Erdos – Open-Source, AI Data Science Ide
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A few months ago, we shared Rao, an AI coding assistant for RStudio (https://news.ycombinator.com/item?id=44638510). We built Rao to bring the Cursor-like experience to RStudio users. Now we want to take the next step and deliver a tool for the entire data science community that handles Python, R, SQL, and Julia workflows.
Erdos is a fork of VS Code designed for data science. It includes:
- An AI that can search, read, and write across all file types for Python, R, SQL, and Julia. Also, for Jupyter notebooks, we’ve optimized a jupytext system to allow the AI to make faster edits.
- Built-in Python, R, and Julia consoles accessible to both the user and AI
- Plot pane that tracks and organizes plots by file and time
- Database pane for connecting to and manipulating SQL or FTP data sources
- Environment pane for viewing variables, packages, and environments
- Help pane for Python, R, and Julia documentation
- Remote development via SSH or containers
- AI assistant available through a single-click sign-in to our zero data retention backend, bring your own key, or a local model
- Open source AGPLv3 license
We built Erdos because data scientists are often second-class citizens in modern IDEs. Tools like VS Code, Cursor, and Claude Code are made for software developers, not for people working across Jupyter notebooks, scripts, and SQL. We wanted an IDE that feels native to data scientists, while offering the same AI productivity boosts.
You can try Erdos at https://www.lotas.ai/erdos, check out our source code on our GitHub (https://github.com/lotas-ai/erdos), and let us know what features would make it more useful for your work. We’d love your feedback below!
The creators of Erdos, an open-source AI-powered data science IDE, share their project with the HN community, sparking discussions about its features, differences from existing tools, and potential applications.
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If you're going to market Erdos as open source, then IMO there should be a github link somewhere on your website.
Specifics (mostly reproduced from above):
1. R/Python/Julia consoles accessible by the user and AI
2. Optimized jupytext system for editing notebooks efficiently
3. Plots pane for viewing and tracking plots
4. Databases pane for managing SQL/FTP connections
5. Environment pane for managing Python/R/Julia packages and environments
6. Help pane for documentation
7. An AI that interacts with all of that.
8. Open source AGPLv3
For me, the biggest difference in the AI usage is that the AI doesn't need to write one-off python scripts for everything and run them from the terminal because it can just use the console directly.
1. Remote development via SSH or containers
2. AI that can connect to ChatGPT, local models, or our backend
3. In-line code execution for Qmd/Rmd files
4. Julia as a first class citizen
5. Multi-agent chats: as many AI sessions as you want and they’ll all run in parallel
6. Windows ARM64 builds
7. Open source AGPLv3 license
8. A bunch of other misc items including read-write data explorer for CSVs and TSVs, plots history sorted by file and time, searchable help, a command history tab, etc
Maybe the biggest difference going forward is that Positron was a ~2 year dev project, whereas Erdos reached feature parity (plus or minus some features) in about ~2 months and is now adding substantial brand new functionality every week.
Out of curiosity, why the name Erdos? AFAIK Erdos was neither a statistician, data scientist nor AI researcher.
He sure solved many probability/combinatorics problems and famously had many many collaborators.
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