I was initially interested in Positron, until checking out the license:
"Positron is licensed under the Elastic License 2.0, a source-available license. This license makes Positron available for free to everyone to use, build on, and extend for personal, academic, and commercial use. Its primary restriction is that you can’t host Positron as a service to third parties without Posit’s agreement. This restriction is necessary for us to build a sustainable business around Positron while also offering it free of charge to the community."
"You may not move, change, disable, or circumvent the license key functionality in the software, and you may not remove or obscure any functionality in the software that is protected by the license key."
This is not a recommendation on whether anyone should use Positron. I think it's fair that people know it's just another piece of proprietary software with a license key. Posit is a public benefit corporation, which sounds nice, but you're still subject to the same games any other for-profit private company plays with its customers.
(I use Posit Cloud in my teaching, so my interactions with their products are as a customer, but I use RStudio, which is open source.)
Feels really strange to lump this together with PowerBI...
Respectfully, I feel like we could all benefit from having better categories than you are offering. Good categories are tools to navigate an increasingly complex world, and this sort of reduction serves very little
> RStudio (now Posit) was founded in 2009 with the vision of creating high quality open-source software for data scientists. We’ve grown exponentially over time but our culture remains unchanged. We invest heavily in open-source development, education, and the community with the goal of serving knowledge creators 100 years from now.
> We want Posit to serve a meaningful public purpose and we run the company for the benefit of our customers, employees, and the community at large. That’s why we’re designated as a Public Benefit Corporation. As a Certified B Corp, we must meet the highest verified standards of social and environmental performance, transparency, and accountability. Our directors and officers have a fiduciary responsibility to address social, economic, and environmental needs while still overseeing our business goals.
So the main news is that they're giving up on develping an independent IDE and turning into another VS code fork. The loss of biodiversity and reliance on a no-so-reliable steward is mildly concerning.
They (reps? devs? I don't remember) have recently mentioned that they won't give up on RStudio, that it will stay separate from Positron. I really hope that stays true.
OTOH, posit funds a lot of development of important packages in the tidyverse and does a lot of community work etc.
So if maintaining RStudio is so much of a burden that it impedes the rest of their work, I don't think it's a bad idea to reduce the amount of work spent trying to compete with VSCode when that's an increasingly tough sell.
I'm not a fan of VSCode personally, but would probably be happy with a tmux setup with a console for R and some minimal output viewer, so people like me should be able to cobble something together that's a workable alternative to Posit.
> so people like me should be able to cobble something together that's a workable alternative to Posit.
There is Rkward[1], available from the repositories in many linux distributions.
Discovered it recently, because I'm currently learning R, and few linux distributions outside the Mandriva family offer Rstudio straight from the repositories, and I'm lazy to download and do a manual install every time.
For many years, Emacs plus ESS was the standard workflow for R users. Vim users have their own plugin. On Windows, Tinn-R was impressive, and last I checked it was still going. Of course there is also Jupyter, PyCharm, and Eclipse, plus every text editor has support for R. If something were to happen to RStudio, there would be many fine alternatives for R.
> We anticipate many RStudio users will be curious about Positron.
Heavily implies it as a seperate thing that will continue to be maintained. They haven't said they're getting rid of support for Rstudio.
I think this is probably more that Posit have been trying to move more and more into the Python space, since that's where most data science is happenening. Rstudio has a great but is obviously very associated with R, so making a similarly intended project that is more explicit is supporting other languages isn't inherently a bad shout.
It will continue to be maintained, but if lots of R people move to Positron then RStudio's features will start to lag, and they'll eventually deprecate it.
Unless this has recently changed, the support for LLM coding tools in RStudio is so bare bones that I would expect many users to switch to Positron just for that.
I sent this to an R friend, and he was like, "yeah, it's been changed for a few years now". Is he missing something or has there been a major version or something?
I hope positron works out fine, last time I checked it was not yet usable.
Replacing Rstudio with something more reliable would be nice, because of some major design flaws Rstudio has.
A lot of the UI stuff runs also in R, so when the R kernel dies, quite often I cannot save unsaved files. So I need to copy the file content to a different text editor when that happens.
