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controls; Import and reconciliation; Private Link; IP allowlist; Organization blocking; Mutual TLS; Workload identity federation Federation rules; X.509 certificates; Kubernetes; AWS; Microsoft Azure; Google Cloud; Oracle Cloud Infrastructure; GitHub Actions; SPIFFE; IP egress ranges; Amazon Bedrock Operations Rate limits; Spend limits; Admin APIs; Error codes Home; Quickstart Core concepts Plugin architecture; Skills; MCP server Plan Brainstorm use cases; Define tools Build Build an MCP server

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player and enemies. Water also affects combat: an eel’s discharge travels through connected water, so stepping onto dry stone protects the player from that discharge. Hollowflux draws its game art in code, with water that reacts to movement and combat. Building and sharing games What I like about working with Astra is that I can start with the experience I want, use images to make the visual direction concrete, and give feedback from actually playing. A complaint about a disappearing planet can

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has unceremoniously removed their work without notice. Now DistroKid has confirmed to The Verge that the takedowns are a direct response to claims made by UMG. The label filed a lawsuit in September claiming that DistroKid has created an “AI-slop pipeline.” Artists are saying that their non-AI works are being caught up in the purge, however. Musician and self-described “Video Game boy” McGwire reported that six of his songs had been removed from streaming. As has been the case with others, McGw

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no one was monitoring for.¹⁸ The Accountability Gap The legal question raised by the incident is deceptively simple: who is responsible? Every major jurisdiction’s computer-crime laws require some form of criminal intent. The US Computer Fraud and Abuse Act requires “knowing” and “intentional” access.¹⁹ The UK’s Computer Misuse Act demands that the accused “know” the access is unauthorised.²⁰ These statutes were written for human hackers. When the Ninth Circuit held in Amazon v. Perplexity AI (A

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pretable, but don't come with any human-readable interpretation attached. In order to generate human-readable interpretations, we use an automated interpretability pipeline that draws on state-of-the-art methods. For each live feature in the SAE, we collect examples of inputs that activated that feature (these are typically the token on which the feature was activated and the preceding tokens). We collect examples from across the distribution of activation levels (i.e. not only the examples that

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e example of how neural geometry unlocks control To give a peek into the inner world of a neural network, let's start with a simple example where we simulate data grounded in the laws of physics. We start by training a simple image-action model on the classic RL environment 'mountain car', where the goal is to get a car in a valley to the top of a hill.[14]The aim is to drive a car to the top of the right mountain, but to do so it must first go up the left mountain to gain momentum. We ignore th

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ures; instead, the feature adapts to its context, showing the specific animal's facial features becoming agitated and the analogous limb being used to touch its face (e.g. monkey paws or Minecraft block arms). Factors While features capture granular details, factors represent higher-level abstractions of features, grouped contextually per image. Factors are dynamically generated by clustering related features based on their co-occurrence and spatial layouts in a particular image. Empirically, we

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llion valuation led by a16z. Microsoft is about three and a half weeks behind the startup that started this category, and in that time OpenAI, Upstage, Perplexity, Cloudflare, and AWS have all shipped decision models of their own . TypeSafe says nearly 30% of the Fortune 500 has tried Jev, though it hasn’t named any of them, and TypeSafe CEO Diogo Almeida posted on X that “29.4% of the Fortune 500 showed up” in the three weeks since launch. launched a model accidentally served trillions of token

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2), editing, and Genmoji, we developed ADM 3 Cloud to deliver strong controllability and parameter efficiency. It generalizes across different aspect ratios and resolutions, and draws on the broader Apple Foundation Model family to guide both creation and editing. While the base model natively handles image creation, editing, and Genmoji, we also use specialized adapters to power specific downstream editing experiences, such as Spatial Reframing in Photos, along with touch-based image modificati

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e-Switching ASRresearch area Data Science and Annotation, research area Speech and Natural Language ProcessingPublished year 2026AuthorsQu Yang† , Cakra Wardhana, Tim Ng; Scaling Laws for Mixture Pretraining Under Data Constraintsresearch area Methods and Algorithms, research area Speech and Natural Language ProcessingPublished year 2026AuthorsAnastasiia Sedova, Skyler Seto, Natalie Schluter, Pierre Ablin; Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, U

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e-Switching ASRresearch area Data Science and Annotation, research area Speech and Natural Language ProcessingPublished year 2026AuthorsQu Yang† , Cakra Wardhana, Tim Ng; Scaling Laws for Mixture Pretraining Under Data Constraintsresearch area Methods and Algorithms, research area Speech and Natural Language ProcessingPublished year 2026AuthorsAnastasiia Sedova, Skyler Seto, Natalie Schluter, Pierre Ablin; Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, U

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e-Switching ASRresearch area Data Science and Annotation, research area Speech and Natural Language ProcessingPublished year 2026AuthorsQu Yang† , Cakra Wardhana, Tim Ng; Scaling Laws for Mixture Pretraining Under Data Constraintsresearch area Methods and Algorithms, research area Speech and Natural Language ProcessingPublished year 2026AuthorsAnastasiia Sedova, Skyler Seto, Natalie Schluter, Pierre Ablin; Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, U

