when you say limit to 100 sended emails, do you have a limit of like 100 within 24 hours or 1 hour or something like this?

mullvad

Huge!

Yep and it looks like China is well on its way to being independent of the USA in the AI arms race, I just saw they are pushing out their own GPU models:

Chinese GPU Manufacturers Push Out Support For Running DeepSeek’s AI Models On Local Systems, Intensifying the AI Race

https://wccftech.com/chinese-gpu-manufacturers-push-out-support-for-running-deepseek-ai-models-on-local-systems/

Gotta say between them going all out on renewables reducing their reliance on oil and gas and all in on EV’s and tech, it really feels like China has just stolen all of the EU’s ideas and is straight up beating them over the head with it :(

I think they need to be built still tbh

but i don’t think this will be a fix, there are more structural issues in the EU

EU is behind on chip making, software, space race, EV’s, AI, <insert next big thing America or China invents>etcetc

They will continue to be behind China and America so long as it’s more difficult to do business and more difficult to raise money

but eu has ai factories now 😃

https://ec.europa.eu/commission/presscorner/detail/en/ip_24_6302

but we had the same thing with Alpaca, Llama2, Llama3, 3.2, Mistral, Phi…

I don’t believe so, or at least, them all getting smaller and/or more intelligent isn’t the point, it’s how they did it

I noted above that if DeepSeek had access to H100s they probably would have used a larger cluster to train their model, simply because that would have been the easier option; the fact they didn’t, and were bandwidth constrained, drove a lot of their decisions in terms of both model architecture and their training infrastructure. Just look at the U.S. labs: they haven’t spent much time on optimization because Nvidia has been aggressively shipping ever more capable systems that accommodate their needs. The route of least resistance has simply been to pay Nvidia. DeepSeek, however, just demonstrated that another route is available: heavy optimization can produce remarkable results on weaker hardware and with lower memory bandwidth; simply paying Nvidia more isn’t the only way to make better models.

https://stratechery.com/2025/deepseek-faq/

Hard to tell at this stage but models may get a hell of a lot bigger if the hardware required to train them is much smaller or it may plateau for a while while everyone else works on training their stuff using significantly less power and hardware

https://www.nomic.ai/blog/posts/gpt4all-scaling-test-time-compute

This release introduces the GPT4All Javascript Sandbox, a secure and isolated environment for executing code tool calls. When using Reasoning models equipped with Code Interpreter capabilities, all code runs safely in this sandbox, ensuring user security and multi-platform compatibility.

I use LM Studio as well but between this and LM Studios bug where LLM’s larger than 8b won’t load I’ve gone back to gpt4all

that’s interesting, in gpt4all they have the qwen reasoner v1 and it will run the code in a sandbox (for javascript anyway) and if it errors it will fix itself

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