* fewer.
Less human AI agents, please
21–30 of 184 posts
Re: Less human AI agents, please
#22[deleted]
Re: Less human AI agents, please
#23A very human thing to do is - not to tell us which model has failed like this! They are not all alike, some are, what I observe, order of magnitude better at this kind of stuff than others. I believe how "neurotypical" (for the lack of a better word) you want model to be is a design choice. (But I also believe model traits such as sycophancy, some hallucinations or moral transgressions can be a side effect of trainin…
Codex in this case. I didn't even think about mentioning it. I'll update the post if it's actually relevant. Which I guess it is. EDIT: It's specifically GPT-5.4 High in the Codex harness.
Also the exact model/version if you haven't already.
Re: Less human AI agents, please
#24The version of this I encounter literally every day is: I ask my coding agent to do some tedious, extremely well-specified refactor, such as (to give a concrete real life example) changing a commonly used fn to take a locale parameter, because it will soon need to be locale-aware. I am very clear — we are not actually changing any behavior, just the fn signature. In fact, at all call sites, I want it to specify a def…
If it’s a compiled language, just change the definition and try to compile.
Re: Less human AI agents, please
#25The version of this I encounter literally every day is: I ask my coding agent to do some tedious, extremely well-specified refactor, such as (to give a concrete real life example) changing a commonly used fn to take a locale parameter, because it will soon need to be locale-aware. I am very clear — we are not actually changing any behavior, just the fn signature. In fact, at all call sites, I want it to specify a def…
That’s my daily experience too. There are a few more behaviours that really annoys me, like:
- it breaks my code, tests start to fail and it instantly says “these are all pre existing failures” and moves on like nothing happened
- or it wants to run some a command, I click the “nope” button and it just outputs “the user didn’t approve my command, I need to try again” and I need to click “nope” 10 more times or yell at it to stop
- and the absolute best is when instead of just editing 20 lines one after another it decides to use a script to save 3 nanoseconds, and it always results in some hot mess of botched edits that it then wants to revert by running git reset —hard and starting from zero. I’ve learned that it usually saves me time if I never let it run scripts.
Re: Less human AI agents, please
#26> There was only one small issue: it was written in the programming language and with the library it had been told not to use. This was not hidden from it. It had been documented clearly, repeatedly, and in detail. What a human thing to do. "Ignoring" instructions is not human thing. It's a bad LLM thing. Or just LLM thing.
It's not necessarily "ignoring" instructions, it's the ironic effect of mentioning something not to focus on, which produces focus on said thing. The classic version is: "For the next minute, try not to think about a pink elephant. You can think about anything else you like, just not a pink elephant."
Re: Less human AI agents, please
#27The version of this I encounter literally every day is: I ask my coding agent to do some tedious, extremely well-specified refactor, such as (to give a concrete real life example) changing a commonly used fn to take a locale parameter, because it will soon need to be locale-aware. I am very clear — we are not actually changing any behavior, just the fn signature. In fact, at all call sites, I want it to specify a def…
If it’s a compiled language, just change the definition and try to compile.
Indeed! You would think it would have some kind of sense that a commit that obviously won't compile is bad!
You would think.
It would be one thing if it was like, ok, we'll temporarily commit the signature change, do some related thing, then come back and fix all the call sites, and squash before merging. But that is not the proposal. The plan it proposes is literally to make what it has identified as the minimal change, which obviously breaks the build, and call it a day, presuming that either I or a future session will do the obvious next step it is trying to beg off.
Re: Less human AI agents, please
#28I think the author is looking for something that doesn't exist (yet?). I don't think there's an agent in existence that can handle a list of 128 tasks exactly specified in one session. You need multiple sessions with clear context to get exact results. Ralph loops, Gastown, taskmaster etc are built for this, and they almost entirely exist to correct drift like this over a longer term. The agent-makers and models are slowly catching up to these tricks (or the shortcomings they exist to solve); some of what used to be standard practice in Ralph loops seems irrelevant now... and certainly the marketing for Opus 4.7 is "don't tell it what to do in detail, rather give it something broad".
In fairness to coding agents, most of coding is not exactly specified like this, and the right answer is very frequently to find the easiest path that the person asking might not have thought about; sometimes even in direct contradiction of specific points listed. Human requirements are usually much more fuzzy. It's unusual that the person asking would have such a clear/definite requirement that they've thought about very clearly.
Re: Less human AI agents, please
#29I know anthropomorphizing LLMs has been normalized, but holy shit . I hope the language in this article is intentionally chosen for a dramatic effect.
Agreed. We should not be anthropomorphising LLMs or having them mimic humans.
It's inherent in the way LLMs are built, from human-written texts, that they mimic humans. They have to. They're not solving problems from first principles.
Re: Less human AI agents, please
#30This has very little to do with someone making the LLM too human but rather a core limitation of the transformer architecture itself. Fundamentally, the model has no notion of what is normal and what is exceptional, its only window into reality is its training data and your added prompt. From the perspective of the model your prompt and its token vector is super small compared to the semantic vectors it has generated over the course of training on billions of data points.
How should it decide whether your prompt is actually interesting novel exploration of an unknown concept or just complete bogus? It can't and that is why it will fall back on output that is most likely (and therefore most likely average) with respect to its training data.