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TNX API – Natural Language Interactions with Your Database

Hacker News

TNX API – Natural Language Interactions with Your Database

Hey HN! I built TNX API to make working with databases as simple as asking a question in plain English. What it does: - You write a natural language prompt (e.g., "List products with price > 20 USD") - Our system turns it into SQL and runs it - You get actual results, optionally visualized - Your data stays private – nothing is stored, the AI doesn‘t see it, and the API forgets immediately after replying Why I made this: Writing SQL for routine questions is https://www.tnxapi.com/UI/login.php still a blocker for many teams. I wanted a privacy-first, plug-and-play API that just works with natural language. TNX doesn’t just translate — it executes the queries and returns actual answers (not just SQL). Examples: - You ask: “Total sales by product category this year?” → TNX replies: [furniture: $43,000, electronics: $12,000] + “Want a chart for this?” - You ask: “Which customers didn’t order in the last 90 days?” → TNX replies with names or IDs and offers follow-up actions Notes: - Built on modern AI models (small + fast) - No need to send full database dumps – just metadata/config + real-time access - Easy API integration - (Bonus: If you should be interested, I‘d handle setup + customization for you) Try it out: https://www.tnxapi.com/UI/login.php (user name: „hi@tnxapi.com“, password „1“ (so it's harder to forget)) (example promts: - „Please give me the name, ShortDescription and price of product with idpk = 20.“ or - „Please list me all product prices from idpk 10 to 20.“ and then - „Please list me all product prices from idpk 10 to 20.“ (I copied some of my databases for this test, I am sorry for the data being in German xd)) Cheers, Lasse Tramann (Feel free to reach out to hi@tnxapi.com : ) )

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90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released
59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: answers, visualize · Missing: mobile apps, ios, personal
52%52% 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
33%33% 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 · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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