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You can check this out https://www.sqlai.ai . It has AI-powered generators for: - Generate SQL - Generate optimized SQL - Fix query - Optimize query - Explain query Disclaimer: I am the solo developer behind it.
Are all those Twitter testimonials fake? None seem to be actual accounts.
(I kid. Hope you do well with the app, just get some real testimonials in there if they aren't already.)
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In general, if something has been around for a very long time and nobody apparently seems to have thought to improve it, then odds are the reason is it's pretty good and genuinely hard to improve on.
in other words, SQL is a shark, not a dinosaur
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I think a lot of people tried just asking GPT-3.5 to "Write me full stack web app no bugs please." when it first came out. When it failed to do that they threw up their hands and said "It's just a parrot." Then GPT4 came out, they tried the same thing and got the same results. I keep seeing comments regarding it not being helpful because "super big codebase, doesn't work, it doesn't know the functions and what they d…
Nail. Head. The amount of code I now "write" (I've started calling it directing) and features I've put into my side projects has been more than the last 5-10 years combined this last year. I successfully created a sold a product within 3 months. Start to finish, because of the productivity power I received. People are misusing it.
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I feel like by the time I could turn it into a product, Microsoft & friends will release something that makes it look like a joke. If there is no one on the SQL Server team working on this right now, I don't know what the hell their leadership is thinking. I am not chasing this rabbit. Someone else will almost certainly catch it first. For now, this is a fun toy I enjoy in my free time. The moment I try to make money…
There’s daylight between personal toolsmithing and a VC-backed startup (both are fun sometimes and a grind sometimes). I’m getting together a bunch of related-sounding stuff in terms of integrating modern models into my workflow to polish up a bit and release MIT. If you’d like to have a hand tidying it up a little and integrating it with e.g. editors and stuff, I think the bundle would be a lot cooler for it!
But that rabbit can't understand the meaning of the data just by looking at column names and table relationships.
Let's say you want to know how sales and inventory are doing compared to last year at your chain of retail stores.
Will Microsoft's rabbit be smart enough to know that the retail business is seasonal, so it must compare the last x weeks this year with the same weeks last year? And account for differences in timing of holidays? And exclude stores that weren't open last year?
Will it know that inventory is a stock and sales is a flow, so while it can sum daily sales, it's nonsensical to sum daily inventory?
The real AI magic isn't generating SQL with four joins, it's understanding the mechanics of each industry and the quirks of your organization to extract the intent from ambiguous and incomplete natural language.
The most success I had with AI+SQL was when I started feeding errors from the sql provider back to the LLM after each iteration. I also had a formatted error message wrapper that would strongly suggest querying system tables to discover schema information. These little tweaks made it scary good at finding queries, even ones requiring 4+ table joins. Even without any examples or fine tuning data.
Please turn this into a product. There's enormous demand for that.
I have been keeping track of a few products like these including some that are YC backed. Interesting space as I am looking for a solution myself: - Minds DB (YC W20) https://github.com/mindsdb/mindsdb - Buster (YC W24) https://buster.so - DB Pilot https://dbpilot.io and now this one
the main problems we see in the space: 1) good interface design: nobody wants another webapp if they can use Slack or Teams 2) learning enough about the business and usually messy data model to always give correct answers or say I don't know.
All these products that pitch about using AI to find insights from your data always end up looking pretty in demos and fall short in reality. This is not because the product is bad, but because there is enormous amount of nuance in DB/Tables that becomes difficult to manage. Most startups evolve too quickly and product teams generally tries to deliver by hacking some existing feature. Columns are added, some columns…
Documenting data definitely helps to close that gap.
However the last part you describe is nothing new (BI teams taking credit, and pushing on problems to data engineers). In fact there is a chance that tools like vanna.ai or getdot.ai bring engineers closer to business folks. So more honest conversations, more impact, more budget.
Disclaimer: I am a co-founder at getdot.ai :)