curious_nile

4 ms·

1. 1.▲
   by curious_nile 6mo ago

   thanks. we map dependencies across the whole codebase first, then generate docs from that. Most tools just read files top to bottom and summarize. The 7-10x coverage gap isn't really a prompting or model thing, we just don't stop
2. 2.▲
   by curious_nile 6mo ago

   I'm Nilesh. My brother Abhishek and I built ProdE. Carnegie Mellon and IIT Delhi. We benchmarked four AI code documentation tools: ProdE, DeepWiki, Claude Code, and Google Code Wiki. ProdE scored highest on usefulness for coding agents
3. 3.▲
   Show HN: We beat Google, Cognition, Claude Code at codebase docs generation(prode.ai)

   2 points by curious_nile 6mo ago|3 comments
4. 4.▲
   by curious_nile 10mo ago

   This is awesome, thanks for sharing and building it. Yeah, that is a challenge we also faced and gave it a good amount of thought. Something like exposing LSP find references capabilities to the agent. Didn't follow this approach thoug
5. 5.▲
   by curious_nile 10mo ago

   Maker here (Abhishek). ProdE does code change impact + root cause analysis for large codebases. Backstory: we tried a bunch of “AI-native” dev tooling and kept seeing the same thing, each tool builds its own partial code map inside its own
6. 6.▲
   Show HN: ProdE – code change impact and root cause analysis for large codebases(prode.ai)

   2 points by curious_nile 10mo ago|3 comments
7. 7.▲
   by curious_nile 1y ago

   Hi HN, Abhishek here, co-founder of ProdE.ai Every team has that one engineer who knows the entire codebase, but they're always buried in requests and meetings. Today, our AI coding tools (Cursor, Windsurf, Copilot) are the ones needin
8. 8.▲
   Show HN: ProdE – Give AI coding tools context for multi-repo codebases(prode.ai)

   2 points by curious_nile 1y ago|1 comments