I also don't understand why the LLM-Chat window is running inside the R console, and then blocks running R code. That makes it completely unusable.
Was looking forward to trying this, but they haven't got inline chunks for rmarkdown to work yet. This I use religiously. Hopefully they'll be able to ship it eventually, but based on the GitHub issue about it it will be a while.
I understand that extensions have some limits in VS Code, but is it clear this needs to be a fork vs. an extension? An extension would allow it to work in Google Antigravity too.
I know "next-generation" is just SEO slop, but I'm going to hyper fixate on this for a moment (so feel free to ignore if you're actually interested in Positron).
I think the future of data science will likely be something else, with the advent of WebGPU[1] (which isn't just a web technology) and the current quality/availability of GPUs in end user devices, and a lot of data computation clearly standing to benefit from this.
The real next generation of data science tools will likely involve tools that are GPU first and try to keep as much work in the GPU as possible. I definitely think we'll see some new languages eventually emerge to abstract much of the overhead of batching work but also forces people to explicitly consider when they write code that simply won't run on the GPU, like sequential operations that are nonlinear, nonassociative/noncommutative (like highly sequential operations like processing an ordered block of text).
I think WebGPU is going to make this a lot easier.
That said I'd imagine for larger compute workloads people are going to continue to stick with large CUDA clusters as they have more functionality and handle a larger variety of workloads. But on end user devices there's an opportunity to create tools that allow data scientists to more trivially do this kind of work when they compute their models, process their datasets.
[1] Other compute APIs existed in the past, but WebGPU might be one of the most successful attempt to provide a portable (and more accessible) way to write general GPU compute code. I've seen people say WebGPU is hard, but having given it ago (without libraries) I don't think this is all that true, compared to OpenGL there are no longer specialised APIs to load data into uniforms everything is just a buffer. I wonder if this has more to do with non JS bindings for use outside the browser/node or the fact you're forced to consider memory layout of anything your loading into the GPU from the start (something that can be abstracted and generalised), just in my experience after my first attempt at writing a compute shader it's fairly simple IMO. Like stuff that always complicated in rendering like text is still complicated, but at least its not a state based API like web/opengl.
It's worth considering what nextgen really would be, but probably VSCode and its forks will dominate for the time being. I recall Steve Yegge predicting that the next IDE to beat be the web browser, and this was around 2008 or so. It's not the reality, but took about 10-15 years for it to actually happen, even though there were earlier shots at it by like Atom.
Interesting question. I don't know much about WebGPU, but I'd posit (heh!) that the GPU on the client devices doesn't matter too much since folks will likely be working over the network anyways (cloud-based IDE, coding agent connected to cloud-hosted LLM, etc) and we also have innovations like Modal which allow serverless lambdas for GPUs.
As long as silicon is scarce it would make sense to hoard it and rent it out (pricing as a means of managing scarcity); if we end up in a scenario where silicon is plentiful, then everyone would have powerful local GPUs, using local AI models, etc.
I guess in my mind I was thinking use cases other than AI. Like statistical or hierarchical scientific models, simulations or ETL work. I also don't know if some of the econometricians I know with a less technical background would even know how to get setup with AWS, and I feel more boardly there's enough folks doing data work in a none tech field who know how to use Python or R or Matlab to do their modelling but likely isn't comfortable with cloud infrastructure, but might have an apple laptop with apple silicon that could improve their iteration loop. Folks in AI are probably more comfortable with a cloud solution.
There are aspects of data science which is iterative and you're repeatedly running similar computations with different inputs, I think there's some value in shaving off time between iterations.
In my case I have a temporal geospatial dataset with 20+ million properties for each month over several years each with various attributes, it's in a nonprofessional setting and the main motivator for most of my decisions is "because I can and I think it would be fun and I have a decent enough GPU". While I could probably chuck it on a cluster, I'd like to avoid if I can help it and an optimisation done on my local machine would still pay off if I did end up setting up a cluster. There's quite a bit of ETL preprocessing work before I load it into the database, I think are portions that might be doable on the GPU. But it's more so the computations I'd like to do on the dataset before generating visualisations in which I think I could reduce the iteration wait time for processing for plots, ideally to the point I can make iterations more interactive. There's enough linear operations you could get some wins with a GPU implementation.
