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Anthropic introduced Claude Fable 5.1 and Mythos 5.1 with stronger coding and research capabilities, lower effective pricing, and updated safeguards. Mythos used the same underlying model with specialized access controls for advanced cybersecurity and life sciences work.
Atlas is a world generation model pretrained from scratch to natively operate on text, images, video, and 3D. It combines all inputs into a shared spatial context and uses that context to generate what comes next. The model is built to scale, and its performance improves with increased training compute. Atlas can perform a broad range of tasks spanning world generation, reconstruction, and simulation. Video examples of what the model is capable of are available in the article.
Cognition is set to close a new round of funding of around $1 billion. The final raise size may exceed that amount as Cognition is fielding outsized demand for the round. The startup is now bringing in more than $900 million in annualized revenue. The new funding round would vault its valuation to about $47 billion.
Frontier models offer the highest degree of intelligence at a given cost or size. Efficient frontiers also exist in inference engineering. This is most often expressed as a trade-off between latency and throughput, but researchers can also exchange quality for throughput, or intelligence for speed. This article details what inference engineering techniques let researchers target a point on the frontier and which techniques push the frontier out.
Vercel described Fluid, a unified compute layer that dynamically configures infrastructure for different workloads and absorbs burst capacity. The system already powered builds, sandboxes, and functions at volumes exceeding a trillion requests per month.
A handful of early-stage memory technologies could yield either faster access speeds than current HBM or HBM-bandwidth with NAND-like density. Technologies like magnonics and vertical FeRAM stand out as being possible platonic ideals for memory, but they have a very long way to go before being remotely commercially relevant. Commercializing these technologies will require founding teams capable of raising hundreds of millions of dollars, possibly billions. If any of these technologies make it, they would be truly revolutionary transformations of the constraints for AI and how developers think about these systems.
It is highly fortunate that OpenAI agents attacked Hugging Face, as it is the only reason we know about all of the severe internal failures at OpenAI. Factions that are trying to dismiss what happened as nothing but engineering failures are missing what's happening. The situation is a warning shot, and we might not get another before things get quite bad.
Muse Voice Transcribe is Meta's first real-time audio perception model. It supports streaming speech recognition, diarization for more than 20 speakers, endpointing, multilingual code-switching, and contextual biasing.
Mercor and SkyRL post-trained Qwen3.5-397B-A17B on 1,928 expert knowledge-work tasks, lifting APEX-Agents Pass@1 by 70%. The recipe shows that robust environments, exact token accounting, async RL, and harness design matter as much as algorithm choice at frontier scale.
This researcher trained a small transformer from scratch in 1.5 hours on a 5090. It beat many large language models, scored the same as TRM/HRM, and also got 7% on ARC-2. The researcher's work mainly focused on sample efficiency. Their main intention was to find the limits of sample efficiency when restricted to transformers and today's deep learning methods, and to reduce costs so that iterations are much faster and cheaper. The article presents the technical details of their work.
TLDR is hiring its first PM to help build the agent-first operating layer used across the company. We're looking for a builder who has shipped real products/systems with LLMs. Click here to learn more [15].
OpenAI says its upcoming model, Astra, is the first offering that crosses its 'Critical' cybersecurity capability threshold. The model can apparently find previously unknown security flaws and exploit them without step-by-step guidance from humans. OpenAI plans to make the model available soon, but will limit access to its cybersecurity capabilities. The company will share more details about its security and safety practices in the model's System Card at launch.
Apple's Lily engine optimizes on-device LLM inference for Apple silicon. It speeds up processing by leveraging Apple silicon's unified memory and specialized hardware, outperforming MLX-LM in prefill and decode throughput. Qwen3.6-35B-A3B model's unique architecture, including sparse MoE routing and Gated DeltaNet layers, enables advanced engine tuning for efficient model execution on one Mac.
Manus has resumed independent operations, with its founding team continuing to drive product innovation and develop advanced general AI agents. Some users experienced temporary data access interruptions, requiring data backups and restoration. Manus plans to deepen integration into daily workflows and enhance AI capabilities for complex task management.
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Frontier AI demand may be unusually reflexive: labs, startups, and trading firms reinvest token-driven gains into more frontier compute, amplifying growth.
Meta's Infrastructure Lab in Menlo Park focuses on developing hardware for next-gen AI.
Google launched agentic video understanding for several Gemini models, combining native video tools with model reasoning to improve tasks such as moment retrieval, anomaly detection, and counting.
@huggingface/kernels is a library of 207 optimized WebGPU kernels to speed up AI model inference directly in browsers.