DaLMatian – Text2sql that works

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Hey HN, we've built DaLMatian, a text2sql product that meets the needs of data analysts working with enterprise data. We built this app because as a data analyst at an enterprise I could not find a text2sql product that was (1) actually useful for my day-to-day and (2) easy to set up on my computer. Existing products either fall apart when tested on gnarly enterprise data/queries or require going through a sales/integration process that I wasn't in a position to push for - I just wanted something that I could quickly set up to help make my job easier. Our goal is to make this a reality for any data analyst that feels the same. There are many constraints that make this reality difficult to achieve. The product needs to scale to databases with millions of columns and extract business logic from very complex queries. It also needs to be fast, at least faster than an analyst would take to write the query. On top of all this, an analyst needs to be allowed to use it from a security standpoint. Our app meets all the key requirements of an enterprise data analyst while also being lightweight enough to run locally on a typical laptop. Here's how it works. To get started, you simply need to open a file of past queries in our IDE (try it here: https://www.dalmatian.ai/download ) and add a file with your database schema (instructions here: https://www.dalmatian.ai/docs#configuration ). There is also an option to connect a database to auto pull your schema (no actual data is seen by the LLM). We do not see anything you input since the app is local and the only external connection is with OpenAI. It's just like asking ChatGPT for help with queries, but in a streamlined way. If you'd download our free IDE and try to break it, we'd love to hear what you come up with!

Visit product View on Hacker NewsMar 21, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting

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: computer, chatgpt, openai · Missing: mac, agents, macos

89%89% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released

58%58% 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: way · Missing: mobile apps, ios, personal

45%45% 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

32%32% 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

21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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