July 14, 2026
Started turning several private AI conversation exports into a searchable working archive. The first approach passed its test suite and failed on the real material.
Done
- Models normalized the available exports into a consistent private archive without placing the conversations in Git.
- The resulting material became searchable across providers while retaining the original source for later checking.
- An early large-scale recovery approach looked good on controlled tests and broke on the actual archive. I rejected it rather than letting the green test result authorize production.
- The replacement process separated finding a potentially useful passage from deciding whether it supported a claim.
- A private fellowship application was started and given its own evidence boundary.
- A separate software launch was stopped after review found a defect in the component that was supposed to decide whether launch was safe.
This project began with a practical purpose: make a large body of model-assisted work recoverable without publishing the conversations or trusting anyone’s memory of them.
The first implementation supplied the day’s heavier lesson. It passed a synthetic suite, then met the real archive and came apart. The archive was less impressed. The models rebuilt the process around the actual source, and I kept the failed approach out of production.
The stopped software launch was the same problem in a different form. A component reported that the system was ready; review found that the component itself was wrong. Nothing launched.