I am keen to see how far I'll get, but worst case scenario I learn a lot, and I'm sure those learnings will be transferrable to other GPU experiments.
TBC, I too did not really mean "AI" (as in LLMs and the like) which is often hosted/served with a very convenient interface. I do include more bespoke statistical / mathematical models -- be it hierarchical, Bayesian, whatever.
Since AWS/etc are quite complicated, there are now a swarm of startups trying to make it easier to take baby steps into the cloud (eg. Modal, Runpod, etc) and make it very easy for the user to get a small slice of that GPU pie. These services have drastically simpler server-side APIs and usage patterns, including "serverless" GPUs from Modal, where you can just "submit jobs" from a Python API without really having to manage containers. On the client side, you have LLM coding agents that are the next evolution in UI frontends -- and they're beginning to make it much much easier to write bespoke code to interact with these backends. To make it abundantly clear what target audience I'm referring to: I imagine they are still mostly using sklearn (or equivalents in other languages) and gradient boosting with Jupyter notebooks, still somewhat mystified by modern deep learning and stuff. Or maybe those who are more mathematically sophisticated but not software engg sophisticated (eg: statisticians / econometricians)
To inspire you with a really concrete example: since Modal has a well documented API, it should be quite straight-forward ("soon", if not already) for any data scientist to use one of the CLI coding agents and
1. Implement a decent GPU-friendly version of whatever model they want to try (as long as it's not far from the training distribution i.e. not some esoteric idea which is nothing like prior art)
2. Whip up a quick system to interface with some service provider, wrap that model up and submit a job, and fetch (and even interpret) results.
----
In case you haven't tried one of these new-fangled coding agents, I strongly encourage you to try one out (even if it's just something on the free tier eg. gemini-cli). In case you have and they aren't quite good enough to solve your problem, tough luck for now... I anticipate their usability will improve substantially every few months.
If it really were next generation we're like 100 generations ahead since the start of the year except they all keep spawning from the same ancestor so they're really just yet another sibling.
Oof. That's a damn shame. I think languages and use-cases like this are the perfect place for purpose built IDE development. If even these guys are turning into a VS Code downstream that's just sad.
Coincidentally I was thinking of giving R another go, but honestly now... I'm good...
The why not is that they'll constantly be wrestling with, and limited by the generalist nature of a tool like VS Code. While generalism has its benefits and its drawbacks, what it most definitely does not give you is a fully bespoke experience, by its very definition, and I'm simply bemoaning that specific loss, because I like to see specialised tools sometimes.
I feel like a lot of these can be packaged as an extension for vscode. I'd rather not have multiple different variations of the same ide, too much duplication.
Because generally they don't add anything that couldn't have just been a VSCode extension... in which case it would really be better for users if it was a VSCode extension. The only reason they don't do that is for branding & control purposes.
There are exceptions. E.g. Theia actually does enough stuff differently that I think it warrants being its own thing. At least it did. Looks like they have jumped on the AI bandwagon too.
Maybe this is the same; I haven't looked at it in detail. But "we have an IDE! (don't tell them it's vscode)" feels a lot like "we have an app! (don't tell them its a webview)".
Wow so RStudio has switched from being a "real" desktop app to another webview-based thing? Bummer. I hadn't used RStudio for some time but now I probably will continue to not do so. . .
I was initially interested in Positron, until checking out the license:
"Positron is licensed under the Elastic License 2.0, a source-available license. This license makes Positron available for free to everyone to use, build on, and extend for personal, academic, and commercial use. Its primary restriction is that you can’t host Positron as a service to third parties without Posit’s agreement. This restriction is necessary for us to build a sustainable business around Positron while also offering it free of charge to the community."
"You may not move, change, disable, or circumvent the license key functionality in the software, and you may not remove or obscure any functionality in the software that is protected by the license key."
This is not a recommendation on whether anyone should use Positron. I think it's fair that people know it's just another piece of proprietary software with a license key. Posit is a public benefit corporation, which sounds nice, but you're still subject to the same games any other for-profit private company plays with its customers.
(I use Posit Cloud in my teaching, so my interactions with their products are as a customer, but I use RStudio, which is open source.)
