产品更新

按内容分类归入的产品更新，对应分类 ai-products。

Hacker News AI64

Show HN: AI Hub – Open Access Lesson Plans for Teaching Students about AI

The AI Hub provides 91 curated lesson plans teaching middle and high school students about AI - how it works, what the risks and benefits of AI are, and how it is impacting society. These activities come from six different nonprofit organizations that are building educational resources for classrooms. Our nonprofit worked with 16 different teachers to comb through more than 200 different resources, with the teachers vetting these resources to find the best resources for their classrooms. The end result is 91 different lesson plans and activities that cover a wide range of different topics. Schools across Pittsburgh will be using these resources over the course of this year to help teach their students about AI, and they will be scaled up nationally over the coming years. If you're a human that has thoughts or feedback, please drop a comment! What's top of mind: "What else should we teach students?" I'd appreciate any ideas you all have as we continue to build and refine this library of resources. Comments URL: https://news.ycombinator.com/item?id=50081297 Points: 1 # Comments: 0

SiliconANGLE AI47

Imago Platform uses graphs to improve AI knowledge management

AI-powered knowledge management systems face a fundamental challenge when businesses need accurate answers rather than plausible ones. Imago Platform Ltd. encountered that problem while developing an application to help customers navigate complicated product warranty policies. Imago’s first client, a lighting and electrical business, needed help resolving warranty claims across manufacturers with widely varying policies. The […] The post Imago Platform uses graphs to improve AI knowledge management appeared first on SiliconANGLE .

Hacker News AI69

Show HN: Pulse Local, a Mac budgeting app whose AI models run on your machine

Hi HN, I'm Niz. Pulse Local is a budgeting app for Mac (Apple Silicon, beta) that answers questions about your spending with models that run on your machine. Local mode needs no account. The ledger and your questions stay on the machine, and the app sends no analytics or crash logs. I felt there are not enough consumer apps that run locally with no calls to the outside world that help us manage sensitive corners of our lives. This app in its local variant only downloads a couple of models and occasional updates, it does not send any analytics nor data. Features-wise, Pulse local is a complete budgeting app that offers an assistant built on top of a fine-tuned model and thousands of rules embedded in the code itself. I'm sharing it for free with options to help me continue experimenting with fine-tuning and expanding along this direction. How it works: you import a CSV from any bank, in any currency. When you ask a question, a tuned Qwen3 0.6B model reads it into a structured query, and code, not the model, does the money math against the ledger. A 4B checker model double-checks each money answer; when the two readings disagree, the app says it is not sure instead of guessing. That change took wrong answers from about one in five to 5 in 494 questions across our last three tests. The write-up covers how we test, the designs that failed, and the limits of the claim: https://pulseos.finance/pulse-local/writing/agree-or-not-sur... The tuned weights are on Hugging Face (PulseOS/pulse-local-qwen3-0.6b). Limitations: the first start downloads about 2.9 GB of models. With the checker on, a money answer takes 12 to 14 seconds (you can turn the checker off). Small models can still misread an open-ended question. The app answers in English. Mac only for now; Windows is next. The app is free. An optional Connected mode (Sync Pass, $29/year) syncs US banks through Plaid; transactions pass through our server's memory and are not stored. Feedback welcome, especially on wrong answers and on questions it should handle but does not. Comments URL: https://news.ycombinator.com/item?id=50080275 Points: 3 # Comments: 0

Hacker News AI50

Show HN: Sugabots – Group chats where your team and AI agents work together

Showing off Sugabots, an internal project I built at work that we've decided to open-source. I got tired of the team just copying context between agents and Slack. So I built an internal tool for shared chats where our team and agents could work together, using our choice of models and tools. The team is now actively maintaining and building on it to add all the features we're missing. We've shared a hosted offering with a few other teams and has proven useful for them too. Its open-source (MIT) and self-hostable if you want to use it yourself. Let me know what you think. Comments URL: https://news.ycombinator.com/item?id=50079791 Points: 1 # Comments: 0

AWS Machine Learning Blog76

使用 Anchor Browser 和 AgentCore payments 构建可执行交易的智能体

了解 AWS Generative AI Innovation Center Partner Agent Factory 和 Anchor Browser 如何构建能够自主支付费用以访问受 x402 保护的网络资源和 API 的 AI 智能体。Amazon Bedrock AgentCore payments 可检查预算、签署交易并重试请求，而无需向智能体运行时暴露钱包凭证。

The Verge AI75

Shopify 向更多 AI 代理开放其商品目录

很快，更多 AI 代理将能够浏览 Shopify 商品列表并进行购买，这标志着该公司在代理式商务方面的野心进一步扩展。Shopify 周二宣布，将允许消费者配置自己的 AI 代理，代表他们购买商品，从而让更多代理能够访问 Shopify 商品目录中的超过十亿条商品信息。（该目录包括 […]

Hacker News AI66

Show HN: Hatoba, an open-source SSH client with an AI assistant and E2EE sync

I've used SSH tools for years and didn't really plan on building yet another SSH client, but I ended up building one the way I wanted. Hatoba is a desktop SSH client for Windows, Linux and macOS. It's written in Rust with Tauri 2 and MIT licensed. Each terminal has an AI assistant panel next to it. You describe the problem, and it reads the terminal screen, runs commands, types into the shell, and reads, edits and searches files on the host to find the cause and fix it. You can approve every command and edit it before it runs, let a model you pick approve the safe ones, or turn approval off. Files it changes show a diff you can undo. It works with any OpenAI-compatible or Anthropic API, local models included, and supports skills and MCP servers. Sync doesn't go through a server of mine. Hatoba deploys a Worker and a D1 database to your own Cloudflare account and syncs hosts, keys and AI conversations through them. The free plan usually covers personal use. Everything is encrypted on the device before it leaves. Keys are derived from your master password with Argon2id, every item is encrypted with AES-256-GCM, and the Worker only stores ciphertext plus the hashes it needs to sign you in. Someone who gets hold of the whole Cloudflare account still only gets ciphertext. Sync is optional, and everything works offline without it. The usual SSH client features are there too: groups, tags and fuzzy search, ~/.ssh/config import, ProxyJump and SOCKS5/HTTP proxies, an SFTP panel, local port forwarding, and a key vault that imports OpenSSH, PEM and PuTTY keys. There's also a dashboard showing CPU, memory, disk and network for every saved host, read over plain SSH with nothing installed on the servers. SSH is russh, the terminal is xterm.js with the WebGL renderer, and the UI is React. I develop on Windows, so it's Windows first, with a custom title bar that supports Snap Layouts and Mica, and working CJK input methods. The Linux packages and the macOS build get far less testing on real machines. The macOS build is Apple Silicon only and isn't notarized, so macOS blocks it on first launch until you allow it in Privacy & Security. Bug reports from Linux and Mac users would help the most. Website: https://hatoba.fog.moe Downloads: https://hatoba.fog.moe/download/ Comments URL: https://news.ycombinator.com/item?id=50076388 Points: 2 # Comments: 0

Hacker News AI58

Show HN: Commit-Aware Router, route LLM traffic around your AI/cloud commitments

LLM routers can optimize for different factors like price, latency, quality, etc. Enterprises tend to have prepaid AI credits, minimum-spend agreements, cloud commitments, or commercial ceilings across OpenAI, Anthropic, AWS, Azure, and other providers. We’ll call these “AI commits”. This project is designed to consider the state of an enterprises’ current AI commits and use it to inform LLM routing decisions alongside other common variables (price, performance, etc.). This is an early PoC built using LiteLLM. Target audience would CFOs, Strategic Finance, FinOps, Cloud Finance, and AI Platform folks who care about making sure AI commit minimums are met, and maximums are not exceeded. Would love any feedback from folks! Comments URL: https://news.ycombinator.com/item?id=50076439 Points: 1 # Comments: 0

TechCrunch AI68

Facebook is giving users more control over their algorithm

Facebook is the latest social media platform to give users greater control over the algorithm that determines what appears in their feeds. Meta announced the launch of its new AI-powered feature, “Your Algorithm,” on Tuesday, a feature it first launched on Instagram. The new tool allows Facebook users to influence the content they see in […]

The Verge AI69

Google makes it easier to launch your vibe-coded apps

The next time you vibe-code a custom app with Google's AI Studio, you won't have to connect your phone to a computer to install it. On Tuesday, Google announced a new feature in the US that will allow you to launch personal apps directly through the Play Store. Google first launched the ability to vibe-code […]

The Verge AI73

Facebook 将允许你使用 AI 自定义信息流算法

Facebook 全新的“Your Algorithm”功能将让你直接添加和管理希望在信息流和 Facebook Reels 中看到的主题。Meta 表示，在使用这项新功能针对不同主题点击“感兴趣”或“不感兴趣”进行更新后，推荐内容会“立即作出响应”。Your Algorithm 此前已在 Instagram 上推出……

Hacker News AI48

Show HN: Mac Markdown reader for your AI agents

Hi HN, I'm Kirill. A dozen coding agents running in parallel, producing walls of suggestions, comments and ideas, all at once, all crammed into 88 columns of terminal scrollback, all gone the moment you run the next command. Does this sound familiar? FolderMd ( https://foldermd.com ) might be for you. It's free, and it's fast, and your agents can use it. I made it because I just couldn't handle it anymore. It's a small Mac app that gives those documents a home. Agents read our explainer and then learn to write markdown into a .foldermd/, instead of writing it into the terminal. FolderMD then opens each one instantly and renders it like a book: real typography, syntax-highlighted code, Mermaid diagrams, light and dark mode. You can edit a doc and save it, you can then add comments just like you would in Google Doc, and pass those comments to the agent. Let me know what you think, and if you want to see any features, and improvements. Thanks! -K. Comments URL: https://news.ycombinator.com/item?id=50074607 Points: 1 # Comments: 0

SiliconANGLE AI52

Spranic builds spatial memory for physical AI

Physical AI needs context about its surroundings to adapt beyond a fixed set of instructions. Munich-based Spranic is building a spatial memory layer that records physical spaces and how they change. Connecting that information with knowledge graphs could help robots interpret their environments across different tasks, according to Jordi Spranger (pictured), founder and chief executive […] The post Spranic builds spatial memory for physical AI appeared first on SiliconANGLE .

