Whelps! Ive been building something really similar. Mines nowhere as complete as Buckaroo I really think embedded apps in notebooks can be very useful.

Thanks for sharing. I like the view you built to visualize the profiling of your data, I think that's indeed key to understand your data.

Yes we use Fill in the middle models (Mistral and our own trained Qwen). And we feed it with your data context - we have our own SQL parser to feed you with the right data schema context depending on where your cursor is in the query.

Thanks for your kind message — and for helping us make nao better!

Thank you! Actually this is exactly what we target, we've seen that data teams have often a longer feedback loop than software engineers. That's a goal for us to shorten it and to bring data the closest to your dev flow.

Yeah at the moment the Tab is made to work the best with raw SQL (either pure SQL files or in a string).

But, if you use the chat/agent you can explain that you're using Kysely and give the warehouse context he will probably handle this.

I did not know Kysely, but from the gif on the project landing it looks like the autocomplete is great? It's different than a tab I agree tho.

Here is our contact page, feel free to contact us and we'll hep you setup: https://docs.getnao.io/docs/support/support Also, we'll be adding a new dbt onboarding flow tomorrow!

The content of the data is never going to the models, unless you specifically grant access. Our server only store embeddings of your codebase and data schema. The content of the data is only accessed locally by your computer. When you ask our agent to run a query for you, it will execute it on your warehouse, then ask you for permission to read the result. If you don't allow it, you'll be able to preview it locally without sending it to the LLM. The enterprise version is for your if you want to be sure the prompts and context you send to nao don't go through a public LLM endpoint and get trained on.

Oh yes! Should be done fairly easily, we have DuckDB coming in the next release, we can also add SQLite. You use SQLite to develop locally I guess?

For the joins, we give the right context so that the model can infer the relationships: the schema of each table + how the joins are already done in your repository/query history. Usage analysis is definitely on the roadmap: we want to access the logs of the data warehouse to mesure usage of each table.

Not yet, but we are willing to develop these specific connectors on ask. You can reach out!

Just one question what makes you pick Exasol rather than going with an open-source warehouse tech (e.g. Clickhouse or a lake with Trino)?

we support Postgres (and DuckDB is coming very soon) so yes probably as Hydra is a mix of both, but I have to try it

Thanks for the kind comments, he's surely a great guy :)

*blushes*

I'm working on a repo listing this, I hope to finish it soon

ignore the SSH tunneling, didn't see that i had to scroll...it's been a long week. regardless, SSL enabled connections would be huge

When it comes to SQL writing we are more relevant, when it comes to speed this is hard to benchmark exactly against Cursor and Windsurf but we are a bit slower (around ~600ms on average) obviously and we know what we have to improve to speed it up.

Next in the list is the next edit suggestion dedicated to data work, especially with dbt (or SQL transformations) where when you change a query you have to change the downstream queries directly.

Glad you liked it!

Thanks for suggesting, we should set this up!

Yes it's in our roadmap, some users already asked for it!

We heard of Julius a lot, but did not know about Cipher42, there are a few others folks around. We feel there is a pain and also data teams are a bit abandoned at the moment when it comes to work using AI so makes sense. Curious to get a feedback about your journey building cipher, did you stop working on it?

Probably in a few months. For now we're focusing to make the experience great for a restricted number of warehouses. But you can reach out by email and we'll keep you updated

Great! Let us know if it's how you imagined it when you try it

Thank you!

Thank you!

We say "data vibing" so it feels unique to the data community! But in all seriousness, this is already an issue, people are already asking ChatGPT (or Cursor/whatever else) to generate SQL for them, but the next steps do not exist, if you "vibe code" for data you want to have the easiest feedback loop you can get to check if the output is good, and that's what we are working on: identifying the downstream impacts in the IDE and proposing fixes, a table diff view, new UI/UX to test your outputs.

The goal for us is to be the best way to do data with AI.