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Godfather of AI: "If you sleep well tonight, you may not have understood this lecture." This 47-minute lecture is the best thing I saw about AI in the last few months. It will definitely help you understand how it actually works and where it's going. Geoffrey Hinton built the neural networks behind every AI alive, then quit Google to warn the world about it. The part nobody wanted to hear: > AI is already developing abilities its creators didn't intend > in most cognitive tasks it's already ahead of us > the question is no longer if it surpasses us but when > the only decision left is which side of that line you're on Right now the average person opens Claude, types something, gets an answer, closes the tab. They think they're using AI. they're using maybe 10% of it. I went through his entire lecture, built a practical system from what he was describing. 18 steps to actually use Claude the right way, with copy-paste prompts that work today. Full guide in the post below.

If you are interested apply here- job-boards.greenhouse.io/anthropic/jobs…

Learn AI to build smarter as an engineer. Learn AI to ship faster as a builder. Learn AI to make better decisions as a leader. Skills shape the path forward.

Google Cloud AI engineer just showed how they go from idea to deployed app at Google in 30-minutes using Claude. 26-minutes. free. by Google AI team. one person + Claude + Google Cloud = a full engineering org running on a laptop. worth more than any $500 vibe-coding course.

The EXACT order I’d learn tech skills from ZERO in 2026:

ANTHROPIC JUST LAUNCHED A TRADING BOT FRAMEWORK FOR PREDICTION MARKETS 🤯 68.4% SUCCESS RATE. $300–$1,500 PER DAY. BOOKMARK THIS.

10 GitHub repos that print money while you sleep: 1. AutoHedge github.com/The-Swarm-Corp… 2. Vibe-Trading github.com/HKUDS/Vibe-Tra… 3. Claude Ads github.com/AgriciDaniel/c… 4. Toprank github.com/nowork-studio/… 5. Fincept Terminal github.com/Fincept-Corpor… 6. Agentic Inbox github.com/cloudflare/age… 7. ClawRouter github.com/mksglu/context… 8. Camofox Browser github.com/jo-inc/camofox… 9. Open Higgsfield AI github.com/Anil-matcha/Op… 10. Hyperframes github.com/heygen-com/hyp… If you’d love to see more content like this, don’t forget to follow @I_am_Aiabir for more AI magic.

Un japonés descubrió antes que nadie cómo aprovechar realmente el potencial de Claude Code. Instala “Find Skills”, explica lo que quiere lograr y el sistema le sugiere automáticamente las mejores habilidades entre cientos de opciones. Gracias a eso, su sistema automatizado de YouTube está teniendo un gran éxito.

How to Become an AI Engineer in 2026 (The Real Roadmap) Most AI roadmaps you see online are incomplete. They teach you tools… but not how to think. They show you concepts… but not how to build real systems. So I took a step back and rebuilt the roadmap based on one goal: 👉 What does it actually take to become a real AI engineer in 2026? Here’s the answer. 1. Strong Foundations (Non-Negotiable) Before AI, you need engineering basics: Python + Data Structures & Algorithms APIs (REST / GraphQL) Git & GitHub Linux fundamentals This is what separates developers from copy-paste builders. 2. Mathematics & Statistics You don’t need a PhD—but you need intuition: Linear Algebra & Probability Statistics & Distributions Hypothesis Testing & Bayes This is how you stop guessing and start understanding models. 3. Machine Learning Basics Core concepts still matter: Supervised & Unsupervised Learning Model training & evaluation Overfitting, regularization, cross-validation Without this, you’re just using AI—not engineering it. 4. Deep Learning & LLM Fundamentals This is where modern AI starts: Neural networks & backpropagation Transformers & attention Tokenization & embeddings Fine-tuning vs prompting vs RAG This is the difference between users and experts. 5. Generative AI & LLM Applications Now we move to real-world power: Prompt engineering RAG (Retrieval-Augmented Generation) Vector databases Document processing pipelines This is where AI becomes useful and scalable. 6. AI Engineering Stack Tools matter but only after fundamentals: FastAPI (serving models) LangChain / LangGraph LlamaIndex Docker, Kubernetes Cloud (AWS, GCP, Azure) Think in systems, not just libraries. 7. Data Engineering for AI Most people skip this. Big mistake. Data pipelines (ETL/ELT) SQL & NoSQL Streaming data Feature stores & versioning AI is only as good as the data behind it. 8. Build Real AI Systems This is where you level up fast: AI chatbots & assistants AI agents & automation systems Microservices architecture Model serving & CI/CD If you’re not shipping, you’re not learning. 9. Evaluation, Observability & Reliability This is what companies actually pay for: LLM evaluation (RAGas, TruLens, etc.) Prompt testing & A/B testing Monitoring (logs, traces, metrics) Cost & latency optimization This is the difference between demo and production. 10. AI Safety, Security & Product Thinking The most underrated layer: Prompt injection & data security AI safety & bias Human-in-the-loop systems UX & business impact Great engineers don’t just build they solve real problems. The future AI engineer is not just a coder. You are a: Builder (you create systems) Architect (you design them) Problem Solver (you deliver value) Innovator (you push boundaries) If you’re serious about becoming an AI engineer, this roadmap is your blueprint. Which stage are you currently in right now?

