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eflating perplexity roughly threefold. The abstract's "3.1% quality cost for 50% cache reduction" becomes roughly 9.4% once corrected. 🚨 False falsifications. Sometimes we found flaws in an attempted reproduction. One logboook claimed dramatically, "the paper's method is 2x slower than the baseline"; this turned out to be an arithmetic bug in the reproduction : per-trajectory time compared against per-batch-of-50 time. Correctly normalized, the participant's own data confirms the paper's claimed

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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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anslates complex ASL into fluent English. Occasional errors remain in rare signs, rapid fingerspelling ("prey" → "grey"), passive constructions, classifier depictions (dropping "claws"), and tense without context ("kicked off" → "start"). We believe in building with the Deaf community, not just for it. Deaf perspectives have shaped every stage of this project — from conceptualization by Sam Sepah, a Deaf Googler, to data collection with Deaf partners, evaluation in Deaf user studies, and impact

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g. Filler, copy text was one of the genuinely useful abilities of an LLM (Sam Altman said so much about the early business of GPT-3 on a recent podcast ). It has seemed like any flaws here were mostly down to a general lack of intelligence in the models, or some other training issue, and all non-fiction and explanatory text would get obliterated by the rapid pace of progress eventually . Having worked with the models as a writing assistant over the last few years, they’ve gotten a bit better, bu

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t of the cash. This support increases the amount of capital available and reduces the return investors demand. Using several developers allows the buildout to proceed faster and draws on scarce resources already controlled by different companies. A powered datacenter site is not interchangeable with an empty parcel of land: The valuable inputs include a large power connection, completed interconnection work, permits, equipment, and an established construction pipeline. By working across several

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of both of these things. But the less verifiable thing of coming up with new ways of thinking about the problem would be harder to induce. Take, for example, the idea of scaling laws. Obviously, there is some end verification loop such that you can train GPT-4 better if you have the idea of scaling laws from 2020. But there is a longer and potentially more compute-laden road to inducing AIs to be like, “Okay, I got to think carefully about how I should be scaling my parameters and data. What ar

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zon's Automated Reasoning Group, launched in 2016, has transformed formal verification from academic research into production systems processing billions of queries daily, proving AWS infrastructure is mathematically correct rather than just tested; ARG's foundational tools like Tiros and Zelkova evolved into customer-facing services including IAM Access Analyzer, Amazon Inspector, and Reachability Analyzer, which millions of customers rely on for security and network analysis with mathematical

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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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chaser of a certain category of product in the world, then as their supplier, you’re in a pretty miserable position—well, “miserable” might not be quite right, it’s just what the laws of supply and demand naturally dictate. When a customer accounts for the overwhelming majority of purchase volume, they gain enormous pricing power, and they will inevitably squeeze the supplier’s margins down to almost nothing. Second, they will dominate the direction of demand and technology. This dominance may l

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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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st suite first Best for: The baseline for any team on GitHub wanting automated dependency bumps with no procurement, usually paired with a context-aware fix tool for source-code flaws. 4. Pixee Pixee is an agentic application-security platform that triages scanner findings and generates context-aware code fixes as pull requests. Its pitch is that most automated security fixes get dismissed for lack of context, and it targets that directly, reporting a 76% merge rate on the fixes it opens. Key fe

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fference in practice. With no Code Connect, Figma’s MCP will pass the coding agent React like this: With Code Connect, the coding agent receives an accurate snippet of code that draws from your existing design system, and all it needs to do is use it in the correct place: How we measured the impact of Code Connect We built an eval harness that runs the same set of design-to-code tasks in two ways: one with no Code Connect, and one with Code Connect templates. For each of our 27 test cases we mea

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a loaded worker can report itself unavailable and stop accepting jobs. A slow join is sometimes just an under-resourced or over-packed host. Avoid burstable instance types such as AWS t3/t4g, which can stall badly even when CPU usage looks low, and use compute-optimized instances instead. Autoscaling is yours to get right. Adjust the load fnc and load threshold values to match the scaling behavior of your environment. Get these values wrong and a subset of workers can become overloaded, leading

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g material. Stephanie Lees, a data protection specialist at Pinsent Masons, said it would add "a further layer of oversight" for employers already juggling GDPR, local employment laws, and the EU AI Act. It could also mean that rolling out the latest AI-powered workforce optimization suite could become as much an HR exercise as an IT one. ® Fire up that iPhone 1 emulation and party like it's 2007 Browser-based fun to remember a time before Apple's annual cash grab became a thing New motherboards

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ment and verify the full data path, including model endpoints, integrations, telemetry, logs, and support access. OpenHands Enterprise self-hosts in your own VPC via Kubernetes on AWS, GCP, or Azure, supports air-gapped environments, and keeps agent actions inspectable and audit-logged, which suits highly-regulated industries. Factory offers a proprietary managed platform with SOC 2 Type II, ISO 27001, and ISO 42001 certifications. Devin and Codex also have team and enterprise tiers. Cloud-based

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broader range of AI hardware, and make it easier for users to build their own customized versions, we plan to open up the model weights in the coming days, subject to applicable laws and regulations. Hardware compatibility has been a key consideration since the earliest stages of H3's design. Multimodal context understanding Real creative work requires blending complex information across modalities, pulling in images, audio, video, and more as input sources. For example, the prompt for the shot

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t be carefully secured. While frontier models make adversarial manipulation more difficult, with sufficient time and expertise, nearly all of them can still be subverted. Common flaws we observed include: weak access control (permitting any user to access the agent), execution and file-write tools that create opportunities for RCE, insufficient network egress controls permitting data exfiltration and reverse shells, and secrets in plaintext inside the execution environment accessible by the agen

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Acknowledgments This work was led by Karish Grover (Amazon AI PhD fellowship '25-'27, Carnegie Mellon University) during his internship at Amazon, with Han Xie (applied scientist, AWS AI), Sixing Lu (applied scientist, AWS AI), Xiang Song (applied scientist, AWS AI), Christos Faloutsos (Amazon Scholar, Carnegie Mellon University), and me. The code is available as open source. Machine learning Multitask learning; Self-supervised learning Feedback control for multi-objective graph self-supervision

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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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