pantelisk

Compute motion vectors, faux depth, and add DLLS5 to it maybe? Could be pretty cool, or pretty horrific. Only one way to find out :)

Brilliant work! Testing it now

LLMs have higher accuracy and are more versatile, but that comes with a cost (slower and more expensive to run). On the other end you can have your own mini classifier something built on top of ModernBert or DIET or even a hash classifier (these are narrow).

Models like JEV fall in the golden middle. Somewhat cheap, somewhat fast, somewhat general.

Many people have replied saying that narrow classifiers (eg bert) are better, and they are better in many ways (essentially free and faster). But... the real world is messy, in my experience it's actually very hard to make a really good classifier that will work across a specific niche domain especially when there is very fuzzy input. And the most interesting real world scenarios are nuanced and overtime there will be edge cases discovered where it fails and that will require retraining and re-evaluation etc etc. It can becomes a full project on its own that eventually collapses into a game of whackamole (improved in some direction but regressed elsewhere). Things like JEV (or similar models) provide ease of mind, just off load the complexity to it and move on to the next challenge type of thing.

Yes, the best way to think of a general classifier like this is like a smart switch statement. Essentially a "JEV" like thing becomes a sort of programming primitive. Once you see it, it's hard to not get excited.

But even if others surpass them and make better solutions, the fact that nobody was able to see it before typesafe is a testament of what they might be able to come up with next.

I sound like a fanboy but I swear I 'm unaffiliated with typesafe. I was building my own version of this way before they announced JEV (mine was ALE and it was mentioned here on HN for a bit), in use for VR gaming (so one can give commands to NPCs with voice and supports multiple commands in sequence in a single pass), but I missed the "killer usecase" of being a new primitive, like everyone else.

TLDR: There's a lot of value in thinking ahead and seeing the future. The clones are nice and exciting but they give me "I could have built this first, yes but you didn't" vibes. I hope they manage to keep it up and push the space forward again

Same thing happened with the release of a certain programming language release a few weeks ago (that seemed fully vibecoded, which is fine - but was making multiple grandiose claims which attracted pushback). Multiple sock puppet accounts defending the poster religiously with attitude that is more reserved for reddit than HN.

It happens more around things related to vibecoding, it seems that influencer culture, empowered by this new tooling is starting to distort tech communities.

More and more of this will be happening. Before AI coding - the fact that something like photopea or pixelr existed, meant that somebody had to commit years of focused work to even reach to the same level of quality before starting to provide value and attract users. That alone made copying a losing proposition, or at least it raised the bar as it meant one had to be a truly exceptional and dedicated builder.

Nowadays it can happen in an evening, so everything successful will get copied and attacked and attempted to be displaced by noise and hype on certain internet circles alone. Like the cheap ridiculous clones of popular ios games and apps of the past, except now on an industrial scale!

I guess this is the new world we are entering, but can we at least keep the astroturfing and bad faith behavior away damnit?

I think what the parent poster is trying to convey, is that let's say the apocalyptic stuff has a 5% chance as you say, but the non-apocalyptic stuff (social-economic chaos, total centralization of power, eradication of social mobility, total information/trust collapse) might have a good 95% chance as we are seeing it starting to unfold already.

But the discourse is dominated by paper clip experiment discussions and not let's say by the fact that new grads have an unprecedented difficult time getting jobs. Unsurprisingly one of those is a sexy hypothetical beneficial to power and the other one is not.

Multiple problems can be important, pointing a different one out doesn't invalid or take away from another one.

Main difference is; this a desktop app, whereas audiomass is web.

In theory native apps have a higher ceiling for long processing tasks, since they can offload buffers on the disk and reuse - versus having to fit everything in browser memory.

No inference providers. It's all local, meaning all on your own device. The app writes no logs on your disk, so technically it's on-device + zdr?

I think the doom demo uses a text representation of the world and it's basically, "projectile coming your way" -> "Strafe". "Enemy ahead" -> "shoot. So it works well when spawned in a room of enemies (as we see in the video).

If self driving is red means stop, green means go, and stay in your lane - then it would work great, but having to actually think and test which maneuver is optimal for a given situation while weighting safety, road rules, random unexpected actions and getting to your destination, I think it's a much bigger problem. A bigger model specifically trained on that maybe would do great, but then the output is not the constraint anymore.

But I haven't tried the model, so I 'm just ballparking and could be very wrong.

But how did they know about it? Did the agents independently discover it? Did the model know about it because this vuln was public somewhere and systems weren't patched yet?