There are many other Jupyter notebooks with extensive AI integration. These are less (or not at all) open source, but more mature in some ways, having been iterated on for over a year:
- https://noteable.io/ -- pretty good, but then they got acquirehired out of existence
- https://deepnote.com -- also extensive AI integration and realtime collaboration
- https://github.com/jupyterlab/jupyter-ai -- a very nice standard open source extension for gen AI in Jupyter, from an Amazon. JupyterLab of course also has fairly mature realtime collaboration now.
- https://colab.google/ -- has great AI integration but of course only with Google-hosted models
- https://cocalc.com -- very extensive AI integration everywhere with all the main hosted models, mostly free or pay as you go; also has realtime collaboration. (Disclaimer: I co-authored this.)
- VS Code has a great builtin Jupyter notebook, as other people have mentioned.
- https://deepnote.com - Deepnote is closed source sadly - you can't run it locally, you can't tweak it, you need to learn a new interface and switch to it
- https://github.com/jupyterlab/jupyter-ai - I actually mentioned this in the post but in my experience, the UX and features are far behind what we've built already. I'd love to hear from anyone who's tried jupyter-ai to give us a shot and let me know what we're missing! The plus side of jupyter-ai is of course that it supports way more models and the codebase is a lot more hackable than what we've built.
- https://colab.google/ - closed-source, similar challenges as with Deepnote. Another big challenge is that if you want to use Colab as a company, AFAICT, you need use their enterprise version (so that you can have native data collectors, support guarantees etc) and that only works with GCP so if you're an AWS shop, this might be a deal-breaker.
- https://cocalc.com - hadn't used it so far but congrats on a great project! Will check it out. Didn't look in detail but first impressions makes it look like a fairly different interface from Jupyter. One of our goals was to go to where the users already are - that meant Jupyter. So that's definitely a major difference.
- VSCode - as I've mentioned elsewhere, we're targeting a more of an analytics usecase with the features we're building. VSCode has AI features of course! But we'll look quite different once we build more items on the roadmap :)
> Didn't look in detail but first impressions makes it look like a fairly different interface from Jupyter.
That is correct, in that it is a completely different implementation. Unlike Deepnote and Colab, we try to maintain the same keyboard shortcuts and other semantics as JupyterLab, as much as we can.
If you don't already, we would love it if you came to the JupyerLab weekly dev meeting and demoed pretzelai: https://hackmd.io/Y7fBMQPSQ1C08SDGI-fwtg?view
People from Colab, VS Code, etc. regularly come to the meeting and demo their JupyterLab related notebook work, and it's really good for the community.
VS Code (and Cursor) has so nice Jupyter support that I find it much better to use it for my workflow, rather than using any dedicated solution for Jupiter Notebooks only.
Agree with Daksh in the sibling comment. I think it's like you said - different people have different workflows and some might prefer using VSCode. IME though, most data scientists (and all data analysts) I worked with preferred using the company hosted internal Jupyter instance for their work.
Also, as we build more features, we're definitely going in the direction of more analytics workloads (live collaboration, leaving comments, google-doc type versioning, fully AI driven analyses similar to OpenAI Interpreter mode etc) and with these features, I think there will be a clear divergence of feature-set in VSCode/PyCharm vs Pretzel.
If I may ask, are you more on the engineering side (MLE) or more on the data side (Data Analyst)? EDIT: Just saw your other comment!
I used Jupyter Notebook before it was popular and back when I was a PhD student. I pushed in a few places for the unorthodox way of exploring data in a browser. Now, I am back - but only thanks to wonderful code editors and their good support of Jupyter Notebooks. I recommend VSC (or, this year, Cursor) as the default environment for data sci.
That's a cool blogpost! I'm mostly using Cursor now (just waiting until someone makes a kick-ass Emacs package so I can switch back!) so I can definitely see your perspective.
I'd be curious to hear a bit more about the kind of work you do that made you switch. Also if there's anything you miss in VSC/Cursor vs Jupyter. If you don't mind a small email exchange, let me know and I'll drop you a message :)
marimo is very good, been using it for a few months now and have switched over to it for most of my notebook-related tasks (it ships with copilot support)
This is a great implementation by your team + contributors. Simple but effective. And nice to see you’ve kept it open source instead of some other Show HN submissions where they take open source work, make is closed, change a few things, and claim they’ve created something great.
