Agent skills have become an important part of real-world AI applications, providing a mechanism — a set of instructions saved in a folder of text-based markdown (.md) files, usually — for models to adapt to specific enterprise use cases and complex workflows. However, optimizing these skills is a slow process and faulty process, as they cannot be trained in the same way as the parameters of the underlying AI model.... Read more ›
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Xiaomi's MiMo AI team has open-sourced MiMo Code V0.1.0, a terminal-native AI coding assistant that the Chinese electronics giant says outperforms Anthropic's Claude Code on key agentic coding benchmarks, especially on long-horizon, multi-step tasks (200+ steps) — at least, according to its own internal beta release and survey of 576 developers. It's also bundling limited-time free access to MiMo-V2.5, its multimodal flagship model with a million-token context window, requiring no... Read more ›
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Context windows are becoming a computational bottleneck. The longer an agent runs, the more tokens accumulate from retrieved documents, reasoning traces and conversation history, and the more memory and compute that growing context demands. Most existing solutions either degrade model accuracy, require the full context to load before compression begins, or produce memory savings that don't translate into real speedups in standard serving infrastructure.A research team from NYU, Columbia, Princeton,... Read more ›
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Presented by F5Enterprise AI teams have spent years solving for compute, securing GPU allocations, negotiating cloud capacity, and benchmarking training throughput. The assumption embedded in that work is that the path between storage and compute will keep up. In production, that assumption increasingly does not hold. Real traffic introduces latency spikes, network jitter, and node degradation that controlled benchmarks fail to capture, resulting in pipelines that perform well in the... Read more ›
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GenAI image generators like Stable Diffusion do not draw a picture pixel by pixel from left to right. They start with noise and iteratively refine the entire image in parallel until it converges, in a process known as diffusion. For years, applying that same principle to text generation had remained out of reach at scale.Standard language models work like a typewriter: one token at a time, left to right, with... Read more ›
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Presented by Capital One Enterprises aren’t struggling to experiment with AI; they’re struggling to make it work in the real world. Moving from promising prototypes to reliable, production-scale systems is where most efforts stall.In my role within Capital One’s AI Foundations organization, I’ve seen firsthand that successful AI implementation isn’t just about adopting the latest models or tools. It requires a disciplined R&D approach that connects foundational research to real-world... Read more ›
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Researchers from the University of California, Berkeley's Center for Responsible, Decentralized Intelligence (RDI), alongside an advisory committee of over 300 domain experts, have launched Agents’ Last Exam (ALE)—a grueling new benchmark built to measure whether artificial intelligence can actually execute economically valuable, long-horizon professional workflows.In a shocking upset, OpenAI’s GPT-5.5 from April, operating through the Codex harness, secured the absolute top spot on the new ALE Leaderboard. Read more ›
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Training a foundation LLM from scratch costs millions and requires internet-scale data — which is why most enterprises don't bother. Sapient thinks it has a cheaper path.To overcome this brute-force scaling dogma, researchers at Sapient developed HRM-Text, which replaces standard Transformers with a highly sample-efficient Hierarchical Recurrent Model (HRM), an architecture they first introduced last year.HRM decouples computation into slow-evolving strategic and fast-evolving execution layers. Instead of b Read more ›
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In a sweeping new essay titled "Policy on the AI Exponential," Anthropic co-founder and CEO Dario Amodei publicly calls for new government regulations governing the release of powerful AI models — specifically comparing AI industry to commercial aviation, which follows regulations enforced by the U.S. Federal Aviation Administration (FAA) — arguing that this is necessary to maintain public safety as AI capabilities and potential misuses grow.Alongside the essay, Anthropic released... Read more ›
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Enterprise AI teams face a dilemma: The best models today might not be the best models a year from now. MassMutual's answer is to stop making long-term bets — and build infrastructure that can swap models as the market shifts.“The world of AI today is extremely dynamic,” Sears Merritt, MassMutual CIO, explained in a new VB Beyond the Pilot podcast. “We wanted to make sure we were positioned to ride... Read more ›
