Pg

Pg_AI_query – AI powered SQL inside PostgreSQL

Hacker News

Pg_AI_query – AI powered SQL inside PostgreSQL

Hey HN! I’ve been working on a PostgreSQL extension that lets developers write SQL queries using natural language directly inside Postgres. GitHub: https://github.com/benodiwal/pg_ai_query Docs: https://benodiwal.github.io/pg_ai_query/ What it does: Converts plain English to real SQL queries Performs query analysis and performance reflection Integrates with the Postgres extension ecosystem Works locally with custom LLM providers (not tied to a cloud) How it fits into workflows: - Data engineers can quickly explore datasets - BI teams can generate SQL faster - App developers can embed NL query interfaces There are a lot of moving parts with some more intergrations. Love, support and contributions are most welcome :)

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Product HuntOn track for Day 1 leaderboard · Strong signals: using, plain · Missing: mac, agents, macos
81%81% 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: ide, io · Missing: https docs, excited, just released
67%67% 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
52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
21%21% 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
18%18% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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