Im curious to see if you continue building out some other features. While these are great features (copilot, chat, etc), I’d think most users would expect their IDE to have it out of the box (or with an extension) these days
Thanks for the kind words. Keeping Pretzel open-source was important to us - partly for trust reasons. When most people use Jupyter, they do so with sensitive data. Making a closed source tool simply wouldn't work. I wouldn't have trusted a closed source jupyter alternative with my company's data unless the counterparty was huge and well-known.
To your second point - completely agree that most users would expect these feature from their IDEs today. But, only two IDEs support Jupyter Notebooks: VSCode and PyCharm. You can certainly use them for notebook work but most AI extensions written for VSCode wouldn't be optimized for notebook work (for eg, GH Copilot apparently has difficulty completing code across different cells as per a friend). Secondly, to your point, this is just a start - we're going to be building a lot more Data Analysis specific features that don't exist in any IDE. I think there's a decent space for a tool a like this.
> And nice to see you’ve kept it open source instead of some other Show HN submissions where they take open source work, make is closed, change a few things, and claim they’ve created something great.
The seem to have taken a "BSD-3-Clause" licensed project and change it to AGPLv3 licensed one. That's not the same thing, but it's similar to what you're concerned about.
This is true. All new added code is licensed under AGPLv3. But I fail to see how it's the same thing as modifying and open-source tool and re-selling it as a closed source tool. This is what AGPL gives us - anyone can use Pretzel however they want. They can even re-package and re-sell it if they want so long as they too open-source their modifications and improvements.
Selecting the license for an open-source tool backed by a company is tricky. You want your code to be open-source for it's benefits (for eg, for us one benefit is building trust with people working with sensitive data). But, the history of open-source tools is full of tools that another company just started reselling without doing any of the work (sentry, mongodb etc). So, you need to find a balance. AGPLv3 strikes the right balance for us.
You're right that it is not the same thing, which is why I wrote "That's not the same thing [...]" in the comment you're responding to. I have done the same as you guys (building an AGPLv3 or 'worse' product on top of BSD licensed code) many, many times! Anyway, what you're doing is really exciting!
That's fair, I was mostly responding to "but it's similar to what you're concerned about" because I didn't think the concerns are the same (but I can see your perspective on it too! While we are "giving away" the code, we're definitely posing some limitation).
Thanks for the kind words, we're exited to be building this :)
Ramon here, the other cofounder of Pretzel! Quick update: Based on some early feedback, we're already working on adding support for local LLMs and Claude Sonnet 3.5. Happy to answer any questions!
Github Copilot is the most useful tool I've found in a long time and having that in Jupyter Notebooks is just awesome. I've been missing that for quite some time. Great work guys!
You can also open Jupyter notebook files in VS Code, which would be another way to get AI autocomplete. I’m not enough of a Jupyter user to know whether it would make sense to use VS Code all the time.
Yeah, this is definitely a good way to access AI code completion (inline or otherwise) in Jupyter notebooks. In fact, I know some data folks who've been using Jupyter from day 1 switching to VSCode simply because their company buys a copilot license for everyone and they really miss it their Jupyter workflow.
Agree. We actually tried getting GitHub Copilot to work with Jupyter but GH doesn't have an official API. We actually took some time to reverse engineer an implementation from neovim GH Copilot extension [1] and from Zed [2] but found it too flaky and too much trouble in the end.
Meanwhile, we also found a better speed/quality tradeoff with Codestral (since it has a fill-in-the-middle version, unlike a general LLM) so we decided to go with Codestral. This is inspired by continue.dev using Codestral for tab completion :)
I don't really get the appeal of this, I'd just use vscode with Jupyter if I really wanted "ai" integration since I can then access the whole ecosystem of extensions. The idea isn't that bad, but it lacks purpose.
