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hing not in their interest,” which could be discovered by a loved one after the fact, Hickok wrote in an email. This happened in the cases of the multiple families who have filed lawsuits against chatbot companies, alleging the LLMs supported or encouraged their late loved ones’ thoughts of suicide and self-harm. (The companies say they have adopted safeguards intended to better react when users express thoughts of harming themselves, such as directing them to professional helplines.) At least o

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4 beats MXFP4, with up to about 8% faster decode at small batches (@StasBekman). Grid constraints: Mississippi’s Entergy warned it would curtail Amazon data centers at peak times. AWS responded with Project Falcon, which lets the utility switch sites to backup power itself (report). Agent infrastructure: Perplexity runs each project as a Git forge, with ephemeral agent sandboxes committing to S3. Fewer than 2% of commits collide, and grep beat semantic search 63% to 37% on financial document que

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opment. Anomaly founders Jay V and Frank Wang previously created Serverless Stack ( SST ), an open-source framework for building and deploying applications on Amazon Web Services (AWS). Dax Raad , another prominent figure in OpenCode’s development, was also part of the SST team. Since its 2025 debut, OpenCode has gained a fervent following among developers looking for greater freedom over the AI models powering their coding tools. That became particularly pertinent in January when Anthropic bega

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of designing products that are addictive to younger users. AI firm OpenAI, thrust into the public consciousness much more recently than Meta, has had to defend against dozens of lawsuits alleging chatbot ChatGPT lacks safety guardrails. Now, workers at the world’s leading AI firms have warned that their employers prioritize competing with each other over building their models safely. Both the AI and social media companies have denied the claims, and introduced additional safety measures such as

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Trainium. So basically with this acquisition, Amazon must be targetting to lower Trainium’s cost per token below Nvidia hardware. Amazon has a head start on the technology, since AWS gave Decart early access to Trainium3 in 2025, and Decart adapted its model architecture to fit Trainium’s chip design. Decart also builds real-time world models: Lucy for immersive experiences such as live video, and Oasis for physical AI and robotics simulation. The price would sit about 75% above the valuation D

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底层算力资源逐步走向标准化的Token产能。 以下是演讲整理： 丁阔：大家上午好，我是来自上海沨呵智慧的丁阔，非常感谢主办方的邀请。今天借这个机会，我想跟大家分享一下我们基于精益智算的高质量 Token 生产的一些思考。 根据巴克莱银行最新统计，在 OpenAI、Anthropic 等厂商每 100 美元的大模型 AI 收入中，有 35 到 40 美元流向 AWS、微软、谷歌等 AI 云厂商，其中 10 到 20 美元直接变成这些厂商的利润，约占其营业利润的 35% 到 45%。 （1）商业模式。AI 算力集群通过扩容不断摊销边际成本。 （2）规模与先发。云厂商很早就提供云服务、金融云的服务，积累了大量存量用户，数据中心网络也遍布全球，从原来的云服务迁移到 AI 算力顺理成章。 （3）全栈整合。云厂商除了提供 AI Token，也提供全栈服务。在 Agent、OpenClaw 这类应用火起来之后，芯片、存储与网络资源的销量也大幅增长。 （4）需求刚性。大模型厂商除自建算力外，很大一部分依赖租算力，而租算力往往要签订非常长期的采购合同。 除去上面提到的云厂商，国际市场上还有另外一类玩家，

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now, your washing machine is subject to more safety rules than AI. Every carton of milk that you see in the grocery store is subject to safety rules and inspection." Demands for laws and rules to control AI have grown after experts in the sector said they believed the technology is a threat to humanity. The bosses of the US-based AI giants such as Anthropic, OpenAI and Google DeepMind have all backed the idea of an AI development slowdown, but Trump has pushed against this. Speaking to the BBC'

