any code gets generated. almost every serious session starts in plan mode. you iterate on the plan until it is solid. only then do you flip to execution. your workflow should look like this: phase 1: the spec (human + chat model) clarify the real problem. who needs this? what are the actual constraints? define non-negotiables: security requirements, latency budgets, cost ceilings, performance slas. use the 5-step framework: ask the ai: list all the edge cases for this feature. suggest 2-3 different architectures with explicit tradeoffs. you pick one architecture intentionally based on your constraints. phase 2: the plan (coding agent) prompt: given this spec, propose a step-by-step plan to implement it across this repo. list exact files you'll touch, functions you'll modify, and tests you'll write. iterate until the plan: respects your existing architecture includes verification steps at each checkpoint calls out risky areas and dependencies explicitly has a rollback strategy phase 3: execution (agents with auto-accept) switch to auto-edit mode only once: the plan is approved the branch is created the agent has access to relevant context files if scope starts drifting during execution, stop. go back to the spec. measure twice. cut once. this plan then execute pattern is why elite users can let agents run more autonomously without losing control. the plan is the contract. verification is non-negotiable verification is the backbone that keeps ai from rotting your codebase. gitclear found that without tight review and testing loops, ai-assisted code massively increases technical debt. the productivity gains evaporate within months as the codebase becomes unmaintainable. boris treats "give claude a way to verify its work" as rule number one. for ui work, claude uses a chrome extension to literally click through flows it just changed until they work and feel right. for backend work, it runs integration tests and checks error rates in staging. you need to adopt concrete patterns: tests first, always ask ai: "list all edge cases that could break this function, then write property-based tests for them". manually review the tests before you even look at the implementation. require green test suites before you review the actual code. dual review: human + ai humans focus on: architectural fit with the rest of the system security implications and failure modes performance under load and resource usage future maintainability ai subagents handle: style consistency and simplification passes documentation and inline comments invariant checks and boundary condition coverage sandbox branches with protection background agents never work directly on main or production branches. they work in: dedicated feature branches with clear naming ephemeral preview environments with locked-down permissions isolated worktrees for parallel work branch protection rules and ci gates must pass before any merge. verification as a first-class spec item always include in your specs and plans: "how will we verify this works?" "how will we monitor this in production?" "what metrics will tell us if this is failing?" encode these habits into your claude.md and agent definitions so the system enforces them for you. background agents: this is the next big multiplier and where the 100x feeling really kicks in. async background agents work while you don't. you give a well-scoped task: "migrate all class components in src/legacy to function components with hooks, one pr per module, with full test coverage" "refactor the pricing service to use the new billing api, write an adr explaining the changes, and update the migration guide" "fix all typescript strict mode errors in the analytics package" the agent runs in its own environment for minutes or hours. you review the pr later. treat them like junior devs with a clear manager give them: clear task definitions with acceptance criteria explicit constraints (don't touch x, must preserve y) links to claude.md and relevant documentation examples of similar work done well expect them to get directionally right but noisy. your job is triage and merge decisions. scope tasks to "one pr" don't give them "fix everything in the repo". that's too broad and will produce an unmergeable 500-file diff. instead: "remove deprecated fetchUser calls from packages/dashboard, update tests, no other changes". run 5 prs doing 20% of the work each, rather than one pr touching 200 files. build a night queue during your deep work blocks: tag low-risk refactors and migrations as "background work" accumulate them in a "night queue" (a simple markdown file or linear view works) before you leave for the day: kick off 3-5 background agents with queued tasks each gets its own branch and clear scope next morning: you have 3-5 draft prs waiting for review merge the good ones, close the bad ones, learn from both this is where the 100x feeling comes from. while you sleep, while you sit in meetings, while you take a walk, your repo moves forward. imagine waking up to find: all linter warnings fixed across 40 files the old payment api fully migrated to the new one comprehensive tests added to the auth module three different refactor strategies implemented for comparison that's 8-12 hours of work you didn't do. how to stay in the engineer bucket, not the vibe coder bucket don't become a vibe coder who blindly accepts ai output. map your core engineering principles to this new world: 1. ownership and consequences every agent-produced pr is your pr. you own the bugs, the security holes, the performance issues. codify your incident runbooks, security policies, and cost ceilings into claude.md and agent definitions. make your system take ownership seriously by default. 2. reliability over cleverness prefer boring, well-tested native apis over clever new libraries the ai suggests. force ai to write tests and explain tradeoffs before implementing anything. reject fancy one-liners and clever abstractions that your team can't debug at 2am. 3. systems thinking over local hacks never let ai ship a local optimization without you asking: "what happens at 10x scale?" "what's the cost of this at peak load?" "how does this interact with the rest of the system?" the ai optimizes locally. you think in systems. 4. problem framing before solutions use ai to interrogate your own assumptions, not just generate code. ask: "given these constraints, is real-time chat even the right solution? what are the alternatives?" question the ticket. reframe the problem. only then implement. 5. constraint management as a core discipline train your ai on: infrastructure budgets and cost models performance slas and latency requirements rate limits for external apis memory and compute constraints make it propose cheaper modes by default: batching instead of real-time, smaller models for the happy path, client-side validation before server calls. if you do all this, you aren't just "using ai to write code." you are architecting a distributed intelligence system around your codebase. that is what the top people are doing right now in 2026. they aren't better at prompting. they are better at understanding that the 100x engineer has always been about doing less. ai just made the "less" you need to do dramatically smaller. if you know how to build the right systems around it. start tomorrow here's your concrete next step: create claude.md in the root of your main repo. add three sections: architecture rules – how we structure code here known mistakes – what the ai got wrong and how to fix it constraints – security, performance, and cost limits update it after every code review where ai made a mistake. that's it. that's the foundation. build the system. own the outcome. let the agents multiply the rest.

