Yeah, we just added support for local models. As I mentioned in an earlier comment, if you have a local model with an OpenAI-compatible v1/chat/completions endpoint (most local models have this option), you can route Erdos to use it in the Erdos AI settings.

Thanks for the suggestions - we'll definitely add those to the dev list. Also, the GitHub is https://github.com/lotas-ai/erdos (and it's on the download page but a bit small).

Nothing R specific. In my experience, Claude is pretty good about using tidyverse for everything. What was is flopping on for you? Our thought on not fine tuning models is that whatever comes out in 6 months is just going to be better than whatever we fine tuned.

A bunch of specific things below, but the main point is that it integrates a bunch of features that data scientists use that don't come with Cursor.

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.

When models edit the raw JSON behind a Jupyter notebook, they often mess up the cell structure by adding extra cells, misaligning code, or making bad edits. We fix this by giving the model the notebook in Jupytext format instead, which tends to make its edits cleaner and more accurate.

FWIW there's a bunch of stuff Erdos has that Positron doesn't (including having solved Positron's top 5 open GitHub issues):

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.

Yep — if you have a local model with an OpenAI-compatible v1/chat/completions endpoint (most local models have this option), you can route Erdos to use it in the Erdos AI settings.

He did contribute to/utilize probability theory. He came up during my undergrad probability class because of this: https://en.wikipedia.org/wiki/Probabilistic_method

Erdos is also widely considered as the most prolific and productive mathematician of all time (in terms of publications and collaborations). Hopefully you can be as productive with Erdos :)