By 11:00 a.m., we hypothesized that a bug in PostgreSQL, triggered by our query volume, was causing the issues we were experiencing.

When we launched our beta, we underestimated the demand and ran out of bandwidth, leading a Cornell student to host our site temporarily while we resolved the issue.

After launching a feature, I noticed users were frustrated by a confusing button, leading us to fix it, which immediately improved our metrics.

We faced unique challenges in scaling our platform, particularly with GPU local attention caching, which led to unexpected negative results during testing.

We were so incompetent about measuring usage that fixing quality of service issues would shrink our views.

We cache results at each layer of the model for every generated message, which allows us to handle user messages more efficiently and maintain performance.

A bug in the torch sum function was discovered, which had been causing various symptoms, and fixing it resolved multiple issues at once, leading to a fun moment for the team.

Frustrated with communication issues with our users, we added a message feature in our product that allowed users to engage with us directly, which significantly improved user interaction.

If you're unsure about a bug, try switching models. Different AI models may succeed where others fail, and if you find the source of a bug, reset all changes and give specific instructions to fix it on a clean codebase.