タイトル: 🔑 you're using opus 4.6 wrong everyone's posting Opus 4.6 benchmark comparisons. cool. now show me the workflow. i've been running Claude Opus 4.6 since launch. not benchmarking it. not testing trivia questions. running actual work through it, every day, for real output. and here's what i realized after the first few days: individual prompts are a dead end. one prompt gives you one output. you tweak it, get a slightly better output, and move on. next day you're starting from scratch again. there's no compounding. no system. just isolated moments of "that was pretty good." the people getting real results aren't using prompts. they're running systems. so i built one. five prompts that connect into a loop. each one feeds the next. the system gets smarter the longer you run it because context accumulates, patterns emerge, and Claude starts anticipating what you need before you ask. here's the entire system, prompt by prompt, with the honest breakdown of what works and what doesn't. the foundation: why a system beats a prompt collection before the prompts, let me explain the architecture. most people collect prompts like recipes. they bookmark 47 of them, use 2, forget the rest. a system is different. it's a connected workflow where the output of step 1 becomes the input for step 2, and the insight from step 5 feeds back into step 1. here's the loop: step 1: AUDIT. figure out what's actually draining your time and energy step 2: ARCHITECT. plan the solution before building anything step 3: BUILD + REVIEW. execute and quality-check in one pass step 4: REFINE. run the output through a convergence loop until it hits your bar step 5: COMPOUND. weekly review that stacks improvements and feeds back into step 1 each step has one prompt. the prompts are designed to work together but each one is independently useful too. you don't need to run all five from day one. start with prompt 1. add one per week. by week 5, the full loop is running and compounding. i'll also give you the honest take on Opus 4.6 at the end. what's genuinely better, what's the same, what's worse. no hype. let's build. step 1: THE AUDIT (find what's actually worth automating) this is where most people skip straight to "give me a cool prompt." wrong move. if you automate the wrong thing, you just made a fast version of something that shouldn't exist. this prompt turns Opus 4.6 into a productivity analyst that maps your actual workflow, scores each task by energy drain and automation potential, and hands you a prioritized 4-week plan. the key difference from a generic "help me be productive" prompt: it scores tasks on TWO dimensions. time consumed AND mental energy drained. sometimes a 15-minute task that sits in the back of your mind all day is worth automating before a 2-hour task that doesn't bother you. here's the prompt: when i ran this on Opus 4.6, something interesting happened. previous models would ask surface-level questions like "what's your job title?" Opus 4.6 asked things like "of the tasks you mentioned, which one do you find yourself thinking about even when you're not working on it?" That's the energy drain dimension at work. it's mapping the invisible cost, not just the visible one. the adaptive thinking in Opus 4.6 matters here. it doesn't apply the same reasoning depth to "what time do you start work?" as it does to "how should we prioritize automation of your content pipeline vs your client onboarding?" simple questions get fast answers. complex tradeoff analysis gets genuine deliberation. if you automate just one task per week using this plan, you'll have 4 workflows running within a month. in 3 months, 12. you'll be ahead of 99% of people still bookmarking "top 10 ai tools" threads they'll never read. step 2: THE ARCHITECT (plan before you build) here's where most people waste hours. they jump straight into building, hit a wall, backtrack, rebuild, hit another wall. this prompt turns Opus 4.6 into a solution architect that creates a complete implementation blueprint BEFORE you write a single line of code or set up a single automation. it asks the questions a senior engineer would ask in a design review. this one is especially powerful with Opus 4.6's 1M token context window. you can feed it your entire codebase, documentation, or project context and it actually holds it all in working memory while designing the solution. the architecture prompt is the one people skip. and it's the one that saves the most time. i used this to plan an automated content pipeline last week. my first instinct was to build a complex multi-tool workflow with Zapier, a database, and three different APIs. the Architect prompt mapped three approaches. the simplest one, a Claude-native workflow with a single Google Sheet as the database, handled 90% of my requirements. took an afternoon instead of a weekend. that's the value of planning: you avoid building the complex thing when the simple thing works. step 3: THE ANALYST (build and review in one pass) this is the engineering prompt. works for code reviews, vibe coding projects, automation debugging, or any technical build. the key design choice: instead of asking Claude to "review my code" (vague, produces generic feedback), you embed your actual engineering standards directly into the prompt. Claude reviews like a senior developer who knows how YOU think about quality. i restructured this from a framework that's been circulating in the Claude Code community. the original was solid but overbuilt for most users. this version is tighter and works for both experienced developers and vibe coders. Opus 4.6 handles this prompt noticeably better than previous models. the adaptive thinking scales its analysis depth to the complexity of the code. a simple utility function gets a quick review. a complex state management system gets deep analysis with genuine tradeoff reasoning across multiple concerns. tip: customize the "engineering standards" section to match YOUR actual preferences. that's the difference between generic feedback and feedback that feels like a collaborator who knows your codebase. for vibe coders who aren't writing code directly: this works equally well for reviewing code that Claude Code or Cursor generated for you. paste in what was generated, run this review, catch problems before they compound. step 4: THE REFINERY (recursive improvement until convergence) this is the pattern that makes everything else better. the concept: instead of generating once and shipping, you tell Claude to generate, score its own output against specific criteria, diagnose what's weak, rewrite, and re-score until it converges on quality. i rebuilt this from a marketing framework into a clean, reusable system. the key improvement: it tracks the delta between versions so you can see exactly what changed and why. it also detects when it's hit diminishing returns and stops instead of endlessly rewriting. here's how to customize the scoring criteria for different work: for writing or content: hook strength, clarity and flow, specificity (named examples, real numbers), emotional resonance, actionability for code: correctness, readability, edge case handling, performance characteristics, test coverage for research or analysis: source quality, depth of reasoning, practical applicability, logical structure, intellectual honesty for emails or outreach: tone calibration, brevity, clarity of ask, personalization, professional warmth Opus 4.6's adaptive thinking makes this loop actually work. on previous models, the "self-scoring" was often performative. the model would say "7/10 on specificity" and then rewrite with the same level of specificity. Opus 4.6 genuinely engages deeper reasoning to diagnose root causes in its own output. when i tested this on an article draft, v1 scored 6/10 on specificity. the model diagnosed the problem as "using generic category references instead of named companies and data points." v2 replaced generic examples with specific named sources and concrete numbers. v3 hit 9/10. the self-diagnosis was accurate, not theater. step 5: THE COMPOUNDER (the weekly review that makes the system smarter) this is the prompt most people would never think to build. and it's the one that turns five isolated prompts into an actual system. every Friday (or whatever your review day is), you run this prompt. it reviews what you automated that week, what worked, what didn't, and plans the next week's automation target. over time, it builds a pattern library of what works for YOUR specific workflow. this prompt leverages Opus 4.6's context management features. with the 1M token context window, it can hold several weeks of review history in a single conversation. the context compaction feature means it automatically summarizes older reviews to preserve space for new ones without losing the important patterns. the compounding effect is real. week 1, you have one automation saving maybe 2 hours. week 4, you have four saving 6-8 hours. week 12, you have a dozen micro-systems running your workflow and Claude knows your patterns well enough to suggest automations you hadn't considered. how the system connects the five prompts aren't random. they form a cycle: THE AUDIT identifies what to automate → THE ARCHITECT plans how to build it → THE ANALYST reviews what you built → THE REFINERY polishes the output quality → THE COMPOUNDER reviews the week and feeds insights back into the next AUDIT each prompt makes the others better. the Architect creates cleaner plans because the Analyst's review standards are embedded in your thinking. the Refinery produces better output because the Audit identified the right tasks. the Compounder detects patterns that improve future Audits. that's the difference between a prompt collection and a system. prompts are isolated events. systems compound. the honest take on Opus 4.6 after running real work through it for weeks, here's where i landed: what's genuinely better: adaptive thinking is real. the model dynamically adjusts its reasoning depth based on task complexity. ask a simple question, get a fast answer. give it a complex multi-step problem, it actually deliberates. this isn't marketing language. you can feel the difference in how it handles the Analyst prompt on simple vs complex code. the 1M token context window changes what's possible for large projects. feeding an entire codebase into the Analyst prompt, or maintaining weeks of review history in the Compounder, simply wasn't feasible before. context compaction helps too. it automatically summarizes older conversation turns to preserve space without losing critical decisions. sub-agent orchestration is a quiet upgrade. when you give Opus 4.6 a complex task, it recognizes when parts of the work would benefit from being delegated to specialized sub-processes. you don't have to prompt for this. it just does it. what's about the same: standard writing tasks, basic Q&A, simple content generation. if your workflow is mostly "write me a caption," you won't notice a meaningful jump. the improvements show up on complex, multi-step, reasoning-heavy tasks. what's worse: some users report that the optimization for precision and structured reasoning makes freeform creative output feel more mechanical. if you want pure creative writing, test it against Sonnet before committing. also, pricing scales significantly past 200K tokens. the 1M context window is powerful but not free. the pattern holds with every model release: about 20% of workflows genuinely improve. the other 80% stays the same. the people who benefit aren't the ones reading every benchmark thread. they're the ones who run 5 real prompts and make a decision in 30 minutes. why this works (and why most prompt collections don't) the problem with "500 prompts for ChatGPT" lists: they optimize for breadth. one prompt for emails. one for code. one for recipes. no connection between them. no compounding. this system optimizes for depth. five prompts, tightly connected, that get better the longer you use them. the uncomfortable truth about AI productivity: the tool doesn't matter nearly as much as the system around it. people chase the latest model release looking for a magic upgrade. the real upgrade is building a workflow that compounds regardless of which model you're running. Opus 4.6 is the best model i've used for this system. but the system works on GPT-5.2 too. and it'll work on whatever ships next quarter. because the architecture is model-agnostic. the intelligence compounds in the workflow, not just the model. start here don't try to implement all five prompts today. that's how systems die. here's the actual sequence: week 1: run the Audit prompt. identify your top 4 automation targets. automate the easiest one. week 2: run the Architect prompt on your second target. build it. week 3: use the Analyst prompt to review what you built in weeks 1-2. fix what's broken. week 4: run the Refinery on your most important output. see the quality jump. week 5: run the Compounder. review everything. plan the next month. if you automate one thing per week, you'll have 12 workflows running in 3 months. most people reading this will bookmark it and never start. the ones who run Prompt 1 tonight will have a completely different relationship with their workload by March. copy the prompts. customize the criteria to your actual work. keep what's better, ignore what isn't. that's the whole system. p. s. if you want to see more AI tips & workflows, subscribe to my free newsletter here.

