351–360 of 559 posts
But I would like to point out that the overwhelming majority of people using LLMs aren’t programmers, don’t care about coding, and couldn’t even be bothered to “vibe code”.
So we should consider the bias of the output of these open weight models, and what that looks like, outside of the context of writing code.
Earlier quoted context omitted.
> A typical session for me with GPT is usually over a hundred dollars. I don't think a $100 session is "typical". I use GPT for months. $20/m plus plan is enough for my daily work.
It's really interesting what "normal" is for folks. I use the $200/month Anthropic subscription and use it within a few percentages of my limit every week. I'd blow through $20/month plan in hours.
I tried GLM many times and it is bad, i have on clue what these people are talking about
Bad for security research or for general coding? Having used GLM 5.2 for non-security software work, I can say it's better than Sonnet (but not Opus), and cheaper than both (because when you steal someone else's IP, you don't have to amortize the cost of their R&D).
Earlier quoted context omitted.
GLM 5.2 is a great model, but if you only want to use the best model available, it isn't there yet. Every lab releases models that memorize benchmark answers, both intentionally and unintentionally. But we consistently find that models from Chinese labs have a wider gap between public benchmarks and our evaluations, which we designed to be less vulnerable to benchmaxxing. In multi-agent coding environments, GLM 5.2 i…
> but if you only want to use the best model available, it isn't there yet I'm trying to wrap my head around exactly why so may people seem to want the best model available when it has recently become clear that most halfway decent models can write damn good code for a fraction of the price. And the frontier models get nerfed constantly so you with open weight you can get something slightly less performant but way mo…
Earlier quoted context omitted.
You can use GLM in OpenCode with a z.ai subscription by default as well. Also it'd be good if you mentioned you were involved with nemesis8.
I think it would be good not to suggest someone run a new Chinese agent on their bare metal. When I posted the comment I was both the first commentor as well as the first person to upvote the submission. That matters. My name is ALSO on the open source repo that allows Opencode to be run in a container. That's transparency, maybe not here, but on a clickthrough to Github it is immediately obvioius.
Not sure a project nobody knows or uses is much better in this regard?
Earlier quoted context omitted.
GLM 5.2 and DeepSeek v4 Pro seem to approach security research differently. This benchmark was with GLM 5.1, but the patterns are similar: https://dualuse.dev/posts/deepseek-v4-thinks-different Overall, I still think GLM 5.2 is the much stronger performer. It's hard to tell the difference between GLM 5.2 and Opus at <120k tokens.
I have found that some models consistently find or miss specific bugs, and which bugs are hard don't completely line up across all models, so I believe that. I just refactored the security bug-finding harness I've been working on completely (not checked in yet, testing it currently) to strongly encourage "multi-model, multi-pass" scans and make them easy to orchestrate with de-dupe and weeding false positives with a…
It’s hard to argue against the open weight models if your only concern is coding. Which, for many of us hackers here in this forum, it is. But I would like to point out that the overwhelming majority of people using LLMs aren’t programmers, don’t care about coding, and couldn’t even be bothered to “vibe code”. So we should consider the bias of the output of these open weight models, and what that looks like, outside…
Earlier quoted context omitted.
> most halfway decent models can write damn good code for a fraction of the price. The difference is how the model is used. With Opus you can give it a long-horizon task (eg build an entire feature) and it will plan it out and implement it and almost always stay on task. This is what people mean when they say "agentic tasks" With the lessor models the code is fine, but they need something else to plan what needs to b…
My whole point is that I don't want it to build an entire feature from one prompt. At most, I want to work with an agent to nail down the spec and then work with an agent that orchestrates the implementation via other agents, same for testing, etc. None of that requires frontier capabilities, it requires a little bit of work on a harness, a little bit more of my input, a little more of my brainpower. I _want_ to buil…
And hey, don't get me wrong, you can get pretty far with just prompting. But the subtle misses and (I'm looking at you GPT) the overengineered 20k line PRs to do a simple thing are going to cost you a lot if you're not vigilant.