记者丨杨松 编辑丨鄢子为10月底，做光互连芯片的澜昆微，拿到数亿元融资。投资名单上，有熟面孔，还出现一个新锐捕手——锦秋基金。其年初就参与了澜昆微的种子轮，这一次，它追加投资。锦秋的背后，站着字节跳动。其创始人杨洁，是创投圈老兵，先在同创伟业、红杉中国工作，后去字节担任财务投资部负责人。杨洁“她是一个非常敏锐、能看懂大方向的人。”数据分析平台FFastMoss创始人史文禄说，其项目在2022年获锦秋投资。史评价杨洁的风格，特别看重创始人，对标的业务路线和未来发展想得清楚，团队决策也快。目前，锦秋约半数项目，均投向AI应用相关。字节班底杨洁专注投资早期项目，出手凌厉。比如锦秋10月还投了一家量...
Hi, Dmitri from Radioso here, an open-source platform to design AI agents for customers and visitors in any channel, in the style of Fin/Ada/Decagon. The repo is https://github.com/radioso-ai/radioso and our website is https://radioso.aiToday, your choices are:1. Buy a very expensive service for your vertical (usually starting with $$ five figures monthly) 2. Get a cheap off-the-shelf chatbot that mostly answers FAQs 3. Build your own, and then also build the inbox, human handoff, audit trail and integrations around itAt Radioso we wanted an open-source option that combines the best of all 3. You can integrate it with your own software via API/SDK or your CRM, or run it standalone, either self-hosted or in our cloud.The engine runs multi-step interactions and takes actions: calling your APIs, updating records, booking things. Maybe you’ve tried Parlant; Radioso is the alternative “with batteries included”: RAG, audit trail, customer service inbox, and integrations like Slack and email.Under the hood it’s plain TypeScript + PostgreSQL, works with OpenAI/Anthropic/other models, and is licensed under Apache 2.0 which includes all features (The only thing we keep under EE license is the multi-tenant layer we use to run our own cloud).We have existing customers running it for technical Q&A, course booking, online product recommendations and goods returns, among others, some reaching 92% of conversations automated. We’re currently working on voice support so we can offer a call center.Our ambition is to be “the WordPress of customer-facing AI” :), where people can contribute their favorite plugins and benefit from the ecosystem.Longer term, we think browsing the web will mostly mean talking to your own agent, and when you need a service or a purchase, your agent will talk to the vendor’s agent to get things done. For this, Radioso already publishes an A2A Agent Card and we’re working on the rest. Your Radioso services are also callable over MCP, for customers who already use it.Thanks for your attention. We’d especially love to hear which integrations you’d want, whether you’d self-host it, and what’s stopped you from deploying a customer-facing AI agent so far. Tell us what you think. Comments URL: https://news.ycombinator.com/item?id=50423820 Points: 1 # Comments: 0
IT之家 11 月 8 日消息，埃森哲（Accenture）被指发布了一份充斥人工智能“幻觉”的研究报告，其中不仅包含大量虚假信息，还出现了多处疑似捏造的研究案例。据 GPTZero 最新分析，这家咨询巨头发布的一份报告涉嫌包含 15 条虚假引用，以及完全虚构的案例研究，因此正受到外界审查。这份名为《跨越式发展》（Leapfrogging D...
Learn how to build a serverless Model Context Protocol (MCP) server on Amazon ECS with AWS Fargate that gives AI agents secure, metadata-driven access to an enterprise data lake. Exposing a new dataset requires only a JSON file, with no per-dataset integration code.
人工智能企业Anthropic于11月5日宣布为Claude推出全新个人理财功能，面向美国地区的Pro、Max及Ultra订阅用户开放灰度测试。该功能允许用户通过自然语言对话安全绑定银行卡，实现支出分类统计、异常账单识别、财务术语解释与定制化数据看板生成，强调仅具只读权限，不提供投资建议，确保资金安全与分析客观性。官方提醒用户核对关键数据，避免依赖模型输出。
蒸完人类上下几千年的数据之后，大模型开始 “蒸人”。文丨高洪浩 陈佳惠编辑丨高洪浩 黄俊杰成都中和与天津侯台都是城郊结合处的老村镇。一群人在这里为大型互联网公司训练大模型，大多月薪不足 1 万元。他们每天从阅读大模型生成的答案开始：核对事实，检查有没有答非所问，打分、比较优劣，有时还要亲手写一份示范。做完一条，再看下一条。模型要学的任务，从财务分析、修改程序，扩展到生物制药、芯片设计，人也得为这些任务准备示范和评分标准。这些重复、琐碎的工作，构成了今天大模型训练中最重要的一环——数据生产。“这家公司最大的创新就是数据。” 一位负责数据评测的人士这样评价 Anthropic。2025 年，当网上...
Been using this in all my sessions the last months. Opus 5.5 (over MCP) spawns Deepseek flash agents - and codex for audits etc. Very curious to hear what the HN crowd thinks/makes of it. Comments URL: https://news.ycombinator.com/item?id=50383189 Points: 1 # Comments: 0
arXiv:2611.06989v1 Announce Type: new Abstract: Enterprises run business transactions through multi-agent AI systems spanning clouds, large language models (LLMs), orchestration harnesses, and systems of record. Finance receives AI cost only at settlement grain, aggregated by SKU and charge period. The business question (what did this order, claim, or case cost in AI?) cannot be answered from billing data, because providers never see enterprise transaction boundaries. The FinOps Open Cost and Usage Specification (FOCUS) gives billing data one schema across providers but remains settlement-grain even as it adds AI token and requester attribution. We formalize this grain mismatch, derive seven requirements, and propose the Two-Ledger Model: a settlement ledger of FOCUS-conformant billing data joined to an attribution ledger of invocation-grain usage records built on OpenTelemetry GenAI telemetry. A layered correlation spine (trace context, A2A/MCP metadata, business-key correlation, probabilistic matching) assigns each record to its business transaction with recorded method and confidence; a versioned rate card prices usage in near real time; period reconciliation ties both ledgers to the invoice, and the unexplained residual identifies shadow AI. Costs are reported per order, claim, or outcome with agentic cost metrics. A simulation with known ground truth shows the spine sustaining cost coverage above 99% as trace propagation falls to zero, the reconciliation residual recovering planted unmetered spend within 0.24 percentage points while flagging provider billing errors, and mid-flight gating cutting p99 transaction cost by up to 61% at a 2-4% false-pause rate. We propose the attribution ledger as an invocation-grain FOCUS profile for standardization.
Anthropic 本周起在美国向 Pro、Max、Ultra 订阅用户灰度开放 Claude 的个人理财功能，用户绑定银行卡后可自然语言查询账户余额、交易明细及支出统计，支持按类别分析、识别异常账单并解释金融术语。该功能仅提供只读权限，不支持资金划转或投资建议，且不设广告与金融产品推广，强调客观性与数据准确性，鼓励用户核对重要信息。
Article URL: https://github.com/zoghlamimostafa/BrowserForge-MCP Comments URL: https://news.ycombinator.com/item?id=50373055 Points: 1 # Comments: 0
