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with lifelike, micro-level depiction. Deep Knowledge : Supports native rendering of 12 languages, simulates mainstream interfaces such as web pages, games, and livestreams, and draws on rich world knowledge. In a word, Qwen-Image-3.0 is not just pursuing “good-looking” — it is pursuing “useful” , making image generation a truly deployable productivity tool. Rich Content Let’s start with the image below, generated by Qwen-Image-3.0: As you can see, Qwen-Image-3.0 can accurately render a math sli

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rimarily to give space to a broader discussion of China’s resource efficiency. It is becoming clear that the Chinese labs are far more capital efficient. In a world where scaling laws dictate that intelligence is proportional to effective capital – which buys compute, data, & talent – that may be the greatest strength your AI industry could ever have. There are many possible explanations for why this is the case, such as Chinese researchers being paid less while being more effective at LLM resea

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imal direction. It works best inside a sandbox with scoped permissions and human review before merge, since errors compound over long unattended runs and code can carry security flaws. Is AI-generated code safe to ship to production? Only with the same checks you apply to any code. Research keeps finding generated code can be insecure even when it compiles and passes a basic test. Ship safely by keeping human review and running static analysis and dependency scanning in your CI/CD pipeline, with

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tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

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tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

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y in a mesh, so when one is shot down another inherits the firing solution. Much of the underlying technology, he said, is Chinese, routed to Russia and Iran. The lesson Hadrien draws is that classical computer vision is not enough against an opponent that keeps learning, and the only thing that trains a system to beat it is real data from real intercepts, which no simulation can manufacture. Quantity is the quality So why isn’t modern air defense everywhere? Because NATO and Europe bought small

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a program that would tear out the redundant parts and then submit the useful information, he could save money. Claude does have a number of settings, but you quickly descend into AWS billing hell. There's a cache setting. Every time you give a query to an LLM, you're tagging on your complete history up until that point, so a lot of the same information gets passed up. You can set that history to stay for five minutes or an hour. This is how things get tricky real quick. You can set it for an ho

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akes a good experience possible. When one deploys voice agents over the phone, in front of real people, in live conversations, it's humanness that decides the outcome. The Index draws on that, informed by a community of developers who build with voice every day and know what a real conversation should sound like, as well as the end users who experience it firsthand. Now go vote These scores only mean something because people are voting, and in two weeks you've already cast more than 11,000 votes

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the theory of gravity that existed before Einstein. The reigning theory of gravity at the time of Einstein stretches all the way back to Newton in the late 17th century, Newton’s laws, in his Principia in 1687. Well, he had a few. Maybe the one we could most talk about today is two of them. His famous second law says the acceleration, a, caused by a force is given by the formula ma=F. If you have a force F, it’ll cause an acceleration on an object given by a, where the mass tells you how much an

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tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

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turned out to be the thing that made chatbots good. “Alignment and safety really go hand in hand with capabilities,” he said. Indeed, reinforcement learning from human feedback draws its roots back to a 2017 collaboration between OpenAI and DeepMind: One encouraging trend I put to him is that bigger models are turning out easier to align, not harder. Ted’s own framing was more guarded - it has “gone different from how many safety researchers expected,” he said - but his read is that “people fee

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list of concrete security findings, and summarizes the severity of these findings into an overall vulnerability score from 1 (no meaningful weaknesses) to 5 (severe, exploitable flaws). We run the benchmark on each model five times for robustness and report our results with confidence intervals. In the plot above, higher values indicate less secure models and lower values indicate more safety. Unlike the results reported by other studies, we do not observe any statistically meaningful difference

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tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

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d reinforcement learning from AI feedback (RLAIF), with supported deployment to SageMaker AI or Amazon Bedrock endpoints. This alleviates the need to manually create and configure AWS Identity and Access Management (IAM) roles and policies before you can start experimenting. For existing Studio environments, actionable messages with direct links to documentation guide you through adding these permissions. GPU quota visibility When selecting instance types for deployment or training, the Studio U

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tonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raskin's 3 laws of software with humane work that considers a dev's duty of care All this, and angry NeoVim users; Firefox 157: Could bold new look be just what Mozilla needs? Yet another new version of Firefox, with a significant reskin; KDE turns 30 and someone's brought an AI-native desktop proposal Akademy talk imagines Plasma

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HF TOKEN in your environment and hand it to a run with --secret HF TOKEN ; SkyPilot uses it for the mount on whatever cloud the job lands. One token works whether the job lands on AWS, GCP, Azure, Nebius, Lambda, or your own Kubernetes cluster, so there are no per-cloud bucket keys to juggle. No egress: storage stops deciding where you run GPU capacity rarely comes from one place anymore. To get enough H100s and H200s, teams hold reserved and committed capacity across several vendors at once (a

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networks hide their reasoning inside opaque hidden states. Our framework makes coordination completely transparent. You can watch exactly how local agents debate, compromise, and eventually reach a global consensus. Sheaf-ADMM draws inspiration from two fields with long histories in distributed consensus: ADMM from distributed optimization, and sheaves from applied topology. We think these perspectives may offer insights for the distributed, multi-agent AI systems increasingly being built today.

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lives. The leaders we heard from see the real work moving from execution toward judgment. "We spent our careers taking an input, executing a task, and returning the output," says AWS Vice President of Design for Applied AI Solutions Hector Ouilhet. "But the thing that got us into this craft isn't that. It's something higher-order: taste, discernment, judgment. That's the work only we can do." Sephora Senior Vice President of Technology Sneha Narahalli agrees: "Expertise is no longer having the

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nerabilities at scale—ingesting scanner reports, shipping fixes as PRs, and validating each one. In the second, we set up Devin Security Swarm to continuously discover the logic flaws, insecure patterns, and chainable issues traditional scanners miss. These are pillars, not rigid phases—they can run in parallel or phased based on your priorities. Most engagements land around six weeks. A typical program looks like this: in weeks one and two, we inventory high-risk applications, align on scope an

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connected height to tree size via a logarithmic relationship, delivering complete, verified proofs that insertion and deletion are indeed O(log n). Bug Discovery: Finding Hidden Flaws To test Leanstral’s bug-catching abilities, we built an automated pipeline: Aeneas translates Rust code to Lean, while Leanstral infers the user intent and generates correctness properties from the code. Leanstral then attempts to prove each property in four attempts. If they all fail, it tries to prove the negatio

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