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Together Link brings frontier open models like GLM 5.3 and Kimi K3 into the coding agent your team already uses, cutting model spend by over 50%.
Ollama now supports decision models, based on TypeSafe's Jev API for fast, typed decisions. Decision models can now be run at no cost with low latency. Based on text, decision models answer yes-or-no questions, choices and scores about it, with a probability for every option.
Mistral opens a Munich hub for Physics AI and Industrial AI research, partnering with German industry.
We just launched our own Jev-like classifier, together/Tev1-4B-experimental, on top of Qwen3.5 4B on Together’s serverless platform. In this blog post we’ll show you how to fine-tune your own version!
Open, private and multilingual AI is coming to your web browser. Mistral and Mozilla team up to put powerful, trustworthy AI where you already browse.
Ollama's Pro, Max, and Team plans now use industry-standard per-token pricing with usage included on every plan.
Claude Desktop can now be configured to work with Ollama as a third-party gateway provider, making it possible to use open models in Claude.
NVIDIA Nemotron 3.5 Lightning is now available on Ollama. It's a 30 billion parameter (3B active) open model built for agents that stay running, gathering context, calling tools, and working through multi-step tasks on your own hardware.
This page aims to provide an overview of AI systems, and in particular general-purpose AI (GPAI) systems, used for the purpose of therapy or emotional support under the AI Act. It explores the obligations of providers of these AI systems both on the system as well as on the model…
The scientific panel of independent experts (the Scientific Panel) is a body established under the EU AI Act to support the enforcement of rules on general-purpose AI (GPAI) models and systems. It sits within the Act’s broader governance architecture as one of the three adv…
The concept of recursive self-improvement (RSI) dates back to I. J. Good (1965) , where he defined an “ultraintelligent machine” as a system that can surpass humans in all intellectual activities and design better machines to improve itself. Yudkowsky (2008) used the …
Scaling laws are one of the most critical empirical findings in deep learning. The observation is simple in form: the training loss $L$ decreases predictably as we scale up model size $N$, dataset size $D$, and compute $C$, following a power-law curve, which appears as a straight…