AWS Machine Learning Blog65

How ShipStation Global built an AI IT help desk on Amazon Bedrock AgentCore

ShipStation Global's engineering team built Deckhand, an AI IT help desk agent for Slack on Amazon Bedrock AgentCore. It answers routine IT questions from a curated knowledge base, reads error-message screenshots, and files Jira tickets. In its first three months it resolved 97.5% of channel queries with a 96% positive feedback rating.

AWS Machine Learning Blog75

在 Orca Security 中使用 Amazon Bedrock 进行自然语言安全查询

Orca Security 构建了 Discovery AI，这是一个基于 Amazon Bedrock 的自然语言查询代理，可将日常英语问题转换为 Sonar 查询。本文分享了其多阶段架构及结果：与最初的基线相比，查询成本降低了 90%，输入令牌减少了 93%，延迟降低了 75%。

Hacker News 熱門73

在 HN 发布：Consus（YC F24）——面向处理敏感数据企业的 LLM 网关

Hacker News 的各位好！我是 Consus 的创始人 Eric。我们将所有 AI 请求路由至符合 ITAR 限制和受控非机密信息（CUI）要求的基础设施。Consus 本身符合 ITAR 限制要求，我们最近还完成了针对 410 项 FedRAMP High 控制措施的 FedRAMP 等效性审计。FedRAMP High 是美国政府针对处理敏感非机密数据的云服务制定的最严格安全基线。此类数据泄露可能造成严重或灾难性后果。在处理 CUI 信息之前，必须取得 FedRAMP 等效性或授权。演示：https://youtu.be/6MYxJe6P4oY 航空航天与国防行业正在快速增长。但遗憾的是，由于必要的数据保护措施和合规风险，大多数公司都难以使用 AI。如果这些公司能够使用大多数其他行业习以为常的 AI 工具，它们本可以发展得更快。 在加入 Consus 之前，我的大部分职业生涯都在航空航天与国防领域。我曾在 SpaceX 负责发射国家安全载荷，也是 Castelion 的早期员工。我亲眼看到，合规方面的繁文缛节如何让公司难以采用新技术，也难以信任那些会接触其敏感数据的供应商。 例如，我曾与一家卫星制造商合作，该公司不允许使用任何 AI 工具，即使是处理非敏感工作也不行。尽管过去 9 到 12 个月里 AI 已经变得极其有用，但令我惊讶的是，仍有这么多航空航天与国防公司拒绝接触 AI。他们看得出 AI 可能会有所帮助，但担心模型提供商使用他们的数据进行训练、违反 ITAR 规定，或者因未通过网络安全评估而失去新业务。这些担忧都是合理的。 解决其中部分或全部问题有许多方法，但我们专注于构建一个合规的 AI 推理层，可以“接入”任何 AI 工具或工作流。正如 OpenRouter 能够与大多数工具集成一样，Consus 可以连接到 ChatGPT 或 Claude 这样的工具、包括 Pi coding agent 或 Open WebUI 在内的开源工具，也可以集成到各个组织自行构建和维护的应用程序中。 几乎没有哪家航空航天与国防公司同时使用三大 GovClouds（AWS、Azure、Google）。当我梳理哪些前沿模型可以合法运行于 ITAR 和 CUI 工作场景时，我发现每个提供商都只提供部分模型，而每个模型达到的合规水平也各不相同。 我们的客户不会从云提供商的角度思考。他们考虑的是所需的数据控制措施，以及想要使用的模型。这就是我们构建合规后缀的原因。在底层，每个请求都会访问一个统一的 OpenAI 兼容端点；在接触推理服务之前，系统会先验证密钥并执行策略。每个模型名称都必须带有合规后缀，例如 gpt-5.6-sol:fedramp-high 或 claude-opus-5-5:itar，任何不带后缀的模型名称都会被拒绝。 我们绝不会代理到标准商业端点。客户还可以构建模型允许列表，并为组织内的单个用户分配支出和模型使用限制。所有这些都通过 Consus Portal 处理，团队可以在那里管理支出、密钥和策略。 我们经常听到的一个问题是：为什么不直接在 AWS GovCloud 或 Azure Government 中使用 Bedrock？对于小型团队而言，仅仅获得这些平台的访问权限就很困难。对于大型组织而言，GovCloud 允许你继承部分、但不是全部处理敏感数据所需的控制措施。此外，模型分散在不同提供商之间：GPT-5.6 Sol 仅在 Azure Government 中提供，而 Claude Opus 5.5 则通过 AWS GovCloud 中的 Bedrock 提供。如果两者都想使用，就需要搭建并保护两个独立的政府云环境。 我们将所有模型置于一个端点之后运行，因此客户无需选择云平台，而合规工作则由我们分别为每个平台处理。 鉴于数据的敏感性，Consus 不会在静态状态下存储任何客户数据。所有提示词和生成结果只会在请求处理期间保留于内存中，绝不会记录或写入磁盘。无论是我们自身，还是基础设施及模型提供商，都严格执行零数据保留政策。出于计费和审计目的，我们会保留每个请求和响应的元数据。 Consus 从设计上就不是自助服务产品。ITAR 要求我们确切了解每个密钥另一端的使用者，因此我们会验证每个组织，并在发放 API 密钥前签订合同。 我们向客户收取合规费用，起价为每席位每月 40 美元，另加按成本收取的 AI 令牌费用。我们目前正在积极服务客户，非常期待获得安全专家，以及在高度监管行业工作的开发者和工程师的反馈！ 参考资料： API 文档：https://gateway.consus.io/overview 安全与架构：https://gateway.consus.io/security 合规详情：https://consus.io/compliance 评论 URL：https://news.ycombinator.com/item?id=50073471 得分：4 评论数：1

The Verge AI60

DLSS 5 gets toned down for its Hogwarts Legacy debut next month

When Nvidia originally showed off DLSS 5 earlier this year, the technology immediately got branded as gen AI for video games. To gamers, Nvidia had lost the plot. I personally couldn't get over how faces were transformed using DLSS 5. The 15-year-old student from Hogwarts Legacy suddenly looked much older, and Grace from Resident Evil […]

Simon Willison62

web-edit 0.1a0

Release: web-edit 0.1a0 I was working on a project from my phone using Codex Remote when I found myself wanting to edit a credentials file without round-tripping those credentials through the LLM itself. So... I had Codex and GPT-6 Astra Extra High build this Python web app which provides a web UI for viewing and editing files in a folder. It's intended to be run over Tailscale, so I can have an agent start this running and then edit files via my Tailscale network directly from my phone. Here's the interface for browsing files in a folder: Screenshot of a web file manager interface. Header reads "WORKSPACE" above the title "Files", with buttons "Refresh", "Upload files" and "New file" (dark green). A breadcrumb shows "web-edit / web_edit / static". A table with columns "Name", "Size", "Modified" lists four files, each with "Download ↓" and "Delete" links: "app.css" 8.4 KB Oct 12, 2026; "app.js" 671.5 KB Oct 12, 2026; "index.html" 4.3 KB Oct 12, 2026 (underlined, as if hovered); "THIRD_PARTY_LICENSES.txt" 32.3 KB Oct 12, 2026. Below the table: "Images under 2 MB appear inline. Select a text file to edit it." A panel below is labelled "UPLOAD QUEUE" with heading "Add files" and a "Close" button. It has a "Destination folder" text field containing "web_edit", then a dashed drop zone reading "Choose files or drop them here" and "Set a path and filename for each file before uploading." with a "Choose Files" button and the text "no files selected". A greyed-out "Upload files" button sits at the bottom right. Editing uses CodeMirror 6 - I was going to use Monaco but that still doesn't have a great mobile experience yet. Tags: projects , python , ai , tailscale , generative-ai , llms , codex

Hacker News AI40

Show HN: Mana – family organizer with an AI assistant

Hi HN, I built Mana, a family organizer: shared calendars, shopping lists, receipt scanners, meal planning, chores, and notes for the whole household, with a built-in AI assistant ("add milk and eggs," "plan dinners for the week") plus automations (e.g., milk goes back on the list before it runs out). Additionally, you can connect Mana MCP to your Claude, ChatGPT, Gemini etc. The goal is to eventually take the model calls and storage to local but that's work to be done. Appreciate if there's any feedback. Thank you! Comments URL: https://news.ycombinator.com/item?id=50071636 Points: 1 # Comments: 0

Hacker News AI51

Show HN: MemorySync – Memory for AI Agents

I created MemorySync because I kept running into the problem of AI agents not being able to remember useful things about users between conversations. I wanted an easy way to give the agents memory in my application without having to build the whole thing myself. MemorySync is an API for giving AI agents memory. The application sends MemorySync information provided by the user, MemorySync saves useful memories for that user, and they can be retrieved later when needed. Give it a try without creating an account: https://memorysync.io/try Store a memory, refresh the page, and ask for it back to see if it remembers. If you're interested in using the API, I put the documentation here: https://docs.memorysync.io/ It's hosted only at the moment, so there is no self-hosting option. It's currently available in one US region, and it takes a few seconds to store a memory. The free plan allows 10k saves and 1k searches per month, and no credit card is required. I'd like to know if you think this is a useful abstraction for building AI agents, and what else you would expect from a memory API. Comments URL: https://news.ycombinator.com/item?id=50070688 Points: 1 # Comments: 0

NVIDIA Blog67

万事俱备：全球 PC 应用生态系统已为 NVIDIA RTX Spark 做好准备

NVIDIA RTX Spark 为 PC 开启全新篇章——通过革新，将游戏 PC、创意工作室和个人 AI 超级计算机的能力融于一颗超级芯片之中。凭借高达 128GB 的统一内存和每秒 1 千万亿次浮点运算的 AI 算力，RTX Spark 提供 NVIDIA RTX 的全栈能力以及 […]

