Thanks for sharing. I hear people make extraordinary claims about LLMs (not saying that is what you are doing) but it's hard to evaluate exactly what they mean without seeing the results. I've been working on a similar project (a static analysis tool) and I've been using sonnet 4.5 to help me build it. On cursory review it produces acceptable results but closer inspection reveals obvious performance or architectural mistakes. In its current state, one-shotted llm code feels like wood filler: very useful in many cases but I would not trust it to be load bearing.

Access to virtually infinite cash had more to do with Android's success than the source being proprietary.

Golang I think (mostly) successfully resisted this temptation

Doesn’t have to be an in house system, just basic redundancy is fine. eg a simple hook that pushes to both GitHub and gitlab

Sure, we are still closer to alchemy than materials science, but its still early days. But consider this blogpost that was on the front page today: https://www.levs.fyi/blog/2-years-of-ml-vs-1-month-of-prompt.... The table on the bottom shows a generally steady increase in performance just by iterating on prompts. It feels like we are on the path to true engineering.