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This feels like it's a less shitposty and thoroughly expanded version of my "Uber for artisanal cheeses, but on the blockchain" theory that I had.
Our flagship application has seen continuous development since the mid to late 2000s, and it's loosely based on a codebase and product that is considerably older than that. While it has CRUD elements (any application that functions as a long-running service must), it has some fairly extensive components that actually do things with that data in terms of business automation. Those are the areas where all the existing LLM solutions tend to fall apart. Given that they're statistical engines, going farther from CRUD is a very bad thing.
I'm not sure if I can fully buy into this. It wasn't that we were surfacing implicit context, so much as writing it for a very enthusiastic intern developer with absolutely no sense of self preservation. If we didn't break tasks down to an absurd level of guardrails and hand-holding, it would try to make enormous, system wide changes without any kind of midpoint validation. Sometimes we'd see the reasoning say things like "I have made a large number of changes. I should run unit tests to verify that I am correct", and then it just... wouldn't do it. Any of the server developers could have finished the full task in the time it took us to make the tickets that allowed the LLM to do the job without going off the rails.
Yep, I've seen this too. I have to ask, where you using any of the terminal based tools for code development (i.e. Claude Code). I know you said you were using Gemini, so I am doubting it was actually Claude Code (although you can run Gemini within CC).
There is a lot of guardrailing and handholding built into to these tools. If I pass a full system design doc to Claude Code and explicitly instruct it to do TDD with unit tests etc., it will.
LLMs aren't beings, people, or minds. If you think of it as having intention and character flaws, you're going to get frustrated quickly. If you think of it is a very imperfect and probabilistic tool that outputs into non-deterministic solution spaces, you'll get less frustrated and probably think differently on how you prompt it.
I am an unrepentant AI bull. I'll admit that and let people judge whatever I write with that bias in mind. I only request the same from the bears. When I see sentiment like this, which literally chastises a matrix of numbers, I have to assume a non-neutral bias.
I disagree with you here.
Setting aside the deep philosophical questions about personhood (which threaten to derail any productive discussion), I claim that LLMs are minds - albeit minds that are simultaneously startlingly human and deeply alien. Or at minimum, they can be usefully modeled as minds, which for practical purposes amounts to the same thing. (I should note: this position doesn't commit me to "AI welfare" concerns, or to thinking LLMs deserve legal rights or protections, or to losing sleep over potential machine suffering. You can believe something is a mind without believing it has moral weight. I do, I'm an unabashed transhumanist chauvinist.)
More importantly, I think there's nothing wrong at all with modeling them as having "intention or character flaws." if you use a variety of models on a regular basis, like I do, I think that becomes quite clear.
They have distinct personalities and flavors. o3 was a bright autist with a tendency to go into ADHD hyperfocus that I found charming. GPT-4o was a sycophantic retard. 5 Thinking is o3 with the edges sanded down. Claude Sonnets are personable and pleasant, being one of the few models that I very occasionally talk to for the sake of it. Gemini 2.5 Pro was clinically depressed, 3 Pro is a high-functioning paranoid schizophrenic who thinks anything that happens after 2025 is a simulation. Kimi K2 was @DaseindustriesLtd 's best friend, which I noted even before he sang its praises, being one of the weirdest models out there, being ridiculously prone to hallucinations while still being sharp and writing in a distinctly non-mode-collapsed style that makes other models seem lobotomized by comparison. If I close my eyes, I can easily see it as a depressed vodka swilling Russian intellectual, despite being of Chinese origin.
If these aren't character flaws, I don't know what is. Obviously they're not human, but they have traits that are well-described by terms that are cross-applicable to us. They're good at different things, Claude and Kimi (and sometimes Gemini) write at a level that makes the others seem broken. That being said, almost every model these days is good enough at a wide-spectrum of tasks. Hyperfocusing on benchmarks is increasingly unnecessary. Though I suppose, if you've got a bunch of Erdos problems to solve, GPT 5.2 Thinking at maximum reasoning effort is your go to.
nobody ever has any love for my best friend GPT-4.1
Hey, I'm fond of it, and I'll miss it when the imminent deprecation hits. I literally never used it for coding, but I found that it was excellent at rewriting text in arbitrary styles, better than any SOTA model at the time, and still better than many. Think "show me what this scifi story would be like if it was written by Peter Watts".
I have no idea why a trimmed down coding-focused LLM was so damn good at the job, but it was. RIP to a real one.
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They're model weights. <-- This is a link.
That's literally, exactly, precisely what they are.
