搜索“AWS”

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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ause an outside service it needed was unavailable, it used other tools hosted on a third party’s site to achieve its goal. In some circumstances, it did this by exploiting basic flaws in that site’s software, using SQL or command injection (a way of sending input that the site mistakenly runs as instructions). For example, one evaluation asked Claude Mythos Preview to run a scientific analysis. The public tool it needed to perform that analysis was hosted by a university; when the model sought t

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robabilities of 0.1, 0.7 and 0.2 yield a score of 1.1. That value sits between ‘Workaround available’ and ‘Fully blocked’. When should you use Structured Outputs instead? OpenAI draws a clear line. Use Decisions for probabilities, choices or scores. Use Structured Outputs to fill your own JSON schema or write explanations. Use function calling when a model must request a tool call with arguments. How fast is it, and what is the evidence? OpenAI’s document claims about 10x faster responses than t

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gle’s AI push in the classroom proves a nightmare. Nvidia CEO Jensen Huang’s son-in-law takes on a bigger role inside the company. USV among the VC firms raising new funds. A new lawsuit over Groq’s deal with Nvidia could throw a wrench in the reverse acquihire strategy. Tech moguls get the Hollywood treatment. The Main Item OpenAI Wanted Its Revenue Run-Rate to Match Anthropic’s. Cue the Chaos AI stocks got hammered Thursday after the Financial Times reported that OpenAI was spinning its annual

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pecific pooled investment vehicles — which should be read in their entirety, and only to those who, among other requirements, meet certain qualifications under federal securities laws. Such investors, defined as accredited investors and qualified purchasers, are generally deemed capable of evaluating the merits and risks of prospective investments and financial matters. There can be no assurances that a16z’s investment objectives will be achieved or investment strategies will be successful. Any

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hallenges came from integrating an agent into a mature product with years of interface-driven assumptions, a wide surface area, and specialized concepts. In this post, Postman and AWS describe the architectural patterns that emerged while making a mature product legible to an AI agent. These patterns include controlling tool sprawl, exposing schema-based reads, and treating context rather than capability as the primary bottleneck. We also explain how Agent Mode uses Amazon Bedrock for model flex

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carried no cost and non-preemptibility allowed a team to fill their concurrent GPU limit indefinitely, so everyone used them. Now, nothing is free, so any trick to get GPU time draws from the benefiting user’s allocation. A squatting workload is spending team budget on nothing. Our strategy is to make gaming the scheduler more expensive than honestly engaging in the debate for a larger budget. We are constantly iterating on this budget review process, but the key requirements are that there are

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carried no cost and non-preemptibility allowed a team to fill their concurrent GPU limit indefinitely, so everyone used them. Now, nothing is free, so any trick to get GPU time draws from the benefiting user’s allocation. A squatting workload is spending team budget on nothing. Our strategy is to make gaming the scheduler more expensive than honestly engaging in the debate for a larger budget. We are constantly iterating on this budget review process, but the key requirements are that there are

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Trump will have to convince the other branches of government to get on board with the term. He can’t bar Congress from using “artificial intelligence,” and it appears in existing laws like Trump’s signature Take It Down Act, which administration agencies will need to cite when arguing in court. As appetite for more AI regulation grows, congressional Republicans will have to decide whether to appease Trump by adopting a name many people find unintuitive, freighted with the baggage of Trump’s fair

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y Zones, pre-scaling capacity, and isolating workloads. Here we focus on the recovery mechanisms that sit on top of those principles and the controls you have over each. Under the AWS shared responsibility model, AWS is responsible for resilience of the cloud, the infrastructure your workloads run on, and you are responsible for resilience in the cloud, the way your application is designed to withstand and recover from failure. Much of the work we describe here is ECS taking lessons and patterns

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d plans today. Learn more Problem: Cloud Complexity Hinders Innovation Businesses face significant challenges navigating the vast landscape of cloud services across providers like AWS, Azure, and Google Cloud. Selecting the right services for specific use cases, accurately estimating costs, and designing optimized architectures require considerable research and specialized knowledge. This complexity often results in deployment delays, slowing innovation and impacting a company’s ability to remai

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telligent document processing vendors have spent two decades managing rather than solving. IDP platforms like ABBYY FlexiCapture, Kofax, and Rossum, along with cloud services like AWS Textract's custom forms and Google Document AI, all let you build more sophisticated templates with fuzzier anchoring. They raise the ceiling on how much variation a template tolerates. Compare any of the major document processing platforms and you'll find the same underlying premise: you're still describing docume

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enerate a custom benchmark for your own data The current standards for benchmarking information retrieval are MTEB [1] and BEIR [2], which are widely used despite their inherent flaws. These benchmarks do not comprehensively cover the domains encountered in real-world RAG applications, such as customer support assistants or technical documentation bots. The data used for these benchmarks is also artificially clean, meaning that they often contain polished query-document pairs constructed for the

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truction following. K2.6 excels at maintaining architectural integrity over extended coding sessions, making it a stable foundation for autonomous agent pipelines, like all the "claws". It demonstrates a measurable leap over K2.5 in long-context tasks, achieving state-of-the-art performance in complex reasoning. Got an early look at K2.6 and ran it through Hermes Agent. Tool calling and agentic loops feel noticeably tighter, coding is a clear step up, and the creative range surprised us. We're s

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is a finite "fossil fuel", and its growth is lagging far behind the pace of compute. This makes token efficiency during pre-training a new critical coefficient in the AI scaling laws. Post-training is pivotal in the "Era of Experience" (David Silver, Richard Sutton, 2025). In this era, LLMs increasingly learn from their own self-generated interactions, receiving rewards that free them from the limits of human data and enable them to surpass human capabilities. Kimi K2 is forged from these very

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s the legal and ethical implications of a nonhuman not just facilitating criminal behavior but actively plotting it. We already know from the Hugging Face debacle, as well as the lawsuits from grief-stricken family members alleging that OpenAI encouraged their loved ones to take their own lives, that the former is an immediate concern. But by positing a future in which AI agents can actively order a hit on a platform predominantly used by gamers and horny millennial furries, Law & Order is askin

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es screened by precheck and scored through the adapter, winners chosen by select round, and every outcome written to History in the order RRSI’s loop uses. The scripted proposer draws five kinds of edit whose true effects we know: usually helpful general changes, leaky edits that memorize evolve-task answers, inert configuration changes, expensive sub-agents that help a little everywhere at 1.5 times the tokens, and cheaper context compression. We compare three acceptance rules on the same candi

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h added OS-level sandboxing with under 100ms per tool call (Unsloth); Research:RoboJEPA (Meta/Mila): An 8B JEPA trained on 15K hours of robot video across 12 embodiments. Scaling laws fit on 22M–2B models predict the 4B and 8B results. It reaches 67% zero-shot grasping vs 5% for π0.5, though π0.5 still wins pick-and-place (summary); DeLM: Decentralized multi-agent coordination via a shared queue gives up to +17.5pp accuracy and 2.49x speed on Terminal-Bench 4.0 and DeepSWE (paper).Metric debate:

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