If you learn just these 9 AI skills… You’ll be ahead of 95% of people in 2026 ⚡👇 1️⃣ Prompt Engineering → get precise, high-quality outputs 2️⃣ Workflow Automation → eliminate repetitive work 3️⃣ AI Image Generation → create visuals in seconds 4️⃣ Vibe Coding → ship fast without overthinking 5️⃣ Custom GPTs → build your own AI assistants 6️⃣ AI Video → turn ideas into content instantly 7️⃣ AI-Assisted Dev → code faster with AI copilots 8️⃣ Agentic Coding → let AI plan + execute tasks 9️⃣ RAG Systems → connect AI to real data Most people dabble. Few people stack skills like this. That’s the difference between: using AI ❌ vs building leverage with AI ✅ Start with 1 skill. Practice daily. Then layer the next. This is how you go from beginner → builder → operator. 📚 If you want to go deeper, here are some insane free resources: → Free AI GTM Kit (credits, workflows, templates) thevccorner.com/p/free-ai-gtm-… → Claude workflows, prompts & templates the-ai-corner.com/t/claude-and-a… → AI Decks (Eleven Labs, Artisan & more) the-ai-corner.com/p/ai-startup-p… → Prompting & Context Engineering the-ai-corner.com/t/prompting-an… → OpenAI's PMF Playbook for AI Startups thevccorner.com/p/ai-product-m… → 2,500+ Angel Investors backing AI thevccorner.com/p/angel-invest… → AI Agents Guides the-ai-corner.com/t/ai-agents → Humanizer Prompt the-ai-corner.com/p/ai-humaniser… → 30 Claude in Excel Prompts the-ai-corner.com/p/claude-excel… Follow me @elora_khatun for more. Bookmark this. Come back weekly. Execute. Because the future won’t reward people who know AI… It’ll reward people who use AI to build unfair advantages. 🚀

Anthropic is offering 13 AI courses & certificates. It's free by following these 13 links: 1 - Claude 101. Learn Claude for everyday work. Core features and best practices. ↳ anthropic.skilljar.com/claude-101 2 - AI Fluency: Framework & Foundations. The foundational thinking course. Must need. ↳ anthropic.skilljar.com/ai-fluency-fra… 3 - Introduction to Agent Skills Build, configure, and share Skills in Claude Code — reusable instructions Claude applies automatically. ↳ anthropic.skilljar.com/introduction-t… 4 - Building with the Claude API Full spectrum: function calling, tool use, streaming, SDKs, and production patterns. ↳ anthropic.skilljar.com/claude-with-th… 5 - Claude Code in Action Integrate Claude Code into your dev workflow. Hands-on, practical, ship-focused. ↳ anthropic.skilljar.com/claude-code-in… 6 - Intro to Model Context Protocol Build MCP servers and clients from scratch in Python. Tools, resources, and prompts. ↳ anthropic.skilljar.com/introduction-t… 7 - MCP: Advanced Topics Sampling, notifications, file system access, and transport for production MCP servers. ↳ anthropic.skilljar.com/model-context-… 8 - AI Fluency for Students AI skills for learning, career planning, and academic success through responsible collaboration. ↳ anthropic.skilljar.com/ai-fluency-for… 9 - AI Fluency for Educators For faculty and instructional designers applying AI Fluency into teaching and institutional strategy. ↳ anthropic.skilljar.com/ai-fluency-for… 10 - Teaching AI Fluency Teach and assess AI Fluency in instructor-led settings. Curriculum-ready. ↳ anthropic.skilljar.com/teaching-ai-fl… 11 - AI Fluency for Nonprofits Increase organizational impact and efficiency while staying mission-true. ↳ anthropic.skilljar.com/ai-fluency-for… 12 - Claude with Amazon Bedrock The full AWS accreditation course, now open to everyone. ↳ anthropic.skilljar.com/claude-in-amaz… 13 - Claude with Google Cloud's Vertex AI Work with Claude through Google Cloud's Vertex AI, from setup to production. ↳ anthropic.skilljar.com/claude-with-go… Repost this to help others access AI courses.

