Fluffy – Answer-Engine for Interacting with Public / OS Repos

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4 years ago, I dove back into coding after a decade-long break and immediately noticed how much had changed. Back then, as a college freshman, I tinkered with code in isolation. Today, I primarily build custom logic into the functionalities extended by open source libraries—standing on the shoulders of giants. However, I encountered three main challenges: A. Sometimes repo are poorly documented or maintained... or really new (I had to watch a tutorial in Spanish on Vercel's AI/SDK, lol) B. Or at times, I’d stumble upon bugs so specific to my use case that I ended up spending a lot of time trying to figure out a fix C. Or at times, you want to quickly check out a new research driven repo that's come out To address these issues, I built Fluffy over the past four months. To make my life a bit easier. What does Fluffy do? A. Ingest Any Repo: Simply ask Fluffy to ingest a repository. B. Fluffy then parses the files (markdowns, docs linked in them, code files, header files et al) to create a graph of the repo ('cause a repo is not like a PDF) C. Depending on your query, you choose between explore, debug or answer modes and type in the query, much like how you would on Google or Stack Overflow - you no longer have to worry about prompting right D. Fluffy uses GPT4 (for now) to generate an answer using the context from the repo. E. It then evaluates the answer it's generated to highlight edgecases / issues in them so that you don't have to squint to figure out if good ol' GPT isn't hallucinating I think of repos as Pokémon—they constantly evolve. Why rely on outdated tech blogs when you can directly interact with the source? I’m want to improve Fluffy and would greatly appreciate your feedback. What do you think?

Visit product View on Hacker NewsJun 25, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: google, new, context · Missing: mac, agents, macos

95%95% 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.

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% 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

18%18% 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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