There were pretty specific rumors about IPOs happening early this year, up to apparent insider bets on prediction platforms. Don't you think the apparent delays in IPOs could have something to do with the open weights/open source competition? The monetary conditions (interest rates,...) were better early in the year, arguably predictably so, so what would be your alternative explanation why they chose to postpone?
CachyOS is probably the new hip one (Arch-based). The new "I use x, btw" would be nixOS. Arguably, the distro matters a lot less if you use containers or flatpaks for most things (which I'd recommend). So there's nothing standing against using just boring Debian stable these days, or Fedora if you want to be more up to date. If you like fancy UI, check out the COSMIC desktop environment, should work on all mentioned.
> The theory of computer science has become a form of mathematics that doesn’t concern itself with computers
Perfectly summarizes the post, which I'd qualify as a rant (the author's main gripe seems to be that it's a galactic algorithm that assumes exact complex multiplication). The theory of computer science literally started with a mathematical model by Alan Turing that has nothing to do with computers (ones that we could physically build, anyway; and even if we brush aside the infinite tape, it's just architecturally completely different from any computers we've ever built for practical purposes).
If you don't like mathematics without direct applicability, you do you, but it's a legitimate mathematical undertaking and cheap way to rant against this AI drop. Do the work and show us if there's a mistake in the argument, missing attribution or whatever actual problems with the paper, but spare us your sour grapes.
Yes, I don't remember what it was, probably something with file sync, but I remember I kept a WSL1 instance for some stuff for a long time. Rust tooling worked great in WSL2, as long as you stay inside the VM, can confirm.
Those machines generally come locked down, so you need permission to install stuff like WSL. I had such a machine and asked if they could unlock WSL, and their answer was no, because Security was busy enough keeping the Windows installs reasonably secure. Which makes sense tbf. Would be surprised if that was much different in other places.
My memory is a bit foggy, but the original WSL promised a much more ambitious integration with Windows, sort of container-like. It had a lot of rough edges when I first tried it, but they promised they'd work on it. That, together with their promise that W10 would be the 'last' Windows and they'd switch to a more OSX-style rolling release model was what pushed me to adopt Windows in ~2016. In the first years of Nadella it really felt like MS would listen to developers. Within less than two years iirc they'd given up on the WSL ambition, with WSL2 being a mere VM with noticeably worse performance for some of my workloads, and completely reneged on their W10 promise, even vowing to make my newly purchased hardware obsolete.
In summary, I really couldn't care less what improvements they make to WSL, and neither should anyone else, imho. If you want Linux, for the love of god just use Linux. Emulation works great the other way round by now.
Sure, or let's say, no one else who was doing it before was noticed at this level. But hype doesn't automaically equal a paradigm shift. In this case, it's going back to what used to be one of the most common ML applications until not so long ago. If anything it feels like going old school (and I really mean that in a good way).
Paradigm-shifting? A classifier? It's great and it's a cool use of Transformers, but AI really didn't only start with LLMs. We used to have an own term for AI models that can handle text instead of just numbers, now a model that outputs numbers instead of text is considered paradigm-shifting.
The "island economy" can be an interesting thought exercise for getting a feel for economic concepts, from money and banking all the way real estate and taxes.
Imagine the first guy arriving on the island claiming the whole land to be his, and any successive arrivers have to rent space from him in perpetuity, at rates he can decide. Not great, but not immediately obvious what a minimally-invasive improvement would look like. Now add a tax that forces the first guy to pay for every acre of land he claims. This incentivizes him to give up some of the space he's not actually using. Even in the most "rational agents in a frictionless vacuum" type of free-market macroeconomic model this is going to lead to a more efficient outcome, even more so in a more Keynesian-style model.
Cool idea! Do you happen to have benchmarks for Strix Halo (AMD Ryzen AI Max+ 395)? I take it that Qwen3.8-Flash-Next is not supported?
And a more general question: does your engine detect and optimize for custom setups like multiple (possibly different) GPUs, eGPUs,...? Because if all you have is a stock major system like a Mac or DGX Spark, that's all you're going to care about, and there are a lot of highly optimized single-hardware engines out there that will be hard to beat in the long run. Something that automatically adapts to custom systems that don't have their own subreddits could really fill a gap.
LLMs are like lossy compression of ~all of written text ever produced, with useful recall. To the extent that the corpus contains labelled examples of the given classification task, it's not unreasonable to think that we'll be able to build a decoder for that, just like we already have a useful decoder for next-token prediction. Extend to image classification the same way we already have multimodal LLMs.
