My

Myriade – Ask your database questions in plain English

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Myriade – Ask your database questions in plain English

Hey HN! Maybe I’m crazy because I’ve been tinkering with this since GPT-3 era... but today, I’m thrilled to share Myriade with you. As a data guy, I’ve always been frustrated by the effort involved in answering simple questions. Not that SQL is hard, but a simple question can easily require 10+ queries and 1h+ of our time. Which means it’s not worth doing - more often than not. Myriade is a natural language interface to analyze your database, which allows you to get answers in seconds/minutes, not hours. Live Demo: https://hn.myriade.ai (explore HN data!) Repository: https://github.com/myriade-ai/myriade What it does: - Ask "Why was there a drop in sales on July 14?” instead of writing JOIN statements - The data analyst agent will explore, try, correct, adapt, inspect, analyze & synthetize a response… beyond the classical NL2SQL. - See the agent's full interaction and “take over” at any moment. - Works with Postgres, MySQL, Snowflake, BigQuery - Self-hostable (so you can control where your data goes) Under the hood - Developed my own agent library for this ( https://github.com/BenderV/autochat ) - Uses Anthropic Claude (also compatible with OpenAI and others) - Postgres/Flask/SQLAlchemy/Vue/Tailwind/Echarts I’ve used it a lot, and it shows great potential that goes beyond just “getting information”. Interesting use cases I've seen: - “What KPIs should I focus on ?” → Select your KPIs - “Detect quality issues in billing data" → Review data quality - “Flatten this table” → Prepare a view - “Make this query run faster: X” → Optimize a query This clearly is not the one-size-fits-all solution to solve BI, but I find it really useful for day to day. Try it for yourself - let me know what you think! I’d love to get any feedback, feature requests and (especially) criticism you may have!

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94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
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Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
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Hacker NewsMay not resonate with HN audience · Strong signals: 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.
TrustMRRLess likely to generate early MRR · Strong signals: answers, way · 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 · Strong signals: host, interface · Missing: plus, platform, intuitive
37%37% 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
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
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