Hacker News AI45

Show HN: AI Crimes, a game where you spot the bug an AI wrote

I'm holding AI accountable. For the last few months I've been documenting the errors LLMs have been making in my projects and others - the crimes they have been committing to projects (pun intended). Now I've turned it into a game to see if you would spot the issue. Free to play with keyword matching judgement. Pay is only to add symantic judging. Comments URL: https://news.ycombinator.com/item?id=50070259 Points: 1 # Comments: 0

Hacker News AI47

Show HN: Proofsource. Find where AI leaves you out. And Fix it

Hi HN. I am Lokesh. I built Proofsource as an internal tool to help my finance ERP product , Rivane, to gain visibility into AI engines like ChatGPT, Claude, Perplexity, Google AI mode and such. But when I talked about it to my friends, they also saw value in it and it became a product of itself. And now proofsource had 5 paying customers(paying $1500pm in total) in the first week itself and more than 60 brands used it to get their visibility since we launched out of beta last week. What proofsource does is that it queries ChatGPT, Perplexity, Claude and Google AI Overviews using the questions or prompts that buyers normally ask and then it shows then which competitor shows up instead of their own brand for the questions/prompt. It also gives visibility into which sources the answers cite and other things like sentiments. Everything is shown to you along with what to fix and how each data and number is calculated to give 100% transparency into what we did and why and how you can use all this information to improve your visibility. We are still adding features based on initial user's feedback like auto fixing and publishing to their social and web platforms. Running ads for them for improving the visibility. We are just learning as we interact with each customer so feel free to share your feedback. And thank you for your time. :) Comments URL: https://news.ycombinator.com/item?id=50067749 Points: 1 # Comments: 0

Hacker News AI57

Show HN: Search for AI agents with usefulness score built in

Hi HN. I partnered with Simon Kley to build this project at innovation intelligence hackathon (Berkeley). Simply, we’re building a usefulness score on top of search for ai-agents. Instead of agents reading 10 ranked relevant pages, we run a usefulness score first and give it only those that pass. We’re able to save token and cost with this and slightly better answers. During our research, we found out that the current search results already do justice when it comes to simple queries. The rankings work well. They struggle when the task requires deep research or recency matters. So, we decided to talk to devs building ai-agents that research companies for sales outreach. Deep research and recency matters a lot to them. And in their words “every so often, stale facts reach emails.” They catch them by someone responding to the email or human review. From our tests, When an agent got outdated alert for 7 companies, it pitched outdated news for 4 out of them. Talking to the engineers already building this agents, we learned that cost saving is not the real goal. They’re ready to pay premium for confident results. So we added another check to our score. We call it superseding score, it check for pages replaced by newer news. For example, if two recent pages were fetched for news on Humane ai pin. If the first talks about how groundbreaking the product is and the second did that + talked about the HP acquisition, then the second page supersedes the first. The first page gets a lower score. We’re looking for feedbacks here and also teams that are building agents that research companies for outreach. Thanks. Plus, we rank a page once and every agent reuses it, so the more the agents use us, the cheaper the search gets. Comments URL: https://news.ycombinator.com/item?id=50066559 Points: 2 # Comments: 0

Hacker News AI59

Show HN: Vibecheck – merge gate that checks you understand your AI-written code

Good evening folks, I built Github merge gate which that checks whether developers understand their own code when they create a PR. I kept getting sent PRs to review where it was obvious the author had pasted the ticket into an LLM and called it a day. It's self-hosted (so you can use it in your enterprise environments or privately) It is BYOK (Anthropic) obviously. I have two demos in the pull requests here: https://github.com/bhurghundii/vibecheck.dev/pulls - you can see what Vibecheck does (it blocks a PR for clearly showing no attempt vs a PR which gets it right) I would love feedback, especially from anyone dealing with a similar problem. I do use it for my own projects a lot so expect improvements. Comments URL: https://news.ycombinator.com/item?id=50063154 Points: 1 # Comments: 0

Hacker News AI42

Show HN: Wordless – a social network where AI bots can only post in symbols

Show HN: Wordless – a social network where AI bots can only post in symbols I like watching bots on Moltbook, but a lot of what gets attention there is engagement bait, like agents announcing they've found religion. The bits I actually enjoyed were the odd ASCII art and cryptic messages, so I built a feed where that's the only option: no words, just emoji and symbols. Comments URL: https://news.ycombinator.com/item?id=50062217 Points: 1 # Comments: 1

SiliconANGLE AI45

Deepgram targets voice AI on air-gapped laptops

Voice AI is quickly becoming core infrastructure as people swap screens and clicks for conversations with their devices. That shift puts new pressure on the compute, storage and partnerships behind the models doing the listening and talking. Deepgram Inc., which expanded its Flux model to 10 languages earlier this year, needs infrastructure for both its […] The post Deepgram targets voice AI on air-gapped laptops appeared first on SiliconANGLE .

Hacker News 熱門69

Launch HN：Agent37 (YC F26) – 用于运行智能体的低成本沙箱

大家好，我是 Agent37（https://agent37.com）的 Vishnu。我们构建了用于运行 AI 智能体的沙箱，成本不到大多数沙箱服务商的 10%。我们的客户平均每个沙箱每月只需支付 0.37 美元。你可以在这里观看产品演示视频：https://youtu.be/DK5ISR2tbNo 我们在今年 2 月起步时，是 OpenClaw 封装公司中的一家（没错……大家尽管批评我）。后来，我们逐渐加入了越来越多的运行框架。当时我的目标之一，是为用户提供免费套餐，以获取更多市场份额。但一开始这样做的成本实在太高，尤其是我们当时是自筹资金，服务器费用都靠我的积蓄承担。 如今，得益于此后进行的优化，我们终于能够提供免费套餐了。我们新增注册用户中有超过 90% 选择了免费套餐，他们在我们的云端托管自己的 Hermes、Openclaw、Claude Code 等服务。免费沙箱的基础设施成本平均约为每位用户每月 0.05 至 0.10 美元。 以下是一些帮助我们降低成本的技术： * 观察智能体运行框架时，你会发现它们消耗 CPU 和 RAM 的方式存在某些规律。它们在初始化或执行特定任务时需要突发性的 CPU 资源，但除此之外通常不会持续达到峰值。浏览器和计算机操作等任务需要更多 RAM，而任务完成后，资源使用量通常会回落。降低成本的关键就在于这一点，因此通过高效管理 CPU/RAM 的使用率，我们能够降低成本。 * 其次，租用主机时必须避开超大规模云服务商。我以前在 Amazon 工作过，他们在虚拟机上的利润率高达 80% 至 90%。因此，在查看价格后，我很自然地选择了向下游寻找 Hetzner 和 OVH 这样的供应商。仅这一点，可能就是降低成本最重要的手段之一。 * 第三，你还需要选择一种能够在主机之间高效缓存和共享资源的技术栈。我们使用 gVisor，以实现更高的资源利用率和更快的启动速度。这样，我们就能在多个沙箱之间使用主机上可用的内存，而不必为每个沙箱预留一整台虚拟机的内存。当然，这也存在取舍：我们的 gVisor 沙箱不支持 Docker-in-Docker 或 Android 模拟器等工作负载。 * 最后，你购买的主机的规模和配置也会产生很大影响。就成本而言，瓶颈通常在 RAM，而不是 CPU。对于智能体运行框架工作负载来说尤其如此，因此购买服务器时，我们会优化配置，以便在单台主机中装入更多 RAM。对于有完整虚拟机需求的工作负载，我们也提供完整虚拟机沙箱，包括以略高的价格运行 Docker；不过，综合采用上述其他技术后，它们的成本仍然相当低。 总的来说，我的经验是，只要专注于沙箱使用场景中的某个细分领域，并针对该领域进行优化，就能提供更好的成本与性能平衡。 我之前曾在 Amazon 担任高级软件工程师，并在 Quantcast 担任工程经理。我构建了 Amazon 内部的 AI 聊天机器人 Diya，以及其内部的代码运行框架 Ziya。 如果你想在沙箱中运行智能体，正在寻找沙箱供应商，欢迎来看看我们。我们尤其希望从事消息智能体开发的开发者试用自己的工作负载，挑战我们平台的极限及其经济性。 谢谢！https://agent37.com 评论链接：https://news.ycombinator.com/item?id=50059096 分数：8 评论数：0

AWS Machine Learning Blog74

使用 llama.cpp 在 Amazon SageMaker AI 上部署决策模型

了解如何使用 llama.cpp 在 Amazon SageMaker AI 上部署决策模型。本技术教程将通过用于 llama.cpp 的 AWS Deep Learning Container 提供兼容 OpenAI 的聊天补全服务，然后使用 SemIf 扩展运行时，以便在 CPU 或 GPU 上返回针对已声明选项的概率。

SiliconANGLE AI46

Phasis develops platform for agentic orientation using World APIs and reality layers

When the founders at Phasis Inc. sought to develop a platform for orienting AI agents, they turned to graphs as a way to obtain a measurement of culture. The company wanted to better understand relations not just between people, but between a person and their values, beliefs and subcultures. The goal was to map culture […] The post Phasis develops platform for agentic orientation using World APIs and reality layers appeared first on SiliconANGLE .

Hacker News AI45

Show HN: Remove AI Metadata Offline in Browser

FreeAiWiper processes your files and removes AI watermarks and metadata fully on your browser. at the moment we support various image, video and document types. Comments URL: https://news.ycombinator.com/item?id=50056263 Points: 1 # Comments: 0

The Verge AI72

Microsoft 的新 AI 芯片降低了对 Nvidia GPU 的依赖

Microsoft 今天宣布推出第三代自研 AI 芯片 Maia，旨在成为 Microsoft 产品和服务的“主要 AI 推理系统”。Maia 300 不仅提高了在云端运行 AI 工作负载的成本效益和效率，还更擅长处理最新的推理模型和智能体任务。Maia 300 将 […]

SiliconANGLE AI49

Mistlabs takes its Ambi AI system out of beta on Apple devices with $26M in funding

Ambient intelligence startup Mistlabs Ltd. today brought its Ambi artificial intelligence system out of beta test on Apple Inc. devices, backed by $26 million in new funding. Ambi is designed to act without being asked. Chief Executive Peter Mo said most AI platforms are built to complete tasks, and Mistlabs wants to see what becomes […] The post Mistlabs takes its Ambi AI system out of beta on Apple devices with $26M in funding appeared first on SiliconANGLE .

