Resources

🎓 Courses The Modern Software Developer By Stanford (CS146S) AI Agents by CMU (11-768) 📚 Books AI Engineering by Chip Huyen 🎥 Talks & Videos Tom Blomfield — “How to vibe code” Y Combinator — “Vibe Coding Is The Future” 📝 Blogs Simon Willison’s Blog Lil’Log (Lilian Weng) Chip Huyen’s Blog Sebastian Raschka’s Magazine The Batch (DeepLearning.AI) Anthropic Engineering Blog Engineering at Meta Netflix Techblog Clouadflare blog VLLM blog 🎓 Articles Famous Deep Learning Papers OWASP Top 10 for Large Language Model Applications 🪛 Good to know :) Explain bash 🌍 Beyond Tech Richard Feynman — “Cargo Cult Science”

These are the notes from talks at Y Combinator. General rule of thumb The best technique is to do what a professional software engineer already does. Small code, modularity, and abstraction are your friends — they help both you and the LLM reason about the project. Note: this advice will likely shift over the next few months. As models get more capable, some of these guardrails will loosen. Plan before you build Don’t ask the model to one-shot the whole project. Instead, create a planning file (a CLAUDE.md or a plain markdown file) that lays out step by step what you want to build. Iterate on it over time and revise it whenever your understanding changes. You can even use a Planner agent to help revise it. ...