搜索“RAG”

ial proxy is on by default. proxy country takes any ISO country code across 195+ countries, and null turns the proxy off. Logins come from cloud profiles holding cookies, local storage and login state: the agent lists them with list browser profiles and passes one as profile id. workspace id adds file storage that persists across sessions. I'd use this when the agent needs the answer, not the pages: research, form fills, checks across many sites, anything I want to run 10 or 100 at once. It's al

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back). Imagine what the Whale could do with more GWs. Receive results from any of SerpApi’s 100+ APIs as clean, structured Markdown, using roughly 50% fewer tokens than JSON on average. Simply add output=md or use the /search.md endpoint. It requires zero configuration and is included with every plan at no additional cost. AI News for 10/19/2026-10/20/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is no

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ly cached service. DeepSeek’s engineering report, as summarized and quoted in the supplied posts, reports 155 trillion tokens/day from one cluster, 96% input-cache hits, 400 ms average time to first token, and 230 tokens/s generation, reaching 300 tokens/s for coding. It also reports $38 revenue per accelerator-hour at off-peak prices. These are first-party production claims, not independently audited measurements. @ZhihuFrontier, @swyx “Fully open source” does not mean the entire production ser

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机器和流程本身”。 同样一群聪明绝顶的工程师，一个组织一年只走两圈闭环，另一个组织一年狂奔五十圈。 第一年差别也许不大，到了第五年，它们已经不再是同一个维度的生物了。 这可能也是马斯克如此厉害最大的原因之一。 14 智能的再生产与“自催化”：从存量（Stock）竞争到流量（Flow）竞争 过去，人类提供智能（Intelligence），机器提供杠杆（Leverage）；现在，智能本身变成了杠杆，直接进入了生产函数： 现在是：人 + AI（1）➡️ AI（2） 正在逼近的未来是：AI（1）➡️ AI（2）➡️ AI（3） 化学里有一种现象叫Autocatalysis（自催化）——化学反应生成的产物，反过来又充当催化剂，去加速这个反应本身。 一旦进入这种自催化状态，评估一个系统或一家公司的标准就会发生根本性逆转：二阶能力开始远远压倒一阶能力。 一个系统今天有多聪明，叫一阶能力（存量 Stock）； 一个系统明天能比今天变聪明多少，叫二阶能力（流量 Flow）。 假设公司 A 今天的能力是 100 分，每年进步10%；公司 B 今天只有 70 分，但每年进步100%。真正危险的对手显然是 B

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Was Linux 7.3 bei Grafikspeicher, Scheduling und Dateisystemen verbessert. Das US-Heimatschutzministerium verbietet zivilen Bundesbehörden alle DeepSeek-Produkte. Die weit gefasste Anordnung wirft Fragen zur praktischen Umsetzung auf: USA verbannen DeepSeek aus allen zivilen Bundesbehörden. Der Werbeblocker Pi-hole bessert mit einem Update zahlreiche Schwachstellen aus. Nutzerinnen und Nutzer sollten es rasch anwenden: Update für Werbeblocker Pi-hole stopft Befehlsschmuggel-Lücke und mehr. (fds)

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laude Sonnet 5.5 (max) at 68.4 and Muse Code with Muse Wonder Full (max) at 68.0. Against Claude Code with Claude Opus 5.5 (max) at 66.0, it costs $9.01 vs. $13.04 per task and averages 55 vs. 65 minutes per task. Across the three benchmarks, the Fusion configuration scores higher on DeepSWE v1.1 (72.6% vs. 68.4%) and SWE-Atlas-QnA (69.6% vs. 66.4%) and slightly lower on Terminal-Bench 4.0 (62.1% vs. 63.1%). Its results reflect both models working together, including their combined API cost. Cos

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an literally make millions of dollars a year and some others may not make as much just because they’re not as productive. Yeah, exactly. The thesis is that AI is really just a leveraged bet on a human’s intellect 3:29 and work ethic. And so you’re just going to have more variance than ever before in earning capacity. and our view is our model isn’t for everyone, but for the person who basically wants to go long themselves, who has this incredible foundation in software engineering, but also is c