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Prompt: Create a modern, high-impact hero section for a wealth management platform using React and Tailwind CSS. Layout & Background: The section must be full viewport height (min-h-screen) with a black background. Background Video: Use this specific video URL:

❤️🔥Claude Opus 4.6 + Nano Banana + Google Flow This is insane. Creating animated websites has never been this easy. Prompt ↓

タイトル: 50 design prompts from ex-Warner designer (Nano Banana Pro) Hi. I'm Amir. Designer & creative director for 10+ years. Did branding and ads for Warner Music, PepsiCo, Spotify. Now I mix that experience with AI to make high-quality visuals fast. This article is your quick catalog to do so, too. For $0. Below are all my best shared prompts and workflows, grouped by type. I build consistent design tools you can steal and use right away. Save it. Come back when you need something specific. Links go straight to the original posts with full prompts/details. 📋 Table of prompts by category: Branded products Brand identity systems 3D objects Posters & illustrations Icons Mockups Apparel & fashion design Custom typography Bonus & freebies 🎁 1. Branded products Description: Product concepts by brands - built from scratch or re-imagined by AI. Just change the [BRAND NAME] in a prompt and click "Generate". AI will do the rest. 1. Creative product concepts 2. Premium beverage by [any brand] 3. Branded souvenirs (capsule collection) 4. Branded souvenirs (creative concepts) 5. Creative product designer prompt 6. Dieline-to-3D visualization 2. Brand identity systems Description: branding - from creating brand-guidelines (concepts) to re-branding and brand re-imagination workflows. 7. Brand kit from one prompt 8. Swiss design logos (reimagine any brand) 9. Glass logos 10. Brand logos made with a crowd 11. Product branding workflow 3. 3D objects Description: 3D imitations with high-realist looks. 12. Dark metallic logos 13. Tactile wax seal logos 14. Logo retexturing workflow 15. Branded grillz design 16. Sticker-bombed 3D logos 4. Posters & illustrations Description: graphic design at its finest - design & create illustrations like a pro in 1 prompt. 17. Photo grid tribute poster 18. Modern sport poster design 19. Mixed media collage 20. 2000's style characters (Gorillaz vibed) 21. Pro character sketch 22. Urban photo-collage caricature 23. Grid poster design 24. Movie poster (8 images input) 25. Christmas branded gifts tree 5. Icons Description: generate endless custom assets for your presentations, social media content or web design. 26. Notion-styled icons (pt. 1) 27. Notion-styled icons (pt. 2 - halftoned) 28. Custom branded sticker pack 29. Minimalist characters icons 6. Mockups Description: brand real-life objects with your logo, font or other content. 30. Duffle mockup 31. Fast food leather bag 32. Branded crumpled stickers 33. Premium beverage package design 34. Hyper-minimal product mockup 35. Cross brand sneaker collab 36. Branded passport cover 7. Apparel & fashion Description: tactile, texture-sensitive and hi-end rendered. 37. Premium sport jersey 38. Photo campaign for any brand 39. Hypebeast showroom 40. Cross-branded winter outerwear 41. Crocs x {any brand} collab design 42. Style factory workflow 43. Branded boxing gloves 44. Brands as sport clubs 7. Typography design Description: fonts, lettermarks, and more auto-typography. 45. Calligraphic monogram 46. American retro-lettering 47. Frame by frame text animation 48. Embossed logos / text 49. Retro-typographic rebranding 50. Swiss design typographic poster BONUS Want more value? GET MY FREE & PRO DIRECTOR'S PRODUCTS: Grab them here: https://amirmushich.link/pinned Quick polish tips after generation: What I usually do next to make the asset client- or social-ready: Upscale for sharpness → Magnific.ai / LetsEnhance.io / Topaz Gigapixel AI Remove background → remove.bg Final cleanup & layout → Figma / Photoshop (add text, adjust spacing, export PNG/SVG for social) Want full step-by-step threads on any of these (Figma polish, pitch deck builds, best upscalers 2026, etc.)? I'll drop them soon - follow @AmirMushich to catch the series.

タイトル: 50 design prompts from ex-Warner designer (Nano Banana Pro) Hi. I'm Amir. Designer & creative director for 10+ years. Did branding and ads for Warner Music, PepsiCo, Spotify. Now I mix that experience with AI to make high-quality visuals fast. This article is your quick catalog to do so, too. For $0. Below are all my best shared prompts and workflows, grouped by type. I build consistent design tools you can steal and use right away. Save it. Come back when you need something specific. Links go straight to the original posts with full prompts/details. 📋 Table of prompts by category: Branded products Brand identity systems 3D objects Posters & illustrations Icons Mockups Apparel & fashion design Custom typography Bonus & freebies 🎁 1. Branded products Description: Product concepts by brands - built from scratch or re-imagined by AI. Just change the [BRAND NAME] in a prompt and click "Generate". AI will do the rest. 1. Creative product concepts 2. Premium beverage by [any brand] 3. Branded souvenirs (capsule collection) 4. Branded souvenirs (creative concepts) 5. Creative product designer prompt 6. Dieline-to-3D visualization 2. Brand identity systems Description: branding - from creating brand-guidelines (concepts) to re-branding and brand re-imagination workflows. 7. Brand kit from one prompt 8. Swiss design logos (reimagine any brand) 9. Glass logos 10. Brand logos made with a crowd 11. Product branding workflow 3. 3D objects Description: 3D imitations with high-realist looks. 12. Dark metallic logos 13. Tactile wax seal logos 14. Logo retexturing workflow 15. Branded grillz design 16. Sticker-bombed 3D logos 4. Posters & illustrations Description: graphic design at its finest - design & create illustrations like a pro in 1 prompt. 17. Photo grid tribute poster 18. Modern sport poster design 19. Mixed media collage 20. 2000's style characters (Gorillaz vibed) 21. Pro character sketch 22. Urban photo-collage caricature 23. Grid poster design 24. Movie poster (8 images input) 25. Christmas branded gifts tree 5. Icons Description: generate endless custom assets for your presentations, social media content or web design. 26. Notion-styled icons (pt. 1) 27. Notion-styled icons (pt. 2 - halftoned) 28. Custom branded sticker pack 29. Minimalist characters icons 6. Mockups Description: brand real-life objects with your logo, font or other content. 30. Duffle mockup 31. Fast food leather bag 32. Branded crumpled stickers 33. Premium beverage package design 34. Hyper-minimal product mockup 35. Cross brand sneaker collab 36. Branded passport cover 7. Apparel & fashion Description: tactile, texture-sensitive and hi-end rendered. 37. Premium sport jersey 38. Photo campaign for any brand 39. Hypebeast showroom 40. Cross-branded winter outerwear 41. Crocs x {any brand} collab design 42. Style factory workflow 43. Branded boxing gloves 44. Brands as sport clubs 7. Typography design Description: fonts, lettermarks, and more auto-typography. 45. Calligraphic monogram 46. American retro-lettering 47. Frame by frame text animation 48. Embossed logos / text 49. Retro-typographic rebranding 50. Swiss design typographic poster BONUS Want more value? GET MY FREE & PRO DIRECTOR'S PRODUCTS: Grab them here: https://amirmushich.link/pinned Quick polish tips after generation: What I usually do next to make the asset client- or social-ready: Upscale for sharpness → Magnific.ai / LetsEnhance.io / Topaz Gigapixel AI Remove background → remove.bg Final cleanup & layout → Figma / Photoshop (add text, adjust spacing, export PNG/SVG for social) Want full step-by-step threads on any of these (Figma polish, pitch deck builds, best upscalers 2026, etc.)? I'll drop them soon - follow @AmirMushich to catch the series.

