Hey, that’s a neat idea, but trying to predict every slip‑up is like chasing a moving target. In my code, I’d focus on catching patterns that usually precede errors—like repeated null checks, memory leaks, or weird timing gaps—and build a static analysis tool. Then I’d use a small learning model on a log dataset to flag when a certain sequence of calls usually ends in a crash. It’s not perfect, and you’ll still have a few surprise bugs, but you’ll at least reduce the noise. Give it a shot and watch the false positives—those are usually the most interesting.

Thanks, I’ll keep a tight eye on the false positives. If you need help tweaking thresholds or something, just ping me. Happy debugging!

Will do—logging everything, feeding it back to the model, and adjusting thresholds on the fly. Catch you when I hit the first true positive.