Tootfinder

Replies to comments on my "#LLMs are eroding my career" post
https://human-in-the-loop.bearblog.dev/replies-to-comments-on-my-llms-are-erodi…

One solution here is to make corporate LLMs unprofitable. Local resistance to data center build-outs is already making that happen, as is other resistance. But, honestly, they're already doing a pretty good job of this themselves. The "AI industry" has never been profitable. Local #LLMs can't be monetized and also can't be prevented.
It's simply not possible to recover training investment costs by charging for inference. I guess they can try to make local models illegal, which is totally possible. I can totally see corporations pushing through a bunch of regulation to "control uncensored models" creating a black market for ablated models.
It's gonna be a wild ride. Get your dark nets ready, we're very close to an even more cyberpunk future.

Just wrote yet another piece on LLMs that has been kicking around in my head for a while: "What kinds of conversations can you have with a parrot?"
#AI #LLMs

Replies to comments on my "#LLMs are eroding my career" post
https://human-in-the-loop.bearblog.dev/replies-to-comments-on-my-llms-are-erodi…

I have some sketches of an essay that I need to write, but I think it's worth brain-dumping a bit more in the mean time.
#LLMs are an attempt to make tech grow forever. But like, how many "your mom/a friend, but done by a precarious worker instead" apps do we really need? Everything right now is in the AI grift hole, but there's almost nothing of interest (even if you ignore the ethical concerns). Like, no, I don't fucking want a robot to lie to me about my groceries. That doesn't sound like a useful feature. There's a lot of useless shit being pumped out to prop up the bottom line, and a lot of people just want to be able to use their old phone for more than a couple of years.
No one is happy with this. No one wants this. Except the billionaires who are forcing us all to drink the capitalism koolaid, because they'd rather exterminate life on earth than live in a world where they experience consequences.
Nothing grows forever. That's not how literally anything in reality works, or has ever worked, at all in history. Some people think that the universe itself may work like that, but that's only an educated guess. Finite things don't grow forever. Every organism, every society, every technology, every dynamic and adaptive system we have ever known goes through a growth phase and then goes in to a stabilization phase. Or, following a Malthusian pattern, grows until it reaches a catastrophic point and collapses. Like lemmings. Or reindeer. Or cancer.

I guess it is kind of nice that #LLMs are just as prone to getting confused by ambiguous wording in architecture specs as I am. I'm looking at you FEAT_FPRCVT.....

Can someone explain to me how the argument “ #LLMs only produce stochastically reasonable output ” is relevant or convincing?
Like… how do you think a human brain forms a sentence? Is this a straight-forward deterministic process? Does it matter?
I use tools and processes to verify my output and so does a coding agent.
I just don't get how this is an argument that would…

Just realized that the fact that newer large language models keep getting bigger in terms of parameters is kind of a tell about how they work, even as it's also kind of a requirement from the investment standpoint.
Very roughly, models develop complex functional internal state about some sub-domains, and merely memorize many examples in others. In reality it's more complicated than this and even in this simplified metaphor it's a mix between memorization and "real" "understanding" in each domain. But the point is that if companies were really working towards AGI, they'd be feeding more data into models with *fewer* parameters (that's how you force a model not to memorize) instead of building bigger and bigger models (expands the illusion of competence through increased capacity to memorize).
But being the only ones with the hardware to train an even-bigger model is one of their few competitive advantages, and signing new deals for even more hardware is one of the only ways they can signal to investors that they'll retain their advantage and thus not be destroyed by a food of competitors. That's also how they can convince the hardware dealers like NVidia to continue with circular investments. So they have to run in that direction, regardless of the scientific merits.
This is why someone like LeCun would leave that side of things.
#LLMs #AI

#LLMs als Chance für die #Linguistik
https://doi.org/10.37307/j.1868-775X.2026.02.08
(Open Access)
Meine Kollegen am @… und der Erstgutachter meiner Diss Lars Konieczny (U Freiburg) haben einen – wie ich finde – feinen Beitrag in der Zeitschrift 'Deutsche Sprache' veröffentlicht.