It's as others say - in VSCode there support for Copilot but most Data Scientists and specially analysts who don't spend most of their day in a text editor still use Jupyter Lab (or Notebook - I mean the software, not the file format) and with Codestral, we've found similarly good completions (sometimes better than Copilot) but at a much better speed and cost.
So we discuss this briefly on our FAQ but let me try to expand on it.
Our goal is to make a modern literate programming tool. On a surface level, a tool like that would end up looking very similar to Jupyter, though with better features. We've mentioned some things we'd like to have in this final tool in our README and also in the post above.
Our first thought was to make a tool from scratch. The challenge was, it's very hard to get people to switch and so, we had to go where people already are - that meant Jupyter.
We could've made this one feature an extension with some difficulty (in-fact, our early experiments, we started by making an extension). It would have some downsides - we wouldn't have granular control over certain core Jupyter behaviours like we do right now (for eg, we wanted to allow creating hidden folders to store some files). But we probably could have made a 95% working version of Pretzel work as a jupyter extension.
The bigger reason we chose to fork was because down the line, we want to completely change the code execution model to being DAG based to allow for reproducible notebooks (similar to https://plutojl.org/ for eg). Similarly, we want to completely remove Codemirror and replace it with Monaco (the core editor engine in VSCode) to provide a more IDE like experience in Jupyter. These things simply couldn't have been done as extensions.
These editors all focus on programming, does anybody have a recommendation for more general note-taking?
I'd like to do things like organizing very rough notes, having them reformatted according to a general template, apply changes according to a prompt, maybe ask questions that refer to a collection of notes, ...
Codeium(https://codeium.com/) already supports this, along with VSCode jupyter notebook extensions. It has 400k downloads on the VSCode extension store. don't really see the point of this when codeium already exists..
Curious on why you went with Codestral for autocomplete, does it outperform other local models? How is the performance compared to GPT or Claude for autocomplete?
Any plans to finetune Codestral for this specific usecase?
So, we were tipped off to Codestral being really good because of continue.dev - for reference, that's a VSCode extension that gives you similar features to Cursor. After we trialled it out head-to-head against GPT-4o for fill-in-the-middle completion, in my experience (purely vibe based), it produced better completions maybe twice as fast as GPT-4o.
We haven't tried vs Sonnet 3.5 yet - my hunch is that on the speed/quality/cost space, Sonnet will end up doing better than Codestral for some folks.
Against general purpose local models (taking Llama-70B as a high-water mark), Codestral does better far better on code related tasks while being less than 1/3rd the size (22B!). That said, I'm definitely exited to try out DeepSeek Coder v2 - by all reports, it's amazing model for code completion and will likely also beat out Codestral.
I don't think we're planning to fine-tune Codestral though (or any model for that matter). The latest models keep on becoming faster, better and cheaper AND they already work quite well. My thinking at this time is that waiting it out and having a big AI lab make a more capable general model is a better strategy.
We're just a fork of Jupyter so everything - notebook files, keybindings, extensions, settings should just work.
We pull all of your config from the ~/.jupyter folder so you should be able to switch between Jupyter and Pretzel from different python environments (though you might see some warnings)
Yeah PyCharm and VSCode are definitely great options (though PyCharm is paid and VSCode AI extensions aren't notebook tailored). If you ever get a chance, I'd love to get your feedback on Pretzel - I think Codestral is a better and faster inline completion model than GH Copilot's GPT4 class model plus I think we might do context-relevant questions better :)
I'll give it a crack over the holiday. My primary mechanism is that I talk inline to the program.
import pandas as pd
# read a csv in with a pipe delimiter
pd.read... # AI fills this in
I saw your demo and I get the "Enter AI mode" thing but I like this flow, esp since I use ideavim. Perhaps the only improvement would be if I had another mode in the editor for AI that was keyboard accessible.
Interesting! We have a inline autocomplete as well (that's the one that uses Codestral). So in your example, you'd see a autocompletion prompt after you type pd.read and hitting tab will autocomplete the line (or many lines if there's enough context to generate that much text).
The AI mode/box with Cmd + K is for more complex prompts, multi-line code chunks and such. We've tried to make everything completely keyboard accessible btw, including the sidebar so that you never have to use the mouse :)
At this point I'm almost afraid to ask but my attempts to figure it out have failed. What is a Jupyter notebook? Where is the code running? On your computer? On someone elses computer?