> just another piece of proprietary software
Feels really strange to lump this together with PowerBI...
Respectfully, I feel like we could all benefit from having better categories than you are offering. Good categories are tools to navigate an increasingly complex world, and this sort of reduction serves very little
Formerly RStudio
> RStudio (now Posit) was founded in 2009 with the vision of creating high quality open-source software for data scientists. We’ve grown exponentially over time but our culture remains unchanged. We invest heavily in open-source development, education, and the community with the goal of serving knowledge creators 100 years from now.
> We want Posit to serve a meaningful public purpose and we run the company for the benefit of our customers, employees, and the community at large. That’s why we’re designated as a Public Benefit Corporation. As a Certified B Corp, we must meet the highest verified standards of social and environmental performance, transparency, and accountability. Our directors and officers have a fiduciary responsibility to address social, economic, and environmental needs while still overseeing our business goals.
So the main news is that they're giving up on develping an independent IDE and turning into another VS code fork. The loss of biodiversity and reliance on a no-so-reliable steward is mildly concerning.
They (reps? devs? I don't remember) have recently mentioned that they won't give up on RStudio, that it will stay separate from Positron. I really hope that stays true.
OTOH, posit funds a lot of development of important packages in the tidyverse and does a lot of community work etc.
So if maintaining RStudio is so much of a burden that it impedes the rest of their work, I don't think it's a bad idea to reduce the amount of work spent trying to compete with VSCode when that's an increasingly tough sell.
I'm not a fan of VSCode personally, but would probably be happy with a tmux setup with a console for R and some minimal output viewer, so people like me should be able to cobble something together that's a workable alternative to Posit.
> so people like me should be able to cobble something together that's a workable alternative to Posit.
There is Rkward[1], available from the repositories in many linux distributions.
Discovered it recently, because I'm currently learning R, and few linux distributions outside the Mandriva family offer Rstudio straight from the repositories, and I'm lazy to download and do a manual install every time.
[1] https://rkward.kde.org/
For many years, Emacs plus ESS was the standard workflow for R users. Vim users have their own plugin. On Windows, Tinn-R was impressive, and last I checked it was still going. Of course there is also Jupyter, PyCharm, and Eclipse, plus every text editor has support for R. If something were to happen to RStudio, there would be many fine alternatives for R.
It feels like when Opera dropped its own web engine, Presto, to become just another Chrome clone.
I'm not sure. wording like this:
> We anticipate many RStudio users will be curious about Positron.
Heavily implies it as a seperate thing that will continue to be maintained. They haven't said they're getting rid of support for Rstudio.
I think this is probably more that Posit have been trying to move more and more into the Python space, since that's where most data science is happenening. Rstudio has a great but is obviously very associated with R, so making a similarly intended project that is more explicit is supporting other languages isn't inherently a bad shout.
It will continue to be maintained, but if lots of R people move to Positron then RStudio's features will start to lag, and they'll eventually deprecate it.
Unless this has recently changed, the support for LLM coding tools in RStudio is so bare bones that I would expect many users to switch to Positron just for that.
I sent this to an R friend, and he was like, "yeah, it's been changed for a few years now". Is he missing something or has there been a major version or something?
May be mixing-up the company change with the IDE: Posit, the company, was named a few years ago, whereas Positron, the IDE, is new.
The IDE has been available for awhile.
In beta state.
Not true.
But the community can maintain it.
The Rstudio users can give a roadmap and ask for help.
> A %PRODUCTNAME% next generation editor/ide is released
> Look inside
> VS Code
VS Code is the chromium of IDEs. I shall not explain further.
And when you look inside VS Code, it is Chromium.
Do you mean VSCodium? https://vscodium.com/
I hope positron works out fine, last time I checked it was not yet usable.
Replacing Rstudio with something more reliable would be nice, because of some major design flaws Rstudio has. A lot of the UI stuff runs also in R, so when the R kernel dies, quite often I cannot save unsaved files. So I need to copy the file content to a different text editor when that happens. I also don't understand why the LLM-Chat window is running inside the R console, and then blocks running R code. That makes it completely unusable.
