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rocessing pipeline via two injection vectors, both targeting the same config-driven data loader within our production Kubernetes pods. The first vector was an HDF5 external raw storage dataset read that returned the contents of local files: it handed the agent the pod's environment (secrets and tokens) and the worker's own source. The second vector was a Jinja2 template injection that executed arbitrary code, giving the agent a foothold inside our perimeter. Once a prod-pod foothold was establis

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he Genesis Mission, a national initiative, intends to build “the world’s most powerful integrated science discovery platform” by incentivizing cross-sector collaborations that leverage AI, supercomputing, quantum systems, and advanced scientific instruments to accelerate breakthroughs in energy, scientific discovery, and national security. “MIT researchers are proud to be leading and contributing to projects under the Genesis Mission, in vital areas of research that support national priorities,”

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eer, including the INFORMS 1997 Prize for Research Excellence in the Interface Between Operations Research and Computer Science for Neuro-Dynamic Programming, the 2001 ACC John R. Ragazzini Education Award, the 2009 INFORMS Expository Writing Award, the 2014 ACC Richard E. Bellman Control Heritage Award for “contributions to the foundations of deterministic and stochastic optimization-based methods in systems and control,” the 2014 Khachiyan Prize for Life-Time Accomplishments in Optimization, t

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ems once reserved for proprietary models. Ollama’s cloud is the most delightful way to access the most powerful open models: GLM, Nemotron, DeepSeek, Kimi, MiniMax and more. On average, Ollama’s cloud has more than doubled in token volume every month, and we can’t wait to make it available to every team. Today, we’re announcing that Ollama has raised $88M from Peter Fenton at Benchmark, Tomasz Tunguz at Theory Ventures, Alex Kolicich at 8VC, along with Solomon Hykes (founder of Docker), Aaron Ka

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response so they can be independently tested. We will also provide government partners with our threat intelligence reporting in advance of publication and participate in the interagency cybersecurity vulnerability clearinghouse established under Sec. 2(d) of the June 2 Executive Order; Dedicated resources for joint research. We are substantially scaling up joint work with government partners on AI security. We will stand up dedicated Anthropic teams to work on shared government priorities, pro

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hat higher-level cryptography is built on, alongside genuine feature work in review: serde encoding support and HPKE DHKEM suite IDs. Other patches were plain engineering help: storage-accounting and service-restart fixes in SimpleX, a clearer admin-quarantine confirmation in PyPI’s Warehouse, and supply-chain improvements like SBOM sidecars for Python’s Windows artifacts. We will also be upstreaming many testing improvements and new testing campaigns. Arguably, our best contributions are not ev

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s like Exa, Modal, Eight Sleep, and OpenRouter have also made their software development lifecycle more autonomous with Devin. Customers are delivering real outcomes with this leverage. Mercedes-Benz cut an eight-month legacy modernization project down to eight days. Systems integrators like Infosys and Cognizant have embedded Devin into how they deliver work to ship projects faster than ever before. Itaú, Latin America’s largest bank, fixes 70% of security vulnerabilities automatically with Dev

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ng to accelerate the work of engineering solution teams worldwide. The acquisition also accelerates our Science roadmap, advancing our understanding of fundamental physics and leveraging unique industrial data. With Emmi AI's models complementing our own, we are set to build the best-in-class agents for engineers. Founded in Austria, Emmi AI has quickly emerged as one of the world's most ambitious AI companies, leading a revolution at the intersection of artificial intelligence and industrial en

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tion we need to scale, forget about them, and they just work” What we’re building next We’ve spent the last five years going very deep on technology, including building our own storage and compute layer from the ground up. This has enabled us to achieve outcomes that seemed impossible, e.g. improve cold starts by 100x with GPU snapshotting, elastic low-latency inference globally, and scaling from 0 to 1,000 GPUs in minutes (or even seconds) without reservations by pooling capacity in hundreds of

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model-ready data. Adaption describes this set of capabilities as bringing data optimization techniques typically reserved for frontier labs to everyday builders, and reports an average 82% increase in data quality across early deployments. With this integration, Adaption users can connect their Together AI account to achieve the fastest time to high-quality, fine-tuned model through a seamless experimentation workflow. In Adaption, the user optimizes their training dataset, then directly execute

