Connor Hayes

For me, I feel like the hardest part isn't choosing between tools like React, Firebase, Railway, etc, it's really deciding which parts of the stack I should commit most of my time and tokens early and which ones may become painful to replace later.

Do you stick with a familiar stack and avoid overthinking it, design around expected scale from day one, let your coding agent influence the architecture, or start with the cheapest setup and migrate later? Also, curious which early technical choice has saved you the most time, and which one you wish you probably shouldn't have overthinked on.

The biggest change is the integration of a music player.

I will continue to update and improve boobar every 1-2 weeks. I would be very grateful if you would like to support my work.

Something I have been thinking about in the AI governance space that I do not see discussed enough: provenance capture is not like most tooling categories.

With most observability or audit tooling, the reasoning is "we should have this so we're better positioned going forward." You can turn it on when the need becomes clear. You lose some history, but the tooling from that point forward is complete.

AI code provenance does not work this way.

The prompt a developer submits to Claude Code exists for a few hundred milliseconds in transit. After the model returns its response and the editor applies the change, that prompt is gone. Git records the diff. Nothing else records the origin by default. There is no reconstruct operation.