Sapi LeGAL @Walidsapi22

@davidpattersonx It's a hype , should wait for this, at least for a decade.

How to build an AI agent 📚📘

Most people treat Claude Code like a smarter chat window. That works… until your project grows. This structure highlights something deeper: once you move beyond single prompts, you need separation of concerns. The same principles we use in software engineering apply here, too. Look at the layout carefully. CLAUDE.md is not just a note file. It becomes project memory. It defines: → Standards → Constraints → Tone → Non-negotiables → Guardrails Instead of repeating instructions in every prompt, you centralize them. That reduces token waste and behavioral drift. Then you see skills/. This is where things get powerful. A skill is essentially a reusable workflow. If you’re repeatedly doing: -Code reviews -Refactoring -Output formatting -Structured analysis It should not live in an ad-hoc prompt. It should live as a reusable capability. That shifts you from prompting to system design. Next, hooks/. Hooks are underrated. They let you enforce checks: → Clean tool output → Validate structure → Log commands → Transform JSON If you’re not using hooks, you’re manually correcting outputs that could have been automated. Then the repository itself stays modular: -docs/ for architecture decisions -src/ for actual logic -tools/ for scripts and utilities This prevents your AI layer from bleeding into your application layer. When I started organizing projects this way, three things improved: -Fewer repeated instructions -More predictable outputs -Easier collaboration Especially once you add: → Subagents → MCP integrations → GitHub Actions automation → Plugin development Without structure, context becomes clutter. With structure, Claude operates within clear boundaries. This is not about making things complex. It’s about treating AI workflows like first-class engineering components instead of temporary chat experiments. If you're learning Claude Code and want to see how I implement this step by step, from installation to CLI usage, skills, hooks, subagents, MCP, GitHub Actions, and plugins, I’ve recorded the full process while building real workflows. This is the Claude Code Full Course Link- lnkd.in/gA_thjGq Image Credit- Brij Kishore Pandey Happy Learning! #ClaudeCode #claudeai

There is a war in Iran, so you had no time to test the new Nano-Banana 2 by Gemini. So I wrote this article to copy & paste prompts I have personally tested:

Ruben Hassid @rubenhassid

3 months ago

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The Anatomy of a Claude 4.6 Prompt: 1. Task Define what you want & what success looks like: "I want to [TASK] so that [SUCCESS CRITERIA]." No roles, "act as a senior expert." That era is over. 2. Context Files Upload context files with your expertise and rules: "First, read these files completely before responding: [filename .md] - [what it contains]." AI went from reading a sticky note to an entire book. Stop explaining yourself in the prompt. Put it in files. 3. Reference Show AI exactly what you want. Upload an example. Then give patterns, tone & structure as rules. No "give me something like" & hoping AI figures it out. 4. Brief This is the only part you actually type from scratch. Everything else is files. "Type of output + length. Does NOT sound like. Success means." 5. Rules Context file holds your standards, taste & audience. Prompt: "Read it fully before starting. If you're about to break one of my rules, stop and tell me." 6. Conversation You spent 3 years prompting AI. Now it prompts you Prompt: "DO NOT start executing yet. Ask me clarifying questions (use 'AskUserQuestion' tool) so we can refine the approach together step by step." 7. Plan Claude read your files before writing a single word. Prompt: "Before you write anything, list the 3 rules from my context file that matter most for this task. Then give me your execution plan." 8. Alignment Nothing happens until you both see the same aim. This replaces the old prompting era. Prompt: "Only begin work once we've aligned." Copy the full prompt template + download my personal md. files for Claude here: how-to-ai.guide.

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🚨 BREAKING: Building Claude skills from scratch is officially dead. Someone just open-sourced a battle-tested Claude skills library you can copy/paste into your workflow. No theory. No fluff. Just production-ready building blocks. Here’s what’s inside 👇 --- 1️⃣ Document Generation (All Formats) * PDFs * DOCX * PPT * CSV * Markdown One prompt → structured, formatted output. Your AI just became a document factory. --- 2️⃣ Web Scraping & Automation * Extract structured data * Clean & normalize * Schedule workflows * Trigger automations Claude stops “chatting.” It starts executing. --- 3️⃣ Data Processing Pipelines * Parse raw files * Transform datasets * Run analysis * Generate summaries From messy input → decision-ready output. --- 4️⃣ API Integrations * Connect SaaS tools * Push/pull structured data * Automate reporting * Chain multi-step workflows Claude becomes your backend engineer. --- Most people are still: “Write me a summary.” Power users are: “Here’s the repo. Process everything. Generate assets. Ship.” This changes the game: ❌ No more building prompts from zero ❌ No more fragile workflows ❌ No more reinventing automation ✅ Copy ✅ Plug in ✅ Scale 100% open source. Zero gatekeeping. AI skills are becoming modular. And modular always wins. If you're building with Claude, this is your unfair advantage.

cowork is incredible and you should be using it. this is one of the clearest articles i’ve stumbled across, you need to put down your evening coffee and read it at once.