Most people using Claude are using 10% of what it can do. This 1-hour course covers the other 90%. Build anything. Automate anything. Free. Watch it instead of Netflix tonight. Bookmark this before you forget. 👇

WE JUST GOT THE ULTIMATE PLAYBOOK FROM ANTHROPIC 🤯 their massive 33-page official guide on Claude Skills is the only guide you need. make sure to bookmark it. link below in the 🧵↓

ANDREJ KARPATHY COULD HAVE CHARGED $2,000 FOR THIS COURSE. He put it on YouTube. The full training stack. Tokenization. Neural network internals. Hallucinations. Tool use. Reinforcement learning. RLHF. DeepSeek. AlphaGo. 3 hours of the most comprehensive LLM education that exists anywhere at any price. Not how to use the tools. How the entire system was built from the ground up and why it behaves the way it does. The engineers who understand this build things the ones who only use the tools cannot even conceive of. The gap between those two groups is not 3 hours. It is everything those 3 hours quietly unlock for the rest of your career.

how to scale an app from zero to millions of users

Stop scrolling. Your AI learning curve is about to 10x. 🚀 These are the 10 YouTube channels dominating AI in 2026 - from fundamentals → cutting-edge research → real-world builds. If you’re serious about AI, bookmark this 👇 🎯 Must-follow AI creators: AI Explained 👉 @aiexplained-o" target="_blank" rel="nofollow noopener">youtube.com/@aiexplained-o… Breaks down complex AI into simple, visual explanations. Andrej Karpathy 👉 @AndrejKarpath" target="_blank" rel="nofollow noopener">youtube.com/@AndrejKarpath… Ex-Tesla, ex-OpenAI. Deep insights on LLMs, neural nets & the future. Cole Medin 👉 @WesRoth/" target="_blank" rel="nofollow noopener">youtube.com/@WesRoth/ Practical AI trends, tools & breakdowns you can actually use. DeepLearningAI 👉 @Deeplearninga" target="_blank" rel="nofollow noopener">youtube.com/@Deeplearninga… Structured learning from the best in AI education. Futurepedia 👉 @futurepedia_i" target="_blank" rel="nofollow noopener">youtube.com/@futurepedia_i… Stay ahead with the latest AI tools & updates. Matthew Berman 👉 @matthew_berma" target="_blank" rel="nofollow noopener">youtube.com/@matthew_berma… AI tutorials, breakdowns & practical use cases. Skill Leap AI 👉 @SkillLeapAI/f" target="_blank" rel="nofollow noopener">youtube.com/@SkillLeapAI/f… Hands-on tutorials to level up fast. Tim Ruscica (Tech With Tim) 👉 @TechWithTim/v" target="_blank" rel="nofollow noopener">youtube.com/@TechWithTim/v… Coding + AI = real-world skills. Tina Huang 👉 @TinaHuang1/vi" target="_blank" rel="nofollow noopener">youtube.com/@TinaHuang1/vi… From beginner → advanced AI workflows & career advice. Two Minute Papers 👉 @TwoMinutePape" target="_blank" rel="nofollow noopener">youtube.com/@TwoMinutePape… Cutting-edge AI research explained in minutes. 💡 Pro tip: Don’t just watch - build. • Pick 1 tutorial • Ship 1 project/week • Share your learnings publicly That’s how you stand out in AI. AI isn’t slowing down. The question is: Are you keeping up? Follow @elora_khatun for more no-fluff AI breakdowns. 🔥

