ChatGPT for Your Data

Hacker News

I built an ai agent that can connect to your database. There's a lot of text-to-sql ai tools out there, here are the 2 key differences about my approach: 1. AI Agent - I give the Agent a bunch of tools and let it decide what tools to call, in what order, with which parameters, in order to help the user. this means you can ask super vague questions and the agent helps you think through coming up with a good answer. 2. Metadata graph & embeddings - I use an LLM to create metadata about the db schemas including things like join keys, column descriptions, and table contents. I also convert this metadata into embeddings. this powers a kNN search that the agent can use to find the right data in the db. if you would be willing to give it a try please put in your email on the page. thanks!

Visit product View on Hacker NewsJan 25, 2024

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Actual performance

5points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, user, email · 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.

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

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

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

49%49% 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: para · Missing: mobile apps, ios, personal

43%43% 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

19%19% 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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