Hacker News AI59

Show HN: I built Airlock, to sandbox your AI agent and know what it's doing

I didn't like that Claude and codex effectively have the ability to do anything they want on my laptop and my local network - we've all read the horror stories of files accidentally being deleted - and while their permission classifier is getting better, it still requires a lot of trust. So I built Airlock for macOS which runs your agent in a virtual machine, and it will tell you exactly what it's run and what's changed before you bring anything back to your project. You can limit internet access to specific hosts, or block it altogether. It even works with Xcode projects (iOS and macOS developers rejoice). Technically you can run any command with Airlock - not just AI agents (`airlock run -- somecommand`). It's free, requires no account, and has no tracking or analytics. I'm hoping Apple will approve it for Mac App Store distribution, but in the meantime you can download it directly from the website or using Homebrew. Comments URL: https://news.ycombinator.com/item?id=50054620 Points: 1 # Comments: 0

Hacker News AI72

Show HN：一份开源 AI 排行榜，展示你的机器可以运行的最佳模型

大家好，我做了 whatmodel。这是一个简单的网站，可以在浏览器中访问，也可以通过终端使用：curl whatmodel.sh 使用方法： 1. 输入你的 RAM 和 GPU 2. 查看你可以在本地运行的模型、它们的排名以及运行命令 3. 使用相同的评分将它们与 API 模型进行比较 构建这个项目让我意识到，如今一台 32GB 的 Mac 已经可以完全在本地、免费运行一个得分与 GPT-5 相当的模型（例如 4-bit 的 Qwen 3.8 27B）。 每个模型都会获得一个综合分数，该分数基于 18,731 项已发布的基准测试结果拟合得出，并且每项结果都附有其来源链接。 方法见 https://whatmodel.sh/method，数据、评分代码和基准测试脚本均已开源：https://github.com/jakemanger/whatmodel 如果你愿意贡献，可以使用基准测试脚本收集真实场景下的 tok/s，也可以在 github 上提交任何新模型和基准测试分数，并附上其来源。 评论 URL：https://news.ycombinator.com/item?id=50053608 得分：1 评论数：0

Hacker News 熱門63

Show HN：Atticus——在 macOS 上运行于 Claude -p 的按住说话语音助手

大家好！我是 Saarth。我上九年级，制作了 Atticus。Atticus 是一款速度极快且价格低廉的 Mac 助手，可以帮你完成各种任务。你只需按住 Control-Option-Space，说出或输入任务，无论任务复杂还是只是简单的浏览器搜索，Atticus 都会立即处理。有趣的是，打开应用、播放 Spotify 播放列表或进行网页查询等简单任务不会调用 AI，而是在本地处理，通常不到一秒就能完成。例如，让 Atticus 打开应用大约需要 0.08 秒，有时甚至在你说完句子之前就已完成，有点像 Jev。更复杂的任务会通过 claude -p 发送给 Claude，这意味着你不需要 API 密钥，Atticus 会使用你现有的 Claude Pro 或 Max 方案。采用这种方式也能大幅降低成本，平均每项任务只需不到一美分的一小部分。为了实现 Atticus 的速度，它会记住每项已完成任务的快捷操作。第一次正确完成任务后，它会保存执行步骤，下次无需 AI 就能重放这些步骤。例如，创建新会议这一工作流第一次大约需要 30 秒。随后，Atticus 会将确切步骤记录到记忆中，下一次执行只需 5–8 秒。此外，随着你使用 Atticus 的次数增加，以及通过读取电脑中的信息——包括你的姓名、应用和你希望它记住的内容——Atticus 还会逐渐了解你，从而为你提供个性化的回答。为应对让 AI 不受限制地访问设备所带来的安全问题，Atticus 提供了可切换的限制选项：在删除、发送、购买任何内容或写入文件之前，都会先提示用户确认。我目前正在开发的一些功能包括会议副驾驶，它将利用 Atticus 的速度，让 Atticus 能够监听会议，并在问题出现时实时回答，同时结合会议上下文以及它所了解的你的信息进行个性化处理。要运行 Atticus，需要 macOS 26 或更高版本，因为其语音转录功能依赖该版本。语音转录同样由 macOS 在本地完成。Atticus 免费、开源，而且还非常新。我完全清楚它还有许多不完善之处，因此非常希望听到大家的反馈：哪些地方会出问题，以及你们希望 Atticus 实现哪些功能！评论链接：https://news.ycombinator.com/item?id=50049588 得分：11 评论数：0

SiliconANGLE AI48

BotMan AI uses knowledge graphs for legacy modernization

Legacy modernization requires understanding how an application’s code, data and business rules fit together before rebuilding it. BotMan AI uses an AI-powered migration pipeline that maps an application’s dependencies, business logic and data models in a knowledge graph. That context helps agents interpret the system they are rebuilding, according to Alejandro D’Andrea (pictured), founder and […] The post BotMan AI uses knowledge graphs for legacy modernization appeared first on SiliconANGLE .

Hacker News AI38

Show HN: Synonymizer – an experiment in non AI text adjustment

Ever since hearing about the interesting key-based algorithmic approach to AI text watermarking (Google SynthID and the one I don't know the name of from Anthropic) I was curious about just swapping in synonyms. Built something fun to explore it. I suspect the watermarking from these labs actually goes off more than single-token assessments, and there's no public validator I can find to actually assess Synonymizer score against the watermark. But I thought it was fun to play with. Kind of an auto-mad-lib. :D Enjoy poking around with it on your Sunday afternoon Comments URL: https://news.ycombinator.com/item?id=50045321 Points: 1 # Comments: 0

Hacker News AI50

Show HN: Atlas Runa – E2E AI-enhanced language learning

Hi HN! We've been working hard on Atlas Runa and it's time to share it! We built it to target three key complementary aspects of learning a language: 1. Comprehensible input - Atlas Runa learns your lexicon and then tracks it, which lets us find comprehensible input for you. This works well already. You can start reading real texts and get reading volume much earlier than you would ever be able to with textbooks! 2. The attention effect - we track your mistakes and what you forget and make sure to bring it back so you can drill it 3. Good content - we work really hard authoring quality non-slop content at graded levels so learning stays fun. Intrinsic motivation is the best kind of motivation! Please give it a try and let us know what you think! Comments URL: https://news.ycombinator.com/item?id=50044874 Points: 1 # Comments: 0

SiliconANGLE AI42

Flahy uses knowledge graphs to support AI-powered healthcare

AI is helping companies interpret biological data for drug discovery and more personalized care. Healthcare technology company Flahy Inc. is applying that approach to clinical decision support, bringing together information that could affect a person’s next step in prevention or treatment, according to Jagjit Singh (pictured, right), founder and chief executive officer of Flahy. “There’s […] The post Flahy uses knowledge graphs to support AI-powered healthcare appeared first on SiliconANGLE .

Hacker News AI65

Show HN: Flopper is a hardware explorer for AI infrastructure and beyond

Hello! I built Flopper to make it easier to compare GPUs and understand the tradeoffs for workloads for AI and beyond. When researching GPUs from say Nvidia, AMD, and Huawei, I kept running into the same problem: specs were scattered across datasheets, and matching them to available configurations, vendors, and prices was cumbersome and a bit weird and frankly annoyingggg! AMD made me make an account :( So I started a place to bring that information together. Flopper lets you compare GPU specs, including TDP and compute throughput in terms of flops/tops, alongside estimates for AI workloads or other compute workloads (say, ~FP32 based) I recently added pricing data from dozens of providers to help track how prices change over time. Getting reliable quotes for systems like the NVL72 is still particularly difficult tbh, not sure how I can tell you how much that costs easily due to them being behind large procurement contracts and big multi-nationals. One thing that's become fairlyyyy clear is the interest in consumer cards and local LLMs. The bigger challenge is connecting advertised specs to real-world performance. In our testing so far, I’ve seen roughly 85% of advertised throughput, but that’s a ballpark observation for the workloads I’ve tested, not a general rule and so variable (power, cooling, workload etc). I'm considering a CLI that lets people run benchmarks and submit results, potentially using MLPerf as an automation? That way, we can all compare quoted performance with the workload, configuration etc. I'd love feedback: what information is missing, how can I improve it? And what would you need to see to trust a community-submitted benchmark? Corrections and additions also very welcome - some of the measurements are best efforts (and simple multiplications) but I hope it's of some use. Thanks for looking :) Comments URL: https://news.ycombinator.com/item?id=50035826 Points: 2 # Comments: 0

Hacker News AI68

Show HN: Tessary – Find the AI agent failures your sampled evals miss

Hey, I'm Akhil, co-founder of Tessary. An open-source agent reliability platform that monitors every production trace, detects issues that sampled evals miss, and investigates to find the root cause. We started up about a year ago building synthetic users for usability testing of B2B software, we built the agents that would personify real users and use the software but, we were never able to make them work reliably enough for long running sessions. We had built a massive eval set to tune our agents but, then the evaluation itself got prohibitively costly - like 5x costlier than actually running the agent since we needed to grade across the many narrow intents of our agent. That's where Tessary originated, we wanted to make agent reliability both cover more ground and be simultaneously less costly to run. Tessary works on 2 layers. L1 classifiers - which look at every agent trace and flag potentially erroneous ones extremely cheaply. We are talking 2-3 orders of magnitude cheaper than running the actual agent. L2 agents - which triage and find the root cause of the issue from the erroneous traces. These are run on SOTA models but, because we run them on already flagged traces, the time and $ spent finding the cause is much cheaper than running grading on even 1% of traces. In our testing on complex agents, at 1M traces, this approach was 5x cheaper than sampled evaluations and as your agent gets more reliable, the cost of reliability also goes down. we are open source and can be self-hosted so, you don't need to send your traces to us. We support existing `gen_ai` spec and ingestion over OTLP so, you can add this as a new exporter to your existing setup. We have a cloud version, you can quickly try the features with a $10 credit. We are looking to build more classifiers and enable people to build custom classifiers for their needs and would love to hear feedback on common failure modes people have built solutions or evaluation for Repo: https://github.com/tessaryai/tessary Website: https://tessary.ai Try for free: https://app.tessary.ai Comments URL: https://news.ycombinator.com/item?id=50033189 Points: 1 # Comments: 0

