Start here

Pick your path. We’ll take it from there.

Three guided tracks through the library, matched to where you are today. Every path starts from first principles and ends with working mastery.

Track 01 · The Curious

I’m new to AI

You use ChatGPT or Claude and want to actually understand what is happening, and get real work out of these tools. No code required.

01AI, From ZeroThe ground floor: what a language model actually is, why it works, and why it fails, explained for someone starting from nothing.10/10 · Beginner→02AI for Business ProfessionalsNo code, no jargon: how to actually use AI at work, the tools, the prompts, the workflows, and the traps.8/8 · Beginner→

Then browse the Explainers. Start with how ChatGPT reads a PDF.

Track 02 · The Builder

I build with AI

You write code or ship products and want to use AI tools properly: prompting that works, retrieval that stays honest, agents in your terminal.

01Prompt Engineering That WorksPast the tips-and-tricks noise: the small set of techniques that measurably change model output, and how to know they worked.7/7 · Intermediate→02RAG, ProperlyWhy retrieval-augmented generation goes wrong in production, and the specific decisions that keep answers grounded in something real.8/8 · Intermediate→03Claude Code, MasteredThe agentic coding tool, feature by feature: from first install to workflows that feel like cheating.10/10 · Intermediate→04MCP in PracticeThe Model Context Protocol, hands on: what it standardizes, how to use servers, and how to build your own.5/5 · Intermediate→05AI Coding ToolsAgents, copilots, and autocomplete: what each tool actually does, how they think, and how to build a workflow that compounds.6/6 · Intermediate→

Then the Playbooks and Protocols articles keep you current.

Track 03 · The Architect

I design AI systems

You are responsible for AI in production: agents that do not loop forever, retrieval that scales, evaluation that catches regressions before users do.

01AI System DesignHow production AI agents actually work: the reasoning loop, memory, tools, guardrails, evaluation, and the unglamorous engineering that keeps them reliable.14/14 · Advanced→02Inference InternalsWhat actually happens between hitting enter and seeing tokens: the caches, the batching, and the economics under every AI product.7/7 · Advanced→03How Vision Models WorkFrom CLIP to diffusion to video: how machines learned to see, draw, and read the visual world.6/6 · Advanced→

Then the Deep Dives library goes as far down as you want.

Pick your path. We’ll take it from there.

I’m new to AI

I build with AI

I design AI systems

Keep up with AI. One email a week.