
Thinking Machines Lab just released Inkling, their first model trained from scratch, weights are open, fine-tunable on Tinker. The lab pitches it as a base for customization. What is Inkling? Inkling is a Mixture-of-Experts transformer with 975B total parameters and 41B active. It supports a context window of up to 1M tokens. Pretraining covered 45 trillion tokens of text, images, audio, and video. Inputs accept text, images, and audio; output is UTF-8 text only. The research team also
Thinking Machines Lab released Inkling, an open-weights artificial intelligence model with 975 billion total parameters that can process text, images, and audio as inputs while producing text outputs. The model uses a Mixture-of-Experts architecture where only 41 billion parameters are active at any given time, allowing it to balance capability with efficiency. A key differentiating feature is "controllable thinking effort," which lets users adjust how many tokens the model spends reasoning about each task, making both cost and latency tunable rather than fixed. The model was trained on 45 trillion tokens of multimodal data and performs competitively on various benchmarks, with deployment options available through multiple frameworks and hosted APIs.

Embedding models decide which passages an agent ever sees. NVIDIA released Nemotron 3 Embed model to work on that layer. It targets production-scale RAG, agentic retrieval, code retrieval, and agent memory. What is Nemotron 3 Embed? The model collection includes three open checkpoints. Nemotron-3-Embed-8B-BF16 is the accuracy-first option. Nemotron-3-Embed-1B-BF16 carries the same design into a smaller footprint. Nemotron-3-Embed-1B-NVFP4 is the Blackwell-optimized 4-bit path. All thre

Moonshot AI just released Kimi K3. It is a 2.8-trillion-parameter model with native vision and a 1-million-token context window. Moonshot calls it the world’s first open 3T-class model. What is Kimi K3? Kimi K3 is a sparse Mixture-of-Experts (MoE) model built on two architectural updates. Those are Kimi Delta Attention (KDA) and Attention Residuals (AttnRes). Both change how information flows across sequence length and model depth. K3 targets long-horizon coding, knowledge work, an
Google DeepMind and Isomorphic Labs are sharing our joint approach to bioresilience and AI models.
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