Auto Added by WPeMatico
Following two years of immense hype in 2023 and 2024, this year felt more like a settling-in period for the LLM-based token prediction industry. After more than two years of public fretting over AI models as future threats to human civilization or the seedlings of future gods, it’s starting to look like hype is giving way to pragmatism: […]
From prophet to product: How AI came back down to earth in 2025 Read More »
Thinking Machines Lab has moved its Tinker training API into general availability and added 3 major capabilities, support for the Kimi K2 Thinking reasoning model, OpenAI compatible sampling, and image input through Qwen3-VL vision language models. For AI engineers, this turns Tinker into a practical way to fine tune frontier models without building distributed training
OpenAI team has released their openai/circuit-sparsity model on Hugging Face and the openai/circuit_sparsity toolkit on GitHub. The release packages the models and circuits from the paper ‘Weight-sparse transformers have interpretable circuits‘. https://arxiv.org/pdf/2511.13653 What is a weight sparse transformer? The models are GPT-2 style decoder only transformers trained on Python code. Sparsity is not added after
The new LiteRT NeuroPilot Accelerator from Google and MediaTek is a concrete step toward running real generative models on phones, laptops, and IoT hardware without shipping every request to a data center. It takes the existing LiteRT runtime and wires it directly into MediaTek’s NeuroPilot NPU stack, so developers can deploy LLMs and embedding models
