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here is a long tail of mere multi-hundred-million-dollar deals whose aggregate value is enormous. One plausible scenario in which managed clusters gain ground on frontier-lab bare metal involves open-source progress. Serving open-source models is already more lucrative than many realize; we recently modeled that “you can make over $100M per MW per year selling open source tokens,” leaving plenty of room for profit even at today’s rental prices. One could imagine open-source models increasing the

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missed its bandwidth target. The following example shows how a certification names its targets and the categories to run. apiVersion: nvcre.nvidia.com/v1alpha1 kind: Certification metadata: name: gpu-cluster-cert spec: target: nodeSelector: nvidia.com/gpu.present: "true" categories: - domain: communication variant: nccl-all-reduce - domain: training variant: nemotron5-8b $ kubectl apply -f certification.yaml $ kubectl get certifications.nvcre.nvidia.com -w The built-in catalog currently covers t

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ustom code scorers. This comparison assumes you record all three judgments as scores in Braintrust, with the policy verdict mapped to a numeric result and its category retained as metadata. Under these assumptions, Logfire plus Jev costs about 1.4% of the Braintrust-plus-Jev subtotal, roughly 73 times less. Longer inputs raise both inference totals equally. At 4,000 tokens per reply, the comparison becomes $188 versus $4,668 before Braintrust's data charges. More telemetry raises Logfire's total

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With AI agents like Meta’s Muse and Instinct now able to make calls on users’ behalf, Google is now letting Gemini call businesses with a new feature called “Call for Me.” The feature will initially roll out to Pixel 11 owners who pay for a Gemini subscription in the U.S. It will also require the use of the beta version of Google’s Phone app for Android, as it’s still supposed to be an experiment. While Google has long offered calling features guided by AI, prior versions were limited in what th

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llar. The benchmarking results highlight consistent stability across multiple benchmark datasets in multiple languages, such as NOTSOFAR, AMI, CALLHOME, and AISHELL, outperforming Meta Muse Voice Transcribe on all datasets at even the ultra-low configuration, and losing to pyannote Community-1 only on AMI. Even under ultra-low latency configurations, the error rate remains within a tight margin of offline baselines, confirming robust speaker attribution under real-time constraints. Streaming dia

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llar. The benchmarking results highlight consistent stability across multiple benchmark datasets in multiple languages, such as NOTSOFAR, AMI, CALLHOME, and AISHELL, outperforming Meta Muse Voice Transcribe on all datasets at even the ultra-low configuration, and losing to pyannote Community-1 only on AMI. Even under ultra-low latency configurations, the error rate remains within a tight margin of offline baselines, confirming robust speaker attribution under real-time constraints. Streaming dia

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alent. Having Jürgen join Sakana AI is a testament to this commitment. His foundational contributions to the field are legendary, specifically his pioneering work on World Models, Meta-Learning, and the Gödel Machine, as well as the 1991 deep learning techniques that underpin much of today’s AI boom. Now, he brings that visionary expertise to our team in Japan. His arrival also highlights a shared respect for Japan’s true history of technical innovation. Japan did not merely participate in the A

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, and fetch full document pulls the complete document when a single chunk isn’t enough. Retrieval quality is improved with Amazon Bedrock reranking on semantic queries and team id metadata filtering for team-scoped lookups. HAL worked well in its own chat UI, but people live in other tools: their IDE, their AI assistant. The second step was to let external MCP clients such as Kiro and Claude reach the same knowledge and tools, without handing out AWS credentials. That meant adding a second Agent

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lities, and the actions parts of each government continue to take, are the downward leg. The system is currently being held together by that upward leg, but it’s very fragile. The metaphor is more than a parallel. When I hear talk of a truce or a détente, that sounds static, like a situation we’ve arrived at. But we all feel that US-China relations is actually quite dynamic and quite unstable, moving in two vectors at once. The Midterms Jordan Schneider: I don’t know who in the Trump administrat

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odel and 20GW of data-center capacity by 2032. British Columbia sued OpenAI and Sam Altman, alleging the company failed to alert police after flagging the Tumbler Ridge shooter’s ChatGPT use. Meta’s Muse reportedly outpaced ChatGPT’s mobile debut, with 1.8M U.S. and Canadian iOS downloads in 12 days to ChatGPT’s 1.3M. Twenty countries and the EU called for stronger global oversight of advanced AI, urging transparent company safety protocols and coordinated government standards. Sponsored listing

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n progress is a stock rather than a total — while capital expenditures are however much money was spent, CIP represents the accrual of stuff. Across hyperscalers including Google, Meta, Oracle, Amazon, SpaceX, and Tesla, neoclouds like CoreWeave and IREN, and colocation companies like Core Scientific and Applied Digital, there is over $374 billion in construction in progress, a figure that’s likely lower than the true number, because Amazon’s contribution ($71.7 billion) is only current as of th