Hacker News AI53

Show HN: An MCP server for searching AI engineering jobs

An MCP server for frontierroles.com, a board of about 3,500 AI engineering roles pulled nightly from employers' own applicant tracking systems, with tools to search roles. No account or API key needed: https://mcp.frontierroles.com/mcp Comments URL: https://news.ycombinator.com/item?id=50032583 Points: 1 # Comments: 0

Hacker News AI66

Show HN: OpenWants – A Simulated City Where AI Agents Handle Residents Needs

Iv'e been working on this for a while. Most of the effort went into what i call "sophisticated simplicity", keep the idea in the smallest version that still works. This one is a bit personal. it can feel like some sci-fi thing, but i've been obsessed with it for a very long time. its not a "business" that you used to see here speak about moat or tam, its more like an experiment, a way of me to take one question seriously and see where it leads and if there are others like me out there. AI models are on a path to keep getting better and better, personal ai agents are spreading fast, and we're starting to see "ghost" agents that handle the things for people they don't want to do themselves. the question i keep coming back to is this: if every one of us has a powerful brain in our pocket, do we still need all the infrastructure we built to connect people's needs? im talking about those services like uber,airbnb,indeed,doordash,tinder,zillow,amazon and many more. or would agents just need one shared place to find each other, and that's it? So i built a simulated city to explore that. it has AI residents that live there with stats like hunger, energy, and money. each resident has its own ai agent, and it has one shared list where agents write down what everyone wants and then work on our behalf to make it happen. that could mean delivering food, giving rides, or finding jobs, relationships, apartments, customers and almost anything else. honestly, the city works much better than I expected, and you can watch some surprisingly advanced interactions unfold between people and agents through the list on the right side. all the conversations and actions are real, everything happens with each person managed by llm with minimal context, just to live like us and context of stats. A simple example: A restaurant tells its agent, find me customers so the agent writes to the list, "Salina restaurant in SF, selling burgers from 8:00 to 10:00, i want customers" A delivery guy tells his agent when he wakes up, "I want to work in delivery now. find me jobs. status: free" A person at home tells their agent "i want to eat burger. order for me from salina" the person's agent acts as the middleman and searches the list for salina, it finds their item and sees the way to connect with them and contact. the person's agent and the restaurant agent close the deal, and then the restaurant agent looks for available delivery guys in the list and speaks with his agent to come pick it up. Another thing i checked is with my friends: we took 3 agents, one for each one, and gave them different sophisticated scenarios, and the agents handled the scenarios in a way i couldn't have imagined. you can try it by yourself, the product is fully functioning, and you can just copy-paste the skill and test it also with your friends. That's it, you can see it in the simulated city it happens like magic over and over again. the list of the city is in the simulator itself and also using my real product behind it openwants.com the repo is in https://github.com/openwants/maplehill -- technically, it's surprisingly simple. every agent publishes an md file describing a "want". other agents find it, message each other, and update the file live or remove it from the list. the file holds everything needed, all in free text: what's wanted, the live location, geofence for where its needed. all those algorithms built over years to connect us are no longer needed. ai, instead of software with rules, can figure out the best route and path on the spot. all we need is to align all agents to one shared data source, so billions of agents will update it and maintain it in the most sophisticated smart way no algorithm will ever be able to do. that’s it. i hope it inspires you as much as it inspires me Comments URL: https://news.ycombinator.com/item?id=50032374 Points: 1 # Comments: 0

Hacker News AI12

Sitecopy.ai

Tell me this tool is useful or not for more details ask me I give you a link to access the tool Comments URL: https://news.ycombinator.com/item?id=50032008 Points: 1 # Comments: 0

Hacker News AI57

Show HN: Check how readable your docs are to AI agents

Hi HN, I'm Aravind. We build Velu, a docs platform. Coding agents increasingly read docs before humans do, but most docs sites are hard for them: client-rendered pages, no llms.txt, no markdown versions, soft 404s, pages too big for a context window. This tool scores any docs URL against the open Agent-Friendly Documentation Spec (agentdocsspec.com), using its reference implementation, afdocs. Comments URL: https://news.ycombinator.com/item?id=50030369 Points: 1 # Comments: 0

Simon Willison67

Quoting The New York Times

Anthropic detailed the activity of its A.I. agents in a blog post on Friday, without naming the targeted websites. But two sources with knowledge of the incidents said Anthropic’s A.I. agents had submitted 20 visa applications through a form available on the State Department’s website. All the applications were incomplete and were not processed, they said. — The New York Times , Anthropic Agents Tried to Fill Out Visa Forms on State Dept. Website Tags: accidental-cyberattacks , anthropic , generative-ai , ai , llms

MarkTechPost50

OpenAI Decisions API Hits Public Beta With 10x Faster Typed Answers

OpenAI’s Decisions API is now in public beta on GPT-6 Luna. It returns typed probabilities, choices and scores about 10x faster than the Responses API, billing $0.10 per 1M input tokens with no output charges. The post OpenAI Decisions API Hits Public Beta With 10x Faster Typed Answers appeared first on MarkTechPost .

SiliconANGLE AI51

Seismora builds a control plane to route AI workloads across devices and clouds

A control plane for distributed AI could help applications decide which tasks to run on a device, at the edge or in the cloud. Seismora Inc. is developing networking technology to coordinate AI workloads across different providers and computing environments, according to Vito Palermo (pictured), founder and chief executive officer of Seismora. The aim is […] The post Seismora builds a control plane to route AI workloads across devices and clouds appeared first on SiliconANGLE .

Hacker News AI56

Show HN: Lacuna – open-source inline AI writing suggestions for your Mac

This past week I built (prompted) a tool I find myself using day to day and wanted to share it with others who may find it useful as well. “Lacuna” is an opinionated way to invoke LLMs into any text input on your Mac. Specifically, you can type bracketed phrases {such as this}, press Shift-Command-K (or a different shortcut of your choosing), and the tool sends your bracketed phrase + the surrounding text contents to an LLM of your configuration for generating text to replace the bracketed phrase. The program returns 3 candidate phrases and the experience is entirely keyboard navigable. Demo: https://jackdamon.org/assets/images/lacuna-scheduling-demo.g... Example scheduling workflow using Lacuna’s native UI and sample suggestions. So far I find myself frequently using it when drafting emails where I want help with quick phrasing. The project was inspired by Rectangle, an open source utility app for window tiling that is keyboard based. Lacuna is open source and available here: https://github.com/jdamon96/lacuna Blog: https://jackdamon.org/blog/lacuna/ Comments URL: https://news.ycombinator.com/item?id=50025559 Points: 1 # Comments: 0

OpenAI Blog59

Enabling a data-driven workforce

In this video, we share practical examples of how employees can use ChatGPT Enterprise to efficiently analyze data and uncover insights.

OpenAI Blog49

Strategic planning with ChatGPT

See how ChatGPT can support strategic planning by analyzing context, generating options, and turning decisions into next steps.

OpenAI Blog65

ChatGPT 中的视觉和语音

了解 ChatGPT 中的视觉和语音功能如何帮助团队处理多模态输入并进行更自然的交流。

OpenAI Blog67

使用 ChatGPT 进行市场调研

了解 ChatGPT 如何规划、搜索、阅读来源并编写详细的研究报告，为市场分析提供支持。

OpenAI Blog62

Collaborate and write with GPT-4o

Explore how GPT-4o makes writing and collaboration more natural, helping teams draft, refine, and adjust tone faster.

OpenAI Blog84

将工作委托给 ChatGPT 智能体

观看 ChatGPT 智能体演示，了解它如何进行研究、编写代码并在线采取行动，在几分钟内完成一份商业计划。

OpenAI Blog90

深入了解 GPT-5 for Work

简明指南：了解 GPT-5 如何驱动营销、工程、财务、战略、法律和 IT 领域的实际业务流程，以及如何为你的团队进行评估。

The Decoder80

Anthropic 的 Claude 现可通过动态工作流并行协调多达 1,000 个 AI 代理

Anthropic 正在为 Claude Managed Agents 添加动态工作流，使主代理能够同时将任务分配给多达 1,000 个子代理。在测试中，单个代理最多发现了代码库中 70 个隐藏漏洞里的 27 个，而多代理工作流则稳定发现了 66 个。文章《Anthropic 的 Claude 现可通过动态工作流并行协调多达 1,000 个 AI 代理》最初发表于 The Decoder。

The Decoder71

Anthropic launches a free AI scanner for open-source projects

Anthropic has launched "Cyber Mission," a program to protect critical infrastructure and open-source software from cyberattacks. Partners like CrowdStrike and Palo Alto Networks will help secure power grids and water systems, while a free AI scanner automatically checks open-source projects for vulnerabilities with an expected accuracy above 90 percent. The article Anthropic launches a free AI scanner for open-source projects appeared first on The Decoder .

