411–420 of 626 posts
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I’m in the Claude code harness for everything boat too. What are the alternatives?
ArtificialAnalysis puts out benchmarks for harnesses now as well, and OpenCode seems to be winning it. https://artificialanalysis.ai/agents/coding-agents#coding-ag... I only found this yesterday, and it inspired me to start testing out OpenCode.
People familiar with the topic, how will models continue to get better? Post training it seems? Labs have already used up internet-scale data, so are there any limits to architecture improvements and post training or can we expect this trend to continue? ByteDance is training a 10T-parameter model. Here, GLM 5.3 outperforms models 3-4x its size of roughly 700B, so parameter count doesn’t seem to be a direct correlati…
Apple will release M7 MacBook Pros / Mac Minis next year, and they will be able to run free LLMs locally at native speed. All software developer notebooks will be replaced to run local models, saving a lot by cancelling Claude Code subscriptions. Developers win. Apple stocks will be rocketing. Everything else will go down. You're welcome.
But there's a significant difference in hardware required between running a 3B parameter model and a 700B-1T+ parameter model.
I might be just reading my positive bias into that text, but is it possible that it is written less like SV marketing hype trash and more like researchers wrote it? It does feel like it respects both me and my time. Thank you, Z.AI. Amazing what difference it makes when the top of your org are actual university professors.
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> I understand that such models can be used by malicious actors, but it’s fair to have it publicly available I feel like there should be some mechanism to prove you own the code/app/site/whatever and it will remove the guardrails from the LLMs allowing them to find and fix these vulnerabilities.
Isn’t this essentially what anthropic is doing, albeit in a manual fashion? They work with code owners to run mythos and find issues.
People familiar with the topic, how will models continue to get better? Post training it seems? Labs have already used up internet-scale data, so are there any limits to architecture improvements and post training or can we expect this trend to continue? ByteDance is training a 10T-parameter model. Here, GLM 5.3 outperforms models 3-4x its size of roughly 700B, so parameter count doesn’t seem to be a direct correlati…
You basically need both. Parameters and good post training. If you keep on growing both, you’ll have good models. LLMs are still surprisingly “easy”. You need maybe a couple dozens of right people, a lot of good quality data and a lot of GPU that you know how to operate. There’s relatively little “secret sauce” needed.
The data is mostly coming from places like Scale/Mercor/etc and net dumps with some filtering and batch prioritization, and RL on verifiable domains like code/math/games.
People familiar with the topic, how will models continue to get better? Post training it seems? Labs have already used up internet-scale data, so are there any limits to architecture improvements and post training or can we expect this trend to continue? ByteDance is training a 10T-parameter model. Here, GLM 5.3 outperforms models 3-4x its size of roughly 700B, so parameter count doesn’t seem to be a direct correlati…
> Labs have already used up internet-scale data Despite this being the topic du jour of 2025, it was never true. Most of the "we've hit a wall with data" came from communicators / media and not researchers. It got popular because negativity sells. It's a false premise for a number of reasons: a) Data curation is as important, if not more important than bulk data. Models becoming better at classification leads to bett…
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Fable finished training 6+ months ago. At this point, Anthropic only needs to release models to the public when the competition forces them to. OpenAI also has a better model (Astra) that they haven't released yet.
Yes it seems like the thread is discounting that frontier providers are likely already baking new, stronger models. I agree that GLM and its ilk are quite good, but having used them I’m not convinced they’re on par with eg Opus in terms of things like tool calling. And they’re fast but less capable so I spend about the same amount of time with them, just with more hand holding. Maybe this is a harness limitation. I k…
It’s whether you absolutely need the latest capabilities (at the cost of very high prices, sending your data to them, and being totally at the whim of 2 companies, that can shut you off anytime for any reason).
With how good LLMs are already, there’s tons of tasks where not being at the absolute bleeding edge doesn’t matter, especially when you add cost/freedom/supply chain risk/not leaking your data.
Even more - there’s increasing number of companies that give you ability to post train open weight model yourself, for your own use case. Given how many of the gains today are from post training, if you post train it for your specific use case, you’re very likely get model that you own, that works for you as good as frontier, at the fraction of the cost.
That’s not something for an average Joe to do, but for any bigger business with big spent it’s only natural thing to look into. Just one example - cursor composer - that’s fine tuned kimi.
It’s not whether frontier labs will stop releasing models. It’s whether they can generate enough profit out of them. 2 years ago (even 1) they basically had monopoly and combined with demand explosion as capabilities exploded - valuations grew to insane levels. But math now looks different - they no longer have monopoly.
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Realistically, you're looking at least 2x DGX sparks to run this at a 2 bit quant, but quantization really lobotomizes models so it's just better to run DSv4 flash at full precision. 4x DGX sparks should let you run this at 4 bit at least and there are some folks who ran GLM 5.2 on this configuration in r/LocalLlama
How fast are 2x or 4x DGX? I only have one and am wondering what the benefits are of getting another. I feel I will be disappointed…
For something like GLM, it's larger, has a larger number of active experts, and doesn't support tensor parallel. This means performance doesn't really scale with more Sparks. You can layer split, but then you are still seeing each layer in series and so if anything performance gets slightly worse. I would not expect more than 10-20 TPS on GLM with 2-4 Sparks.