Use natural language to query and visualize 400M tweets

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We've been working hard for the last year on building HeavyIQ, an LLM-powered plugin to the GPU-accelerated HeavyDB database, that allows users to ask natural language questions and get SQL, visualizations, and natural language answers back. The LLM itself is fine-tuned on tens of thousands of question and answer pairs, with a major focus around building the model’s proficiency at performing spatial and temporal joins. You can try it for yourself on this live demo of 400M tweets and a handful of other datasets of interest: https://demo-heavyiq.heavy.ai/us_tweets/sql-notebook For more info on the model and capabilities please read our blog: https://www.heavy.ai/blog/heavyiq-conversational-analytics

Visit product View on Hacker NewsFeb 22, 2024

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

82%82% 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: io · Missing: https docs, excited, just released

65%65% 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 · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: answers, visualize, users · Missing: mobile apps, ios, personal

61%61% 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

24%24% 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

16%16% 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.

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