Matt Warden

The AI Lab

The “AI experts” I meet fall into two camps: machine learning engineers who have never run a business, and business people who use AI constantly without understanding how it works. That gap will not resolve quickly, and waiting twenty years for today's ML graduates to acquire domain expertise is not a plan.

So: real competency, now. Not the latest social media post about the top ten ChatGPT tricks; what an LLM actually does, what persists between conversations and what doesn't, how tool use works, what RAG and embeddings are for, token economics, evaluation. The glossed-over nuts and bolts that make AI useful rather than magically impressive.

Karpathy describes LLMs as an emerging operating system — software 3.0 — which gave me language for something I had already been feeling. The Claude projects I build are not workflows, they are programs, and I would like to share them the way developers share code.

I am not closing the expert gap. I am documenting what I am actually doing, in enough detail that it might be useful to someone else. Including the parts that failed.

Case studies & war stories

A specific problem, what I tried, what broke, and whether it was worth it.

Generating a slide deck from a whitepaper

A task that sounds trivial and isn't. Where the model's structural choices were better than mine, where they were confidently wrong, and how many passes it took before the output was something I would actually present.

A microcontroller learning plan

I used my own learning-plan generator on a subject I knew nothing about, then followed the plan to see whether it held up. Write-up includes where the sequencing was wrong.

Research & analysis

Longer pieces, only when I have something substantive to say.

The LLM psychology experiments

A series of tests borrowing methods from psychology to probe how these models behave under pressure — with the caveat, stated in the piece, that behavioural resemblance is not evidence of an inner life.

Claude detects silent disagreement

What happens when a model is asked to notice that its interlocutor has stopped agreeing without saying so. The result surprised me enough to write it down carefully.

Prompts & assets

Downloadable. Take them, adapt them, no attribution needed.

The prompt itself, with notes on what each section is doing
Packaged so it runs without being pasted in every time
Learning plans

The sequence I actually followed, not a reading list I aspire to.

Current explorations

What I am working on right now, including the things I have not figured out.

Passed, July 2026 Claude Certified Architect exam
Working through Karpathy’s Neural Networks: Zero to Hero — doing the exercises, not watching them
Building Strategies for Ed‑Fi API tool use by LLMs — still unsure whether MCP is the right shape or overkill
Open question Hallucination detection: current strategies, and whether LLM‑as‑judge holds up in production
Open question Output validation and evaluation for anything I would put in front of a customer
Learning to do Read benchmarks and leaderboards critically enough to know when a number means nothing
Full exploration history