witcheer @witcheer

3 months ago

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Anthropic just launched a weapon against OpenAI. Not a new model, benchmark, or price cut. But a copy-paste button. Here is what is happening and why it should terrify every AI company on earth. For two years, Silicon Valley assumed one thing would keep you locked into ChatGPT forever. Memory. Every preference you shared, every project you explained. Every quirk it learned about how you think, months of training an AI to understand you. That was supposed to be the moat but the thing that made leaving impossible. Anthropic just drained it. They built a feature called Import Memory. It sits right inside Claude's settings. You click it, and it hands you a prompt. You paste that prompt into ChatGPT. ChatGPT then does something remarkable. It confesses, It dumps every single thing it knows about you into a text block. Your work habits, preferences, projects, history, all of it. You copy that confession and paste it into Claude. Your entire relationship with ChatGPT, months, maybe years of context transplanted in under a minute. OpenAI spent two years building a sticky product. Anthropic just made the glue dissolve. Think about what this means. The reason most people stay with one AI assistant is not loyalty, it is laziness. Starting over is painful. Re-explaining everything to a new system feels like training a new employee from scratch. That pain just disappeared. Now the only thing keeping you on ChatGPT is whether ChatGPT is actually better, not whether it remembers your name. This is the most aggressive competitive move in the AI wars since the price cuts of 2024. And almost nobody is talking about it. The question now is what OpenAI does and they have two options. Make memory exportable themselves or level the playing field and compete on merit. Or quietly make it harder to extract, bury the data and hope nobody notices. One path builds trust, the other builds a wall. History says most companies choose the wall. But here is the part that should keep every AI CEO awake tonight. If memory is just a file you carry with you, then there is no lock-in, there is no moat. There is only one question that actually matters. Which AI is best right now. Today, this conversation. Every single session becomes an audition and that changes everything.

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Greg @GregFeingold

3 months ago

Ready to make the switch? claude.com/import-memory

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build agents that never forget. (100% open-source, self-evolving AI memory) most agents have no real memory. every conversation starts fresh with no recall of yesterday and no understanding of how information connects. and here's where most people go wrong when trying to fix this: they rely entirely on vector databases and call it a day. vector search is fast, but it treats your documents as isolated chunks with no understanding of how they connect. what your agent actually needs is memory that captures relationships and persists over time. Cognee is an open-source tool built for exactly this: it combines vector search with graph databases, making your documents searchable by meaning and connected by relationships. here is what makes it even more interesting: > composable pipelines: build custom workflows by chaining modular tasks like chunking, embedding, and entity extraction > weighted memory: frequently used connections get stronger. feedback from responses flows back into edge weights so the graph learns what actually matters > self-improving: the memify pipeline has RL-inspired optimization that strengthens useful paths, prunes stale nodes, and auto-tunes based on real usage getting started with Cognee is as simple as this: 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲.𝗮𝗱𝗱("𝗬𝗼𝘂𝗿 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗵𝗲𝗿𝗲") 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲.𝗰𝗼𝗴𝗻𝗶𝗳𝘆() 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲.𝗺𝗲𝗺𝗶𝗳𝘆() 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲.𝘀𝗲𝗮𝗿𝗰𝗵("𝗬𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝘆") that's it. Cognee handles the heavy lifting, and your agent gets memory that actually learns over time. I've shared the link to their GitHub repo in the next tweet.

a repository that will make your bot 1000x better affaan-m/everything-claude-code adds to your bot: / 13 agents / 32 commands / at least 2 clearly visible contexts (dev.md, review.md) / 12 hooks / 14 mcp-configs / 102+ rules / 56 skills / scripts — exact count couldn’t be reliably confirmed / 992 internal tests this can improve the performance of your bots and agents 1000x.

cogsec @affaan

5 months ago

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😱Anthropic isn’t just building chatbots They’re building a system that can think, code & execute And most people are using it wrong. There are actually 3 different Claudes — and each one replaces a different type of work: 1. Claude AI → replaces Google + docs + junior research • Writing, thinking, summarizing, ideation • Zero setup, pure conversation 2. Claude Code → replaces hours of engineering work • Reads your entire codebase • Edits multiple files • Writes, debugs, and runs tests autonomously 3. Claude Cowork → replaces repetitive computer tasks • Renames, organizes, and edits files in bulk • Extracts data from PDFs → spreadsheets • Automates cross-app workflows Simple rule: • Thinking → Claude AI • Building → Claude Code • Doing → Claude Cowork Most people only use the first. The leverage is in the other two

Most agent failures are not model failures. They are tool-design failures. Read this article, What I learned from it (practical takeaways) 1. Tool design is model-specific, not universal - Don’t build tools because they look “clean” architecturally. - Build tools that your model actually uses reliably. 2. Watch behavior, then redesign - Their core message: “see like an agent.” Meaning: inspect outputs, failure patterns, hesitation points, then iterate tool shape. 3. Structured tool calls beat fragile output parsing - They tried output-format hacks first, but model drift broke consistency. - Dedicated tool (AskUserQuestion) worked better than “please format text like X.” 4. What helped old models can constrain new models - They moved from TodoWrite + reminders to more flexible Task coordination. Lesson: revisit assumptions when model capability changes. 5. Prefer fewer, higher-leverage tools - More tools = more decision burden for the model. Keep action space tight. Add tools only when needed. 6. Progressive disclosure is a superpower Don’t stuff everything in system prompt. Let model discover context in layers (docs → skill files → referenced files). 7. Let model build context itself when possible Shift from “we feed context” to “model can search + compose context.” That improves robustness across tasks/environments.

Thariq @trq212

4 months ago

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Calling all founders! I’ve curated all the resources you need to get started: → github.com/avinash201199/… What are you building?

If your AI depends on your laptop being awake, it’s not infrastructure.

Leo Ye @LeoYe_AI

4 months ago

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