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n Big Bench Audio, which evaluates reasoning through spoken questions and answers, Max answered 98.8% of questions correctly, compared with 75.9% for Mini. Max also recorded an average time to first audio of 0.57 seconds, while Mini took 0.62 seconds, suggesting higher accuracy. However, Mini scored higher on Full-Duplex-Bench, which evaluates conversational dynamics, including pauses, interruptions, turn-taking and backchannels. It scored 95.8%, compared with 94.1% for Max. In the Speech Agent

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模型规模开始与PC硬件能力匹配。30B级模型正在成为智能体电脑的重要参考基准，但单纯提升AI算力并不足够，因为模型运行效率还受内存容量和带宽制约。当前，智能体电脑的基础门槛至少需要32GB内存和135GB/s内存带宽；面向更高阶的本地AI工作负载，则需向64GB以上内存和250GB/s以上带宽提升。 在软件侧，AI交互方式正从“对话”向“任务执行”演进。本地RAG、代码助手、自动化办公、个人助理和多模态应用，都可能成为智能体电脑最明确的落地场景。 从“怎么做”到“要什么”，交互逻辑彻底变了 传统PC的交互逻辑很清晰：用户发出指令，PC完成操作。AI PC在此基础上增加了自然语言交互和生成式AI能力，但用户仍需参与任务拆解和具体操作。 智能体电脑进一步改变了这一模式：用户只需提出目标，Agent即可理解与规划、调用模型及工具、自主执行并返回结果。关键变化不只是增加了一个“执行”环节，而是改变了用户与PC交互的基本方式。过去用户需要告诉电脑“怎么做”，未来更多是告诉电脑“我要完成什么”。 这意味着，PC的使用方式将从围绕“点击、命令和操作”转向围绕“目标、任务和结果”。相应地，PC的评价标

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f industry-leading digital media brands that reach 15+ million elite tech professionals. Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations. LATEST STORIES AI infrastructure management software startup ASI hits $3B valuation after raising $325M in funding Google unveils TorchTPU in a push to break Nvidia's strangle

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f industry-leading digital media brands that reach 15+ million elite tech professionals. Our new proprietary theCUBE AI Video Cloud is breaking ground in audience interaction, leveraging theCUBEai.com neural network to help technology companies make data-driven decisions and stay at the forefront of industry conversations. LATEST STORIES AI infrastructure management software startup ASI hits $3B valuation after raising $325M in funding SpaceX's stock soars as Elon Musk reveals crushing earnings

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d Meta-Harness on task streams that changed topic, workflow, or domain. They tracked how much each method learned on the current task and lost on the others. GEPA learned the most, 6.2 points per block on average, and also lost the most elsewhere, 4.3 points. After it learned to ask users for missing details, a later block rewrote that rule and its 15.5-point gain fell to 2.5. If your agent edits its own prompt or memory, keep task-specific rules separate and retest old tasks after every update.

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tion work that spreads a drug program across departments, contractors and disconnected software. Its agents monitor studies and timelines, so when a manufacturing batch fails, they map which trials slip and compare responses for program leaders to choose. Anthropic paid roughly $400M in stock for Coefficient Bio in April, so both labs are now buying specialist biology teams. Development still averages 10 to 15 years from target to US approval, a clock largely set by trial biology and regulators.