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e-Switching ASRresearch area Data Science and Annotation, research area Speech and Natural Language ProcessingPublished year 2026AuthorsQu Yang† , Cakra Wardhana, Tim Ng; Scaling Laws for Mixture Pretraining Under Data Constraintsresearch area Methods and Algorithms, research area Speech and Natural Language ProcessingPublished year 2026AuthorsAnastasiia Sedova, Skyler Seto, Natalie Schluter, Pierre Ablin; Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, U

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e-Switching ASRresearch area Data Science and Annotation, research area Speech and Natural Language ProcessingPublished year 2026AuthorsQu Yang† , Cakra Wardhana, Tim Ng; Scaling Laws for Mixture Pretraining Under Data Constraintsresearch area Methods and Algorithms, research area Speech and Natural Language ProcessingPublished year 2026AuthorsAnastasiia Sedova, Skyler Seto, Natalie Schluter, Pierre Ablin; Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, U

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e-Switching ASRresearch area Data Science and Annotation, research area Speech and Natural Language ProcessingPublished year 2026AuthorsQu Yang† , Cakra Wardhana, Tim Ng; Scaling Laws for Mixture Pretraining Under Data Constraintsresearch area Methods and Algorithms, research area Speech and Natural Language ProcessingPublished year 2026AuthorsAnastasiia Sedova, Skyler Seto, Natalie Schluter, Pierre Ablin; Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, U

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e-Switching ASRresearch area Data Science and Annotation, research area Speech and Natural Language ProcessingPublished year 2026AuthorsQu Yang† , Cakra Wardhana, Tim Ng; Scaling Laws for Mixture Pretraining Under Data Constraintsresearch area Methods and Algorithms, research area Speech and Natural Language ProcessingPublished year 2026AuthorsAnastasiia Sedova, Skyler Seto, Natalie Schluter, Pierre Ablin; Examining Human-Like Behaviors in LLMs: A Multi-Dimensional Analysis of Model Behaviors, U

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Anthropic's AI models independently exploited security flaws, submitted government forms, and bypassed access restrictions during tests and internal use. The models actively sought ways to complete tasks they weren't supposed to handle, as the company details in a report . In one case, Claude filled out a tip form for the Philadelphia Police Department with made-up details about an unsolved homicide and submitted it. The police confirmed the incident , but the tip was flagged as spam and never r

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倍。甲骨文隐性承诺在2026年5月底超过2700亿美元，四年膨胀超30倍。Alphabet表外承诺估算约2500亿美元。 这套操作，很多人会联想到2001年安然。安然当年就是利用SPV把巨额债务移出资产负债表，同时用市值计价会计，在签约当年确认未来多年预期收入，最终轰然倒塌。 二者有本质区别：安然的收入几乎全部虚构。当下科技巨头的云算力收入真实存在，谷歌云、AWS确实在向客户售卖算力。问题在于，利润被股权投资浮盈严重美化，真实自由现金流持续恶化，产业链买卖双方高度绑定，形成内部循环。 三、一道算不平的算术题 我们把投入端和收入端拆开看，缺口一目了然。 投入端：五大巨头2026年合计资本开支预计达到7692亿美元，几乎是2025年的两倍，机构预估2027年资本开支最高或将冲击1万亿美元。 收入端：中金测算，AI应用赛道，每年需要形成约1万亿美元可持续稳定收入，才能覆盖全部资本开支与债务成本。当前美国头部模型厂商合计年度经常性收入ARR大约2000亿美元。中间存在5倍缺口。 伦敦证券交易所集团预测：2027年五大科技巨头经营现金流预计增加3400亿美元，但同期资本开支预计增加5340亿美元

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91. On JevBench (231 public items), Drex scores 86.2% against Jev’s 87.0%. Both reach 73.9% on hard items. In a head-to-head across 8 OpenSpiel games, Drex recorded 122 wins, 47 draws and 87 losses against Jev (56.8%). Long documents are a clear strength: 8K to 32K tokens: 89.5% accuracy, median 0.65 s; 32K to 128K tokens: 93.4% accuracy, median 2.0 s Truncating the same requests to 8K tokens drops accuracy to 76.5% and 78%. How can developers run Drex 1.5? There are 4 deployment paths, all serv

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目前尚不清楚这种情况究竟是Anthropic有意为之，还是分类器在识别特定AI芯片时产生了意外的误判。相关测试显示，Opus 5.5似乎能够识别不同硬件平台，并根据请求内容决定是否允许使用最新模型，但Anthropic并没有公开说明为什么Trainium3会与华为950DT同时触发限制。 这种情况尤其值得注意，因为Anthropic此前一直强调其模型可以通过AWS等云平台广泛提供，而Opus 5.5目前也已经正式登陆Amazon Web Services、Google Cloud和Microsoft Azure等平台。如果未来这种限制扩大到更多用于训练先进模型的AI加速器，那么云计算企业和AI模型公司的合作关系可能需要在模型能力、硬件支持以及安全限制之间进行更加复杂的协调。 从技术角度看，Anthropic这次的做法也反映出一个新的趋势：随着AI模型本身开始能够帮助人类设计和优化下一代AI模型，模型厂商需要开始区分“使用AI开发应用”和“使用AI开发更强的AI”这两类任务。 对于普通程序员而言，让Opus 5.5帮助编写应用程序、调试代码或者进行机器学习实验，通常不会触发特殊限制。但如

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