I searched for a simple, self-hosted journal app for my girlfriend and everything I found was either too complex, too feature-heavy, too feature-less for what I needed or required trusting a cloud service.
So I built Piruetas (it means lollipops in Spanish - she chose the name btw).
It's a day-per-page diary with rich text editing, drag-and-drop image uploads, auto-save, public share links, and a clean mobile UI. It can be set up for Personal or Multi-user usage via docker compose deployment.
She seems to like it so I decided to give back to the community and make it available for everyone (after some QA)
Live demo: https://piruet.app (login: demo / piruetas — data resets every 30 min!) GitHub: https://github.com/patillacode/piruetas
by Jean-Philipe ·
Hi *,
My daughter is 14 and wants to get into developing games. What's your go-to framework/engine to recommend? I feel like she's old enough to start with something "real" and not a game engine specifically targeted towards kids. Still, it should be fun and easy to approach. I was thinking gbstudio, godot or even some javascript/html based engine.
She likes Stardew Valley and Minecraft, so I guess we'll go for something pixel artsy. I can be there to support, but I don't want her to be blocked by waiting for me to solve a problem.
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 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.
