CLI app for document summary, search and natural language queries

Hacker News

This is a CLI app that I wrote - it will download and summarise all the issues in a github repository using a local LLM or OpenAI, index the results using vector embeddings, and then allow you to search and ask questions about the specs. I've provided windows and linux binaries, plus source to build and tweak yourself - you can use it with a local install of MarqoDB. I work in a company with a knowledge in over 8500 github issues software specs, I wrote this to allow me to easily search them and ask direct questions about the functionality. Things like searching for login issues, and getting a summary of the top results, and asking for patterns. Or finding an issue with a particular feature, and asking for that exact implementation to check the original intent of a feature. I've given an example of the usage with an open source repository, and it should be easy to swap out the document store or experiment with different ways of querying if you're familiar with c# - I've designed it to experiment with ways of getting information out of a large sets of documents, it's been fun to play with.

Visit product View on Hacker NewsJul 15, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: openai, using, open · Missing: mac, agents, macos

84%84% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, io · Missing: https docs, excited, just released

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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