Oh, they are. It's just that the harness around it is able to pick up the commands it "autocompletes" and runs them for you. LLM can't run anything, it never could.
If you wonder why your Copilot subscription has new limits that you hit every few days, it's because of PhDs like Adam.
Could Adam use a local model hosted on his own box? Probably yes. But he preferred to waste the service we all use just to produce a weak blog post that introduces absolutely no knowledge and serves no other purpose than to tell everyone that the author likes to waste resources and calls it "fun".
> Ridiculous? Yes. Wasteful of tokens? Sure. Fun? Oh yeah!
Do you really think it's fun to be one of these people who are the reason why the rest of us gets more limits?
That's why LLM will eventually be used only for initial interaction between the user in their language, to prepare the data to a specialized model.
Imagine face recognition to work like a text chat, where the PC gets the frame from the camera and writes in the chat: "Who's that? Here's the RGB888 image in hex: ...".
Huh? The images are tokenized in the same way language is and it’s just fed into one single model. Not multiple smaller expert models.
Image gets rasterized into smaller pieces (eg 4x4 pixels) and each of those is assigned a token, similarly how text is broken up into tokens. And the whole thing is fed into a single model.
> Imagine face recognition to work like a text chat, where the PC gets the frame from the camera and writes in the chat: "Who's that? Here's the RGB888 image in hex: ...".
The experts in MoEs aren't specialized in any meaningful task sense. From level of what we would think as tasks MoEs are selected essentially arbitrarily per token and per block.
It’s unsupervised, yes, but “unspecialized in any meaningful task sense” is incorrect, that’s the whole point. It’s just not in the sense of “this is a legal expert, this is a software developer”.
Now do the equivalent of just in time compilation. Claude sees that we need to respond to a lot of pings and writes a program to compute it instead of thinking about each one.
Wouldn't this be faster with an agent skill that has code?
/skill-creator [or /create-skill] Write an agent skill
with code script(s) that use an existing user space IP library that works with your agent runtime, to [...]
You could read about that in 1992 "A Fire Upon the Deep" by Vernor Vinge. There is prompt injection in communication, in the book certain protocols for information communication can not be deterministic so if someone is too smart you get hacked.
Do some people still claim "LLMs are just dumb auto completers"?
Because this seems to disprove that claim pretty convincingly?
Oh, they are. It's just that the harness around it is able to pick up the commands it "autocompletes" and runs them for you. LLM can't run anything, it never could.
This is cool, let aside the token usage, perhaps it can help analyze tcp throughput by redirect wire shark/to dump result
Opus 4.6 is already very good at troubleshooting all kinds of network problems if it has access to the command line tshark tool and the pcap files.
How quickly claude responds when it acts like a user space LLM chatbot?
If you wonder why your Copilot subscription has new limits that you hit every few days, it's because of PhDs like Adam.
Could Adam use a local model hosted on his own box? Probably yes. But he preferred to waste the service we all use just to produce a weak blog post that introduces absolutely no knowledge and serves no other purpose than to tell everyone that the author likes to waste resources and calls it "fun".
> Ridiculous? Yes. Wasteful of tokens? Sure. Fun? Oh yeah!
Do you really think it's fun to be one of these people who are the reason why the rest of us gets more limits?
Don't hate the player, hate the game.
think about how much faster it would've been with a small local model!
Modulo Anthropic messing with the model for load mitigation, I wonder how stable this result is.
1,000 pings, how many correctly ponged?
>Fun? Oh yeah!
I think this author and I have different definitions of fun.
That's why LLM will eventually be used only for initial interaction between the user in their language, to prepare the data to a specialized model.
Imagine face recognition to work like a text chat, where the PC gets the frame from the camera and writes in the chat: "Who's that? Here's the RGB888 image in hex: ...".
That's actually how vision language models already work, pretty much.
And there's a reason nobody uses them for face recognition
Vision language models are an incredible achievement in the generality and usability. But they pay a hefty price in fidelity and speed
Huh? The images are tokenized in the same way language is and it’s just fed into one single model. Not multiple smaller expert models.
Image gets rasterized into smaller pieces (eg 4x4 pixels) and each of those is assigned a token, similarly how text is broken up into tokens. And the whole thing is fed into a single model.
Yes I'm saying
> Imagine face recognition to work like a text chat, where the PC gets the frame from the camera and writes in the chat: "Who's that? Here's the RGB888 image in hex: ...".
that's p much how it works.
But that isn’t a specialized model like the grandparent claimed, but rather a single, multi-modal model.
Yes, the "imagine" was showcasing the opposite of a specialized model to call it a bad idea.
Do you know that MoE is a thing?
The experts in MoEs aren't specialized in any meaningful task sense. From level of what we would think as tasks MoEs are selected essentially arbitrarily per token and per block.
It’s unsupervised, yes, but “unspecialized in any meaningful task sense” is incorrect, that’s the whole point. It’s just not in the sense of “this is a legal expert, this is a software developer”.
Now do the equivalent of just in time compilation. Claude sees that we need to respond to a lot of pings and writes a program to compute it instead of thinking about each one.
Wouldn't this be faster with an agent skill that has code?
/skill-creator [or /create-skill] Write an agent skill with code script(s) that use an existing user space IP library that works with your agent runtime, to [...]
ComposioHQ/awesome-claude-skills: https://github.com/ComposioHQ/awesome-claude-skills
anthopics/skills//skill-creator/SKILL.md: https://github.com/anthropics/skills/blob/main/skills/skill-...
/.agents/skills/skill-name/SKILL.md, scripts/{script_name.py,__init__.py}
https://agentskills.io/what-are-skills
Well, yeah, of course it would be.
Even faster would just to be use code in the first place!
Next up: Claude replacement to handle simdjson processing.
Perhaps one day, all network services will be provided by LLMs natively. Truly, that would be a day in the future.
You could read about that in 1992 "A Fire Upon the Deep" by Vernor Vinge. There is prompt injection in communication, in the book certain protocols for information communication can not be deterministic so if someone is too smart you get hacked.
"Perhaps" doing enough lifting to participate in a bodybuilder contest, in that sentence
why? We already have more efficient specialized hardware.
I mean, we did decades of JavaScript, so... I mean... anything is possible, right? :)