The library.
You're trying to learn with AI. So is everyone else. The problem: AI generates answers, not structure. These pages are the structure — quick covers of every discipline, written for people who want to build real things.
No courses. No paywalls. No 40-hour video playlists. Just the map, the vocabulary, and the gaps you need to fill if you want to be dangerous in a real codebase.
Front-end
HTML, CSS, JavaScript, TypeScript, React, Next.js, performance, accessibility. The layer users actually touch.
Back-end
APIs, databases, authentication, caching, queues, security, scaling. The engine room.
Infra
AWS, Docker, Kubernetes, CI/CD, observability, networking, secrets. The foundation everything sits on.
Agents
LCEL, agents, tools, memory, RAG, streaming, evaluation, deployment. The framework layer for LLM apps.
Design
Typography, color, layout, components, UX patterns, accessibility, design tokens. Making it usable.
Data engineering
ETL, warehouses, pipelines, modeling, orchestration, data quality. Turning raw data into decisions.
Data science
Statistics, ML, experimentation, feature engineering, model deployment. From correlation to causation.
How to use this library.
- 01
Pick a track.
Start with whatever's closest to what you're trying to build right now.
- 02
Read the map.
Each page lists concepts, tools, and failure modes in order of importance.
- 03
Ask AI the right questions.
Use these pages as prompts. Copy the vocabulary. Ask Claude to explain what you don't recognize.
- 04
Build something.
The only way to know you understand it is to ship something that breaks and fix it.
These pages update. If something is wrong or missing, drop it in the webinar Q&A or email me.