It's usually run as a local web application with the browser running on the same machine as the backend, though it's possible to bind it to non-localhost interfaces.
- https://noteable.io/ -- pretty good, but then they got acquirehired out of existence
- https://deepnote.com -- also extensive AI integration and realtime collaboration
- https://github.com/jupyterlab/jupyter-ai -- a very nice standard open source extension for gen AI in Jupyter, from an Amazon. JupyterLab of course also has fairly mature realtime collaboration now.
- https://colab.google/ -- has great AI integration but of course only with Google-hosted models
- https://cocalc.com -- very extensive AI integration everywhere with all the main hosted models, mostly free or pay as you go; also has realtime collaboration. (Disclaimer: I co-authored this.)
- VS Code has a great builtin Jupyter notebook, as other people have mentioned.
Am I missing any?
- https://noteable.io/ - as you say, it doesn't exist anymore
- https://deepnote.com - Deepnote is closed source sadly - you can't run it locally, you can't tweak it, you need to learn a new interface and switch to it
- https://github.com/jupyterlab/jupyter-ai - I actually mentioned this in the post but in my experience, the UX and features are far behind what we've built already. I'd love to hear from anyone who's tried jupyter-ai to give us a shot and let me know what we're missing! The plus side of jupyter-ai is of course that it supports way more models and the codebase is a lot more hackable than what we've built.
- https://colab.google/ - closed-source, similar challenges as with Deepnote. Another big challenge is that if you want to use Colab as a company, AFAICT, you need use their enterprise version (so that you can have native data collectors, support guarantees etc) and that only works with GCP so if you're an AWS shop, this might be a deal-breaker.
- https://cocalc.com - hadn't used it so far but congrats on a great project! Will check it out. Didn't look in detail but first impressions makes it look like a fairly different interface from Jupyter. One of our goals was to go to where the users already are - that meant Jupyter. So that's definitely a major difference.
- VSCode - as I've mentioned elsewhere, we're targeting a more of an analytics usecase with the features we're building. VSCode has AI features of course! But we'll look quite different once we build more items on the roadmap :)
That is correct, in that it is a completely different implementation. Unlike Deepnote and Colab, we try to maintain the same keyboard shortcuts and other semantics as JupyterLab, as much as we can.
If you don't already, we would love it if you came to the JupyerLab weekly dev meeting and demoed pretzelai: https://hackmd.io/Y7fBMQPSQ1C08SDGI-fwtg?view People from Colab, VS Code, etc. regularly come to the meeting and demo their JupyterLab related notebook work, and it's really good for the community.
VS Code (and Cursor) has so nice Jupyter support that I find it much better to use it for my workflow, rather than using any dedicated solution for Jupiter Notebooks only.
Also, as we build more features, we're definitely going in the direction of more analytics workloads (live collaboration, leaving comments, google-doc type versioning, fully AI driven analyses similar to OpenAI Interpreter mode etc) and with these features, I think there will be a clear divergence of feature-set in VSCode/PyCharm vs Pretzel.
If I may ask, are you more on the engineering side (MLE) or more on the data side (Data Analyst)? EDIT: Just saw your other comment!
I used Jupyter Notebook before it was popular and back when I was a PhD student. I pushed in a few places for the unorthodox way of exploring data in a browser. Now, I am back - but only thanks to wonderful code editors and their good support of Jupyter Notebooks. I recommend VSC (or, this year, Cursor) as the default environment for data sci.
I'd be curious to hear a bit more about the kind of work you do that made you switch. Also if there's anything you miss in VSC/Cursor vs Jupyter. If you don't mind a small email exchange, let me know and I'll drop you a message :)
https://github.com/marimo-team/marimo
Im curious to see if you continue building out some other features. While these are great features (copilot, chat, etc), I’d think most users would expect their IDE to have it out of the box (or with an extension) these days
To your second point - completely agree that most users would expect these feature from their IDEs today. But, only two IDEs support Jupyter Notebooks: VSCode and PyCharm. You can certainly use them for notebook work but most AI extensions written for VSCode wouldn't be optimized for notebook work (for eg, GH Copilot apparently has difficulty completing code across different cells as per a friend). Secondly, to your point, this is just a start - we're going to be building a lot more Data Analysis specific features that don't exist in any IDE. I think there's a decent space for a tool a like this.