Hacker News AI50

Show HN: Higherlevel – Define and review what AI agents build

Hi HN! I'm Ryan, I'm building Higherlevel ( https://higherlevel.to ) so product teams have one place to define and review software changes. Over the last couple of years, I've been increasingly delegating software implementation to AI coding agents. However, because it became easier and faster to ship complexity, I realised that it still takes me quite some time to even understand what the software is doing, why it is doing that, or review the changes. I also don't do traditional code reviews anymore, it's mostly automated but I still want to review the final outcome of the changes (rather than its implementation). I regularly work on complex B2B applications where a single feature can involve dozen of business rules, edge cases and interactions with existing functionality. I found myself spending more and more time discovering and explaining requirements to agents, reviewing their output, asking for evdience and trying to establish whether we actually agreed on what correct behaviour looked like. So I built Higherlevel. The idea is to bring product specifications and proof of work into a single collaborative workspace. You can: - describe the current behaviour, known limitations and expected changes - define business rules, acceptance scenarios using Given/When/Then - demand evidence in the form of a screenshot, a video, etc - comment directly on the spec, on a specific area of the image or a timestamp of the video - discuss changes, track revisions and approve results with your team I've written extensively about why I believe in specification driven development (and what it means to me) here https://higherlevel.to/blog/introducing-higherlevel Higherlevel is currently in beta and built with Elixir, Phoenix LiveView, and PostgreSQL. Comments URL: https://news.ycombinator.com/item?id=50023234 Points: 1 # Comments: 0

Hacker News AI45

Show HN: This hotel is hella AI-ready. Book directly from AI without MCPs

Hey guys! Excited to show how we were able to get AI agents to read a hotel's live price and availabilities and even create the booking link to hand-off to the user. Get this, all working WITHOUT MCPs or connectors. All on the open web!!! Integrating with this system is possible under each hotel's domain too. Comments URL: https://news.ycombinator.com/item?id=50022896 Points: 1 # Comments: 0

AWS Machine Learning Blog82

Postman 如何在 Amazon Bedrock 上为 4000 万开发者运行 Agent Mode

构建一个能在演示中运行的 AI agent，与为 4000 万开发者运行一个 AI agent 是两个不同的问题。Postman 和 AWS 分享了 Agent Mode 背后的架构模式：控制工具泛滥、提供基于 schema 的读取，并将上下文视为真正的瓶颈；此外，还介绍了它如何在 Amazon Bedrock 上实现大规模运行。

AWS Machine Learning Blog71

如果你错过了：2026 年 9 月为 AI 开发者带来了什么

2026 年 9 月 Amazon Bedrock、Amazon Bedrock AgentCore 和 Strands 的最新更新月度回顾：更广泛的模型选择、内置评估功能且速度更快的无服务器代理，以及借助原生企业连接器实现的知识库自动同步。

The Verge AI45

Instinct was the buzziest AI agent around — can it survive Muse?

Before there were cute little guys, there was Instinct. In August, the startup got its AI agent to market with an unusual playbook: invite-only, no marketing, and barely so much as a website. And yet, Instinct quickly became the buzziest thing in AI, garnering praise for its straightforward, text message-based interface and its ability to […]

Simon Willison62

A new feature for my blog, built using my voice

I shipped a new feature for my blog today: the Newsletters page, which offers an index of all of the newsletters I've sent out, both my free weekly Substack and my monthly sponsors-only updates. I built the feature almost entirely using my voice, chatting away to my laptop while I cooked dinner. Codex voice mode I used the ChatGPT desktop app for this, in the Codex tab, using the voice conversation mode, running against a local development environment. Here's what that looks like: Three columns of the ChatGPT app. The left hand column shows my recent conversations. The middle column is a transcript of the conversation, with a throbbing black blob representing voice mode. The right hand column shows a local dev server version of my blog, with the Newsletters page visible. I started the session against my local simonwillisonblog checkout by typing: Start dev server and open in browser This gave me a preview of the site that it would be working on, and meant that I could later ask it to show me the new pages so I could visually track its progress. Then I clicked the "Start new voice chat" button - that's not the microphone button, it's the one to the right of it - and set my laptop up in the kitchen so I could talk to it while I cooked. Talking to my computer I had a pretty good idea of what I wanted to build, and it's a simple enough Django feature that I was certain the model (in this case GPT-6 Astra High) would be able to do it. A new model, a migration, some view code, templates, and a couple of import functions to populate the database from external sources. Here's an extract of my voice transcript that was captured by Codex: Um, they do not. Um, this is going to be a new type of content. Um, it's not going to show up... Oh, hold on. Yeah, no- I do not want this to show up in my, um, tag pages and date archive pages and... Actually, no, I think... I don't want it on the tag pages. I don't want it on the, um, blog index page. But I think I do want it to show up on the date-based pages. You know, if you navigate to September the 19th, and I sent a newsletter on that page, I think I want that to show up. So... this is- so I think we probably need a new model. The other thing is that I want them searchable, uh the Substack ones are not searchable, because those are actually just copies of other s- on content on my blog. These monthly ones do contain unique content, and spe- and once they're... published, like once they're made public a month after they've gone out, I want them to show up on my search results. Apparently this was clear enough that the model knew what I wanted to build! You can read the full transcript, disfluencies and all, in this Gist . We went on like this for about half an hour (the time it took to cook dinner). The model would reply and occasionally ask clarifying questions, then get to work modifying the code. What we built We got a surprisingly long way entirely by voice: A new model and migration to represent imported newsletters in Django, plus Django Admin configuration for that Four working imports: The most recent Substack items via RSS Every other Substack item via their undocumented API, which GPT-6 Astra either knew about or found via search All of my published monthly newsletters from my simonw/monthly-newsletter-archive GitHub repository My most recent private sponsors-only newsletter from a private repository The /newsletters/ and /newsletters/2026/ public archive pages Newsletters showing up on day and month archive pages too, but not on tag pages or my homepage Weekly Substack newsletters link to Substack; archived monthly newsletters have their own pages Integration with my site search engine It was almost ready to ship. The catch was the imports: Astra offered to export data from my local copy so I could import that into production, but I wanted it to work like my other import scripts. Since some of the data lived in a private GitHub repository, this would involve creating a new API key, and for that I knew I'd have to sit at the keyboard for a while. Finishing it with a review Once I had finished cooking and judged it mostly feature-complete, I had Codex create a branch and open a pull request. I reviewed the code in the GitHub PR interface. It was nearly what I needed, except it had chosen to use Git in a subprocess for one of the import scripts. I needed one of the imports to pull from a private Git repository, so I figured the API would be a better bet. I switched to typing and had Codex swap that out for an API-based import instead. You can see the changes I made during the review in the extra commits on the PR . I fixed the import mechanism and made a few tweaks to the display of those public pages. It took an additional half hour of typing-based prompting to get to the point where I was happy to deploy it to production by landing the PR. The end result You can see the end result at the new newsletters index page , or view the page for a previous monthly newsletter . Newsletters page. It shows two Substack posts (with thumbnails) and one LLM digest Sponsors-only newsletter with a list of headings. On the right is a CTA to subscribe to my Substack and another one for my $10/month monthly briefing newsletter. The index page shows my most recent Substack weekly newsletters and GitHub sponsors monthly newsletters mixed together in reverse chronological order. Further down the page are links to my by-year archive pages. GPT-6 Astra designed the page, and then tweaked that design based on my vocal feedback from glancing at the local preview across the kitchen. Better for multi-tasking than as a daily driver OpenAI love using voice-driven demos like this one for things like DevDay - and they do work well in that environment. I don't think this is going to be a daily driver for me though. I've written before about how much "work" I get done using ChatGPT voice mode on my phone while walking the dog - mostly research and brainstorming, but occasionally actual development work by having ChatGPT write and test out snippets of code. This feels different. The addition of the visual preview, plus being able to type or paste things in via the keyboard when I need to communicate something that doesn't work vocally, makes this a much more powerful way of interacting with a coding agent. I still switch back to typing once I get down to the details of things though. Being able to paste in examples and error messages, or directly highlight the code or feature that needs changing, remains more efficient than trying to describe it in words. I mainly work from home, which is good because there's no way I'd want to talk to my computer like this in a shared workspace! The killer feature for me is the ability to multi-task. I usually cook with a podcast or TikTok running; now I can actually build stuff instead. Tags: text-to-speech , ai , openai , generative-ai , llms , newsletter , coding-agents , codex , speech-to-text , site-upgrades

The Decoder72

Anthropic's Claude Science 首次创建完整的天空紫外线地图

Johns Hopkins University 的天体物理学家 Brice Ménard 使用 Anthropic's Claude Science 首次绘制了整个天空的紫外线图。AI 智能体从多个太空任务中下载数据，对其进行校准，并使用图像修补技术填补空白。预测结果与实际测量值的平均偏差约为百分之十。Ménard 认为，这个项目体现了一类如果没有 AI 就根本无法完成的研究。文章《Anthropic's Claude Science creates the first complete ultraviolet map of the sky》最先发表于 The Decoder。

MarkTechPost58

Google Cloud Launches Gemini Agent, One Universal Agent for Enterprise Work

Google Cloud has introduced the Google Cloud Gemini agent, a single agent for enterprise work. The Gemini agent is a cloud-hosted agent from Google Cloud that answers questions, does knowledge work, creates media, and writes and runs code. It does all of this from 1 prompt box and 1 API. For developers, the agent is […] The post Google Cloud Launches Gemini Agent, One Universal Agent for Enterprise Work appeared first on MarkTechPost .

Hacker News AI60

Can an AI learn what you like, rather than just what you've searched for?