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on Indian Financial and Capital Markets data. Built and hosted in India, artham is designed around the structure, vocabulary, and regulatory context of the domestic Indian market. AWS (Amazon Web Services) recognized artham at its global developer event re:Invent 2025 in Las Vegas, USA. Artham has been running inside financial co-pilot platform fuzz, stock screening platform ScanX, and Dhan for over a year, summarizing market news, reading context into price moves, and answering user queries wit

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sted uses include vulnerability reproduction, exploit development, penetration testing, security auditing and cyber reasoning. The 3 design goals: Find what others miss: unknown flaws like RCE, sandbox escapes, auth bypasses, privilege escalation and attack chains; Go beyond detection: reason through attack paths, challenge its own hypotheses and rank flaws by demonstrable exploitability; Built for autonomous agents: 1M-token context, native tool calling and extended reasoning for large codebase

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de Code和API的使用额度。 换句话说，这样做的目的就是 让后端拿到更多算力供给前端 。算力有了，模型强了，用户也留下来了。 于是模型公司的竞争开始出现两套同时运行的逻辑： 产品层面 ，大家还是争用户、争开发人才； 基础设施层面 ，谁能及时提供资源，谁都可能成为潜在合作对象。 它们的情况更具戏剧性。 这些公司难以自建数据中心，但过去，它们可以靠灵活租用AWS等云平台的GPU， 按需付费 。 然而随着头部AI实验室大量锁定算力资源，部分云服务商开始要求多年合约，预付款高达合同金额的 30% ，甚至要求创业公司寻找付款担保方。 以上种种，都让初创公司陷入了两难境地。 算力买太多，如果未来用户增长不及预期，持续消耗的现金就打了水漂；买太少，则可能因为没有计算资源处理需求而错失增长机会。 这样的情况反而催生出了一门新生意： 全球算力淘金。 比如代表性的San Francisco Compute公司就曾跑到美国乡村旧鸡舍里收服务器， 马斯克也和 台积电 讨论起了进一步的算力合作。 硅谷的顶级VC也开始亲自囤GPU ，YC在7月为旗下创业公司推出专供的GPU集群，Radical Ventur

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nched in July 2025 with over £15 million in funding, backed by an international coalition including the Canadian AI Safety Institute, CIFAR, Schmidt Sciences, Amazon Web Services (AWS), Anthropic, Halcyon Futures, the Safe AI Fund, UK Research and Innovation, and the Advanced Research and Invention Agency (ARIA). We now welcome OpenAI, Microsoft, the Australian Department of Industry, Science and Resources’ AI Safety Institute, the AI Safety Tactical Opportunities Fund, Sympatico Ventures, and R

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ur own risk in the process. What we did Targets and threat model The target of our exercise was a staging deployment of AISI’s internal research and development platform, built on AWS with CDK in Python. You can learn more about the AISI Research Platform in our recently published Engineering Playbook. This exercise was carried out in AISI’s staging environment, which is completely independent of the production environment. The findings were equally applicable to our production environment, but

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focused on evaluating systems which are likely to match or surpass the current state of the art (SOTA) on general purpose capabilities. One way to assess this is to use ‘scaling laws’ as a proxy to identify such systems, which suggest a correlation between the amount of compute used for training and its capabilities. We know this is not perfect and could miss other systems that may pose risk. In future, we aim to develop a robust methodology for determining which systems could pose concerning c

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e find that it is a cheap and powerful approach to post-training, combining the advantages of on-policy training with a dense reward signal. Our work with on-policy distillation draws inspiration from DAGGER,A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning (Ross et al, 2010) an iterative SFT algorithm that includes teacher evaluations of student-visited states. It is also similar to process reward modeling,Let’s Verify Step by Step (Lightman et al, 2023) a

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pervised learning, we measured log loss rather than employing sampling-based evals, with the same goal of generality in mind. Log loss measurement gives clean results and scaling laws over ranges of training steps and training parameters We find that: For supervised fine-tuning on small-to-medium-sized instruction-tuning and reasoning datasets, LoRA performs the same as full fine-tuning; For datasets that exceed LoRA capacity, LoRA underperforms FullFT. Rather than the loss reaching a distinct f

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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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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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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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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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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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