401–410 of 559 posts
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you can however, have fun with it. oil workers buy 100k trucks they do not-much with. why not a 100k in computer?
Because car loans can’t be used to buy computers
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> But in general it's self-evident that training the model on information that is irrelevant to your use case does not necessarily improve ability, otherwise you'd have AGI just from reinforcing your model on memorizing the first 10^50 digits of pi. It's hardly self-evident, and your counter-example is hardly applicable. The first 10^50 of pi is not the same as having BREADTH of information in the training data, whic…
It is self-evident. Bringing up Kolmogorov complexity is irrelevant, we're talking about rote memorization, but if you can't ignore the given example then replace "digits of pi" with "bits of output from a true random number generator". There's an infinite amount of information that we could shove into a model, and a finite amount of bits with which to store any of that information such that it can be usefully recall…
The memorization of say 100000 world facts through training texts, which enrich model associations all around, is absolutely not the same as rote memorization on 10^50 digits of pi. Not for a human, and even more so, not for an LLM.
An LLM trained with digits of pi and one trained with books and posts, even if they both have the exact same amount of bytes of training input, would not be comparable in any way in utility and reasoning capabilities.
>There's an infinite amount of information that we could shove into a model, and a finite amount of bits with which to store any of that information such that it can be usefully recalled or form useful logical associations.
Which is irrelevant. Anyway, the amount of information that doesn't form useful logical associations is even larger (e.g. actual human books vs possible permutations of characters and spaces). Just like those (random) possible permutations of characters aren't good for LLM input to get logical associations out of it, pi isn't either (logical associations of the kind we care for and expect, not of the kind related to pi's sequences).
Also it's not only not self-evident, it's also apparently wrong.
I have taken another look on these open models after the fiasco of Fable and GPT 5.6 this weekend and... GLM-5.2 truly is a good workhorse model for daily programming. I consider myself a heavy user of LLMs and a seasoned developer. A typical session for me with GPT is usually over a hundred dollars... This weekend I programmed a matrix bot with encryption and a Rust agent with some tools. Because I need one and Open…
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…
Just want to express how amazing that is. Opus 4.6 is an amazing model. That an open weight model like GLM 5.2 competes with it is nothing short of outstanding.
This is because of the safeguards and not the model capabilities. If these folks signed up for the proper cyber service offered by Anthropic where refusals are removed then the open weight model wouldn't look as capable.
And just like Linux lost to Windows in consumer market due to devs/creator's stubbornness, same will happen with closed vs open LLM. In the end the one that is used the most will be the one that you train your kids on and therefore the one that wins the market. Eventually the closed one with too much guardrail will be left behind because people will stop using it. You need to read the market. Linus didn't read it in…
The only niche where it doesn't utterly dwarf the competition is personal computers and it looks like we're all getting priced out of that anyway
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>Don't worry though, open source evangelists will tell you that these will be running on your phone in the next 3 years. Not sure if you're being sarcastic, but I can run a quantised version of Gemma or Qwen on my 16GB M1 Macbook Pro that beats GPT-4 from 2023 hands-down. I wouldn't be surprised if, in another 3 years, you'd be able to run something as powerful as Opus 4.5 or GLM-5.2 on standard consumer hardware - s…
This is clearly where the industry is going, imho. Everyone who is playing with LLMs wants a laptop with enough grunt to run a decent model locally. We've been sat with basically the same PC specs for ~20 years - our current specs are within an order of magnitude of the ones we could buy back in 2010. This is not really constrained by tech, as we could have much, much, larger machines. It's more because there's no ma…
My productivity profits from the best intelligence available, a decent context size, and a batch size of four.
While my MacBook has 48 GB of RAM, not only do I want the above requirements at a decent speed, but I also need my machine to run the development tools and test suites, ideally without the fans blasting at full load.
For the foreseeable future I will stay with providers rather than local inference, apart from niche use cases.
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We've spent some time trying to understand this anomaly, even re-running Sonnet 4.6 through our evaluations to see if that would bring down its scores... and it didn't. I don't know what they did differently, but it's basically Opus 4.6 with more temperature variability (some great responses, some less great, with an approximately frontier median response in agentic work specifically). It is smart, methodical and exc…
That tracks with my experience. 4.7 was so bad, I locked a bunch of my machines to 4.6. I haven’t bothered locking the 4.8 machines to 4.6. There was a HN thread a while back where they run swe bench a few times a day and measure success rate and latency. It showed opus getting significantly dumber for the week before a recent launch. It wouldn’t surprise me if they’re quantizing to improve margins or to hype models…
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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…
That changes as soon as the developer is the one paying for a model. Then it's a classical engineering trade-off between money and quality, and that's where open models are clear winners.
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> 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…
> 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…
I am still struggling how to deal with sub agents and different roles for each model. I still think Claude or Codex are overall better models, but everything around them transpires such weird vibes, including, and this one kills me, that at certain times they feel like dumbed down.
I keep changing these things often, but I have basic subscription to codex (20$ plan) which I use with GLM 5.2 to do some high level planning of what I intend to do, and then leave Deepseek do the coding. Or something along those lines.
Point is, GLM 5.2 is now at a point where I cannot tell you if it's better or worse. I can tell you however one thing: no matter when I use it, it's consistent in what it does and how it works.
Then there is the Fable thing, but as with many things, I think the past has distorted the reality. It lasted two days, but Anthropic said it clearly for plan users it would only be there for two weeks. It was great for doing what you can already do with other tools: doing all the planning, and reviews, and launching a million subagents talking to each other. I sometimes wonder if it was really a new model, or just Opus 4.9 wrapped with some fancy model driven harness.
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I think people are grouping into two flows. One group is trying to get the LLM to basically one shot everything and not properly reviewing the output. Others are using the LLM to assist their human intelligence in a tight loop. If you’re doing the former you really do need the best model available because that’s still right on the edge of what LLMs can do at best, and at worst you’re just shipping pure unmaintainable…
Well put. I belong to the latter group as I feed small, granular tasks that I describe thoroughly to the LLM. I tried, however, to just give it a bigger scope task. Even best models produce sloppy code. While the single functions/classes/structs/... can be well though out the code tends to lack cohesion, and especially maintainability. For instance, it never thinks: "I could put this logic in an interface/trait so th…
SoTA models can do reasonably good jobs on each ticket, but over time the architecture of the application starts degrading without a human in the loop.
The entropy increases slower with better models but the trend is always towards slop
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This is clearly where the industry is going, imho. Everyone who is playing with LLMs wants a laptop with enough grunt to run a decent model locally. We've been sat with basically the same PC specs for ~20 years - our current specs are within an order of magnitude of the ones we could buy back in 2010. This is not really constrained by tech, as we could have much, much, larger machines. It's more because there's no ma…
It seems relevant for playing with LLMs, but for actual work this seems far off for me. My productivity profits from the best intelligence available, a decent context size, and a batch size of four. While my MacBook has 48 GB of RAM, not only do I want the above requirements at a decent speed, but I also need my machine to run the development tools and test suites, ideally without the fans blasting at full load. For…
I'm in Australia, so we're probably not getting access to Fable again. We're learning that a faster model + better harness/framework > smarter model. So being able to run GLM5.2 locally and super-fast would be great.