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Query your database using plain English, fully on-premises

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Query your database using plain English, fully on-premises

Hi folks, My friend Sami and I recently built Vizly, a Mac application that allows anyone to query their databases using plain English. Vizly is built on Llama 2, llama.cpp, and runs fully on-prem (edit: meaning everything is local and your data never leaves your own computer). We are running two Llama models, one for natural language to SQL translation, and another that uses the results from the SQL to render visualizations. That means there are no external APIs and all the AI models are running locally on your MacBook. We tried to make Vizly very easy to share as well. Every Vizly instance creates a share link that can be accessed by anyone on the same network as you. Just send the share link to anyone on the same network and they will be immediately able to run AI-powered queries, hosted from your device. Vizly previously used to be a hosted solution for querying CSVs and now we are on-prem specifically focussed on databases. Would love if you could try it out and give us any feedback!

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, computer · Missing: agents, macos, agent
94%94% 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
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: llama, io · Missing: https docs, excited, just released
75%75% 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 · Strong signals: host · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
28%28% 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
19%19% 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
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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