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Apple’s new Siri AI, unveiled yesterday at Apple's annual Worldwide Developers Conference (WWDC 2026), may look like a consumer product story on the surface. But for enterprise developers and IT leaders, the bigger news from WWDC26 is that Apple is turning Siri into a systemwide AI interface for apps, data and workplace actions across iPhone, iPad, Mac, Apple Watch and Vision Pro, as revealed in the WWDC26 Apple Intelligence developer... Read more ›
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Engineering teams building agentic coding pipelines now have a concrete open-source alternative to managed models like Claude Fable 5 — one that runs on a single H100. The tradeoff: Cohere's North Mini Code, which launched Tuesday, generated three times the output tokens of comparable models in independent testing, a verbosity cost that compounds in high-volume production workloads.The new open-source model is a 30 billion parameter mixture-of-experts (MoE) model with 3... Read more ›
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On-device AI models have stayed small because the entire weight set has to live in DRAM, capping practical parameter counts well below what server-side deployments use. Enterprise architects evaluating agentic workloads have had to choose between capable cloud-dependent models and limited on-device ones. Apple's third-generation foundation models, announced at WWDC26, break that constraint by moving the weight set off DRAM entirely.The AFM 3 family was developed in collaboration with Google... Read more ›
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Anthropic today launched two new AI models — Claude Fable 5 and Claude Mythos 5 — marking the company’s first broad release of the powerful “Mythos-class” AI capabilities it previously made available only to participating organizations in its restricted cybersecurity program, Project Glasswing, which it announced two months ago.The company says Fable 5, which is the version most users and developers will get starting today, exceeds every Claude model it... Read more ›
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Presented by Norton For 39 days this summer, the planet will be doing roughly the same thing at the same time. The 2026 World Cup spans 104 matches across 16 cities in the United States, Canada, and Mexico, with billions of people likely to watch over the course of the tournament. It could very well be one of the largest shared events the internet has ever been asked to carry.... Read more ›
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A joint research collaboration between researchers at the University of Illinois at Urbana-Champaign (UIUC), UC Berkeley, and the open source AI-native vector database platform Chroma unveiled Harness-1, a 20-billion parameter open-source search agent built atop OpenAI's gpt-oss-20B open source model that fundamentally redesigns how AI executes complex retrieval tasks. Harness-1 achieves a massive leap in performance, scoring 73% average on its ability to recall relevant information correctly from a curated Read more ›
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In Q1 2026, VentureBeat's Pulse Research surfaced the “Governance Mirage”: the gap between the governance org charts enterprises had drawn and the control layers they had actually built. Forty-three percent said a central team owned AI governance; 23% couldn't agree on who owned it at all; and 31% named vendor opacity as the single biggest obstacle.This new wave of research asks the next question: Once you've admitted the governance problem,... Read more ›
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Our system did one thing, and it did it well: It turned natural-language questions into API calls.The users were analysts, account managers, and operations leads. They knew what data they needed, but assembling it manually meant pulling from four dashboards, two BI tools, and a Salesforce report builder. With our system, they typed the request in plain English. A request like "Compile a report on sales volume for January through... Read more ›
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Agentic AI is now a core part of the engineering process, driving massive execution leverage and helping us generate more code than ever before. Yet, a difficult question I’ve increasingly heard from business leaders is: if we’re shipping code faster than ever, why aren’t our products improving at the same rate?The reason is that writing code was never the rate limiter. Defining the right requirements, integrating with complex systems, and... Read more ›
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Our system did one thing, and it did it well: It turned natural-language questions into API calls.The users were analysts, account managers, and operations leads. They knew what data they needed, but assembling it manually meant pulling from four dashboards, two BI tools, and a Salesforce report builder. With our system, they typed the request in plain English. A request like "Compile a report on sales volume for January through... Read more ›
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25.07.2026 15:33
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