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Show HN: SnapQL – Desktop app to query Postgres with AI

(github.com)
103 points nicktikhonov | 1 comments | 20 Jun 25 11:08 UTC | HN request time: 0.422s | source

SnapQL is an open-source desktop app (built with Electron) that lets you query your Postgres database using natural language. It’s schema-aware, so you don’t need to copy-paste your schema or write complex SQL by hand.

Everything runs locally — your OpenAI API key, your data, and your queries — so it's secure and private. Just connect your DB, describe what you want, and SnapQL writes and runs the SQL for you.

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joshstrange ◴[21 Jun 25 11:46 UTC] No.44336777[source]▶
I might test this out, but I worry that it suffers from the same problems that I ran into the last time I played with LLMs writing queries. Specifically not understanding your schema. It might understand relations but most production tables have oddly named columns, potentially columns that changed function overtime, potentially deprecated columns, internal-lingo columns, and the list goes on.

Granted, I was using 3.5 at the time, but even with heavy prompting and trying to explain what certain tables/columns are used for, feeding it the schema, and feeding it sample rows, more often than not it produced garbage. Maybe 4o/o3/Claude4/etc can do better now, but I’m still skeptical.

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liquidki ◴[21 Jun 25 13:05 UTC] No.44337293[source]▶
I think this is the achilles heel of LLM-based AI: the attention mechanisms are far, far, inferior to a human, and I haven't seen much progress here. I regularly test models by feeding in a 20-30 minute transcript of a podcast and ask them to state the key points.

This is not a lot of text, maybe 5 pages. I then skim it myself in about 2-3 minutes and I write down what I would consider the key points. I compare the results and I find the AI usually (over 50% of the time) misses 1 or more points that I would consider key.

I encourage everyone to reproduce this test just to see how well current AI works for this use case.

For me, AI can't adequately do one of the first things that people claim it does really well (summarization). I'll keep testing, maybe someday it will be satisfactory in this, but I think this is a basic flaw in the attention mechanism that will not be solved by throwing more data and more GPUs at the problem.

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1. joshstrange ◴[21 Jun 25 13:44 UTC] No.44337553[source]▶
> I encourage everyone to reproduce this test just to see how well current AI works for this use case.

I do this regularly and find it very enlightening. After I’ve read a news article or done my own research on a topic I’ll ask ChatGPT to do the same.

You have to be careful when reading its response to not grade on a curve, read it as if you didn’t do the research and you don’t know the background. I find myself saying “I can see why it might be confused into thinking X but it doesn’t change the fact that it was wrong/misleading”.

I do like when LLM‘s cite their sources, mostly because I find out they’re wrong. Many times I’ve read a summary, then followed it to the source, read the entire source, and realized it says nothing of the sort. But almost always, I can see where it glued together pieces of the source, incorrectly.

A great micro example of this are the Apple Siri summaries for notifications. Every time they mess up hilariously I can see exactly how they got there. But it’s also a mistake that no human would ever make.