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Show HN: Mljar Studio – local AI data analyst that saves analysis as notebooks
Hi HN,<p>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.<p>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.<p>What MLJAR Studio does:<p>- Sets up a local Python environment automatically, runs on Mac, Windows, and Linux<p>- Installs missing packages during the conversation<p>- Built-in AutoML for tabular data (classification, regression, multiclass)<p>- Works with standard Python libraries (pandas, matplotlib, etc.)<p>- Works with any data file: CSV, Excel, Stata, Parquet ...<p>- Connects to PostgreSQL, MySQL, SQL Server, Snowflake, Databricks, and Supabase.<p>For AI: use Ollama locally (zero data egress), bring your own OpenAI key, or use MLJAR AI add-on.<p>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<p>Demos:<p>- 60-second demo: <a href="https://youtu.be/BjxpZYRiY4c" rel="nofollow">https://youtu.be/BjxpZYRiY4c</a><p>- Full 3-minute analysis: <a href="https://youtu.be/1DHMMxaNJxI" rel="nofollow">https://youtu.be/1DHMMxaNJxI</a><p>Pricing is $199 one-time, with a 7-day trial.<p>Curious if this is useful for others doing real data work, or if I’m solving my own problem here.<p>Happy to answer questions.
Mljar Studio offers a compelling local-first alternative to cloud-based AI data tools, leveraging a proven open-source AutoML foundation to ensure reproducibility. While the technical approach is strong and addresses privacy concerns, the one-time pricing model and intense competition from major LLM providers and established notebook platforms limit its traditional venture scalability.