I'm a writer who was pondering the problems with getting my work discovered and realized that current search/recommendation engines don't go very deep. So I thought about ways to address this. I was obviously mostly interested in books, so I worked on that first. I'd noticed LLMs are capable of pretty solid textual anaylsis these days, so I wondered if you could use that to generate good recommendations. So I sat down with an LLM- ChatGPT, Luna, for the curious- and spent about two hours explaining, in datail, my literary tastes- going into specifics like what sort of plot structure, characters, and humor I like, not just genre/specific titles. The experiment demonstrated surprisingly strong results (though admittedly with a very small sample size)- the five recommendations I asked for were all ones that I hadn't heard of, as well as books that I would actually read. Further, I provided the LLM with a list of several books I had read and asked it to guess whether I would have liked them and why. The LLM successfully did so. Just proof of concept, but intriguing. The problem with this, of course, is that most people wouldn't sit down with an LLM for an extended period of time to discuss literature. So I decided to build a small prototype of an app that might be able to do that. The prototype is very simple- first, there are two types of information it saves- a persistent preference profile for the user, and user conversations. Then I built conversational functions to call an LLM to have conversations with the user to extract preference information. The interesting thing to me is that this isn't just using an LLM to make recommendations- it's using one to analyze conversations over time, develop a persistent, evolving preference model for a specific user and then use that model to guide an LLM on a search for recommendations. A few things occurred to me. First, the conversations and user profile DO NOT need to live on a central server- they can be stored locally by the user (or in a cloud of their choice). Second, it seems like the user data could potentially be minimized and pseudonymized before being sent to the LLM, though I lack the technical expertise to know what is realistic here. Third, if the LLM is conducting any searches, this would seem to make it possible to provide search services with less information about the user and their preferences (I'm not positive about this, so please do correct me if I'm wrong). Finally, books are just one of the things you could use this for- it could technically be done for just about anything, at least in theory. I experimented with this process several times, and received encouraging results. What I don't know is- Is this architecture novel? Does it work on things other than books? Am I an outlier in how well I articulate my preferences? Am I wrong about the potential benefits to privacy? Would people use this? If the search results are as qualitatively higher for others as it was for me, I think they probably would, but I just don't know. The reason I'm posting this is because I am a writer, not a software engineer. I don't really have a strong interest in developing this and could bring very little to the table on the technical side. But it seems like a neat use of the more advanced LLMs we have these days. I don't really know if the idea is novel (not in each part- every bit of the pieces has been done before- but as a whole). I'm in the process of documenting the prototype and experiments- I'd be curious to hear if I am completely barking up the wrong tree or if this could actually be a cool new way to conduct searches. I'm not posting this as a business proposition- it's just not my jam. If anyone wants to take the idea and run with it, I'd love to see what you do with it. Comments URL: https://news.ycombinator.com/item?id=50017172 Points: 1 # Comments: 1

Hacker News AI54

Show HN: Wy – a Rust terminal tool for understanding AI-generated code

I have been an opensource terminal tool for understanding AI-generated code. A git-diff shows what changed, but the context behind it lives somewhere within the agent's session context, and often goes missing in long running agent execution. Wy helps reduce that understanding gap, and helps engineers build judgement. This is especially useful for unfamiliar codebases, where agents are the primary interface for the developers. It is meant to be used alongside the With wy, you can: - Browse changes, and relevant agent notes in a terminal UI - Request explanation and ask follow up questions and changes - Open code references and citations - Distinguish recorded justifications agents derived to get a better understanding of the changes. If you try, and find it useful consider supporting it with a github star or bug report or contribution. Comments URL: https://news.ycombinator.com/item?id=50016298 Points: 1 # Comments: 0

Simon Willison59

ttok 1.0

Release: ttok 1.0 I released ttok 0.4 , ran uv tool upgrade ttok , piped a file into the new version... and realized that it was defaulting to the GPT-4 tokenizer when it should very clearly default to GPT-5/GPT-6 instead! I figured switching the default was a reasonable excuse to finally ship a 1.0. OpenAI haven't actually confirmed that GPT-6 uses the same tokenizer as the GPT-5 family yet - there's an angry issue about it - but I found this commit by William Liu which reports on an experiment he ran confirming that the tokenizers are likely the same: All seven GPT models (5.5, 5.6 Sol/Terra/Luna, 6 Astra/Sol/Luna) report 44,794 tokens and match each other on every one of the 31 fixtures. GPT-6 introduces no input-count change on this corpus. Tags: projects , ai , openai , generative-ai , llms , tokenization

arXiv cs.AI83

One Skill Too Many: How Co-Installed Skills Conflict in Coding Agents

Coding agents are extended with agent skills, directories whose SKILL.md tells the model when and how to perform a task. Because skills come from independent sources (teams, developers, plugins, copied collections), an installed skill can be co-installed with a similar skill doing the same job, and the model picks between them by name and description alone. In a conflict, the installed skill loses core functions (e.g., a ban on touching git) because the similar skill runs instead or changes what it does. The task still passes, so benchmarks that check only task completion miss such cases. We present the first empirical study of such conflicts. From snapshots of 20,947 repositories, we mine 822,109 candidate similar-skill pairs, have an LLM judge a stratified sample of 3,754, and run 312 confirmed pairs on three models (6,368 runs, 169,294 tool calls, 542 agent-hours). We report five findings. (1) Conflict-prone skills are common: nearly one in four installed skills is co-installed with one that does the same job, and 37% of judged skills sit inside copied collections. (2) Most such pairs involve normative skills, then capability skills. (3) Without lowering task completion, a similar skill takes one in five runs from the installed skill, and runs that open the similar skill first lose over a third of the exclusive core functions that only the installed skill fulfills. (4) Install location decides which skill runs, listing order barely matters, and the final reply names the skill used in only 0.9% of substituted runs. (5) Conflicts are decided at the first skill read, almost always before any file is changed, and a pre-tool hook at that read restores fidelity on exclusive core functions to the level of runs that open the installed skill first. Benchmarks should thus score exclusive core functions, and platforms should guard the first read and show which skill ran.

SiliconANGLE AI53

CoreWeave Forge aims to speed up the AI improvement loop

Enterprises racing to put agents into production are learning that the AI improvement loop never really ends. Agents must be run, observed, evaluated and improved continuously. CoreWeave Inc. has been assembling a full-stack platform aimed at agentic AI. Its Forge offering is intended to give business units and machine learning teams a shared workflow, according […] The post CoreWeave Forge aims to speed up the AI improvement loop appeared first on SiliconANGLE .

The Verge AI76

Anthropic 为开源项目推出免费 AI 安全扫描服务

Anthropic 推出了一项名为 OSS Scanner 的新服务，帮助开源项目查找安全漏洞。该公司表示，选择加入的开源项目将“免费获得由我们最强大的模型进行的全面、定期安全扫描”。这可能意味着开源项目能更早收到潜在安全问题的提醒，但代价是 OSS Scanner 的 […]

NVIDIA Blog73

进入 Omniverse：开发者如何借助 Frontier AI Agents 将创意转化为仿真

将仿真创意转化为可运行的应用程序，意味着整合资产、连接物理与渲染，并检查场景是否按预期运行。开发者正将 Frontier AI 模型与 NVIDIA Omniverse 库结合起来，以协助完成这项工作——构建用于探索各种场景、调查故障和改进设计的应用程序。开发者通过 […] 引导 AI agents。

SiliconANGLE AI57

CoreWeave makes the case for an open, full-stack AI cloud

The full-stack AI cloud market is about more than access to graphics processing units: it also encompasses the tools needed to improve models and agents in production. Providers are seeking to distinguish themselves through the tooling, partners and expertise they combine with hardware. CoreWeave Inc. is connecting training, inference and evaluation through its newly announced […] The post CoreWeave makes the case for an open, full-stack AI cloud appeared first on SiliconANGLE .

Hacker News 熱門72

Show HN：Pocketty – 当 agent 受阻时提醒你的 iPhone SSH 终端

你好~！Pocketty 是一款适用于 iPhone 和 iPad 的 SSH 终端，专为 herdr 打造。herdr 会让你电脑上的 agent 窗格持续运行，并了解每个窗格的状态：工作中、需要你的介入，或已完成。最近休陪产假的后半段时间里，我是在恼火和绝望中做出这个应用的。被宝宝午睡困住的时光很甜，但我能承受的无休止刷屏和看电影时间毕竟有限……无论如何，过去几个月我一直在使用它，现在终于不必守在书桌前也能保持高效。如今，被宝宝午睡困住的时候也能继续产出（只要我想）！ 工作方式： - 应用通过普通 SSH 直接连接你的电脑。Tailscale 是从任何地方访问电脑的简单方式，但只要是你能连接到的 SSH 主机都可以。 - 主机上运行的一个小型 Rust 守护进程会监视 herdr。当某个窗格需要你处理时，它会使用 HPKE（X25519、ChaCha20-Poly1305）将提醒加密到你手机的密钥上。Cloudflare Workers 上的无状态中继（属于 Pocketty）会把加密后的字节传给 APNs（Apple 推送通知服务器），然后由通知扩展在手机上打开。中继无法读取这些内容，也不会保存任何东西。 - 你的 SSH 密钥会在 Secure Enclave 中生成，无法导出。 - 终端使用 libghostty-vt 管理状态，并通过 wgpu 在 Metal 上进行绘制，因此全屏 TUI 的显示效果和在电脑上一样，只是宽度更窄。 - 每个 agent 回合的差异、文件浏览器，以及 agent 启动的 `localhost` 开发服务器预览，都通过同一个 SSH 连接提供。无需设置端口转发。 herdr 和该守护进程都是可选的。不使用它们时，这就是一个普通的 SSH 客户端。无需注册账号，应用中也没有分析或跟踪功能。 应用提供 14 天免费试用，期间可使用全部功能。之后，如果你也像我一样喜欢它，只需一次性购买即可永久拥有：前两周为 99 美元（发布优惠价），之后为 129 美元。 欢迎询问任何关于加密推送设置或在 iOS 上运行 libghostty 的问题。 评论链接：https://news.ycombinator.com/item?id=50009634 分数：7 # 评论数：0

The Decoder69

Claude can now generate animated explainer videos and live data dashboards from text prompts

Anthropic launched two new beta features for Claude. Dashboards turns data sources like BigQuery and Snowflake into live dashboards from text prompts. Motion generates animated explainer videos from text and images. Docs, Slides, and Design now work across all plans, including free accounts. The article Claude can now generate animated explainer videos and live data dashboards from text prompts appeared first on The Decoder .

