Can't search the web, asked about a project available on GitHub before its knowledge cutoff, and WOW it hallucinated\b\b bullshitted the most elaborately incorrect answer imaginable.
Immediately deemed irrelevant to me, personally.
GLM-5: Targeting complex systems engineering and long-horizon agentic tasks
41–50 of 540 posts
Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks
#42It's looking like we'll have Chinese OSS to thank for being able to host our own intelligence, free from the whims of proprietary megacorps. I know it doesn't make financial sense to self-host given how cheap OSS inference APIs are now, but it's comforting not being beholden to anyone or requiring a persistent internet connection for on-premise intelligence. Didn't expect to go back to macOS but they're basically the…
> doesn't make financial sense to self-host I guess that's debatable. I regularly run out of quota on my claude max subscription. When that happens, I can sort of kind of get by with my modest setup (2x RTX3090) and quantized Qwen3. And this does not even account for privacy and availability. I'm in Canada, and as the US is slowly consumed by its spiral of self-destruction, I fully expect at some point a digital iron…
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Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks
#43Grey market fast-follow via distillation seems like an inevitable feature of the near to medium future. I've previously doubted that the N-1 or N-2 open weight models will ever be attractive to end users, especially power users. But it now seems that user preferences will be yet another saturated benchmark, that even the N-2 models will fully satisfy. Heck, even my own preferences may be getting saturated already. Op…
In some ways, Opus 4.6 is a step backwards due to massively higher token consumption.
For me, it's just plain worse.
Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks
#44It's looking like we'll have Chinese OSS to thank for being able to host our own intelligence, free from the whims of proprietary megacorps. I know it doesn't make financial sense to self-host given how cheap OSS inference APIs are now, but it's comforting not being beholden to anyone or requiring a persistent internet connection for on-premise intelligence. Didn't expect to go back to macOS but they're basically the…
> Didn't expect to go back to macOS but their basically the only feasible consumer option for running large models locally.
Framework Desktop! Half the memory bandwidth of M4 Max, but much cheaper.
Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks
#45I hope Cerebras offers this soon. Working with GLM-4.7 from Cerebras was a major boost compared with other models.
Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks
#46It's looking like we'll have Chinese OSS to thank for being able to host our own intelligence, free from the whims of proprietary megacorps. I know it doesn't make financial sense to self-host given how cheap OSS inference APIs are now, but it's comforting not being beholden to anyone or requiring a persistent internet connection for on-premise intelligence. Didn't expect to go back to macOS but they're basically the…
> Didn't expect to go back to macOS but their basically the only feasible consumer option for running large models locally. I presume here you are referring to running on the device in your lap. How about a headless linux inference box in the closet / basement? Return of the home network!
Not feasible for Large models, it takes 2x M3 512GB Ultra's to run the full Kimi K2.5 model at a respectable 24 tok/s. Hopefully the M5 Ultra will can improve on that.
Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks
#47It's looking like we'll have Chinese OSS to thank for being able to host our own intelligence, free from the whims of proprietary megacorps. I know it doesn't make financial sense to self-host given how cheap OSS inference APIs are now, but it's comforting not being beholden to anyone or requiring a persistent internet connection for on-premise intelligence. Didn't expect to go back to macOS but they're basically the…
> It's looking like we'll have Chinese OSS to thank for being able to host our own intelligence, free from the whims of proprietary megacorps. I don’t know where you draw the line between proprietary megacorp and not, but Z.ai is planning to IPO soon as a multi billion dollar company. If you think they don’t want to be a multi billion dollar megacorp like all of the other LLM companies I think that’s a little short s…
> Also Chinese companies aren’t the only companies releasing open weight models. ChatGPT has released open weight models, too.
I was with you until here. The scraps OpenAI has released don't really compare to the GLM models or DeepSeek models (or others) in both cadence and quality (IMHO).
Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks
#48It's looking like we'll have Chinese OSS to thank for being able to host our own intelligence, free from the whims of proprietary megacorps. I know it doesn't make financial sense to self-host given how cheap OSS inference APIs are now, but it's comforting not being beholden to anyone or requiring a persistent internet connection for on-premise intelligence. Didn't expect to go back to macOS but they're basically the…
Yeah that sounds great until it's running as an autonomous moltbot in a distributed network semi-offline with access to your entire digital life, and China sneaks in some hidden training so these agents turn into an army of sleeper agents.
What if the US government does instead?
I don't consider them more trustworthy at this point.
Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks
#49How do you get a domain like z.ai?
Re: GLM-5: Targeting complex systems engineering and long-horizon agentic tasks
#50Earlier quoted context omitted.
> Didn't expect to go back to macOS but their basically the only feasible consumer option for running large models locally. I presume here you are referring to running on the device in your lap. How about a headless linux inference box in the closet / basement? Return of the home network!
Apple devices have high memory bandwidth necessary to run LLMs at reasonable rates. It’s possible to build a Linux box that does the same but you’ll be spending a lot more to get there. With Apple, a $500 Mac Mini has memory bandwidth that you just can’t get anywhere else for the price.
With Apple devices you get very fast predictions once it gets going but it is inferior to nvidia precisely during prefetch (processing prompt/context) before it really gets going.
For our code assistant use cases the local inference on Macs will tend to favor workflows where there is a lot of generation and little reading and this is the opposite of how many of use use Claude Code.
Source: I started getting Mac Studios with max ram as soon as the first llama model was released.