AI Brain Bank – The Ultimate 'Chat with Your Data' App

Hacker News

Hello HN, this is my first full stack AI web application that combines the power of LLMs and persistent long-form data to allow you to chat with large amounts of novel data. Chat with any text file, pdf, audio file, web page URL, YouTube video, or even entire YouTube channel. Your input data is saved, so you can log in and chat with it any time you'd like. Here's how it works: you create a context (a folder), and choose a variety of data sources to upload - if they are not native text, they get transcribed into text. From there, you ask the AI any questions you want, and it intelligently searches through all the data you've uploaded to come up with an answer. Some quick example applications: - Asking questions about specific details in a large PDF file, - Chatting with YouTube channels to see how a particular individual/knowledge base would answer, - Creating a context with the financial data of multiple companies, and comparing them simply with natural language questions ("Compare company X's revenue with company Y") - Course notes and studying for school: upload all your course materials and textbooks into a context, and ask away! Looking forward to your feedback, and please let me know if there are any issues with the site. Cheers!

Visit product View on Hacker NewsJun 14, 2023

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

89%89% 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

85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, way · Missing: mobile apps, ios, personal

54%54% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

nativeThis product was originally launched on this platform.

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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

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, audio · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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