221–230 of 367 posts
I used it with Cerebras inference at a time when it had a good coding plan at a low price, and delivered tons of stuff using it.
Earlier quoted context omitted.
The streetlight effect: > A policeman sees a drunk man searching for something under a streetlight and asks what the drunk has lost. He says he lost his keys and they both look under the streetlight together. After a few minutes the policeman asks if he is sure he lost them here, and the drunk replies, no, and that he lost them in the park. The policeman asks why he is searching here, and the drunk replies, "this is…
The minute an open model breaks through and beats Claude Opus/Fable, it's over. There are far more opportunities that can be served when the world's intellectuals have the raw weights and can fine tune, splice, distill, and reapply. Imagine having raw unfettered access to Fable. It can be refit to structural biology. It can be fine tuned on the repo for smaller context requirements. It can be run cheaper and air gapp…
I seriously dont' know all this big hullabaloo about one shot prompting. by definition, a single prompt wont' constitute the complexity of a software project. ergo, what you'll get is a series of assumptions made by the model based on preexisting code in its training corpus. I'd rather see a coding agent that can follow steps in a plan file to a T while following guardrails and adhering to the proper coding conventio…
But for a more practical issue, the ultimate goal of LLMs is to replace software engineers, or at least enable everybody to become a software engineer, to use a more up-beat phrasing that's no less accurate. And so an LLM's ability to reliably construct something from a poorly defined, contradictory, or otherwise flawed prompt, while accurately inferring intent is probably the first finish line.
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That's precisely the difference between an engineer and a business guy. The business guy would say "hey build me this and that" and would get _something_ to show of. An engineer will have a long conversation with a llm about the exact requirements, tech stack, tradeoffs. He would understand what is built, how is it built, and refine on the fly until he gets something sensible. It won't be as fast as "build this", but…
what you said above is only true when the AI is not as smart/professional/knowledgable as that engineer.
And yet, even the smartest AI in the world would give an alternative solution every time you invoke it. And you still need someone to judge what is right and what is not.
Earlier quoted context omitted.
The streetlight effect: > A policeman sees a drunk man searching for something under a streetlight and asks what the drunk has lost. He says he lost his keys and they both look under the streetlight together. After a few minutes the policeman asks if he is sure he lost them here, and the drunk replies, no, and that he lost them in the park. The policeman asks why he is searching here, and the drunk replies, "this is…
The minute an open model breaks through and beats Claude Opus/Fable, it's over. There are far more opportunities that can be served when the world's intellectuals have the raw weights and can fine tune, splice, distill, and reapply. Imagine having raw unfettered access to Fable. It can be refit to structural biology. It can be fine tuned on the repo for smaller context requirements. It can be run cheaper and air gapp…
Earlier quoted context omitted.
I disagree fundamentally. If I do that, I'm literally slower then just doing the change without sufficiently specifying it to the model. I can see how a junior dev or generally someone that's not particularly knowledgeable about the language or framework they're working with may benefit from such usage, but for experienced people there is very little value in that approach. I say this because I've just had to face th…
I disagree, fundamentally . I see little value in throwing a ton of context at an llm and waiting 10-20 minutes for a coin flip on whether or not its going to produce junk. I'd rather do quick 60 second turns, get most of the way there and fix the rest myself if I have to. I'd rather honestly just not use them.
Earlier quoted context omitted.
The streetlight effect: > A policeman sees a drunk man searching for something under a streetlight and asks what the drunk has lost. He says he lost his keys and they both look under the streetlight together. After a few minutes the policeman asks if he is sure he lost them here, and the drunk replies, no, and that he lost them in the park. The policeman asks why he is searching here, and the drunk replies, "this is…
Sure, for casual evaluation, I agree. But are there serious analyses that are evaluating this kind of thing? I mean, these are the kinds of things I evaluate in my own work when a new model comes out, or when I'm evaluating a harness. But this is all very ad hoc and intuitional. I'd love to start bringing rigor to it, but I haven't found much prior art on this. In another thread someone said that's because it's proba…
[1] https://blog.neurips.cc/2025/09/30/reflecting-on-the-2025-re...
I seriously dont' know all this big hullabaloo about one shot prompting. by definition, a single prompt wont' constitute the complexity of a software project. ergo, what you'll get is a series of assumptions made by the model based on preexisting code in its training corpus. I'd rather see a coding agent that can follow steps in a plan file to a T while following guardrails and adhering to the proper coding conventio…
It's a relatively objective way of testing LLMs, and I think it's pretty representative of how strong models are overall.
The outcome of this test mirrors how GLM 5.2 and Opus 4.8 work for me: they're both similarly capable of fully executing a given task, but Opus tends to have a bit more "taste" in how it handles unstated details or implicit requirements.
> what you'll get is a series of assumptions made by the model
Yes, but that's why we use these models in the first place. We don't want to explicitly write down all the details because that would mean writing code. So we write a higher-level, human-language spec, and let the LLM fill in the blanks. The question is how good they are at doing that.
Earlier quoted context omitted.
The streetlight effect: > A policeman sees a drunk man searching for something under a streetlight and asks what the drunk has lost. He says he lost his keys and they both look under the streetlight together. After a few minutes the policeman asks if he is sure he lost them here, and the drunk replies, no, and that he lost them in the park. The policeman asks why he is searching here, and the drunk replies, "this is…
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