A pre-trained universal classifier that can replace specifically-trained ones would have been considered just as much science fiction in the 2010's as the capabilities of modern LLMs. I'm not sure Jev is actually there yet, but at least it sounds theoretically doable today.
That being said, one thing having been unrealistic 10 years ago and just about possible today doesn't mean that it's going to change the world the same way another technically related, previously-impossible thing did. The Jev hype gives me a bit of the "you're still early to crypto" vibes of some later altcoins. I really like the idea, I think it's going to open up possibilities for using classifiers where we wouldn't or couldn't have trained one before. I'm crossing my fingers for an open weights version to drop. But it's still just a classifier, people have built similar things before Jev, the one thing that really stands out about it is their ability to generate hype.
Can't speak to it myself, but I'm not sure how up to date your infos are. I read a lot of reviews and it seems that the vast majority of the Steam catalogue should run fine on Linux today. The most important exceptions are games that require kernel-level anti-cheat (so if you're into online multiplayer games that could be a dealbreaker).
You're saying this as if emulation (which Wine isn't really, more of a Win32 API compatibility layer; WINE literally stands for 'WINE Is Not an Emulator') was a bad thing. In reality, a lot of games benchmark better on Linux than on Windows today.
Two projects that I found brought nice, more incremental changes to the desktop were COSMIC and Omarchy. Both use tiling, COSMIC's mixed tiling+floating workflow is amazing and my new daily driver. I personally don't use Omarchy for a variety of reasons, but I'd love to be able to install its launcher and keyboard integrations.
My source: https://news.ycombinator.com/item?id=49783999#49785351 I don't have an account so couldn't check, a friend couldn't replicate with a one-letter attempt (first letter A 50%) - performed by his agent, though, so not sure what he did exactly.
You can also just acknowledge that we don't know what they did (and that is because they chose not to tell us). Apparently, until not so long ago Jev would spell out its identity as Q-w-e-n if asked, so we might as well assume that what they did is at least similar to what this blog poster did (who chose to tell us).
Extremely fast hype and a very short-lived moat, those are the parallels that I see. The OpenClaw guy was smart to cash out at peak hype, and chances are Jev will be overrun by big players and open-weights pretty soon too. I really love the idea, but just don't see any sustainable moat here. People are already coming knocking [0], it should only be a matter of time until we get something like Qwen3.9-Classify (or a classify mode just baked into a multimodal LLM).
Technically, Q is picked because it has the highest probability of all letters. But it makes a difference whether the probability for Q is barely above a uniform 1/26~3.8% or whether that one letter concentrates >50%. What I remember from reading the docs is that Jev gives you the full probabilities (and the confidence, which is something like normalized entropy).
But in general, we might be reading too much into this. If I were to build something like this, a Qwen model would be among the first things I'd reach for too. Initially just prompted inside a little harness to guarantee you get the desired output. Next step would be finetuning, finally training your own foundation model, if you can muster the funding. In this fast-moving space, I think it's quite understandable that they'd go public with an MVP asap, so likely not much training on their own. And even if they're finetuning, Qwen's baked-in answer (through Alibaba's finetuning) seems likely to survive unless it was explicitly overridden.
Once the first letter is Q, the rest is probably pretty determined. Can you see the confidence for the first letter (don't want to accept the ToS to follow your link)?
I mean, there's centuries' worth of mathematical prose to train on. But that's presumably already in the training data, so if it isn't good enough today, it might not get better fast enough to keep track with how fast they'll get better by training on formally verified math. But then again, the prose in Terry's conversation I linked above seemed pretty useful. But it's also a problem requiring famously little advanced mathematics.
> Separately, our group has been exploring approaches along these lines to make such evaluations more scalable
Actually, that sounds like an interesting idea for peer review in general, to include an interview between referees and authors. If it saves one round of rebuttals/reactions, it needn't even consume a lot more of everyone's time if you're doing those things properly. What it would undermine would be blindness, but something's gotta give, and it was already on its way out.
> we might imagine what it could look like to have an analog of the Millennium Prize Problems for open exposition problems
The core idea seems to me that we should shift the standards for professional evaluation from generating proofs to generating explanations. Makes sense that such a proposal would come from the 3B1B guy, and I actually agree with it, irrespective of AI. But what eludes me is how that could be a defensive mechanism against AI automating humans out of mathematics. AI is likely no less good at producing natural language explanations as it is at generating rigorous proofs. It's telling that even Terrence Tao turned to AI to understand AI-generated results [0]. It seems that the essay doesn't address that issue at all.