Was looking forward to trying this, but they haven't got inline chunks for rmarkdown to work yet. This I use religiously. Hopefully they'll be able to ship it eventually, but based on the GitHub issue about it it will be a while.
I understand that extensions have some limits in VS Code, but is it clear this needs to be a fork vs. an extension? An extension would allow it to work in Google Antigravity too.
Reading the title, I expected this to be the successor of Electron.
Or at least a positive version of it...
Each Positron installation annihilates one Chrome clone from the PC and frees up 1-2 Gigajoules of RAM in the process.
I know "next-generation" is just SEO slop, but I'm going to hyper fixate on this for a moment (so feel free to ignore if you're actually interested in Positron).
I think the future of data science will likely be something else, with the advent of WebGPU[1] (which isn't just a web technology) and the current quality/availability of GPUs in end user devices, and a lot of data computation clearly standing to benefit from this.
The real next generation of data science tools will likely involve tools that are GPU first and try to keep as much work in the GPU as possible. I definitely think we'll see some new languages eventually emerge to abstract much of the overhead of batching work but also forces people to explicitly consider when they write code that simply won't run on the GPU, like sequential operations that are nonlinear, nonassociative/noncommutative (like highly sequential operations like processing an ordered block of text).
I think WebGPU is going to make this a lot easier.
That said I'd imagine for larger compute workloads people are going to continue to stick with large CUDA clusters as they have more functionality and handle a larger variety of workloads. But on end user devices there's an opportunity to create tools that allow data scientists to more trivially do this kind of work when they compute their models, process their datasets.
[1] Other compute APIs existed in the past, but WebGPU might be one of the most successful attempt to provide a portable (and more accessible) way to write general GPU compute code. I've seen people say WebGPU is hard, but having given it ago (without libraries) I don't think this is all that true, compared to OpenGL there are no longer specialised APIs to load data into uniforms everything is just a buffer. I wonder if this has more to do with non JS bindings for use outside the browser/node or the fact you're forced to consider memory layout of anything your loading into the GPU from the start (something that can be abstracted and generalised), just in my experience after my first attempt at writing a compute shader it's fairly simple IMO. Like stuff that always complicated in rendering like text is still complicated, but at least its not a state based API like web/opengl.
It's worth considering what nextgen really would be, but probably VSCode and its forks will dominate for the time being. I recall Steve Yegge predicting that the next IDE to beat be the web browser, and this was around 2008 or so. It's not the reality, but took about 10-15 years for it to actually happen, even though there were earlier shots at it by like Atom.
I guess my mind is wasn’t so much on the editor but that was what the article is was about and I don’t disagree.
Interesting question. I don't know much about WebGPU, but I'd posit (heh!) that the GPU on the client devices doesn't matter too much since folks will likely be working over the network anyways (cloud-based IDE, coding agent connected to cloud-hosted LLM, etc) and we also have innovations like Modal which allow serverless lambdas for GPUs.
As long as silicon is scarce it would make sense to hoard it and rent it out (pricing as a means of managing scarcity); if we end up in a scenario where silicon is plentiful, then everyone would have powerful local GPUs, using local AI models, etc.
I guess in my mind I was thinking use cases other than AI. Like statistical or hierarchical scientific models, simulations or ETL work. I also don't know if some of the econometricians I know with a less technical background would even know how to get setup with AWS, and I feel more boardly there's enough folks doing data work in a none tech field who know how to use Python or R or Matlab to do their modelling but likely isn't comfortable with cloud infrastructure, but might have an apple laptop with apple silicon that could improve their iteration loop. Folks in AI are probably more comfortable with a cloud solution.
There are aspects of data science which is iterative and you're repeatedly running similar computations with different inputs, I think there's some value in shaving off time between iterations.
In my case I have a temporal geospatial dataset with 20+ million properties for each month over several years each with various attributes, it's in a nonprofessional setting and the main motivator for most of my decisions is "because I can and I think it would be fun and I have a decent enough GPU". While I could probably chuck it on a cluster, I'd like to avoid if I can help it and an optimisation done on my local machine would still pay off if I did end up setting up a cluster. There's quite a bit of ETL preprocessing work before I load it into the database, I think are portions that might be doable on the GPU. But it's more so the computations I'd like to do on the dataset before generating visualisations in which I think I could reduce the iteration wait time for processing for plots, ideally to the point I can make iterations more interactive. There's enough linear operations you could get some wins with a GPU implementation.
