Terminal world models have many applications, but empirical observation of successful pretraining or fine-tuning can be significant for encapsulating from rather expensive Bash traces, such as ones obtained from systems with scarce accelerators, etc.
That seems as likely as anything, but that just moves the problem to counting, doesn't it? The model gets the duration, but I was under the impression LLMs struggled with the arithmetic that would make that useful.
Idk about Qwen 2.5 Coder that this is based on, but modern models don't struggle with arithmetic. I see Qwen 3.8 do long-hand math in thinking traces (ie. it outputs 17+35+12=64 in the thinking trace) so I would imagine this is something they train into the model specifically. Sometimes it creates and runs a simple python script to check its work.
I regretfully suspect this actually is the future of UI. You just need smart local models running fast enough to keep the framerate high, but that’s inevitable in the long term, isn’t it?
Why would you do that instead of having the model do the task you're using the UI for? It could generate UI widgets on-the-fly as needed to take richer inputs.
It might be fine for one-off applications, but it's incredibly inefficient for something that has to be done over and over.
Why would you use a GPU and gigabytes of VRAM running a non-deterministic process for something a program can do on the CPU in milliseconds with kilobytes of memory?
The logic here is a bit different. Consumer software is slow because it's the user paying for the compute to run those bloated Electron apps. Unless we're all running local inference in the future, it would be the vendor paying for the compute to run the AI-based UI.
Of course they would pass that along to the consumer, but nobody is going to pay token-based pricing for something that used to be free and ad-supported, or a cheap monthly subscription.
We had decades to work on making software more performant, but instead most users' space-age 2nm chips spend most of their time running Javascript because it was faster to make software that way.
>I used Qwen2.5-Coder-0.5B as the starting point. I needed a small model to get close to realtime performance. Being a coding model, it had also already seen plenty of CLI usage.
Interesting. I wanted to ask, how that model compares with the fine-tuned version, but I just saw the author provided benchmarks. Interesting that fine-tuning improved the memory so significantly, wouldn't have expected that.
Though, on second thought, I had an experience where I was able to, as a human, fine-tune my memory beyond what I previously thought was possible!
Reminds me of one of my favorite jokes.
Excuse me, can you tell me how to get to Carnegie Hall?
I wonder how much a small model could do if it had a tool calling API for a filesystem, an embedded busybox, and a single output token for 'emit what busybox would do here'. Then presumably the model parameters could learn everything that diverges from what busybox can handle.
Very very cool! Impressed that it can be done with only fine tuning
On a very similar theme, I recently built a “lossy git”, which stores your code by training a neural network, then it generates the code back to you on checkout
The web demo runs entirely on the browser, both the version control and the neural network training/inference. You can commit and checkout both images or code - you can also paste/“upload” your own images, no server requests (everything clears from the page on reload)
That's very cool project.
I read this blog post a few days ago about one way to look at memory encoding in models https://alexwang.ai/posts/associative-memory/ that you might find relevant
I tried it and is not working remotely as close as the demo gif. It keeps telling me that "test1" folder that I want to create already exists (not true), then letting me do "mkdier test2" (sic) but then the folder is not there when ls.
I mean all its expected, this has no state, follow no rules and is making up things from the current context, but is not as "magical" as I thought when watching the demo.
obilgic | 2 hours ago
prodigycorp | 2 hours ago
andai | an hour ago
https://www.youtube.com/watch?v=z3pV6FHvcgM
htoz1 | 2 hours ago
dotancohen | 2 hours ago
mehmetoguzderin | 2 hours ago
YmFzZTY0ZGFkZHk | 2 hours ago
good-idea | 2 hours ago
aghuang | an hour ago
antonvs | an hour ago
wartywhoa23 | 30 minutes ago
Not unlike that American tractor pulling, but for geeks.
Clouds of smoke, weeeeee!
oilkillsbirds | 2 hours ago
psandor | 2 hours ago
https://www.microsoft.com/en-us/research/articles/whamm-real...
soulofmischief | 2 hours ago
__mp | 2 hours ago
prodigycorp | 2 hours ago
The tmux thing had me confused for a moment until i realized it was emulating tmux. Super fun. Is the model window width aware?
serje1480 | an hour ago
yjftsjthsd-h | 2 hours ago
peder | an hour ago
simedw | an hour ago
sprobertson | an hour ago
Time is an input too. If nothing is pressed, the model gets a WAIT token with a duration instead of a key, which is how snake keeps moving on its own.
yjftsjthsd-h | an hour ago
skohan | an hour ago
thorum | 2 hours ago
gr_norm | an hour ago
skohan | an hour ago
Why would you use a GPU and gigabytes of VRAM running a non-deterministic process for something a program can do on the CPU in milliseconds with kilobytes of memory?
antonvs | an hour ago
Edit: oh, I saw you wrote a related comment after I posted this. So you already know that direct LLMs for UIs are all but inevitable...
hnlmorg | an hour ago
It’s cheaper to hire developers, and quicker to ship software. The user experience be damned.
skohan | an hour ago
Of course they would pass that along to the consumer, but nobody is going to pay token-based pricing for something that used to be free and ad-supported, or a cheap monthly subscription.
skohan | an hour ago
winrid | an hour ago
If I had to complain about any site/app it'd be datadog... each tab uses like 1gb in firefox.
latentsea | 31 minutes ago
andai | an hour ago
Interesting. I wanted to ask, how that model compares with the fine-tuned version, but I just saw the author provided benchmarks. Interesting that fine-tuning improved the memory so significantly, wouldn't have expected that.
Though, on second thought, I had an experience where I was able to, as a human, fine-tune my memory beyond what I previously thought was possible!
Reminds me of one of my favorite jokes.
Excuse me, can you tell me how to get to Carnegie Hall?
—Practice, man... Practice...
simedw | an hour ago
treetalker | an hour ago
Please elaborate.
Lerc | an hour ago
alex7o | 30 minutes ago
Lerc | 24 minutes ago
On thin that would be useful is an entirely new permission system for files to allow for users and agents acting for users.
nico | 52 minutes ago
On a very similar theme, I recently built a “lossy git”, which stores your code by training a neural network, then it generates the code back to you on checkout
* web demo: https://nicobrenner.github.io/lossy-git/demo.html
* cli version: https://nicobrenner.github.io/lossy-git/
The web demo runs entirely on the browser, both the version control and the neural network training/inference. You can commit and checkout both images or code - you can also paste/“upload” your own images, no server requests (everything clears from the page on reload)
simedw | 34 minutes ago
donperignon | 46 minutes ago
gitowiec | 33 minutes ago
odo1242 | 16 minutes ago
ohadpr | 9 minutes ago
I did it for web apps last year, basically a web server that feeds all requests into an LLM.
https://www.ohad.com/blog/voidware/