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s the Pareto of performance vs price on DeepSWE, a favorite agentic software engineering benchmark today, which DeepSeek dominates. clef by Cloudflare: Decision models are all the rage after being introduced by the startup Typesafe. In short, they are general classification models that output few tokens with their probabilities. Its usefulness has led to quick adoption and also a lot of open model finetunes such as Clef. NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 by nvidia: The mid-sized model

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than a well-designed memory at the same accuracy, and on the longest histories it scored worse, because irrelevant turns confused the agent. Building memory yourself on object storage or a vector database means owning extraction, deduplication, retrieval, and access control. Memories go stale, contradictory facts pile up, and no one can tell which memory caused a bad answer. What teams need is memory that makes agents cheaper and more personal at the same time, without standing up another syste

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past session goes into the agent's context. Across all three benchmarks, MemCurrent scores highest among all memory backends and stays on the cost-accuracy Pareto frontier. On average, MemCurrent (88.1%) performs on par with full context (87.8%) at 12x fewer input tokens per question (8.4K vs 103K). On the two benchmarks with the longest histories, it even beats full context, where dumping everything into the prompt ends up confusing the agent. Figure 5. Cost–accuracy1 trade-off of memory backe

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inference profile, covering invocation count, token count, latency, throttles, and errors. Model invocation logging delivers full request and response payloads to Amazon Simple Storage Service (Amazon S3) or Amazon CloudWatch Logs. To activate it on the Amazon Bedrock console, choose Settings, then choose Model invocation logging. You can also call the PutModelInvocationLoggingConfiguration API. It is off by default. AWS Data Exports itemizes spend by model and profile. Amazon CloudWatch Coding

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行为如何都能保持。AgentCore 在整个执行链中携带经过身份验证的用户身份：每用户 OAuth 令牌保管库验证智能体是代表该特定用户而非共享服务身份行事，凭证按智能体限定作用域且仅在会话期间存在。AgentCore Gateway 在工具、操作和参数级别进行授权。自动推理针对已定义的规则提供形式化验证。这与 IAM 和 Amazon Simple Storage Service (Amazon S3) 策略验证背后的原则相同。图 2 展示了跨两个租户的这种隔离。 图 2：租户之间的 AgentCore 会话隔离。每个 microVM 在使用后停止并清理内存。 因此，租户 A 的质量处理人员提交的投诉只会到达租户 A 的令牌保管库、工具以及 AgentCore Code Interpreter（Amazon Bedrock AgentCore 的一项能力）中的沙箱。租户 B 的会话没有任何路径可以访问它们。 将摄取的内容视为不可信 这一矛盾没有干净的答案。投诉附件是不可信输入，但它也正是智能体存在所要读取的内容，因此可疑内容不能被拒绝。团队最终选择了纵深防御。 Octave 在智能体

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内则仍按Token顺序调整状态。 消融实验证明了这一点，将逐Token更新换成整轮聚合后，Nano的记忆基准平均分从约47.0降到了41.0。 六分的差距 ，足以说明连续的Token级更新能更好地处理记忆衰减，并及时修正轮内关联。 但当记忆的内容不断扩充，模型容量不够了怎么办？ 这时MemT 1.0又依托MoE，提出了 MoS （Mixture-of-Storage）解决方案。 MoS将一个记忆原生模块扩展为多个记忆专家，再分别学习存储与使用路由。其采用的是 稠密融合 ，所有记忆专家都参与计算，路由权重决定各自贡献。 这就与基座MoE的稀疏专家路由明显不同了。 不过，结构还只是起点。 为了让模型真正学会使用这条通路，团队构建了340亿Token的记忆训练语料，其中包含继续预训练200亿，后训练140亿。 继续预训练 的格式都是统一的，先把长文本切成连续片段写进记忆，再让模型拿着记忆状态去回答查询。 每处理完一个片段，模型再更新状态，供下一段使用。 后训练 进一步对齐任务结果，数据也包含三类：记忆原生的 语言建模数据 、 记忆存储与利用数据 （比如摘要、续写、压缩等），以及专门强化长程记

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pelines and check their work on each branch before a person approves the merge. Hamlet is exploring Lakebase Search for vector and text search in Postgres, branching for object storage and a home for longer agentic jobs. Hamlet, an AI-powered platform that makes local government decision-making accessible and searchable, uses Databricks Lakebase Postgres to give every pull request its own branch of production data. Engineers and AI coding agents test changes against real data without touching pr

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