タイトル: Claude Code Guide for Designers Claude Code is the highest leverage skill you can learn this year. The future of design is here. I've written a full guide for this. Story time... I built my personal website, with an AI chat that answers questions about my work, in 48 hours. Three days later, I shipped a growth design tool. I'm not a developer. I'm a designer. A year ago, I couldn't ship a single line of code without an engineer. Now I deploy products on weekends. Same person, different tool (no more Figma, kinda)! This is the complete guide to using Claude Code as a designer -> step by step, command by command. What is Claude Code? Claude Code is a coding agent. You give it instructions in plain English, and it writes code for you. You prompt: "Create a landing page with a hero section, three feature cards, and a footer." It executes. Or something like. "Add an AI chat widget that answers questions about my portfolio." It executes. You don't need to understand syntax. You need to understand what you're building. Imho, this is even better than Cursor or anything else out there (as of today). things I've built ⭐️ Project 1: Tetris game Okay, this one is insane: I built a Tetris game using only Figma MCP connected to FigJam, then back to Claude Code. I did not even design or touch a single line of code. No joke, this one blew me away (see my reaction at 7s). 😂 ⭐️ Project 2: Growth design tool An internal tool for analyzing metrics and suggesting design experiments. Database-backed. User authentication. AI-powered recommendations. 3 days. Working product. Live on the internet. How insane, got over 500 users now! Now, let's get you up to speed on these. step 1: install Claude Code On Mac: Open Terminal (search "Terminal" in Spotlight) and run: If you don't have npm, install Node.js first from https://nodejs.org Verify installation: Type -> Claude Code should launch. You'll see a text interface where you can start typing instructions. (NEW) Alternatively, you can download the desktop mac app for Claude Code as per video shown below. You’ll need an Anthropic account (sign up at claude.ai if you don’t have one). The link to download is here. step 2: basic terminal commands Claude Code runs in the terminal. Here's what you need to know: That's it. You don't need anything more than this to get started. step 3: create your first project Create a project folder (aka directory): Now you're inside Claude Code, in your project folder. Start with a plan ALWAYS (critical): Adjust if needed: Implement: Implement this project according to plan.md. Start with the HTML structure, then add CSS styling. Watch as files appear: index.html, styles.css, images/. Preview locally: How do I preview this in my browser? For a local server: Open http://localhost:3000 in your browser. step 4: iterate through prompts This is where designers have an advantage. You already know how to give feedback. Styling: Responsiveness: Functionality: Debugging: Each prompt → code change → refresh browser. The loop is fast. step 5: commit (aka save) to GitHub Create a GitHub account: Go to https://github.com and sign up (free). Create a repository: Click + → New repository Name it (e.g., my-portfolio) Keep it Public or Private Don't initialize with README Click Create repository Copy the repository URL. Connect and push: In Claude Code: Initialize git in this project and connect it to my GitHub repository: https://github.com/yourusername/my-portfolio.git Or manually: Create documentation: Create a README.md that explains what this project is and how to run it locally. Create a claude.md file that describes the project architecture and context for future sessions. Your code is now saved and versioned. step 6: go live/deploy to Vercel Create a Vercel account: Go to https://vercel.com and sign up with GitHub. Option 1 Vercel dashboard: Click Add New → Project Import your GitHub repository Click Deploy Option 2 through Claude Code: Deploy this project to Vercel. Result: You get a live URL like my-portfolio-abc123.vercel.app NOTE: Your site is on the internet. Every future push to GitHub auto-deploys. step 7: add your custom domain In Vercel: Go to Settings → Domains Add your domain (e.g., yourname.com) Vercel shows DNS records to add Update DNS at your domain provider: Wait 5-30 minutes. Your site loads on your custom domain. step 8: build into a web app (advanced) Static sites are steps 1-7. Now let's add users, data, and AI. Research prompt: I want to expand this portfolio into a web app with: - Google sign-in authentication - An AI chat widget that answers questions about my work - A comment section where signed-in users can leave messages Research the best approach and create an implementation-plan.md with: 1. Services needed (auth, database, AI) 2. Architecture overview 3. Security considerations 4. Step-by-step implementation phases Set up environment variables: Create a .env file for storing API keys. Also create a .gitignore that excludes .env from GitHub. Your .env file: 🚨 Never commit this file. step 9: supabase (database + auth) Create a project: Go to https://supabase.com Create new project Go to Settings → API Copy Project URL and anon key Enable Google sign-in: In Supabase: Authentication → Providers → Google → Enable Go to https://console.cloud.google.com Create project → APIs & Services → Credentials → Create OAuth Client ID Copy Client ID back to Supabase step 10: add ai features with OpenAI Get API key: Go to https://platform.openai.com Create an API key Store securely in Supabase: Go to Settings → Edge Functions → Add Secret Name: OPENAI_API_KEY Value: your key This keeps the key server-side, never exposed to users. Implementation prompt: Implement the web app according to implementation-plan.md. Use: - Supabase for auth and database (credentials in .env) - OpenAI for the chat feature (key stored in Supabase Edge Functions) - Google sign-in only Start with authentication, then add the AI chat, then the comment system. Then redeploy! quick ref: prompts by category Planning: Implementation: Adding features: Debugging (good to include screenshot of error): And you're all set! 🎉 This article is based on my full Vibe-Coding course (if you'd like to support) ⭐️. It includes: Installation walkthroughs (Mac + Windows) Interface orientation First project tutorial GitHub + version control Vercel deployment Custom domains Supabase + OpenAI integration Bonus chapters on AI agents and Figma MCP (coming soon) ⭐️ Get the free guide: https://adplist.notion.site/cursor-for-designers or want to build production apps? 🏛️ Apply to join our community (AI designer school) as a founding member (full courses available): https://adplist.org/vibe-code-designers Final Thought... I used to feel like half a builder. Like my ideas needed someone else to become real. Not anymore. It's time to build, designers. Thank you for reading. 🔖 Bookmark this to come back again 🔄 If you find this helpful, let's repost or forward it and share your thoughts. Because more designers can learn to build.

