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.
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.
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.
German AI company Black Forest Labs released Flux 3, a multimodal foundation model trained on images, video, and audio together. It can generate videos up to 20 seconds long with native audio for the first time. In early tests, it beat several rivals including Luma Ray 3.2 and Runway Gen-4.5. The company also developed Flux-mimic, a video-action model for robotics, now being tested at Audi.
Google DeepMind has introduced three new Gemini models: 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber. The 3.6 Flash model offers improved coding and multimodal performance with 17% fewer output tokens and lower cost. The 3.5 Flash-Lite is the fastest in its series at 350 output tokens per second, designed for high-throughput agentic tasks. The 3.5 Flash Cyber, fine-tuned for cybersecurity, will be available exclusively to governments and trusted partners via the CodeMender agent.
Kimi has released K3, a multimodal open-weight model with 2.8 trillion parameters and a one million token context window. In benchmarks, it approaches Claude Fable 5 and GPT-5.6 Sol but trails them slightly. Priced at $3 per million input tokens and $15 per million output tokens, K3 signals an end to ultra-cheap Chinese AI models, though it remains cheaper than top Western competitors.
Thinking Machines Lab, the startup founded by former OpenAI CTO Mira Murati, has released Inkling, an open-weights multimodal model with 975 billion parameters. The model leads U.S. open-source models on the Artificial Analysis Intelligence Index but trails top Chinese models. It shows strong agentic performance but has a 63 percent hallucination rate.
Meta has released Muse Spark 1.1, an upgraded coding AI model that can detect complex bugs and handle multimodal inputs. The model is available through the Meta AI app and a new Meta Model API preview for US developers, with $20 in free credits.
OpenAI has launched GPT-5.5 Instant, replacing GPT-5.3 Instant as the default model for ChatGPT. The update cuts down hallucinations in fields like law, medicine, and finance while keeping low latency. It also boosts performance on math and multimodal benchmarks, adds context tools, and shows memory sources to users.
NVIDIA has released Nemotron 3 Nano Omni, an open multimodal model that combines vision, audio, and language processing. It offers up to 9x higher throughput than other open omni models, topping leaderboards in document intelligence, video, and audio tasks. The model supports agentic workflows like computer use and is available now on multiple platforms.
Alibaba launched Qwen3.6-27B, a 27 billion parameter dense open-source model. It surpasses the much larger Qwen3.5-397B-A17B on almost all coding benchmarks, including 77.2 on SWE-bench Verified versus 76.2 and 59.3 on Terminal-Bench 2.0 versus 52.5. The model excels in text and multimodal tasks and runs more easily than MoE architectures.
Alibaba released Qwen3.6-35B-A3B, an open AI model that tops Google's Gemma 4-31B across agentic coding benchmarks like SWE-bench Verified at 73.4 versus 52.0. The mixture-of-experts model uses only three of its 35 billion parameters at once to reduce compute costs. It also performs well on reasoning tests and matches Claude Sonnet 4.5 in some multimodal areas.