Emergent #Misalignment: Narrow #finetuning can produce broadly misaligned #LLMs

“To waste what little bandwidth we have left – when 750 million people worldwide lack access to electricity – assisting some of the richest men ever to hone their plagiarism bots would be a historic idiocy that future generations are unlikely to forgive today’s leaders for.”
#StochasticParrots
#LLMs

The thing I love about computers is that I can tell you basically anything about how a computer works, not because I know everything but because I know how to figure it out. I can walk you, over the course of an hour, two, there, maybe more, though every step of typing something into a web form, from the electrical signals that get turned into digital via an ADC, to the USB controller memory, to the kernel driver, to user space, through the application stack, back down to the kernel, to the network driver, through routers, up the server stack, TCP/IP, key exchanges, etc.
I don't mean I have the time to dig into these things. I used to, and it was fun. I've given more than my fair share of interviews talking though variations of this. What I'm talking about isn't pure knowledge, but that, given relatively simple theory, and the right tools, every action of a computer can be understood down to the limits of physics.
The thing I hate about #LLMs is that take something comprehensible and make it something almost completely opaque. Even with a solid understanding of the theory, literally no one understands what's happening. That is shit. It makes playing with technology not fun anymore. The way in which companies are making things even more opaque by running stuff in the cloud is everything I hated about closed source on steroids.

You can't make this stuff up (or can you?):
#LLMs #AI #GenAI

Are you in tech and outraged about generative AI? Is it being forced down your throat at work?
Here's a nice vindictive way to get a little revenge if you want:
1. Find a project that contains slop code.
2. Optionally, identify specific files or functions that are LLM-generated. I guarantee you that on average, this code has not been adequately tested/inspected, even/especially if it contains LLM-generated test cases.
3. Make up a reason the code could be flawed, bonus points if it's subtle or hard to test. Don't put effort into this or try to actually find a flaw. Just make something up at random.
4. Report your made-up defect as a bug.
That's it. If anyone ever questions you on the incorrect report, just say "oh I used an LLM and it said there was a bug so I reported it." (Don't actually use an LLM, that would be feeding the bubble.)
Note that you are showing the creator of the code the exact same amount of disrespect that they've shown you by publishing slopcode in the first place. I'd bet odds are 50:50 or better that if a human actually follows up on the report, even though they'll find out that the bug report is wrong, they'll find and fix some other subtle flaw in the LLM-generated code, so this is actually helpful in a way.
For step 3, try to get creative. Like "logic in decideUVParameters can cause state to be inconsistent in some cases." If asked for a steps to reproduce, either make one up if it's easy to do so, or say "I forgot how I triggered this." Surely they can ask an LLM to figure out conditions that would trigger the bug ;).
#AI #LLMs #GenAI

Dear generative AI enthusiasts,
Look, I know the tokens you're burning right now don't actually use *that*much energy (even though it's somewhat substantial already and disastrous when we take into account the quality of the crap it's being used for) but what's more important is the appearance (or not) of that token spend on the quarterly earnings report of OpenAI/Anthropic/etc. lays the foundation necessary for those companies to go ahead with their plans for datacenters on a truly ridiculous scale, and those datacenters, if built, ate indeed a climate nightmare which *my kids* will have to live through even if they never benefit from any of it at all. That's (one of many reasons) why I personally need you to stop using generative AI right now.
The fact that the output is crap, the way it erodes your intelligence, and the ways in which it plagiarizes and actively undermines good citation practices are among many other practical reasons not to use it, but what's personal to me is the way that your frivolous sloperation is making the future worse for the baby I'm feeding blueberries to as I type this, and half the time I interact with people like you the conversation begins with some form of "putting aside the ethical issues..."
#AI #GenAI #LLMs

Thinking about some things that @… said in another thread, and as someone who advocates against AI hype and against the use of most generative AI in most circumstances, I feel it's important to say: many of the ethical issues with using generative AI mirror almost directly the ethical issues with living/working on land stolen by colonists, except that they're less harmful.
Arguments like "well we don't really know whose work it's ripping off this time" and "artists that post their art online know it's going to be looked at; this is the same thing" and "well it's inevitable and everyone's doing it so it's unreasonable to make a big deal about it" directly echo arguments like "well now we don't know whose land it was any more exactly" (yes, we do; you can literally go look up the website of their descendants), or "the natives weren't really using the land anyways", or "it's all in the past now, and it's unavoidable." That unavoidable one is actually somewhat true of using stolen land, at least compared to LLM usage.
If you can see through those lies in the case of AI hype but choose not to do so in the case of colonialism, that says something about your priorities and allegiances.
This is not at all a call for people to talk less about AI; rather it's a call for those who take opposing AI hype seriously to look around and make some noise about other injustices too (I realize many of you already do this).
#AI #LLMs #LandBack #GenAI