I'm absolutely right

OK, so I love this, because we all recognize it.

It's not fully just a tic of language, though. Responses that start off with "You're right!" are alignment mechanisms. The LLM, with its single-token prediction approach, follows up with a suggestion that much more closely follows the user's desires, instead of latching onto it's own previous approach.

The other tic I love is "Actually, that's not right." That happens because once agents finish their tool-calling, they'll do a self-reflection step. That generates the "here's what I did response" or, if it sees an error, the "Actually, ..." change in approach. And again, that message contains a stub of how the approach should change, which allows the subsequent tool calls to actually pull that thread instead of stubbornly sticking to its guns.

The people behind the agents are fighting with the LLM just as much as we are, I'm pretty sure!

> The LLM, with its single-token prediction approach, follows up with a suggestion that much more closely follows the user's desires, instead of latching onto it's own previous approach.

Maybe? How would we test that one way or the other? If there’s one thing I’ve learned in the last few years, it’s that reasoning from “well LLMs are based on next-token prediction, therefore <fact about LLMs>” is a trap. The relationship between the architecture and the emergent properties of the LLM is very complex. Case in point: I think two years ago most of us would have said LLMs would never be able to do what they are able to do now (actually effective coding agents) precisely because they were trained on next token prediction. That turned out to be false, and so I don’t tend to make arguments like that anymore.

> The people behind the agents are fighting with the LLM just as much as we are

On that, we agree. No doubt anthropic has tried to fine-tune some of this stuff out, but perhaps it’s deeply linked in the network weights to other (beneficial) emergent behaviors in ways that are organically messy and can’t be easily untangled without making the model worse.

The human stochastic parrots (GP, not you) spouting these 2023 talking points really need to update their weights. I’m guessing this way of thinking has a stickiness because thinking of an LLM as “just a fancy markov chain” makes them feel less threatening to some people (we’re past the point where it could be good faith reasoning).

Like, I hear people say things like that (or that coding agents can only do web development, or that they can only write code from their training data), and then I look at Claude Code on my computer, currently debugging embedded code on a peripheral while also troubleshooting the app it’s connected to, and I’m struck by how clearly out of touch with reality a lot of the LLM cope is.

People need to stop obsessing over “the out of control hype” and reckon with the thing that’s sitting in front of them.

I think there’s a bit of parroting going around but LLMs are predictive and there’s a lot you can inuit a lot about how they behave just on that fact alone. Sure, calling it “token” prediction is oversimplifying things, but stating that, by their nature, LLMs are guessing at the next most likely thing in the scenario (next data structure needing to be coded up, next step in a process, next concept to cover in a paragraph, etc.) is a very useful mental model.