41–50 of 318 posts
Why is everyone expecting LLMs to be like the Star Trek computer? I wonder if anyone's ever measured what the hallucination rate of a human is.
From how they measure it, a model that simply answers "I don't know." to any prompt would be the one hallucinates the least. So it's not surprising at all that a smaller model can perform better.
My anecdotal experience differs (though I hold ground that LLM evaluations are highly subjective and benchmarks are just as useful for LLMs as they are for dating websites users). GLM 5.2 tends to stray way more than and 5.1. It also hallucinates you things subtly: morphs requirements, makes unfounded conclusions. This output is not something I experienced in any model I seen so far. In coding it's especially annoyin…
I've had more success with creating a plan first and then implementing it in (short-lived) sub-agents.
Ironically good software architecture patterns (small functions, single responsibility) heavily impact the performance of these models as well. They do surprisingly well in well architectured codebases.
They do very poorly in anything that's a mess where Opus and GPT 5.5 still get reasonable performance.
Earlier quoted context omitted.
Those numbers are abysmal. Should we really be using LLMs to write our code? I have a theory- LLMs can spit out code that gets the job done and looks ok, maybe even great, but contains small “anomalies” that compound over time. An enterprise app developed entirely with LLM-happy devs might end up virtually unmaintainable. I’m not sure how to explain it, but the more I see LLM-written code the more I feel it’s bad cod…
Not my observation. If you never look at the code and dont have basic guardrails in place (linters, architecture tests, some guidelines for best practices) - probably. But as soon as you do minimal reviews and high-level corrections, applications turn out just fine. Can there be bugs? Sure. That's the price of not reading or understanding every line. It should depend on the criticality of your software how much of th…
Hallucination rate scores are a little tricky to interpret because they're conditional on the model not knowing the answer. That means they don't measure the probability of your encountering a hallucination in everyday use, since that also depends on the probability of the model not knowing the answer, as well as how well your distribution of tasks aligns with the distribution tested in the eval. I'd also hesitate to…
Those numbers are abysmal. Should we really be using LLMs to write our code? I have a theory- LLMs can spit out code that gets the job done and looks ok, maybe even great, but contains small “anomalies” that compound over time. An enterprise app developed entirely with LLM-happy devs might end up virtually unmaintainable. I’m not sure how to explain it, but the more I see LLM-written code the more I feel it’s bad cod…
My observation is that they are equally bad and hard to maintain or even more so than the new ones.
One thing I’ve noticed is that the LLM assisted ones have a lot more comments which is nice but take more time to read.
Earlier quoted context omitted.
Those numbers are abysmal. Should we really be using LLMs to write our code? I have a theory- LLMs can spit out code that gets the job done and looks ok, maybe even great, but contains small “anomalies” that compound over time. An enterprise app developed entirely with LLM-happy devs might end up virtually unmaintainable. I’m not sure how to explain it, but the more I see LLM-written code the more I feel it’s bad cod…
Not my observation. If you never look at the code and dont have basic guardrails in place (linters, architecture tests, some guidelines for best practices) - probably. But as soon as you do minimal reviews and high-level corrections, applications turn out just fine. Can there be bugs? Sure. That's the price of not reading or understanding every line. It should depend on the criticality of your software how much of th…
I've yet to come across a human developer who's output would meet this standard, despite writing every line.
In fact, having an LLM review our code is catching quite a few bugs before it reaches QA.
> For the non technical, this is like asking a delivery driver to drop off packages at 3 houses at the same time without ever stopping the truck. I'm already hallucinating about how this could work and it involves catapults
Earlier quoted context omitted.
Not my observation. If you never look at the code and dont have basic guardrails in place (linters, architecture tests, some guidelines for best practices) - probably. But as soon as you do minimal reviews and high-level corrections, applications turn out just fine. Can there be bugs? Sure. That's the price of not reading or understanding every line. It should depend on the criticality of your software how much of th…
> Can there be bugs? Sure. That's the price of not reading or understanding every line. I've yet to come across a human developer who's output would meet this standard, despite writing every line. In fact, having an LLM review our code is catching quite a few bugs before it reaches QA.
The humans may skip unit tests and need reminding; the AI always write unit tests once it's in AGENTS.md or whatever, but my experience* was that 5-10% of the time the LLM's attempt at a "test" would, instead of executing the code and examining the results, open the source code as a text file and run a regex to find/exclude certain substrings.
* At the start of this year, because Anthropic and OpenAI were both offering free trials. IDK how much things have changed since then, some things change fast in this domain, other things don't.
Earlier quoted context omitted.
> Hallucination rate scores are a little tricky to interpret because they're conditional on the model not knowing the answer. That means they don't measure the probability of your encountering a hallucination in everyday use, since that also depends on the probability of the model not knowing the answer, as well as how well your distribution of tasks aligns with the distribution tested in the eval. Do you have a cite…
there is a difference between a human knowingly bullshitting and being confident because he misremembers something