Andrej Karpathy on the shift to agentic engineering

If you're serious about AI engineering (in 2026), then learn these 13 concepts: 1 How Vector Database Works → newsletter.systemdesign.one/p/what-is-a-ve… 2 How RAG Works → newsletter.systemdesign.one/p/how-rag-works 3 Design Personal Chat Assistant → newsletter.systemdesign.one/p/ai-chat-assi… 4 LLM Concepts - A Deep Dive → newsletter.systemdesign.one/p/llm-concepts 5 How to Design an AI Agent → newsletter.systemdesign.one/p/how-do-ai-ag… 6 What is Reinforcement Learning → newsletter.systemdesign.one/p/what-is-rein… 7 LLM Evals 101 → newsletter.systemdesign.one/p/llm-evals 8 Context Engineering 101 → newsletter.systemdesign.one/p/what-is-cont… 9 AI Coding Workflow 101 → newsletter.systemdesign.one/p/ai-coding-wo… 10 Agentic Patterns, Simply Explained → newsletter.systemdesign.one/p/agentic-desi… 11 How AI Agents Work → newsletter.systemdesign.one/p/ai-agents-ex… 12 Multi-Agent Architectures, Clearly Explained → newsletter.systemdesign.one/p/multi-agent-… 13 How MCP Works → newsletter.systemdesign.one/p/how-mcp-works What else should make this list? === 👋 PS - Want my System Design Playbook (for Free)? Join my newsletter with 200K+ software engineers now: → newsletter.systemdesign.one/join === 💾 Save now & repost to help others learn AI engineering. 👤 Follow @systemdesignone + turn on notifications.

How to duplicate yourself into Claude in a weekend: (even if you've never written a prompt before) 1. Download the desktop app. ☑ Go to claude .com/download ☑ Set Opus 4.7 as default ☑ Turn ON Extended Thinking 2. Open Cowork mode. ☑ Cowork = where your voice lives ☑ Click the top left tab ☑ Create your "Voice" folder inside 3. Install Wispr Flow (it's free). ☑ Turns your voice → text ☑ Voice = faster and more honest ☑ Typing kills the truth. 4. Run the interview. ☑ Paste Prompt 1 from ruben.substack.com/p/youre-just-a…. ☑ 100 questions, 7 categories ☑ Push past every vague answer 5. Compress the dump. ☑ Paste prompt 2 from ruben.substack.com/p/youre-just-a…. ☑ 20K words → 4K tokens ☑ Save as [your_name] .md 6. Test it in a blank chat. ☑ Open a fresh Claude chat ☑ Run a prompt only you would write ☑ If it sounds like you → ship it 7. Drop it into Cowork. ☑ Move [your_name] .md into your folder ☑ Claude now reads it on every turn ☑ Every draft = your voice, automatically 8. Port it everywhere. ☑ Upload to ChatGPT, Grok, Gemini ☑ Same file = same voice in every AI ☑ Hand it to your team or ghostwriter 9. Edit it forever. ☑ Install Obsidian (free) ☑ Open Cowork as a vault ☑ Update as your taste shifts Full guide + prompts at ruben.substack.com/p/youre-just-a…. (save this to clone yourself into any AI)

Claude Code ships with 5 architectural layers most engineers never open. Not features. Not settings. Layers — each solving a distinct problem that LLMs alone can't solve. And four of them have nothing to do with prompting. Here's the full Agent Development Kit: Layer 1 — CLAUDE.md → The Memory Layer Architecture rules, naming conventions, test expectations, repo map. Always loaded. Always active. Two scopes: • ~/.claude/CLAUDE.md → global • .claude/CLAUDE.md → project This isn't context you paste in before every session. It's context that never needs repeating. The agent's constitution. Layer 2 — Skills → The Knowledge Layer Each SKILL.md carries a description. Claude matches it at runtime and forks the skill into an isolated subagent. On-demand, never always-on. Task-specific knowledge without inflating your main context window. Modular by design. Layer 3 — Hooks → The Guardrail Layer PreToolUse → PostToolUse → SessionStart → Stop → SubagentStop This is the layer most teams skip. And the one they regret skipping first. Hooks are NOT AI. They're deterministic event-driven shell commands. • Auto-lint on every Write • Hard-block on rm -rf • Slack notification on Stop Event fires → Matcher checks → Command runs Quality enforced at the infrastructure level. Not the prompt level. Layer 4 — Subagents → The Delegation Layer Each subagent gets its own context window, model, tools, and permissions. Main agent delegates down. Receives results up. That's it. No infinite recursion — subagents can't spawn subagents. Main context stays clean. Hard boundaries by design. Layer 5 — Plugins → The Distribution Layer Bundle your skills + agents + hooks + commands into a plugin. One install. Whole team inherits the behavior. Think npm packages — but for what your agent knows how to do. Wrapping everything: → MCP Servers on the left (GitHub, databases, APIs, custom integrations) → Agent Teams on the right (parallel execution, message passing, shared permissions) The 5-layer stack in one line: CLAUDE.md sets rules → Skills provide expertise → Hooks enforce quality → Subagents delegate work → Plugins distribute to the team Most production failures in agentic systems trace back to one missing layer. Which one is the gap in your current setup?