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I spent a day debugging a personal AI voice assistant where the telemetry database became the shared context with the agent. The pattern that fell out — telemetry in a queryable shape — turned out to be more portable than the specific stack: the same loop works with local logs, a local database, or an observability stack, as long as the agent has a programmatic connector.

I wrote a careful set of rules to make an AI a better writer for my blog. It followed every rule. It also let about twenty facts go out the door wrong. Writing rules and verification rules are two different disciplines, and I had only written one of them.

Some AI tools feel like they're quietly siphoning from you; others send real viewers and subscribers back to you. The difference isn't a feature you add later — it's a shape baked into the architecture long before anyone writes a privacy policy. Here's what that shape could look like.

I added a 'universal coverage' backup source to my AI's grounding pipeline, expecting it to fill the gap on the games Wikidata didn't cover. The hypothesis behind it didn't hold. What that taught me about 'more data' vs. 'the right data.'

I spent a few weeks measuring where an AI confidently made things up — and it turns out 'hallucination' wasn't one thing. It was two.

Moving off an ASP.NET template I never customized. Here's why Next.js made sense.