Have you done any fine-tuning or prompt-customization for the R-specific work? I've found the models worse on R when compared to Python, especially for more complex tasks. This looks cool, thanks for sharing!
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.
We started with a product like this at Definite (https://www.definite.app/), but it became clear there weren't enough people willing to spend real money on a product like it when Cursor / VS Code already have good coverage on data science.
I think Rao is more appealing to me since Positron already has that kind of integration, while RStudio doesn’t. Plus, Posit probably won’t ever add an AI Chat feature to RStudio anyway.
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.
This is a good idea, although IMO source control, compute, and MLOps integration are bigger but less flashy pain points for data scientists than AI in notebooks.
If you're going to market Erdos as open source, then IMO there should be a github link somewhere on your website.
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).
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.
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.
Have you done any fine-tuning or prompt-customization for the R-specific work? I've found the models worse on R when compared to Python, especially for more complex tasks. This looks cool, thanks for sharing!
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.
We started with a product like this at Definite (https://www.definite.app/), but it became clear there weren't enough people willing to spend real money on a product like it when Cursor / VS Code already have good coverage on data science.
I think Rao is more appealing to me since Positron already has that kind of integration, while RStudio doesn’t. Plus, Posit probably won’t ever add an AI Chat feature to RStudio anyway.
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.
This is a good idea, although IMO source control, compute, and MLOps integration are bigger but less flashy pain points for data scientists than AI in notebooks.
If you're going to market Erdos as open source, then IMO there should be a github link somewhere on your website.
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).
I see Google acquiring Iotas in the future, that's how good it gets
Do you have the option to run on a local model? Lots of firms don't want data or prompts going outside the local network
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.
I can't tell how this differs to Cursor from your website. How is it different?
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.
I can't tell how this differs from Cursor on your website. How is it different?
Looks like this comment got duplicated, answered above
Another VS code for.
Yet another VSCode fork