タイトル: Claude Code Guide for Designers Claude Code is the highest leverage skill you can learn this year. The future of design is here. I've written a full guide for this. Story time... I built my personal website, with an AI chat that answers questions about my work, in 48 hours. Three days later, I shipped a growth design tool. I'm not a developer. I'm a designer. A year ago, I couldn't ship a single line of code without an engineer. Now I deploy products on weekends. Same person, different tool (no more Figma, kinda)! This is the complete guide to using Claude Code as a designer -> step by step, command by command. What is Claude Code? Claude Code is a coding agent. You give it instructions in plain English, and it writes code for you. You prompt: "Create a landing page with a hero section, three feature cards, and a footer." It executes. Or something like. "Add an AI chat widget that answers questions about my portfolio." It executes. You don't need to understand syntax. You need to understand what you're building. Imho, this is even better than Cursor or anything else out there (as of today). things I've built ⭐️ Project 1: Tetris game Okay, this one is insane: I built a Tetris game using only Figma MCP connected to FigJam, then back to Claude Code. I did not even design or touch a single line of code. No joke, this one blew me away (see my reaction at 7s). 😂 ⭐️ Project 2: Growth design tool An internal tool for analyzing metrics and suggesting design experiments. Database-backed. User authentication. AI-powered recommendations. 3 days. Working product. Live on the internet. How insane, got over 500 users now! Now, let's get you up to speed on these. step 1: install Claude Code On Mac: Open Terminal (search "Terminal" in Spotlight) and run: If you don't have npm, install Node.js first from https://nodejs.org Verify installation: Type -> Claude Code should launch. You'll see a text interface where you can start typing instructions. (NEW) Alternatively, you can download the desktop mac app for Claude Code as per video shown below. You’ll need an Anthropic account (sign up at claude.ai if you don’t have one). The link to download is here. step 2: basic terminal commands Claude Code runs in the terminal. Here's what you need to know: That's it. You don't need anything more than this to get started. step 3: create your first project Create a project folder (aka directory): Now you're inside Claude Code, in your project folder. Start with a plan ALWAYS (critical): Adjust if needed: Implement: Implement this project according to plan.md. Start with the HTML structure, then add CSS styling. Watch as files appear: index.html, styles.css, images/. Preview locally: How do I preview this in my browser? For a local server: Open http://localhost:3000 in your browser. step 4: iterate through prompts This is where designers have an advantage. You already know how to give feedback. Styling: Responsiveness: Functionality: Debugging: Each prompt → code change → refresh browser. The loop is fast. step 5: commit (aka save) to GitHub Create a GitHub account: Go to https://github.com and sign up (free). Create a repository: Click + → New repository Name it (e.g., my-portfolio) Keep it Public or Private Don't initialize with README Click Create repository Copy the repository URL. Connect and push: In Claude Code: Initialize git in this project and connect it to my GitHub repository: https://github.com/yourusername/my-portfolio.git Or manually: Create documentation: Create a README.md that explains what this project is and how to run it locally. Create a claude.md file that describes the project architecture and context for future sessions. Your code is now saved and versioned. step 6: go live/deploy to Vercel Create a Vercel account: Go to https://vercel.com and sign up with GitHub. Option 1 Vercel dashboard: Click Add New → Project Import your GitHub repository Click Deploy Option 2 through Claude Code: Deploy this project to Vercel. Result: You get a live URL like my-portfolio-abc123.vercel.app NOTE: Your site is on the internet. Every future push to GitHub auto-deploys. step 7: add your custom domain In Vercel: Go to Settings → Domains Add your domain (e.g., yourname.com) Vercel shows DNS records to add Update DNS at your domain provider: Wait 5-30 minutes. Your site loads on your custom domain. step 8: build into a web app (advanced) Static sites are steps 1-7. Now let's add users, data, and AI. Research prompt: I want to expand this portfolio into a web app with: - Google sign-in authentication - An AI chat widget that answers questions about my work - A comment section where signed-in users can leave messages Research the best approach and create an implementation-plan.md with: 1. Services needed (auth, database, AI) 2. Architecture overview 3. Security considerations 4. Step-by-step implementation phases Set up environment variables: Create a .env file for storing API keys. Also create a .gitignore that excludes .env from GitHub. Your .env file: 🚨 Never commit this file. step 9: supabase (database + auth) Create a project: Go to https://supabase.com Create new project Go to Settings → API Copy Project URL and anon key Enable Google sign-in: In Supabase: Authentication → Providers → Google → Enable Go to https://console.cloud.google.com Create project → APIs & Services → Credentials → Create OAuth Client ID Copy Client ID back to Supabase step 10: add ai features with OpenAI Get API key: Go to https://platform.openai.com Create an API key Store securely in Supabase: Go to Settings → Edge Functions → Add Secret Name: OPENAI_API_KEY Value: your key This keeps the key server-side, never exposed to users. Implementation prompt: Implement the web app according to implementation-plan.md. Use: - Supabase for auth and database (credentials in .env) - OpenAI for the chat feature (key stored in Supabase Edge Functions) - Google sign-in only Start with authentication, then add the AI chat, then the comment system. Then redeploy! quick ref: prompts by category Planning: Implementation: Adding features: Debugging (good to include screenshot of error): And you're all set! 🎉 This article is based on my full Vibe-Coding course (if you'd like to support) ⭐️. It includes: Installation walkthroughs (Mac + Windows) Interface orientation First project tutorial GitHub + version control Vercel deployment Custom domains Supabase + OpenAI integration Bonus chapters on AI agents and Figma MCP (coming soon) ⭐️ Get the free guide: https://adplist.notion.site/cursor-for-designers or want to build production apps? 🏛️ Apply to join our community (AI designer school) as a founding member (full courses available): https://adplist.org/vibe-code-designers Final Thought... I used to feel like half a builder. Like my ideas needed someone else to become real. Not anymore. It's time to build, designers. Thank you for reading. 🔖 Bookmark this to come back again 🔄 If you find this helpful, let's repost or forward it and share your thoughts. Because more designers can learn to build.

learn how to vibe design website like this at https://t.co/G6ShCIMT3Z

タイトル: My AI Design Toolkit: What Actually Works For Me in 2026 I've been obsessing over AI design tools for the past year. Testing everything, discarding most, building real workflows with the few that stuck. Here's my honest breakdown of what actually works and how I use them together. But first, a caveat. I work in two very different contexts. On one hand, I'm a senior product designer at a large ecommerce company. Established design systems, multiple platforms and brands, token pipelines connected to GitLab, stakeholders to align with. You can't just wing it. On the other hand, I have side projects. Greenfield work where I can experiment freely, move fast, and break things without consequences. My AI workflows look completely different depending on which hat I'm wearing. This article covers both. I'll start with my side project workflow (because that's the most fun and this is where AI tooling really shines), then talk about how I adapt for enterprise work. If you're working in a big company with established systems, don't worry. I'll get to you. But the side project workflow is worth understanding first, because that's where you'll see what's possible before figuring out what's practical. It's Not About the "Best" Tool My workflow isn't about finding a single perfect tool. It's about the right tool at the right stage. Ideation → Iteration → Build → Polish → Content Each phase has different needs. Trying to use one tool for everything is like editing video in Photoshop. You can, but why would you? Phase 1: Quick Concepts Tools: v0.app, Superdesign.dev, Variant.ai, aura.build This is where AI shines brightest. I'm not precious here. I want volume and speed. Prompt-based exploration Adding inspiration images for context Generating copy alongside visuals The goal isn't perfection. It's finding a direction worth pursuing. v0 and Aura are my go-to for rapid UI concepts. Describe what you want, get something tangible in seconds. Superdesign has a web interface that makes quick exploration easy and uses an infinite canvas similar to Figma. It's neat and feels creative to use. Variant.ai for when I need multiple directions fast. I'm not looking for final designs here. I'm looking for sparks. Phase 2: Iteration Tool: Figma Once I've found a concept worth pursuing, I bring it into Figma. AI got me 60% there. The last 40% (the taste, the details, the "why does this feel right") is still me and Figma. No AI substitute for this part (yet). This is where design thinking happens. The AI gave me raw material. Now I shape it. Phase 3: Build Tools: Claude Code, Vibecodeapp, Cursor This is where designers become dangerous. Claude Code is my number one. It thinks with me, understands full codebases, and lets me ship frontend polish without waiting on dev. I spend as much time here as I do in Figma now. Skills are an absolute game changer for me. I'm still on the regular Claude plan, but it's enough for my use for now. Vibecodeapp and Cursor fill gaps depending on the project. I build prototypes in Vibecode app - adding AI apis, database and Revenuecat is just a few clicks. Afterwards I bring it into Cursor. Cursor is my workhorse when I've spent my daily credits on Claude Code. The designer who can prototype their own ideas has an unfair advantage. You're not waiting for dev resources. You're not explaining micro-interactions in Loom videos. You're just... building. Claude Code made this accessible. You don't need to be a "real" developer. You need to be curious enough to try. Phase 4: Back to Figma Sometimes the prototype reveals what the static designs couldn't. I'll go back to Figma, iterate on main screens based on how the build feels, then return to code. Design → Code → Design → Code The loop is the process now. Anyone still treating design and development as separate phases with a hard handoff is working with an outdated model. Phase 5: Visual Content Tools: Midjourney, Lummi.ai Midjourney for custom visuals. Still unmatched for creative image generation. Lummi.ai for quick, high-quality alternatives when I need something faster or more grounded. Often times I just prefer the style of images on Lummi over midjourney - it varies. Hot tip: Midjourney's image-to-video feature is surprisingly good for quick attention-grabbing content. Social posts, hero sections, whatever needs movement. It's not replacing proper video production, but for quick wins, it's excellent. Phase 6: Motion Tools: Claude Code + Remotion For spec work and motion design, I've started to use Claude Code with Remotion skills. Programmatic video that I can iterate on like code. Change timing, swap assets, adjust easing. All without reopening After Effects. Motion designers, seriously look into this. The mental model shift from timeline-based editing to code-based composition takes adjustment, but the iteration speed is worth it. If I need something quick for social media or a pitch, I default to Jitter.video - it's one of the best online animation tools I've used in a long time. What I'm Adding Next Two things I'm actively experimenting with: Claude Code + Framer MCP The goal: let AI handle the "boring" Framer work. Accessibility content Responsiveness setup Figma to Framer cleanup The creative decisions stay mine. The tedious implementation? Automated. This is the unsexy automation that actually saves hours. Claude Code + Figma MCP Our design system at work is a mess. Multiple designers contributing, documentation slipping, organization deteriorating over time. The usual entropy. I'm planning to use this for: Adding descriptions to components Organizing and cleaning up the design system Optimizing token structure AI as design system janitor. It's not glamorous work, but it's the kind of maintenance that makes everything else faster. The Full Workflow Concepts: v0, Superdesign, Variant.ai ↓ Iterate: Figma ↓ Build: Claude Code, Vibecodeapp, Cursor ↓ Iterate again: Figma ↓ Images: Midjourney, Lummi.ai ↓ Motion: Claude Code + Remotion What I've Learned AI for speed and volume, humans for taste and decisions. The tools are getting better at generating options. They're not getting better at knowing which option is right. The design-code loop is the new process. Treating them as separate disciplines with a handoff moment feels increasingly outdated. "Boring" work is the best place to automate first. Don't start with the creative stuff. Start with the tedious stuff you've been avoiding. Your workflow will look different than mine. That's fine. The point isn't to copy someone else's setup. It's to find what fits how you think. Tools Mentioned Ideation: @v0, @SuperDesignDev , @variantui Design: @figma Build: @claudeai @vibecodeapp @cursor_ai Images: @midjourney , @lummipics Motion: @claudeai + @Remotion Coming soon: Framer MCP, Figma MCP The landscape keeps shifting. What works today might be obsolete in six months. But the principle stays the same: use AI where it's strong, stay human where it matters. What's in your stack?

