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Before I started using advanced IDEs that could navigate project structures very quickly, it was normal to have a relatively poor visibility -- call it "fog of
by throwaway2037 1y ago
Before I started using advanced IDEs that could navigate project structures very quickly, it was normal to have a relatively poor visibility -- call it "fog of war/code". In a 500,000 line C++ project (I have seen a few in my career), as a junior dev, I might only understand a few thousand lines from a few files I have studied. And, I had very little idea of the overall architecture. I see LLMs here as a big opportunity. I assume that most huge software projects developed by non-tech companies look pretty similar -- organic, and poorly documented and tested.
- markus_zhang 1y agoWhat do you think about software such as source insight that gives developers an eagle eye of view of the project?
- throwaway2037 1y agoYou raise a good point. I had a former teammate who swore by Source Insight. To repeat myself, I wrote: <<Before I started using advanced IDEs that could navigate project structures very quickly>>. So, I was really talking about my life before I started using advanced IDEs. It was so hard to get a good grasp of a project and navigate quickly.
- markus_zhang 1y agoThis makes sense.
- guappa 1y agoThat's what documentation is for. If you don't have that, AI won't figure it out either.
- jrochkind1 1y agoI'm not sure that's true?
- guappa 1y agoSuch a project is way too large for AI to process as a whole. So yes it's true.
- throwaway2037 1y agoI have a question: Many people have spoken about their experience of using LLMs to summarise long, complex PDFs. I am so ignorant on this matter. What is so different about reading a long PDF vs reading a large source base? Or can a modern LLM handle, say, 100 pages, but 10,000 pages is way too much? What happens to an LLM that tries to read 10,000 pages and summarise it? Is the summary rubbish?
- guappa 1y agoIt can't handle large contexts, so the way they often do it is file by file, which loses the overall context.
- randomjoe2 1y agoLots of models CAN handle large contexts, gemini 2.5 pro their latest model can take 1 million tokens of context
- srazzaque 1y agoGet the LLM to read and summarise N pages at a time, and store the outputs. Then, you concatenate those outputs into one "super summary" and use _that_ as context. Theres some fidelity loss but it works for text, because there's quite often so much redundancy. However, I'm not sure this technique could work on code.