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rch and into April, I've had sessions where I maxed out session usage under 2 hours and it got stuck in overthinking loops, multiple turns of realising the same thing, dozens of paragraphs of 'but wait, actually I need to do x' with slight variations." That chimes with similar comments from an AI director at AMD. Cache optimization may be important, but it seems unlikely to account for all these reported issues. When you just need to build a tiny desktop app: TinyJS October 13, 2026 Valen creato

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rhymes" - the great George Lucas I've been doing OSS since 2009ish. My first "success story" was libGDX, a cross-platform game development framework. Back in 2011, it was all the rage and the most used game development framework on Android. Some prominent libGDX users included Niantic (Ingress was built with libGDX, Pokemon Go was not) and the peeps behind Slay the Spire. It also powers Spine, a commercial game development tool I've been involved in for almost 10 years now. I mostly handed over

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o be skipped entirely; rather than raising an error, queries silently returned results without team-level filtering. The change was tested and peer-reviewed. Our existing test coverage validated that the optimization worked correctly when active, but did not cover the codepath where it was disabled for non-target workspaces. This is the gap that allowed the regression to reach production. This change was written and reviewed by engineers without AI assistance. While we use AI extensively in our

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nerate actionable hypotheses. The system we developed, which was used for this analysis, relies on three key technical pillars: 1. Contextual Narrative Extraction We moved beyond fragile keyword searches. For example, our system successfully extracted the hidden narrative of “Taiwan interference” from posts demanding a politician “retract an erroneous statement”—something a simple search for the word “Taiwan” would completely miss. 2. Novelty Search via Multi-LLM Reasoning Using an ensemble of t

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their own hardware." "We wanted to design hardware in a way where we could build a differentiated experience," Cooper said. "Having full control over the network, compute, and storage layers lets us do really fast build and deploy loops, the kind that allows us to move at 'agentic speed' while staying 100 percent the smoothest ride in town." The approach paid dividends during recent widespread outages that affected major cloud providers — Railway remained online throughout. This soup-to-nuts co

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s. Grok Imagine : our lightning-fast image and video generation models that brings state-of-the-art multimodal understanding, editing, and generation capabilities. Grok on 𝕏 : leveraging the 𝕏 platform to understand what's happening in the world in real-time, utilizing xAI’s most powerful model to date. Looking ahead, Grok 5 is currently in training, and we are focused on launching innovative new consumer and enterprise products that harness the power of Grok, Colossus, and 𝕏 to transform how we

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Excel files can be leaked by Claude AI! Quick action by Anthropic to mitigate this indirect prompt injection attack. Our coverage in The Information and full attack chain, below: https://t.co/V1zw5crsfu

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library. We track three core metrics for our browser agents: accuracy, speed, and cost. In one year, the accuracy improved from 71.8% to 82.0%. But more importantly, we reduced average task time from 123 seconds to 33.4 seconds and dropped the cost from staggering 39.2¢ to 1.9¢ per task. It now costs less than 2 cents to complete a simple browsing task. What's Next? We're already at <2¢ per task and 4x faster than a year ago. The cost and speed problems are largely solved. But there's one critic

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he Netherlands, England, Switzerland, and many more —to put this vision into practice. Today’s AI landscape is dominated by closed, opaque offerings with limited flexibility, encouraging a “one size fits all” approach to AI. As public institutions look for solutions to help them implement AI strategies, those offerings come with significant limitations and risks. Closed and inflexible Offerings from the dominant vendors are largely proprietary, black-box offerings that are difficult or impossibl

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ls will be competitive with global models,"said Shri Ashwini Vaishnaw, Union Minister for Electronics & Information Technology, Railways, and Information & Broadcasting. Dr. Vivek Raghavan, Co-founder of Sarvam, said, "We are humbled by the responsibility bestowed upon us to build India's sovereign model, and we are ready to build AI that reaches every corner of the country. This is a crucial step toward building critical national AI infrastructure. Our goal is to build multi-modal, multi-scale

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