While I can see that working well for echoing keystrokes in a terminal, I'm not sure how it would work when you actually enter commands into the terminal. Same for opening files in the IDE.
This is why most IDEs nowadays ask you something about "trusting files" when opening a project. They tend to lick and run on everything in there (at least for dynamic-ish languages, and maybe not "run" intentionally but do stuff which is arbitrary code execution more or less by definition) to analyze the code.
I have! It's pretty interesting and handles a lot of the problems discussed here, but is a little young for us. For one thing, it doesn't have fly replay, so we'd have to build a separate proxy again.
If we were starting from 0, I would definitely try it. My favorite thing about it is the progressive checkpointing- you can snapshot file system deltas and store them at s3 prices. Cool stuff!
1. The pools are very shallow- two machines per pool. While it's certainly possible for 3 tasks to get requested in the same region within 30 seconds, we handle that by falling back to the next closest region if a pool is empty. This is uncommon, though.
2. I haven't considered it, but yeah- the caching seems to work great for us.
3. The tokens are generated per-task, so if you are worried about your token getting leaked, you can just delete the task!
One of the perennial problems with on call situations I encountered was that at some point everyone knew that a production incident was going on and people were either trying to help or learn by following along running the same diagnostics the on point people were running, and exhausting the available resources that were needed to diagnose the problem.
Splunk was a particular problem that way, but I also started seeing it with Grafana, at least in extremis, once we migrated to self hosted on AWS from a vendor. Most times it was fine, but if we had a bug that none of the teams could quickly disavow as being theirs, we had a lot of chefs in the kitchen and things would start to hiccup.
There can be thundering herds in dev. And a bunch of people trying a repro case in a thirty second window can be one of them. The question is if anyone has the spare bandwidth to notice that it’s happening or if everyone trudges along making the same mistakes every time.
I'm a huge GCP fan, but cloud run wouldn't fit our use case because of the routing and ephemeral nature. I think you would have to try to build something yourself using GKE + gVisor
This is a valid concern, but astral just has an amazing track record.
I was surprised to see the community here on HN responding so cautiously. Been developing in python for about a decade now- whenever astral does something I get excited!
I agree in principle, but in this case uv is open source and SO MUCH better than pip it would be insane not to use it on those grounds.
With uv the worst case is it goes closed source in a few years and we all switch to a fork.
With pip the best case is that maybe in 10 years they have improved it to fix all the issues that uv has already fixed, and you only spend 10 years using shitty software and putting up with endless bugs and paper cuts.
Frankly, it’s weird. You can find this business model all over the open-source world but for some reason Astral in particular is singled out for way more criticism on this than anything else I’ve seen, despite being unambiguously great contributors who have never put a foot wrong as far as I can tell.
Microsoft – who invented embrace, extend, and extinguish – own NPM, but I don’t see people wringing their hands over them in every thread that mentions NPM. But you mention Astral here or on Reddit and people line up to tell you it’s only a matter of time before they fuck people over. Why the disparity?
NPM has always been commercial (rather than managed by a foundation), and it was nominally acquired by GitHub rather than Microsoft, so at some level as long as GitHub is not causing issues (noting the recent GitHub changes should maybe also imply some consideration of problems for NPM), NPM is "safe".
Astral on the other hand has basically been rewrites in Rust of existing community-based open source tools, for which there is always the question of how such work is funded. PYX (which is an interesting choice of name given the conflicts with pyrex/cython filenames) from what we can see here appears to be in a similar vein, competing with PyPI and making changes which seemingly require their client (uv) be used.
Anaconda/ContinuumIO was also treated with similar suspicion to Astral, so I don't think it's Astral in particular, it's more they both are operating in the part of the ecosystem where it is comparatively easy to lock out community-based open source tools (which the Python ecosystem appears to have been better at setting up and maintaining than the JS ecosystem).
pyx doesn't compete with PyPI; it's a private registry that companies can use e.g. to host internal-only packages, or to provide curated views of things like PyPI for compliance reasons.
> making changes which seemingly require their client (uv) be used
That's an explicit non-goal: "You won't need to use pyx to use uv, and you won't need to use uv to use pyx."
Astral are not causing issues though. Why does “as long as Astral is not causing issues” not apply?
> Anaconda/ContinuumIO was also treated with similar suspicion to Astral
I haven’t observed this. I have seen condo talked about a fair amount but any issues have always revolved around it being awkward to use. But practically every discussion here or on Reddit about Astral has FUD.
Sorry, the quotes around safe were supposed to imply GitHub is not that safe in my opinion, but it's possibly why other people aren't concerned about NPM (also, being for a different programming language and community may help).
Anaconda/ContinuumIO (the company) was absolutely treated with suspicion, see e.g. https://www.mail-archive.com/numpy-discussion%40scipy.org/ms... (and you'll find many such threads around that time on mailing lists/forums that were where the scientific python community was), and while the sky didn't fall in, their history hasn't been spotless. In many ways Astral is the more "webby" version of Anaconda/ContinuumIO, and so assuming Astral will behave (and evolve) in a similar way to Anaconda/ContinuumIO seems to me at least to be a rational thing to do?
While I agree with this particular point, it's weird not to share the slides, everything else rings true for me. I graduated college about a year ago, and so much of this I just took for granted. The class would just get smaller as the semester went on and more people 'disappeared'. In a lecture hall of 200 people, do you really think that my classmates weren't on their phones constantly?
Empirically, literacy rates are dropping. The anecdotes match the data. Why are you trying to negate this article?
This phrase in my comment: perhaps not it kind but at least degree
That not negation of the article and is instead questioning the extent to which their observations are accurate vs caricatures influenced by an outlook on their customers that is already, in software terms, “user hostile”.
The last time I taught in college was about 8 years ago at a school with a similar demographic fit, and I can recognize a fair bit of what the author say but not at all to this degree. I still work in the industry and there’s a post-Covid shift that I think strongly explains a sharp downshift in students feeling attendance is important, but I think that aught to resonate with the HN crowd with respect to a now-common feeling that dogmatic adherence to mandatory full work-from-office isn’t necessary or worker friendly. Consider all the more how that feeling would take hold for young students that spent significant formative years just prior to college being fully or highly remote.
On literacy, that’s an area I have some analytical experience in. As far as I have seen, at least a fair bit of this perception is from the fact that students view homework etc as low-stakes writing but higher stakes get more attention and the end product reflects more ability than might otherwise be shown. Also, the professor in this article may simply not be adept at getting the best results from a group of students that sense the dislike aimed their way. However my analysis side also predates ChatGPT.
I think the good news is that we can adapt to enjoy how warm it is in hell. So it’s bad news that we’re going to hell, good news is that we’ll eventually like it.
