Customizable, embeddable Chat GPT based on your own documents

Hacker News

Hi Hacker News! My name is Bea, I built a site called Libraria that uses GPT to do a few things 1. Let you spin up multiple assistants based on your own documents. You can make it public, private, or protected. It has its own subdomain and landing page. 2. Respond in full markdown always, so it can output images, links, code, and more 3. Let you upload articles on the fly within the Chat, so you can ask it questions 4. Make it embeddable in your site with one line of code 5. Let you update it for fun / with your branding 5. Enable syncing for any URLs you let us scrape, so that you can make sure it's always up to date 6. Let you upload multiple file types I've been working on this for about a month now by myself and you can keep track of my feature updates here: https://libraria.dev/feature-updates I would LOVE your feedback on anything, and If you're willing to try it out I'm looking for a few beta users that can provide me more continuous feedback that I would gladly waive the fee for!

Visit product View on Hacker NewsMar 29, 2023

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

86%86% 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

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

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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released

45%45% 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, users, way · Missing: mobile apps, ios, personal

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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.

Incorrect prediction on native model

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