AI-native systems
How AI fits into real workflows without turning everything into a black box — especially systems where AI assists, but deterministic logic still owns the final action.
- I Automated Astrology in Six Languages. I Don't Believe a Word of It.
I built a machine that writes, voices, and publishes horoscopes in six languages every night, and I'm an atheist. The astrology was never the point — it was the perfect payload to stress-test a localization engine. And localization, it turns out, is the part that refuses to become a pipeline stage.
- Your Site Has Two Readers Now
For twenty years you wrote for a human and for Googlebot, and they wanted roughly the same thing. Now a third party reads your page, answers the question itself, and never sends the visitor. I built a scanner to see my own site the way that reader does — and scored a 93 for search engines and a 79 for answer engines.
- A Council Isn't a Debate
One model has only one gear: forward. It answers, extends, agrees — it has no reflex that says 'wait, are we even on the right road?' I built a tool to make several models argue instead, and learned that fanning a question out to a panel and having them argue are completely different things.
- The Backlinks You Draw Yourself
Obsidian and Notion sell 'connected thinking,' but you draw every connection by hand — the graph just displays the links you already typed. I built the tool that finds them for you instead, then spent months perfecting its architecture and never once checked whether it helped me write.
- The Model Has No Memory. That's a Data Problem.
Every model is a stateless brain riding a stateful transcript — close the tab and it forgets the world you built with it. Giving it durable memory isn't a prompting trick; it's an infrastructure problem, and the first thing it teaches you is that facts and stories have to be stored completely differently.
- You Can Check a Cookie
We baked a batch that refused to set no matter how long it stayed in the oven. The recipes came from two places — bakers with a track record, and an AI. Guess which ones failed. A cookie, it turns out, is a set of ratios you can eat.
- The AI Wrote the Graphics Code. I Still Had to Know Graphics.
I vibe-coded a browser 3D-text generator in a weekend. It ran on the first try and rendered garbage in three specific, invisible-until-you-look ways — flipped holes, striped shading, opaque 'transparent' PNGs. Each fix needed exactly the graphics knowledge the AI didn't apply.
- Weight Isn't Agreement
RAG hands an AI a stack of documents and lets it produce one confident answer — hiding that the sources disagree, that five of them are the same article reposted, that it should have said 'I don't know.' I built an evidence layer around one rule: never give the weight without the fight.
- How I Learned to Distrust My Own Demo
The most useful skill I built across a decade of projects isn't a technique. It's the reflex to disbelieve an impressive result until something that doesn't care about my feelings confirms it. That's science, worn as an engineering temperament.
- I've Been Building the Same Control Loop Since Netcode
The architecture I built to hide network latency in a game — optimism at the edge, authority at the core, reconciliation between them — is the exact one I now reach for to keep AI inside boundaries. Same three moves, different decade.
- I Taught an LLM to Query Data in English. The Hard Part Was Trust.
A proof-of-concept that let researchers ask questions of sensitive data in plain language. Two things decided whether it was a tool or a liability: a semantic layer that gave the model meaning, and a verification boundary the model was never allowed to cross.
- The Tutorial Is the Easy 5%
I pointed a 200-line Keras 3D-CNN tutorial at real brain MRIs. The demo ran in minutes; making it touch real data took months, three preprocessing pipelines, 25 GPU-hours, and landed at an honest 61% — which taught me more than a 95% would have.
- AI Is Good at the Work You Can Check
The same coding assistant that flies through one of my projects crawls through another. It isn't about difficulty. The dividing line is whether a machine can tell when the output is wrong.