This is the most frustrating thing LLMs do. They put wide try:catch structures around the code making it impossible to actually track down the source of a problem. I want my code to fail fast and HARD during development so I can solve every problem immediately.
Seems like there's a need for github to create a separate flow for AI-cretaed PRs. Project maintainers should be able to stipulate rules like this in English, and an AI "pre-reviewer" would check that the AI has followed all these rules before the PR is created, and chat with the AI submitter to resolve any violations. For exceptional cases, a human submitter is required.
Granted, the compute required is probably more expensive than github would offer for free, and IDK whether it'd be within budget for many open-source projects.
Also granted, something like this may be useful for human-sourced PRs as well, though perhaps post-submission so that maintainers can see and provide some manual assistance if desired. (And also granted, in some cases maybe maintainers would want to provide manual assistance to AI submissions, but I expect the initial triaging based on whether it's a human or AI would be what makes sense in most cases).
This is my number one complaint with LLM produced code too. The worst thing is when it swallows an error to print its own error message with far less info and no traceback.
In my rules I tell it that try catches are completely banned unless I explicitly ask for one (an okay tradeoff, since usually my error boundaries are pretty wide and I know where I want them). I know the context length is getting too long when it starts ignore that.