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Mljar Studio

local AI data analyst that saves analysis as notebooks

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Hi 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 basedDemos:- 60-second demo: https://youtu.be/BjxpZYRiY4c- Full 3-minute analysis: https://youtu.be/1DHMMxaNJxIPricing 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.

HN · launched May 2, 2026 · 73 upvotes (top 13%) · 0 views

Niche: specialized ai analytics platforms · 121 launches, 15 in the last 7 days ▲ +7% · Explore this niche →

Category and tags

  • Horizontal
  • Analytics & BI
  • Prosumer
  • AI-native
  • Commercial product

Visit website ↗ Source listing ↗

Could you build this?

Partial While the Electron/React desktop UI and LLM chat wrapper are easily vibe-coded, the underlying tabular AutoML engine (mljar-supervised) involves years of specialized ML research and algorithmic engineering.

What it would actually take: Desktop wrapper built on Electron or Tauri communicating with a local Python runtime executing Jupyter/IPython kernels and the `mljar-supervised` library. The hard part is the AutoML engine itself: ensemble algorithms, stacking, feature engineering heuristics, and robust hyperparameter search pipelines. Requires machine learning systems expertise and data science engineering to reproduce reliably.

Discussion

18 comments analyzed.

Competitors mentioned: Deepnote (cloud notebooks), Jupyter MCP Server (open source), marimo notebooks, Claude Code

Concerns raised: Notebooks aren't reproducible (out-of-order execution, hidden state), High-risk decisions without expert code review; LLM hallucination mistakes, Data scientists may lack skills to verify AI-generated analysis safely, Pricing ($200 USD) not justified vs. free local model alternatives, Data privacy/legal risks sending data to third parties

Feature requests: Better mechanisms to prevent mistakes when LLMs hallucinate, Support for code inspection without requiring expert code review skills

Competitors

Other products that read as similar to this one : 186 launches clear the similarity bar, closest 8 shown.

Attention rank: #25 of 187 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 180 days after the earliest competitor.

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