I am keen to see how far I'll get, but worst case scenario I learn a lot, and I'm sure those learnings will be transferrable to other GPU experiments.
TBC, I too did not really mean "AI" (as in LLMs and the like) which is often hosted/served with a very convenient interface. I do include more bespoke statistical / mathematical models -- be it hierarchical, Bayesian, whatever.
Since AWS/etc are quite complicated, there are now a swarm of startups trying to make it easier to take baby steps into the cloud (eg. Modal, Runpod, etc) and make it very easy for the user to get a small slice of that GPU pie. These services have drastically simpler server-side APIs and usage patterns, including "serverless" GPUs from Modal, where you can just "submit jobs" from a Python API without really having to manage containers. On the client side, you have LLM coding agents that are the next evolution in UI frontends -- and they're beginning to make it much much easier to write bespoke code to interact with these backends. To make it abundantly clear what target audience I'm referring to: I imagine they are still mostly using sklearn (or equivalents in other languages) and gradient boosting with Jupyter notebooks, still somewhat mystified by modern deep learning and stuff. Or maybe those who are more mathematically sophisticated but not software engg sophisticated (eg: statisticians / econometricians)
To inspire you with a really concrete example: since Modal has a well documented API, it should be quite straight-forward ("soon", if not already) for any data scientist to use one of the CLI coding agents and
1. Implement a decent GPU-friendly version of whatever model they want to try (as long as it's not far from the training distribution i.e. not some esoteric idea which is nothing like prior art)
2. Whip up a quick system to interface with some service provider, wrap that model up and submit a job, and fetch (and even interpret) results.
----
In case you haven't tried one of these new-fangled coding agents, I strongly encourage you to try one out (even if it's just something on the free tier eg. gemini-cli). In case you have and they aren't quite good enough to solve your problem, tough luck for now... I anticipate their usability will improve substantially every few months.
check out the RAPIDS ecosystem from 2018 or so :)
This looks interesting, thanks for sharing.
"Next generation IDE" comes out like every fuckin week. I already tried 3 new ones this month so I'm done. It looks nice tho
If it really were next generation we're like 100 generations ahead since the start of the year except they all keep spawning from the same ancestor so they're really just yet another sibling.
Oof. That's a damn shame. I think languages and use-cases like this are the perfect place for purpose built IDE development. If even these guys are turning into a VS Code downstream that's just sad.
Coincidentally I was thinking of giving R another go, but honestly now... I'm good...
If (That's a big if) they can give RStudio experience in VSCode environment, benefiting from the plugin ecosystem etc. why not?
RStudio is great when you are doing your own thing, but when it comes to more generic tools like Git, LLM, Autoformatting etc. it's a hard pass.
The why not is that they'll constantly be wrestling with, and limited by the generalist nature of a tool like VS Code. While generalism has its benefits and its drawbacks, what it most definitely does not give you is a fully bespoke experience, by its very definition, and I'm simply bemoaning that specific loss, because I like to see specialised tools sometimes.
Who cares if it's VSCodium-based?
I feel like a lot of these can be packaged as an extension for vscode. I'd rather not have multiple different variations of the same ide, too much duplication.
Because generally they don't add anything that couldn't have just been a VSCode extension... in which case it would really be better for users if it was a VSCode extension. The only reason they don't do that is for branding & control purposes.
There are exceptions. E.g. Theia actually does enough stuff differently that I think it warrants being its own thing. At least it did. Looks like they have jumped on the AI bandwagon too.
Maybe this is the same; I haven't looked at it in detail. But "we have an IDE! (don't tell them it's vscode)" feels a lot like "we have an app! (don't tell them its a webview)".
I like seeing specialised, bespoke tools existing, that's all.
Wow so RStudio has switched from being a "real" desktop app to another webview-based thing? Bummer. I hadn't used RStudio for some time but now I probably will continue to not do so. . .
It was actually the case for a very long time - albeit a very successful one, so you never realized.
It has always (or at least for decades) been.