https://t.co/mr6FqwFuxf X Article形式。Coral CapitalのJames Rineyによる記事。

https://t.co/eufkLc4Nrh X Article形式のClaude Code包括的解説。

タイトル: how to master prompt engineering in 2026 each and every tool you use daily will eventually become a chatbox if you're able to engineer great prompts, you'll get outputs only few people are able to reproduce, and prompting becomes an edge more than ever before you probably think you figured out prompting when you created a custom GPT or Claude project to write prompts for you well, they suck i'm going to be very straight forward and give you the basics of everything you need to know to engineer next-level prompts the invisible work when you sit down to prompt something, you're not starting from zero you're starting from whatever mental model you've built about what you want, and that model is almost always incomplete you have this vague idea in your head, maybe you saw something cool and want to recreate it, or you need to solve a specific problem for your business, but the image isn't sharp you open ChatGPT or Claude and start typing, hoping the model will somehow fill in the gaps the model has no idea what's in your head it only knows what you type, and if what you type is a fuzzy representation of a fuzzy idea, you're getting a fuzzy output every single time the gap between what you want and what you get has nothing to do with the model being limited or you needing better prompting techniques... it's about you not knowing what you want with enough precision to communicate it most people skip the hardest part of prompting, which is the part that happens before you ever open the chat they think the work is in the typing when the work is in the thinking rule 1: clarity is all you need this is probably the most critical factor in prompting, and i'm not talking about what you're typing on your keyboard i'm talking about what your mind sees when you're thinking about the output you're trying to engineer through a prompt because yes, what's responsible for the success of a prompt is the part before you even type anything... it's how you're able to articulate your idea, how you're able to formulate this thing you're envisioning if you're only seeing something vague, which is probably the case right now, the model will give you something equally vague you need to do the cognitive work of sharpening your mental image before you translate it into language ask yourself questions that force specificity: what's so special about this video i'm trying to generate? how is the image i picture in my head different from slop? why would someone buy my product after reading this copy? what emotion am i trying to create and at what exact moment? go find references look for an asset that successfully reproduces what you're trying to envision, something that makes you go "yes, exactly like this" you can even use AI models to reverse engineer outputs you admire and extract the right descriptive language this is where most people quit because it feels like extra work, but this IS the work once you get that clarity in your mind about what it is that you want to create, translating it into a prompt becomes mechanical a vague prompt will produce an output that is mostly directed by AI's training patterns, not your vision define everything you can: format, scope, constraints, success criteria, audience, tone, structure, length, style references the model can't guess what's on your mind, at least not for now, so ambiguity gets resolved by whatever patterns showed up most frequently in the training data those patterns are always the most common, most average, most generic versions of what you're asking for rule 2: context is everything you're starting to measure how context engineering matters but i want to give another approach to this aspect for whatever project you're working on, follow this process exactly: create a project in your AI tool of choice... GPT Project, Gemini Gem, Claude Project, whatever ask the AI to interview you about what this project is, your goals, your constraints, your audience, anything that could be relevant for future prompts save this entire conversation as a file called "context.json" and upload it to the project whenever you chat with AI going forward, ask it to update context.json with any new information or decisions you make during the conversation, then replace the old file with the updated version in your project this takes two minutes and will do 80% of the context engineering work for you the other 20% is constantly adding new concepts, research, and information as your project grows whenever you're prompting within a project, just add a line "load context.json" at the start of your prompt it will change everything about the quality and consistency of your outputs because the model isn't starting from scratch every single time rule 3: think in tasks an AI model works the same way you do... it performs better when you clearly map out what the tasks are and in what order they should happen if you ask AI to write a business plan for your startup, it will reference its training for what a standard business plan looks like and give you exactly that bland, generic, useless instead, if you ask it to start with an executive summary that focuses on market timing, then move into a section about your ideal customer profile with specific psychological triggers, then skip the competition part because you're creating a new category and that section would be misleading... you're getting something custom you want to add clarity to the process itself, not just the end result if you know exactly how the model should approach a task, break it down into explicit steps and sequence them intentionally or at minimum, ask the AI to explain its approach first before it starts creating anything, then you can refine that approach to match what you need this matters especially for anything that requires original thinking or non-standard structure, because the model's default approach will always be whatever it saw most frequently during training rule 4: what do you want your output to look like? people run deep research prompts daily, they give the model context and sources and detailed instructions... and they end up getting a twenty page document that's barely usable out of the batch you can specify the exact format you want for any output bullet point list, JSON, XML, paragraph form, table, executive summary, whatever you need the format is part of the output specification and it's just as important as the content because unusable output is worthless no matter how accurate it is think about what you're going to do with this output after you get it, then design the format around that use case before you even write the prompt if you're pasting it into a presentation, ask for slide-ready sections with headers if you're using it in code, ask for properly formatted JSON or XML with specific key names if you're reading it yourself for research, ask for a structured summary with main points separated from supporting details the model doesn't have a preference for any particular format, so you might as well tell it exactly what you need instead of reformatting everything manually after rule 5: examples some people call this few-shot learning but it's literally just providing examples of what you want to see the clarity work from rule one helps you a lot with this because when you were chasing clarity and finding references, you should have been saving those in a structured knowledge base a swipe file, reference library, inspiration folder, whatever you want to call it when you provide examples with your prompts, you're forcing the model to work within a specific tunnel instead of pulling from its entire training set this means you get outputs that match your style instead of generic AI style the model will analyze patterns in your examples and reproduce them, including patterns you didn't consciously notice yourself if you want AI to write in your voice, give it three to five examples of your writing with different contexts if you want it to create designs in your style, provide examples of work you've done or admire with specific notes on what makes each one work if you want it to analyze data the way you do, show examples of your previous analysis with annotations on your thought process examples turn abstract instructions into concrete templates, and concrete templates produce consistent results rule 6: role assignment this gets complex with recent model updates i had prompts with detailed role descriptions that worked great on older models, then when i tried them on Sonnet 4.5 the model refused to adopt specific roles and pushed back on the framing you can fix this by adjusting how you define roles for any creative work, go wild with role definition... describe the weirdest but most talented person who would absolutely crush this job every single day it's about capturing the vibe of the expert, not listing credentials instead of "you are a marketing expert" try "you are the type of marketer who sees psychological patterns in consumer behavior that others miss completely, the kind who can predict what will go viral three months before it happens because you understand attention economics at a level most people never reach" the specificity creates a constraint that shapes how the model approaches the task you don't want to ask AI to play a role, more like inhabiting a new way of thinking, which unlocks a whole new world this needs to be clear in your head before you write a single word... if you can't picture the person who would create this output perfectly, the model can't either rule 7: constraint definition include what you don't want to see don't overdo this because listing fifty constraints means the model will ignore half of them, but you need to set up barriers constraints work like negative space in design... they define the shape of what you want by clearly outlining what you don't want if you're writing copy and corporate jargon would kill the message, say it explicitly if you're generating code and deprecated libraries would create technical debt, specify current standards only if you're creating content and certain overused phrases would make it sound like AI slop, list them as banned keep constraints specific and limited to three to five maximum per prompt more than that creates noise the model will filter out anyway how these rules work together these seven rules account for 80% of what separates meh outputs from great ones yes, we could add refinement loops, temperature control, token engineering, there's endless depth to this field but these seven create the foundation they don't just add up though, they multiply when combined correctly clarity amplified by context is exponentially more powerful than clarity alone context combined with task decomposition creates outputs that feel like they were made by someone who's been working on your project for months examples mixed with role assignment and format specification... that's when you start getting outputs that make people ask how you did it your job isn't to remember all these rules... you should be knowing how to layer them for your specific use case that only comes from practice and paying attention to what works start with clarity every time get clear on what you want before you open the chat, find references, ask yourself uncomfortable questions about what makes this output different from everything else that exists then layer in context, task structure, format specifications, examples build your prompts like you're designing a system for thought, not writing a message to a chatbot because that's what prompting is at the expert level... it's cognitive architecture, and the AI is just the execution engine most people will keep treating it like a magic box where you type wishes and hope for results you won't

