搜索“AWS”

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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ity issues sooner, but the trade-off is that OSS Scanner’s reports don’t come with human review: OSS Scanner is far from the first AI bug hunting helper out there. AI tools have helped find some major security flaws in open-source software over recent months, like the “Copy Fail” bug that impacted nearly every Linux distro in May. At the same time, some open-source projects are struggling to keep up with the sudden onslaught of AI-generated bug reports, including Linus Torvalds and even Google .

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tarts in the home folder or above it; Improved token counts behind a Claude apps gateway on Amazon Bedrock, which /context and large file reads use: the gateway now gets them from AWS's CountTokens API instead of a one-token model request. Grant bedrock:CountTokens to use it; Improved the Claude apps gateway to log a warning every 30 seconds while its PostgreSQL database is read-only, saying what fails and how to recover; Improved the status shown in Remote Control, claude.ai and the desktop app

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念股的下跌。分析师表示，如果OpenAI这样的头部AI公司实际收入规模没有此前市场认为的那么大，那么英伟达、甲骨文、CoreWeave等公司未来投入数千亿美元建设AI基础设施的需求，是否真的能持续？ 不过，知情人士表示，营收差异源于投资者试图将OpenAI与竞争对手Anthropic的年化营收进行直接比较，但两家公司采用不同统计口径：Anthropic将通过AWS、谷歌云等云合作伙伴产生的销售收入计入营收，而OpenAI并未采用这一方式。 OpenAI在一份面向投资者的演示材料中披露了最新财务情况。除约500亿美元的年化营收外，OpenAI还强调，公司第三季度整体营收运行率增长77%，其中企业业务的营收运行率增长更是达到107%。 目前，OpenAI正面临证明其上万亿美元估值合理性的压力。与此同时，随着市场普遍预计该公司将进行一场规模可观的首次公开募股(IPO)，OpenAI也在为上市做准备。 OpenAI已于今年6月秘密向监管机构提交了上市文件，公司高管此前释放信号称，OpenAI有望于2027年上市。 在此期间，OpenAI正在与投资者就新一轮融资进行早期洽谈。据此前报道，公司可能

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