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Gurubase – AI-Powered Q&A Assistants for Any Topic

Hacker News

Gurubase – AI-Powered Q&A Assistants for Any Topic

Hey everyone, A couple of months ago, we created Gurubase.io to build RAG-based Q&A agents focused on open-source tools, with the goal of helping developers learn and troubleshoot more effectively. Since then, it has gained momentum, with hundreds of repositories already using it and showcasing it to their users.. https://github.com/Gurubase/gurubase?tab=readme-ov-file#used... Today, we've released the entire system as an open-source project, allowing you to install it in your own infrastructure and create "Gurus" on any topic you choose. Current capabilities of Gurubase: * You can create a Guru by powering it with PDFs, web pages, YouTube videos, or GitHub repositories. * We present Binge, which visualizes your chat history as a node graph. You can navigate through it and create a personalized path. * The system includes an instant evaluation mechanism to minimize hallucinations in generated answers as much as possible. * You can also embed your Guru into your website using an "Ask AI" widget. Check out https://getanteon.com to see it in action. * Although we initially focused on GitHub repositories, you can now create Gurus on any topic, website, or whatever you want by providing the related data. We appreciate any feedback. Thanks in advance! GH Repo: https://github.com/Gurubase/gurubase

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor
83%83% 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: lua, ide, io · Missing: https docs, excited, just released
63%63% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, video, month · Missing: mobile apps, ios, entrepreneurs
54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
25%25% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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