News reporting focused on AI and machine learning, covering the companies behind these technologies, their real-world applications, and the ethical concerns they raise. This includes areas like generative AI (large language models, text-to-image and video), speech tech, and predictive analytics.
AWS has open-sourced Dogwood, a policy language extending Cedar to govern sequences of agent tool calls. It introduces temporal conditions with four operators for rate limits and sequence-level rules, addressing concurrency challenges. The Apache 2.0 release is for exploration, not production, with a roadmap for absolute-time windows and orchestration policies.
Meta AI Research has open-sourced Muse Glimmer, a 30B-parameter agentic model designed for always-on local workflows. Released under Apache 2.0, it runs on consumer GPUs via dynamic quantisation and speculative decoding, offering strong tool-use reliability and failure recovery. The model supports multimodal inputs and is available across major local inference frameworks.
Meta released Muse Glimmer, an open-weight 30B-parameter multimodal agent model under Apache 2.0, optimized for local deployment on consumer GPUs. CEO Mark Zuckerberg published an essay on Personal Superintelligence, outlining Meta's vision for accessible AI and addressing labor, geopolitics, and compute. The release signals Meta's return to open-weight leadership.
Meta has released Muse Glimmer, a 30B parameter language model under the Apache 2.0 license, optimized for local use. Developer Simon Willison tested the model on his own machine, generating images and running coding tests, and praised its performance and the permissive license. The model's 18.16 GB size fits comfortably in systems with 32GB+ RAM, making it a practical choice for developers.
Meta released Muse Glimmer, an open-weight 30B parameter AI model designed to run local agents on consumer hardware. The model, available under Apache 2.0, supports text and images across 100+ languages, enabling on-device tasks like scheduling and coding. CEO Mark Zuckerberg's vision of personal superintelligence is realized through this release, balancing open weights with the proprietary Muse Spark.
Thinking Machines released Inkling Small, a compact open-weights reasoning model that scores nearly as high as its larger sibling on the Intelligence Index (40 vs. 41) and outperforms it on specific reasoning benchmarks like Humanity's Last Exam and GPQA Diamond. The model is highly token-efficient, supports multimodal inputs, and is available under Apache 2.0 with browser-based fine-tuning.
IBM's Granite Team details the construction of Granite 4.1, a set of decoder-only LLMs in 3B, 8B, and 30B sizes trained on about 15 trillion tokens through five pre-training phases. The models receive supervised fine-tuning on 4.1 million curated samples and multi-stage reinforcement learning. The 8B instruct version equals or beats the prior Granite 4.0-H-Small 32B-A9B MoE model using a basic dense setup with fewer parameters. All models appear under Apache 2.0 license.
Researchers Nick Levine, David Duvenaud, and Alec Radford released talkie, a 13B language model trained on historical English text before 1931. The base version used 260B tokens, while the instruction-tuned model powers a chat interface. Both models carry an Apache 2.0 license, with training data free from copyright restrictions.