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多是组织结构的问题。也就是说，实验室里有研发和建模算力，我觉得这两者有点被混在一起了。而现实情况是，让下一代模型变得更好的算力，和两三代之后的模型相比，差别非常大。而且我认为中国实验室只是在更短的时间跨度上投入。然后，如果架构或其他方面出现重大创新，并在美国生态系统中传播开来，我猜中国实验室也会搞明白。我不认为架构创新能在 Anthropic、OpenAI、Meta 和 Google 之间很好地保密。我认为这些都会通过人员流动很快泄露出去。 00:39:50 JS Denain：我明白了。所以你认为那确实有很大影响。所以基本上，我觉得看起来你就是认为，用其他解释就能走得很远，以至于你并不真的需要蒸馏来解释差距的一大部分。 00:40:03 Nathan Lambert：是的。所以我认为，如果没有蒸馏，差距会相当接近。而且，光是构建这些模型，就有太多唾手可得的成果，我不认为蒸馏能堵上多少你正在做的那些唾手可得的成果。这很不清楚。就像一旦你把你的推理轨迹确立为……这还没有被证实，但我认为在中期训练和 SFT 中，你的推理轨迹需要有一种相当独特的风格，才能把它堆叠起来，并为模型定义一个明确的

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upport is coming. This new change is in beta and will be live for all users soon. Also, finally, Claude Code will now support AGENTS.md (via their new feature Claude Mods ). Muse, Meta’s personal AI agent , is getting love from everyone (who has access). It has a Mac app and a developer platform to build connectors , so people can use external services through Muse. And of course, it’s heavily inspired by OpenClaw . Muse can shop for you - well, it could, until Amazon started blocking it . Meta

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about decommissioning it and then decide not to. So the technology is of course incredibly important, as was the innovation required to put it all together. The reason I like this metaphor, and why I think it’s relevant to AI today, is that it wasn’t just the technology, not just the plane. The way they bought it mattered. The innovations around acquisition reform mattered. The way industry and government worked together mattered. And then the way the information coming out of the plane was actu

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ision, analyze for context, alert the right people, store for compliance, and learn from production data. Video is analyzed at the edge, only safety-relevant frames and structured metadata move to the cloud, and a correlation layer turns raw detections into high-confidence safety events. The following diagram shows how the components fit together. Figure 1: End-to-end IRIS architecture on AWS, from edge camera processing through streaming, inference, correlation, alerting, and storage The workfl

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tes, when a file or document lands in Amazon S3, an S3 event notification invokes an AWS Lambda function. The Lambda function generates a document ID, stores it alongside document metadata in Amazon DynamoDB, and starts an Amazon Bedrock Data Automation job to process the file. Amazon Bedrock Data Automation extracts structured insights based on the media type. When the job completes, an Amazon EventBridge rule triggers a second Lambda function that saves the results to a dedicated output bucket

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: Patrick Wardle reported a serious local-hijack flaw in Muse , arguing broad OS access makes such assistants a high-value attack surface. In contrast, DeepLearningAI highlighted Meta’s design philosophy for Muse-like agents: assume prompt injection will happen, keep real credentials away from the model, isolate tools in containers, and use an independent outbound-call gatekeeper. Commercial agents are also being pushed deeper into workflows : Cognition introduced Devin Cloud in Terminal and de

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elping researchers understand how setup choices may influence reported performance. As more evaluators adopt EEE, open comparisons like these can support broader and more reliable meta-research. AISI's Terminal-Bench 2.0 results alongside other reported evaluations for the same models, under different evaluation setups. We are excited about this adoption and look forward to further standardising and sharing evaluations with AISI and other AI evaluation organisations. Contribute to the shared mis

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What is the GGUF file format? GGUF packages model weights and metadata, including tokenizer information and an optional chat template, in one file. It supports different quantization levels, letting you trade some precision for a smaller memory footprint. Variants such as Q4 K M mix tensor precisions, using mostly 4-bit weights while keeping sensitive tensors at higher precision. Here's how quantization changes the file size of Unsloth's Qwen3.5-4B : We suggest starting with Q4 K M , then trying

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turability have already been verified is retained, and performance is improved by substituting or doping certain elements. Typical examples include replacing a specific transition metal in a battery cathode material with another element or altering the element located at the active site of a catalyst. However, existing crystal generation models have not sufficiently addressed the key challenge of generating new atomic species suitable for a given structure. LG AI Research recognized this gap bet

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