Claude Code's creator said something that stopped me cold: "I don't prompt Claude anymore. I write loops — and the loops do the work. My job is to write loops." Most developers are still crafting the perfect prompt. The person who built the tool moved past prompting entirely. In 30 minutes Boris reveals his actual daily Claude Code setup. Claude Code loops dynamic workflows. Worth more than any $500 vibe-coding course. Watch it. Then read this - everything you need to know about loops to actually apply what he says ↓ Bookmark both. This is your weekend.

supervision just hit 40,000 GitHub stars! it now powers over 6.5k open-source computer vision projects, including all my demos like basketball AI link: github.com/roboflow/supervis…

LLM Wiki Is Not a Magic Knowledge Machine by Mark Chen medium.com/p/llm-wiki-is-not…

Why Andrej Karpathy’s ‘LLM Wiki’ is the Future of Personal Knowledge by evoailabs medium.com/p/why-andrej-karp…

The Definitive Guide to Understand Anything: Turning Code and Knowledge Into Graphs That Teach by @roanbrasil medium.com/p/the-definitive-…

Why Obsidian Won as the Base for the Personal LLM Harness (and When You Shouldn’t Pick It) by @roanbrasil medium.com/p/why-obsidian-wo…

RAG Is Dead. LLM Wiki — Andrej Karpathy’s Idea — Is What Comes Next by Jahangir blog.stackademic.com/rag-is-…

I Used Claude Code to Build a Personal Knowledge Base — Inspired by Karpathy’s LLM Wiki Pattern by Mark Chen medium.com/p/i-used-claude-c…

When a DOM event happens in the browser, code runs in response to that action. This is because JavaScript code uses an event-driven programming pattern in the browser. Here @CodeWithNathan teaches you how to listen for & respond to DOM events using JS. freecodecamp.org/news/dom-ev…

DEIMv2-Wholebody49 が完成した。パイプラインがバグったまま作った不完全なモデル。概ね 14ms で物体検出 骨格検出 インスタンスセグメンテーションをリアルタイムに実行できた。モデルは推論１回で３タスク実行のユニファイドモデル。明日終日出張なので帰宅後にリリースするかもしれない。

I Tried Andrej Karpathy CLAUDE.MD File (That Got 157K GitHub Stars) by @joenjenga_ medium.com/p/i-tried-andrej-…

Anthropic AI engineer just dropped a live masterclass on how to ship a team of production‑ready AI agents. 37 minutes. Free. From the Anthropic team. here’s what she covers: • 3 building blocks: brain, hands, sessions • server-side loop, so nothing breaks on refresh • agent teams shipping to production 10–15x faster • why agents die before production brain (persona) hands (environment) sessions = a production agent out of the box. most people are still babysitting fragile agent scripts, while the people who figured this out ship agents that just stay running. Watch this workshop, then read the full guide below.

How to Use Graphify: Turn Any Folder Into a Knowledge Graph by Ana Bildea, PhD medium.com/agentic-builders/…

How to Use lat.md: Turn Any Folder Into a Validated Knowledge Graph by Ana Bildea, PhD medium.com/agentic-builders/…

If you’re building AI agents and haven’t watched this Anthropic talk yet, you’re already behind. In 22 minutes, Claude’s team exposed where the entire industry is heading next: → tool orchestration → memory systems → observability → long-running agents → production infrastructure Most developers are still focused on demos. Anthropic is building for autonomous systems at scale. The last few minutes are the real gold 👇 Watch the full talk first. Then read my complete roadmap on becoming an AI Agent Engineer in 2026 if you want to build what the market will actually need next.

A Google Cloud engineer just showed how to build a full app with Claude from scratch he spent 26 minutes showing exactly what one person with Claude can do, completely free worth more than any $500 vibe-coding course here's what he covers: > raw idea to deployed app in a single session > using Claude as the entire engineering team > the exact workflow they use at Google > no big team, no prior experience needed the people who figure out what Claude can actually do are building things everyone else thinks requires a team that's exactly why I put together a guide on Claude features most people have no idea exist the guide is in the article below

Deep learning gets a lot less intimidating when the tools stop fighting you. PyTorch has become a favorite for building neural networks and LLMs because it keeps things flexible, efficient, and surprisingly Pythonic. Deep Learning with PyTorch, Second Edition covers the fundamentals, transformers, generative AI models, and hands-on projects to help you put it all into practice. First chapter breakdown: hubs.la/Q04hz11S0

🚨 Anthropic just showed a 24-minute workshop on how to actually do prompts for Claude. Taught by the people who built it. Free. No registration. No paywall. I've seen $300 courses that don't cover what they teach in the first 8 minutes. Watch it and bookmark it now.

Anthropic's Head of Product just dropped a 28-minute masterclass on agent production. Prompt caching. Tool search. Programmatic tool calling. Compaction. Advisor strategy. 28 minutes. Free. Worth more than 100 YouTube videos combined. Watch it first. Then read this. The masterclass teaches you how agents work. This teaches you what to build with them — a 5-agent content pipeline that does the work of a $300K creative team. Full pipeline below ↓ Bookmark this. Start this weekend.

Ask Claude to map your entire app's architecture into a single HTML page and JSON file. The HTML is for you. The JSON is for the next agent working on a new feature. Your codebase now explains itself.