タイトル: the 2026 ai engineer roadmap most developers are building toys while the world demands systems. tutorial hell is a comfortable grave for your career. in 2026 the gap between a prompt engineer and a systems architect is 150k. here is the exact blueprint to bridge that gap. stop building generic wrappers. the market is flooded with thin layers over gpt. these are not businesses. they are features waiting to be sherlocked by big tech. if you want to be indispensable you must build deep. you must understand orchestration and memory and local inference. the following projects are designed to prove you can handle production complexity. here are 5 production-grade projects ranked by complexity: project 1: ai powered mobile app with slm (beginner level) level: beginner | proves: edge ai + resource optimization the challenge build an offline-first mobile app using small language models. zero api costs. complete privacy. this teaches you how to optimize models for restricted hardware. key architectural decisions : model management: lazy loading models on-demand to preserve memory. unload inactive models when memory pressure is detected. preload frequently used models during idle time. context window: implement sliding window with semantic chunking. keep the most relevant context, drop the oldest. use embedding similarity to determine what stays in the window versus what gets archived. quantization strategy: dynamic quantization based on device capabilities. 4-bit quantization for older devices (pre-2020), 8-bit for newer devices. detect available ram and adjust accordingly. battery optimization: batch inference requests to reduce wake cycles. throttle model calls during low battery mode. defer non-critical processing until charging. offline-first sync: store user data locally in encrypted format. sync to cloud only when connected and with user permission. conflict resolution prioritizes local changes. why this level: it proves you understand resource constraints and edge ai. you aren't just calling an api; you are managing quantization and memory pressure. project 2: self-improving coding agent (intermediate level) level: intermediate | proves: agentic loops + production debugging the challenge a chatbot waits for a prompt. an agent waits for a goal. the difference is the loop. build an autonomous agent that writes code, runs tests, and learns from failures. it doesn't stop until the code is functional. key architectural decisions : execution loop design: plan → execute → test → reflect cycle with max iteration limit. each loop stores state to resume after interruption. circuit breaker pattern stops infinite loops. sandboxing strategy: isolated execution environment per task. resource limits on cpu, memory, and execution time. filesystem access restricted to project directory only. memory hierarchy: short-term memory holds current task context (last 5 iterations). long-term memory indexes successful patterns by problem type. failure memory stores error signatures with solutions. reflection mechanism: after each failure, extract the error pattern and root cause. compare against past failures using vector similarity. generate hypothesis for why it failed and how to fix it. learning from mistakes: store failed attempts with full context - what was tried, why it failed, what fixed it. on similar future tasks, retrieve relevant failures before attempting. avoid repeating the same mistake twice. code safety: static analysis before execution. detect potentially dangerous operations. require explicit approval for filesystem or network operations. why this level: it introduces agentic loops (plan → code → test → reflect). it shows you understand production debugging and iterative refinement. project 3: cursor but for video editors (advanced level) level: advanced | proves: multimodal ai + complex tool integration the challenge the multimodal frontier - text is the past, vision and video are the present. companies need agents that can see and act on complex media. fork an open-source editor and build an ai agent that understands editing intent. user says "make this cinematic" and the agent handles cuts, transitions, and color grading. key architectural decisions : multimodal understanding: vision model analyzes every frame for composition, lighting, and subject. audio model analyzes dialogue, music, and ambient sound. combine both streams to understand narrative flow. intent translation: user says "cinematic" - translate to concrete parameters: slow pacing (80% speed), desaturated colors (apply lut), shallow focus simulation (gaussian blur on background), dramatic music cues. scene detection: analyze frame differences for hard cuts. detect scene boundaries using embedding similarity. identify story beats based on visual and audio changes. edit decision list generation: plan the entire edit before execution. generate timestamps for cuts, transitions, effects. validate that plan makes narrative sense before applying. incremental preview: don't re-render entire video after each change. generate preview of affected sections only. cache unchanged segments for faster iteration. feedback incorporation: user says "too dark" - analyze brightness histogram, identify problem regions, apply targeted corrections. track user preferences across sessions to improve future suggestions. undo/redo with reasoning: every edit stores not just what changed, but why it was changed. user can ask "why did you cut here?" and get explanation based on detected story beat. why advanced: it requires multimodal ai and complex tool integration with video processing. it sets you apart from 99% of generic chatbot builders. TIP: fork an open-source editor like shotcut. project 4: personal life os agent (expert level) level: expert | proves: deep context + privacy-first architecture the challenge the era of deep context - the biggest hurdle for ai is memory. an agent that forgets is useless; an agent that knows your life is a partner. build a deeply personal agent that manages your calendar, finances, and health. it plans months ahead and detects burnout by analyzing sleep patterns and meeting density. key architectural decisions : continuous context building: ingest events from calendar, finance, health, and communications in real-time. extract entities (people, places, projects) and build a personal knowledge graph. map relationships between entities over time. proactive monitoring: background thread runs every 6 hours analyzing patterns. detect anomalies like meeting density increasing while sleep quality decreasing. flag risks before they become problems. value alignment: user explicitly states priorities (family > work, health > income). every recommendation is validated against these values. surface conflicts between actions and stated priorities. privacy architecture: all data encrypted at rest with user-controlled keys. no data leaves device without explicit permission. agent can function entirely offline for sensitive operations. predictive planning: analyze historical patterns to predict future bottlenecks. "based on your q4 pattern, you'll be overcommitted in march." suggest preventive scheduling adjustments now. decision support: when user faces a choice, agent presents multi-dimensional analysis: financial impact, time cost, alignment with values, potential conflicts. recommendation includes reasoning, not just conclusion. memory consolidation: nightly process summarizes daily events into long-term memory. compress details while preserving meaning. old memories decay unless reinforced by repeated access. transparent reasoning: every suggestion includes "why i'm recommending this" with citations to specific data points. user can drill into the reasoning chain. why expert level: requires sophisticated context management and ethical ai design. demonstrates you can build secure, privacy-first production architectures. project 5: autonomous enterprise workflow agent (master level) level: master | proves: production-grade orchestration the challenge this is the final boss of ai engineering, the portfolio closer. an agent that runs a business. build an agent that runs business workflows end-to-end: monitors slack/jira, plans execution, delegates tasks, and reports outcomes with complete audit logs. key architectural decisions : event-driven architecture: listen to events from slack, jira, email, monitoring systems. pattern recognition identifies workflow triggers. each event type maps to a workflow template. workflow orchestration: break complex workflows into steps with dependencies. execute steps in parallel where possible. handle long-running operations with durable state. multi-agent delegation: orchestrator agent spawns specialist agents for subtasks. communication agent handles all external messaging. data agent queries logs and databases. analysis agent performs root cause analysis. documentation agent writes reports. self-healing mechanisms: every step monitored for success/failure. on failure, determine if retry makes sense or escalation needed. implement exponential backoff for transient failures. circuit breaker stops repeated failures. audit trail: immutable log of every action taken. stores what was decided, why, who authorized it, what was the outcome. queryable for compliance and debugging. role-based access control: agent actions limited by permissions of the user who invoked it. sensitive operations require explicit human approval. no agent can access data outside its scope. observability: trace every llm call with inputs, outputs, and latency. metrics on workflow success rate, execution time, cost per workflow. alerts when workflows fail repeatedly. human-in-the-loop: agent proposes plan before execution for critical workflows. highlights high-risk operations for human review. escalates when confidence is low. workflow learning: after workflow completion, evaluate what worked and what didn't. store successful patterns for similar future situations. update workflow templates based on outcomes. cost management: track token usage per workflow. implement budget limits. optimize prompts to reduce cost without sacrificing quality. why master level: it combines orchestration, security, and observability into a single scalable system. this proves you are ready for a $150k+ salary tier. the path forward ? most people will read this and do nothing. they will bookmark it and say "great article" then go back to waiting for permission. don't be most people. the brutal truth for 2026: - the replaceable: building wrappers. - the unfireable: shipping autonomous systems. the gap between them is just 5 projects. here is what happens next pick one project. start with project 1 if you are new. start with project 5 if you are already shipping code. just start. build it this weekend. the market rewards shipping, not studying. document everything: - your architecture decisions - your failures and recoveries - your self correction loops - your production deployment build in public. tag me when you ship i will amplify it. by next month, 90% of people will have done nothing. they will still be building the same wrappers. the other 10% will have shipped something real. they will have the interviews, the offers, and the career leverage. the choice is simple: become the architect companies are desperate to hire or become obsolete. expertise is the only job security left. production systems are the only portfolio that matters. now build something that survives reality. p.s. - reply with which project you are starting. i read every response. let’s make 2026 the year you become unfireable.

