Show HN: Mljar Studio – local AI data analyst that saves analysis as notebooks
mljar.comHi HN,
I’ve been working on mljar-supervised (open-source AutoML for tabular data) for a few years. Recently I built a desktop app around it called MLJAR Studio.
The idea is simple: you talk to your data in natural language, the AI generates Python code, executes it locally, and the whole conversation becomes a reproducible notebook (*.ipynb file). So instead of just chatting with data, you end up with something you can inspect, modify, and rerun.
What MLJAR Studio does:
- Sets up a local Python environment automatically, runs on Mac, Windows, and Linux
- Installs missing packages during the conversation
- Built-in AutoML for tabular data (classification, regression, multiclass)
- Works with standard Python libraries (pandas, matplotlib, etc.)
- Works with any data file: CSV, Excel, Stata, Parquet ...
- Connects to PostgreSQL, MySQL, SQL Server, Snowflake, Databricks, and Supabase.
For AI: use Ollama locally (zero data egress), bring your own OpenAI key, or use MLJAR AI add-on.
I built this because I wanted something between Jupyter Notebook (flexible but manual) and AI tools that generate code but don’t preserve the workflow. Most tools I tried either hide too much or don’t give reproducible results and are cloud based
Demos:
- 60-second demo: https://youtu.be/BjxpZYRiY4c
- Full 3-minute analysis: https://youtu.be/1DHMMxaNJxI
Pricing is $199 one-time, with a 7-day trial.
Curious if this is useful for others doing real data work, or if I’m solving my own problem here.
Happy to answer questions.
Notebooks as the output format is funny because notebooks are famously bad for reproducibility. Out of order execution, hidden state, etc. You're solving "chat isn't reproducible" with a format that also isn't really
How does this compare to open source Deepnote[0]? We use the cloud version (BYOC) at my previous company to replace self-hosted Jupyter notebooks, and it's pretty great.
[0] https://github.com/deepnote/deepnote
This is one of those product areas I would call high-risk without a human in the loop. So I am glad you kept a person in the loop. It's really easy to lose tons of money making decisions based on bad statistics or models. Anyone remember how much money zillow lost because of automatic time series models?
I do have concerns about the workflow. Data people aren't usually the best programmers. Models hallucinate and make mistakes sometimes subtle sometimes not. Can you think of a way to prevent data scientists from having to be expert code reviewers? I feel like taking away the code gives them the chance to find and fix mistakes in their reasoning but I have no evidence for that.
Really cool. If somebody doesn't want to adopt a new platform, take a look at open source Jupyter MCP Server[1]. Once integrated with Claude, it can execute code on the live notebook kernel.
I just let Claude write notebooks, run top to bottom, debug & fix errors & only ping me when everything is working.
[1] https://github.com/datalayer/jupyter-mcp-server
IME "real data work" doesn't involve notebooks.
I like starting most of my projects on marimo notebooks now and slowly moving parts of it to the main codebase + db.
By the end of it I might remove the notebook entirely but usually I keep it for some visualisation + running stuff as a cli tool.
This is one shot with Claude Code. What’s the moat?
Not the op or affiliated but.
You really shouldn't and often cannot legally send off data or information about data to 3rd parties. Maybe schemas are okay but 1 mistake and your company can be in serious trouble. So local models is a good idea.
This is a safer workflow if implemented correctly to prevent certain types of mistakes when LLMs inevitably hallucinate or make a mistake.
That said, 200 usd? I don't believe the value is there. Someone can run a local model very easily, 1 command line call and do this themselves. For free.
My guess is you can't.
the best you can do is show them the code and hope they catch mistakes. Data scientists who can't read code probably shouldn't be running AI generated analysis on real data.