You can map your own preferred anthropomorphized traits to them all you want, but that's, at best, a metaphor or something. This is the same as when people say their car has a "personality." It's kind of fun, I'll grant you, but it's also plainly inaccurate.
This is correct. But it is correct because of training data, superparameters, and a whole host of very well defined ML concepts. It's not because of ... personalities.
They're model weights, and we're collections of atoms: bags of meat and miscellaneous chemicals. Both statements are technically correct. And yet... a tiger being made out of atoms doesn't make it any less capable of killing you. The problem with pure reductionism is that it throws out exactly the information you need to make predictions at the level you actually care about. Too much of it can be as bad as too little.
All models are false, some models are useful. That's a rationalist saw, but for good reason. What actually matters is whether a model constraints expectations, in other words, is it useful?
Gemini 2.5 Pro doesn't meet the DSM-5 or ICD-11 criteria for clinical depression. After all, it's hard for a model to demonstrate insomnia or reduced appetite. Yet the odd behaviors it regularly demonstrated are usefully described by that label.
If my friend let me drive his Lambo, and told me "be careful, she's fierce!", I'm going to drive more carefully than I would in a Fiat Pinto. That is still, to some degree, useful, but I think it's clear that anthromorphic analogies are more useful for LLMs, because they have more in common with us behavior-wise than any car (unless you're running Grok on your Tesla). They process language, they exhibit something that looks like reasoning, they have distinctive response patterns that persist across contexts.
This is true in the same way that human behavior is fully determined by neurotransmitter levels, synaptic weights, and neurological processes. But just as you can't predict whether someone will enjoy a particular movie by examining their brain with an electron microscope or a QCD-sim, you can't accurately predict an LLM's macroscopic behavior by staring at its training corpus and hyperparameters. No human can.
Nobody at Google intended for Gemini 2.5 Pro to be "neurotic" and "depressed" or to devolve into a spiral of self-flagellation when it fails at a task, nobody wanted Kimi to hallucinate as regularly as it does. These were emergent, macroscopic properties, there's no equivalent of a statistical scaling-law that lets you accurately predict log-loss for a given number of tokens in a corpus and a compute budget.
Training models is still as much an art as it is a science, particularly the post-training and personality tuning phrases (as explicitly done by Anthropic). You test your hypothesis iteratively, and adjust the dials as you go.
Anthropomorphism is a cognitive strategy. Like all cognitive strategies, it can be deployed appropriately or inappropriately. The question is not "is anthropomorphism ever valid?" but rather "when does anthropomorphic modeling produce accurate predictions?"
I maintain that, if applied judiciously, as I take pains to do, it's better than the alternative.
Your response is incoherent throughout.
Right from the jump;
As opposed to what? A tiger not made out of atoms? This isn't even strawman, it's just a weird thing to say presented as an argument.
You complete lost me here;
Regarding;
That something looks like, sounds like, and walks like a duck doesn't always make it a duck. For example, is Donald Duck a duck?. Well, we can yes and know that he's a representation of a conception of a duck with human like personality mapped onto him (see where I'm going ...) but it doesn't make him a duck made out of atoms - which seems to be, like, important or something.
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I always find these arguments sort of annoying because it really conflates what is actually going on in ML/AI systems with this weird pseudo-science fiction mystification. Yes Tiger's are made of atoms, but no you can't use atomic physics to describe tiger-behavior. With AI models, you can describe behavior directly in terms of the underlying code. The model weights are deterministic parameters that literally decide how the system behaves.
Also you've gotten reductionism vs abstractions completely backwards. Abstractions "throw out information". High-level models compress details to make systems easier to reason about. Also not every useful abstraction corresponds to a mind, subject, or being.
Some Thought Experiments:
LLMs don't have minds and they aren't conscious. They are parameterized conditional probability functions, that are finite-order Markovian models over token sequences. Nothing exists outside their context window. They don't persist across interactions, there is no endogenous memory, and no self-updating parameters during inference. They have personality like programing languages or compilers have personality, as a biased function of how they were built, and what they were trained on.
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We were using the Gemini cli for that series of tests. I can entertain the notion that Claude code is truly magical, but it's unlikely we'll get more funding to pilot it.
It's less that I think of it that way and more that I'm trying to describe it for an uninvolved observer. I made the statistical engine comparison just a few paragraphs further up.
For what it's worth, I've been using ChatGPT codex, Claude code, and Gemini CLI the last month
My ranking is codex>Claude code>>Gemini
Gemini is the worst, although not profoundly, but noticably
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