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The average Fortune 1000 company has more than 30,000 employees and engineering, sales and marketing teams with hundreds of members. Equally large teams exist in government, science and defense organizations. And yet, research shows that the ideal size for a productive real-time conversation is only about 4 to 7 people.The reason is simple: As groups grow larger, each person has less opportunity to speak and must wait longer to respond, increasing their frustration that their views are not sufficiently considered. This is true whether groups collaborate in person, by video or teleconference, or even by text chat (which buries users in a backlog of messages that reduce participation and undermine deliberation).  Simply put, produ

Your developers are already running OpenClaw at home. Censys tracked the open-source AI agent from roughly 1,000 instances to over 21,000 publicly exposed deployments in under a week. Bitdefender’s GravityZone telemetry, drawn specifically from business environments, confirmed the pattern security leaders feared: employees deploying OpenClaw on corporate machines with single-line install commands, granting autonomous agents shell access, file system privileges, and OAuth tokens to Slack, Gmail, and SharePoint.

Researchers at Nvidia have developed a technique that can reduce the memory costs of large language model reasoning by up to eight times. Their technique, called dynamic memory sparsification (DMS), compresses the key value (KV) cache, the temporary memory LLMs generate and store as they process prompts and reason through problems and documents.While researchers have proposed various methods to compress this cache before, most struggle to do so without degrading the model's intelligence. Nvidia's approach manages to discard much of the cache while maintaining (and in some cases improving) the model's reasoning capabilities.Experiments show that DMS enables LLMs to "think" longer and explore more solutions without the usual penalty in speed or memory costs.The bottleneck of reasoningLLMs improve their performance on complex tasks by generating "

Chinese AI startup MiniMax, headquartered in Shanghai, has sent shockwaves through the AI industry today with the release of its new M2.5 language model in two variants, which promise to make high-end artificial intelligence so cheap you might stop worrying about the bill entirely. It was made open source on Hugging Face under a modified MIT License requiring that those using the model (or custom variants) for commercial purposes "prominently display 'MiniMax M2.5' on the user interface of such product or service." But that's almost beside the point given how cheap MiniMax is serving it through its API and those of partners.For the last few years, using the world’s most powerful AI was like hiring an expensive consultant—it was brilliant, but you watched

OpenAI on Thursday launched GPT-5.3-Codex-Spark, a stripped-down coding model engineered for near-instantaneous response times, marking the company's first significant inference partnership outside its traditional Nvidia-dominated infrastructure. The model runs on hardware from Cerebras Systems, a Sunnyvale-based chipmaker whose wafer-scale processors specialize in low-latency AI workloads.The partnership arrives at a pivotal moment for OpenAI. The company finds itself navigating a frayed relationship with longtime chip supplier Nvidia, mounting criticism over its decision to introduce advertisements into ChatGPT, a newly announced

When an AI agent visits a website, it’s essentially a tourist who doesn’t speak the local language. Whether built on LangChain, Claude Code, or the increasingly popular OpenClaw framework, the agent is reduced to guessing which buttons to press: scraping raw HTML, firing off screenshots to multimodal models, and burning through thousands of tokens just to figure out where a search bar is.That era may be ending. Earlier this week, the Google Chrome team launched WebMCP — Web Model Context Protocol — as an early preview in Chrome 146 Canary. WebMCP, which was developed jointly by engineers at Google and Microsoft and incubated through the W3C's Web Machine Learning community group, is a proposed web standard that lets any website expose structured, callable tools directly to AI agents through a new browser API: navigator.modelContext.

Lowering the cost of inference is typically a combination of hardware and software. A new analysis released Thursday by Nvidia details how four leading inference providers are reporting 4x to 10x reductions in cost per token.The dramatic cost reductions were achieved using Nvidia's Blackwell platform with open-source models. Production deployment data from Baseten, DeepInfra, Fireworks AI and Together AI shows significant cost improvements across healthcare, gaming, agentic chat, and customer service as enterprises scale AI from pilot projects to millions of users.The 4x to 10x cost reductions reported by inference providers required combining Blackwell hardware with two other elements: optimized software stacks and switching from proprietary to open-source models that now match frontier-level intelligence. Hardware improvements alone delivered 2x gains in some deployments, according to the analysis. Reaching larger cost reductions required adopting low-precision