build logs & notes

I train healthcare workers on clinical systems for a living. Here's what AI actually does well in healthcare, what it doesn't, and what the people using it day-to-day actually need.

Building alone is genuinely isolating in ways that are hard to describe to people who haven't done it. Here's the honest version.

Generating AI outputs is easy. Knowing whether they're good is the hard problem. Most teams skip evaluation and wonder why their AI products are inconsistent.

AI wrapper businesses had a moment. Most didn't survive it. Here's the structural reason they fail and what the ones that work have in common.

My kids watch me build AI systems. They have questions. Here's how I've tried to answer them honestly — without the hype and without the fear.

AI memory is one of the most talked-about features in agentic systems and one of the most poorly implemented. Here's what the different types actually do and where each one fails.

Building after a full day of work is different from building as your main job. The constraints are real. Here's how to work inside them instead of fighting them.

The chatbot had its moment. It's over. Here's what replaced it, why the shift happened, and what it means if you're still building chat interfaces.

Model Context Protocol is getting a lot of hype. Here's what it actually does, why the architecture matters, and what it means for how AI systems get built.

Everyone says to build in public. Nobody tells you it's a full creative job on top of the technical one — and if you treat it like a side effect, it won't work.

API pricing looks fine until it doesn't. The math changes faster than most teams expect — here's when it changes, why it matters, and what to do before the bill surprises you.

Three tools, three different jobs. Most teams pick one and wonder why it doesn't work for everything. Here's the actual decision logic.

There's a specific kind of broke that hits when you can build almost anything and none of it is paying you yet. It has its own flavor. Let's talk about it honestly.

Claude Fable 5 went dark on June 12 after a government export-control order tied to a jailbreak. It came back July 1 — 19 days later, with a new classifier, a higher false-positive rate on coding tasks, and the same Opus 4.8 fallback baked deeper. Here's what changed and what it means for builders.

A fine-tuned 7B model running on your own hardware, trained on your context, always available, zero API cost — that's not a compromise. That's a different product than any frontier model.

Most people implement naive RAG, get mediocre results, and blame the model. The bottleneck is almost always retrieval. Here are the five RAG architectures that matter and when to reach for each one.

Most people building AI tools are building for the market. I'm building for my kids. That's a different constraint — and it shows up in every decision I make.

Andrej Karpathy's Software 3.0 framing isn't about prompt engineering. It's about a complete shift in what software development is — and most people are still building skills for Software 1.0.

Steve Yegge mapped eight levels of AI adoption. Most teams think they're at level five or six. Most teams are at level two. Here's the honest read.

AI just gave you 10x output. Congratulations. Now they expect 20x. Here's what's actually happening — and how to stay alive.

Most AI agents don't fail because the model is bad. They fail because the data underneath them is unstable. The fix is boring and it's the part nobody wants to build: a stable layer between the agent and the raw data.

If your AI agents keep writing bad code, the model usually isn't the problem — the system around it is. The fix is a small set of guardrails that take trust out of the loop.

When you start turning out sharper work in half the time, you brace for praise and get side-eye instead. The resentment is almost never about the tool. Here's what it's actually about, what defuses it, and a real on-ramp for the people who feel behind.

The people getting durably good results from AI tools aren't using a smarter model. They write the system down first — what it does, the rules, what's done — and the agent only ever executes against that.

Anthropic shipped its strongest public model on June 9. I had it in my own stack for about two days before it was gone. The first version of this post guessed at why. Turns out I didn't have to guess — the real reason came out, and it's a bigger deal than the one I made up.

The fastest-feeling way to ship AI-written code is the most expensive way six months out. The people who keep winning with AI use it as a coder, not as their judgment.

The standard fix for a shaky AI pipeline is to add a checker agent. New research says that if the checker is the same model as the thing it's checking, you didn't add a check — you added an echo. The fix isn't more agents. It's different ones.

Anthropic shipped its strongest public model — twice the price of Opus 4.8, with a safety layer that automatically routes about 1 in 20 requests to a weaker model and tells you it happened. The capability gain is real. So is the fine print.

AI stopped being autocomplete a while ago. It's a fast, eager junior developer now. The people who win with it aren't the ones with the fanciest tools — they're the ones who learned to manage it.

The biggest opportunity in AI for business isn't the model. It's that wiring software into real workflows got an order of magnitude cheaper — and the trap is pretending the verification layer was never holding any weight.

AI isn't quietly deleting your job. It's deleting the middle of your job — and handing you the start and the end, where the judgment lives. The people who win figure out which part is theirs to keep.

Why I'm starting this blog, and what to expect from it.