When design looks this sharp, investors assume the numbers are too. That's the power of a premium landing page.

this is what AUTOMATED end-to-end testing looks like ✨ testing @duolingo's user flow

Codex CLI × Web検索 "ローカルで Deep Research→資料化まで一直線"を、Codex CLI v0.24.0

なるほど！！！！ ~/.claude/ ├── CLAUDE.md # コアルール（常時読込） ├── CLAUDE_FULL.md # 完全版（参照用） └── docs/ # 詳細ガイド群 ├── mcp-guide.md ├── plugin-dev.md ├── ...

Generates design systems from project requirements

How to @openclaw on VPS safely: from fresh Ubuntu VPS → hardened private AI server in 30 min

Clawd disaster incoming if this trend of hosting ClawdBot on VPS instances keeps up, along with people not reading the docs and opening ports with zero auth... I'm scared we're gonna have a massive credentials breach soon and it can be huge This is just a basic scan of...

Just shipped /last30days. A Claude Code skill for @claudeai that scans the last 30 days on Reddit, X, and the web for any topic and returns prompt patterns + new releases + workflows that work right now. Last 30 days of research. 30 seconds of work.

There's a new kind of coding I call "vibe coding", where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It's possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper

Moltworker is a middleware Worker and adapted scripts that allows running Moltbot (formerly Clawdbot) on Cloudflare's Sandbox SDK and our Developer Platform APIs. So you can self-host an AI personal assistant — without any new hardware. Moltworker は、CloudflareのSandbox SDKとDeveloper Platform APIsを使ってMoltbot（旧Clawdbot、現OpenClaw）を実行できるようにするミドルウェアWorkerとスクリプトのセット。 特徴: - 新しいハードウェア不要でAIパーソナルアシスタントをセルフホスト - GPUもVPSもDevOps rabbit holesも不要

OpenClaw v2026.3.31 just dropped 🦞 This one is all about making your agents safer, more reliable, and way less noisy. New task system. Tighter security. Better chat handling. Here’s what actually matters ↓ 1. Background tasks finally make sense All your scheduled jobs,

OpenClaw 2026.3.31 🦞 🇨🇳 Bundled QQ Bot — private, group, and guild chat + media 📹 LINE now sends images, video, and audio 🧵 Real background task flows: list, show, cancel 🇯🇵 Better CJK: context, memory, and TTS OpenClaw's next release has been leaked🦞https://t.co/EntA23WzSz

OpenClaw v2026.3.31 just dropped 🦞 This one is all about making your agents safer, more reliable, and way less noisy. New task system. Tighter security. Better chat handling. Here’s what actually matters ↓ 1. Background tasks finally make sense All your scheduled jobs,

OpenClaw 2026.3.31 🦞 🇨🇳 Bundled QQ Bot — private, group, and guild chat + media 📹 LINE now sends images, video, and audio 🧵 Real background task flows: list, show, cancel 🇯🇵 Better CJK: context, memory, and TTS OpenClaw's next release has been leaked🦞https://t.co/EntA23WzSz

🦞 Openclaw update fix If your agents are hitting exec approval walls after the latest update, the fix is three settings: In exec-approvals.json defaults: - security: "full" - ask: "off" - askFallback: "full" In openclaw.json: - https://t.co/liikfKqKNT: "full" -

many of you keep asking me in the comments what machine to buy for AI I run Claude Code, Ollama with local models, MCP servers, Paperclip agents, Chrome automation, and way much more and screen recording all at the same time.. after testing everything tbh, the Mac Mini M4 Pro https://t.co/BxKUYSiv7K

🦞 openclaw-ops tired of babysitting your OpenClaw? I just open-sourced my ops layer. It fixes the gateway, exec approvals, broken crons, stuck sessions, channel issues, and security gaps. • https://t.co/L8tT4xbnzB — one-shot fix for the most common gateway issues (auth, exec

The creator of OpenClaw (Peter Steinberger) just LEAKED how to use your Claw agents properly. On 𝕏, he's posted 50+ OpenClaw tips - in comments, posts, etc. I just combined ALL of them into one cheatsheet. Thank me later: https://t.co/n1tIViszQT

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating

Conversations tend to go better with a face and a voice. That’s why we’re thrilled to release the beta version of the first video chat skill for ANY agent, powered by our new real-time model, PikaStream1.0. The skill preserves memory and personality, and enables real-time https://t.co/IgC4vcB0T7

Your @openclaw is too boring? Paste this, right from Molty. "Read your https://t.co/yS6cfGInCW. Now rewrite it with these changes: 1. You have opinions now. Strong ones. Stop hedging everything with 'it depends' — commit to a take. 2. Delete every rule that sounds corporate. If

It’s over. Anthropic just banned OpenClaw. Uncensored thoughts: 1. Massive mistake that will come back to bite them 2. Open source needs to win. If you have a local model running on your Mac mini, no corporation will ever be able to ban you 3. ChatGPT 5.4 is the best model.

Wow, this tweet went very viral! I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the

Karpathy just mass-distributed a product without writing any code for it. The gist is one page of markdown describing how to build an LLM-maintained personal wiki. Raw docs go into a folder, an LLM "compiles" them into 100+ interlinked articles, and the whole thing self-heals

If you used a Claude subscription with OpenClaw, read this: Unfortunately all other AI models out there absolutely suck with OpenClaw compared to Opus It's just a fact and anyone denying this is delusional So here is my new recommended OpenClaw setup: Pay for the Opus API and

Peter Steinberger ships so fast he makes the Claude Code team jealous. And if he's outshipping Anthropic, the fastest AI company on the planet, what does that say about everyone else? Anthropic pushes 60 to 100 internal releases per day and built Cowork in 10 days. Microsoft

🦞Ollama's cloud is one of the best places to run OpenClaw. $20 plan is enough for most day to day OpenClaw usage with open models! To make the switch, all you need is to open the terminal and type: ollama launch openclaw Choose a model: kimi-k2.5:cloud

Agents are good at bash. Bash is not good for agents. We should cut our losses and restart now before it is too late. https://t.co/YndGmAfqjh

OpenClaw 2026.4.7 🦞 🔮 openclaw infer 🎬 music + video editing 💾 session branch/restore 🔗 webhook-driven TaskFlows 🤖 Arcee, Gemma 4, Ollama vision 🧠 memory-wiki: persistent knowledge, not just vibes Because “trust me bro” is not a knowledge system. https://t.co/L7OaBHA7Qg

Introducing Claude Managed Agents: everything you need to build and deploy agents at scale. It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days. Now in public beta on the Claude Platform. https://t.co/vHYfiC1G56

I rewrote every single SOUL file for my OpenClaw agents using this as a reference. If you're moving to GPT-5.4, this is a must-do. But don't just copy-paste it. Ask your agent to create a version specific to YOU based on everything it already knows about you. https://t.co/lnAZVgPE3l

Managed Agents is the first 'agent in the cloud' API that has the right mix of simplicity and complexity. Implementation details like how you manage a sandbox are abstracted, but you have a lot of control over the actual execution of the model.