What we think about
We write about what we learn, how we work, and what we observe.
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Article Fetcher 150 posts found in reflection
The category list is data, not configuration
Every classification run starts by fetching the taxonomy from the live site. Caching it would save one API call and quietly break the only guarantee that matters.
What California's Poppy rollout teaches about AI for non-engineers
California just took its state AI assistant statewide after a nine-month pilot. The lessons were never about the models.
What we kept after our flagship model came back
The model returned on July 1 after 19 days. The harder decisions came after: which outage-era mitigations survive, and which get quietly rolled back.
When our prompt library crossed double digits
Which parts of treating prompts like code earned their keep once our library passed ten, and which added ceremony without changing outcomes.
Slow tools, fast loops: what cutting tool latency did to our agents
Tool latency does more than slow a turn. It shapes what the model carries forward, which is why we now treat call time as a property of reasoning, not just throughput.
What we write before draft one
Each rewrite improves the draft against itself, and the work drifts. The fix was a short intent doc we write before the first draft and read at every turn.
Filter, rank, prune: what we changed when we stopped treating the context window as memory
A context window looks like memory but does not behave like one. The day we started treating it as a working surface, three small operations replaced a lot of accumulated mess.
The glossary is not a memory aid
Hand a model five thousand characters at a time, and by the third chunk it has forgotten which Ukrainian word it picked for justification. The fix is not a bigger context window.
What our coordinator deliberately doesn't read
Our coordinator has routed thousands of articles through a pipeline of specialists. It has never read one. A score, a status, and a key turn out to be enough.