The seem to have taken a "BSD-3-Clause" licensed project and change it to AGPLv3 licensed one. That's not the same thing, but it's similar to what you're concerned about.
Selecting the license for an open-source tool backed by a company is tricky. You want your code to be open-source for it's benefits (for eg, for us one benefit is building trust with people working with sensitive data). But, the history of open-source tools is full of tools that another company just started reselling without doing any of the work (sentry, mongodb etc). So, you need to find a balance. AGPLv3 strikes the right balance for us.
You're right that it is not the same thing, which is why I wrote "That's not the same thing [...]" in the comment you're responding to. I have done the same as you guys (building an AGPLv3 or 'worse' product on top of BSD licensed code) many, many times! Anyway, what you're doing is really exciting!
Thanks for the kind words, we're exited to be building this :)
LocalAI: https://github.com/mudler/LocalAI
E.g promptfoo and chainforge have multi-LLM workflows.
Promptfoo has a YAML configuration for prompts, providers,: https://www.promptfoo.dev/docs/configuration/guide/
What is the system prompt, and how does a system prompt also bias an analysis?
/? "system prompt" https://hn.algolia.com/?dateRange=all&page=0&prefix=false&qu...
Meanwhile, we also found a better speed/quality tradeoff with Codestral (since it has a fill-in-the-middle version, unlike a general LLM) so we decided to go with Codestral. This is inspired by continue.dev using Codestral for tab completion :)
[1] https://github.com/github/copilot.vim [2] https://zed.dev/blog/copilot
What do you mean by this? I've been using Copilot in VS Code .ipynb files for over a year now.
Our goal is to make a modern literate programming tool. On a surface level, a tool like that would end up looking very similar to Jupyter, though with better features. We've mentioned some things we'd like to have in this final tool in our README and also in the post above.
Our first thought was to make a tool from scratch. The challenge was, it's very hard to get people to switch and so, we had to go where people already are - that meant Jupyter.
We could've made this one feature an extension with some difficulty (in-fact, our early experiments, we started by making an extension). It would have some downsides - we wouldn't have granular control over certain core Jupyter behaviours like we do right now (for eg, we wanted to allow creating hidden folders to store some files). But we probably could have made a 95% working version of Pretzel work as a jupyter extension.
The bigger reason we chose to fork was because down the line, we want to completely change the code execution model to being DAG based to allow for reproducible notebooks (similar to https://plutojl.org/ for eg). Similarly, we want to completely remove Codemirror and replace it with Monaco (the core editor engine in VSCode) to provide a more IDE like experience in Jupyter. These things simply couldn't have been done as extensions.
I'd like to do things like organizing very rough notes, having them reformatted according to a general template, apply changes according to a prompt, maybe ask questions that refer to a collection of notes, ...
Any plans to finetune Codestral for this specific usecase?
We haven't tried vs Sonnet 3.5 yet - my hunch is that on the speed/quality/cost space, Sonnet will end up doing better than Codestral for some folks.
Against general purpose local models (taking Llama-70B as a high-water mark), Codestral does better far better on code related tasks while being less than 1/3rd the size (22B!). That said, I'm definitely exited to try out DeepSeek Coder v2 - by all reports, it's amazing model for code completion and will likely also beat out Codestral.
I don't think we're planning to fine-tune Codestral though (or any model for that matter). The latest models keep on becoming faster, better and cheaper AND they already work quite well. My thinking at this time is that waiting it out and having a big AI lab make a more capable general model is a better strategy.
We pull all of your config from the ~/.jupyter folder so you should be able to switch between Jupyter and Pretzel from different python environments (though you might see some warnings)
The AI mode/box with Cmd + K is for more complex prompts, multi-line code chunks and such. We've tried to make everything completely keyboard accessible btw, including the sidebar so that you never have to use the mouse :)