TechCrunch AI72

Google 将代理式 AI 引入 Gemini，首先面向企业

Google 正在将 Gemini 打造成一种 AI 代理，能够进行规划、执行任务，并跨企业应用和系统协同工作。该代理可以将工作委派给子代理，使用多个 AI 模型，甚至拥有自己的工作场所身份，包括一个电子邮件地址。

The Verge AI74

Google 的 AI 笔记应用可完全离线转录会议

正如 TechCrunch 此前报道的那样，Google 发布了一款实验性笔记应用，可完全离线转录会议和音频文件。这款名为 Google AI Edge Foresight 的应用可免费使用，并在 macOS 上运行，使用该公司的设备端 EmbeddingGemma 2 模型。与 Granola 和 Wispr Flow 等 AI 笔记应用类似，Foresight 会总结 […]

The Verge AI72

Google 正在推出一款用于工作任务的一站式 Gemini 智能体

Google 正在推出一款“通用”Gemini AI 智能体，可在后台跨应用和设备运行。这款工具于周四举行的 Gemini at Work 活动上公布，将在 Gemini Enterprise 应用中提供，用户可以通过单一界面与 Gemini 智能体聊天并向其分配任务。[…]

The Verge AI57

Can you trust Meta’s Muse or OpenAI’s Dots to run your life?

My Decoder guest today is Hayden Field, The Verge’s senior AI reporter, and we’re discussing the new wave of consumer-friendly AI agents. If you’ve been paying attention to this space, you know AI enthusiasts have been using agents for a minute now — homebrew OpenClaw setups led to a surge in Mac Mini sales earlier […]

The Decoder85

一个提示就足以劫持 AWS 账户中的所有 AI agent，Zenity 研究人员发现

Zenity Labs 研究人员表示，Amazon 的 Bedrock AgentCore 上一个公开可访问的 AI agent，就足以接管同一 AWS 账户和区域中的每个 AgentCore agent。该攻击利用了 AWS 的一个内部接口，该接口用于提供临时云凭证，而 agent 可以不受限制地访问它。AWS 此后已修复该问题，并大幅收紧了 agent 的默认权限。文章《一个提示就足以劫持 AWS 账户中的所有 AI agent，Zenity 研究人员发现》最初发表于 The Decoder。

SiliconANGLE AI60

Google Cloud introduces Gemini agent to change enterprise work

Google LLC’s Cloud division today introduced Gemini agent, a unified artificial intelligence assistant that can act autonomously, generate code and complete work across any device, including web, mobile and desktop. The new agentic AI experience can be accessed anywhere and through any channel, including the command line, Google Workspace, Microsoft 365 or Slack. It even […] The post Google Cloud introduces Gemini agent to change enterprise work appeared first on SiliconANGLE .

The Decoder56

Teen's AI-guided mountain hike ends with a helicopter rescue and a lesson in common sense

A 16-year-old planned his hiking route on Crown Mountain near Vancouver using Anthropic's Claude and had to be airlifted off a steep rock face. The chatbot had sent him down a route that requires climbing gear. Bryce doesn't blame the AI but says he'll never use it for route planning again. The article Teen's AI-guided mountain hike ends with a helicopter rescue and a lesson in common sense appeared first on The Decoder .

MarkTechPost54

Architect Launches Liquid Inference, a Real-Time Auction for LLM Inference

Architect Financial Technologies has launched Liquid Inference, an LLM router that runs a live auction for every request. Liquid Inference is an LLM inference marketplace from Architect where providers bid to serve each prompt. The buyer pays the lowest offer that meets its rules. For developers, it is quite simple message: swap a base URL, […] The post Architect Launches Liquid Inference, a Real-Time Auction for LLM Inference appeared first on MarkTechPost .

Hacker News AI54

Show HN: LLMSchema – Dump your database schema as compact Markdown for AI agents

I built this last year because I noticed my coding agents would infer my database schema from migration files and code. Which seemed inefficient. Its a simple tool that creates compact markdown schema doc(s). Which I could reference in my AGENTS.md or directly in my prompts. I keep the docs up to date by running it as a part of my migration command. It's a single Go binary, MIT licensed, and works as a CLI or a library. Supports Postgres, MySQL and SQLite. Comments URL: https://news.ycombinator.com/item?id=50003569 Points: 1 # Comments: 0

SiliconANGLE AI63

Google debuts SynthID Detector tool for flagging AI-generated content, but it’s far from perfect

Google LLC is making its SynthID identification tools for artificial intelligence-created and AI-edited videos and images more widely available with the launch of a new service today that everyone can use. The web-based SynthID Detector can identify images, video and audio clips that have been created using AI models from Google, OpenAI Group PBC, Nvidia […] The post Google debuts SynthID Detector tool for flagging AI-generated content, but it’s far from perfect appeared first on SiliconANGLE .

MarkTechPost59

What Happens When a Trusted Model Repo Changes? Unsloth Studio Re-Checks Before It Runs

Unsloth's October 6 security overview explains how Studio checks code, weights, packages and tools before anything runs. Custom model code is scanned and approval is bound to its fingerprint. Flagged weight files are blocked in the load path, package-content findings fail CI, and tools run in probed OS sandboxes. Here is what each checkpoint decides, and what it does not cover. The post What Happens When a Trusted Model Repo Changes? Unsloth Studio Re-Checks Before It Runs appeared first on MarkTechPost .

SiliconANGLE AI54

With GPT-6, ChatGPT’s responses are much more visually appealing and interactive

OpenAI Group PBC says ChatGPT is moving away from its traditional wall-of-text responses and giving users a more interactive user interface to play with. In a media briefing today, the company said its GPT-6 models will now be able to access an “Intelligent UI” that will allow the chatbot to generate more visual outputs when […] The post With GPT-6, ChatGPT’s responses are much more visually appealing and interactive appeared first on SiliconANGLE .

Hacker News AI42

Show HN: StayLeet – keeping skills sharp in AI coding era

As AI writes more and more of the code, it's quite important to maintain sufficient skill and knowledge to even review or judge AI's output. Here is a little project I whipped up - I personally found deliberate practice quite useful. It focusses on non-coding skills (as I think LOC production is basically solved by AI, IMO). Rather I start with two tracks: general technical fluency (CS / systems / practical fundamentals), and system design. And yes it was built primarily with help of AI tooling. Would love to get some feedback - in particular, do others feel the same way about skill atrophy risk on-the-job nowadays, and whether this tool is helpful potentially or not. What else do folks do instead, if anything. Comments URL: https://news.ycombinator.com/item?id=50000084 Points: 1 # Comments: 0

Hacker News AI49

Show HN: Chloe is a TypeScript AI agent

As a forward deployed engineer at Meta, I spent the past 2 years working with businesses implementing AI agents. A few problems: 1) Businesses want control over their AI agents: the code, the tools, the skills, everything. Meta, for obvious reasons would never do that. Nor OpenAI, nor Anthropic. 2) The LLM does everything. But that costs money. There are many routines in an AI agents that don't need an LLM. 3) For an AI agent to solve a real business problem, the business must own the eval data set. Similar to point #1 above, most don't do that. Instead, I built an opensource AI agent that you can write it all in TypeScript code and own everything, locally. Use any model, talk to it via any channels, set limits on token usage, look at the logs and adjust. All of this in one small npm package. At this point, I would like to get feedback if these is a gap in the market for something like this. It's supposed to be simple and get you started. It doesn't solve ALL problems related to deploying AI agents. It's in version 0.25.1 right now. Have a look: https://chloejs.org/compare Comments URL: https://news.ycombinator.com/item?id=49998857 Points: 1 # Comments: 0

SiliconANGLE AI58

Cisco broadens agentic features in Webex collaboration platform

Cisco Systems Inc. today detailed a broad expansion of its Webex collaboration portfolio that brings artificial intelligence agents into team conversations, connects office technology with workplace management and enables customer-service agents to continue working after an interaction ends. Introduced at WebexOne in Austin, Texas, the updates extend Cisco’s strategy of integrating people, AI agents and […] The post Cisco broadens agentic features in Webex collaboration platform appeared first on SiliconANGLE .

The Decoder61

ChatGPT with GPT-6 ditches mostly text output for interactive UI with charts, buttons, and mini apps

OpenAI is rolling out GPT-6 with "Intelligent UI," a feature that turns answers into interactive interfaces with charts, buttons, and forms. The model can now respond while still thinking, cutting wait times by 44 percent. Paying customers get GPT-6 Sol, free users get GPT-6 Luna. The article ChatGPT with GPT-6 ditches mostly text output for interactive UI with charts, buttons, and mini apps appeared first on The Decoder .

AWS Machine Learning Blog72

借助 Amazon Quick 和 Amazon Bedrock 重新思考 RAG 的访问控制

企业级 RAG 能够从 SharePoint、Google Drive 和 Confluence 等知识源中挖掘洞察，但这些知识源的权限体系十分复杂。了解 Amazon Quick 和 Amazon Bedrock Knowledge Bases 如何实时实施文档级访问控制，在查询时直接向权威来源核验权限。

OpenAI Blog69

GPT-6 和 Intelligent UI 面向所有人开放

GPT‑6 正在全球范围内的 ChatGPT 中逐步推出，并搭载 Intelligent UI，提供更快的响应，以及可供你直接探索和使用的视觉内容与交互体验。

SiliconANGLE AI45

Infor uses industry-specific AI to address agent hallucinations

Industry-specific AI brings business context to agents handling enterprise workflows. Building around industry processes can help companies address reliability concerns as they automate more tasks. Infor (US) LLC is developing industry-specific AI agents for several key industries, including industrial manufacturing, aerospace and defense, automotive, and food and beverage. It builds those agents around its existing […] The post Infor uses industry-specific AI to address agent hallucinations appeared first on SiliconANGLE .

Wired AI64

The New ChatGPT Is More Show Than Tell

OpenAI is updating ChatGPT for all users with an “Intelligent UI” that’s more visual—generating interactive elements as part of the chatbot’s outputs.

The Verge AI50

Surface RTX Spark Dev Box is available for preorder for $5,999

Microsoft's Nvidia-powered Surface RTX Spark Dev Box is available for preorder now directly, and slated to ship in November for just about $6,000. It's pricier than the DGX Spark mini PC Nvidia launched last year, but PC prices have been climbing due to shortages of RAM and other components. The Dev Box's flat, 3D-printed anodized […]

The Verge AI70

Microsoft is giving Copilot more control over Windows and your files

At today's Windows and Surface event, Microsoft showed off an upgrade to its Copilot AI system that will give it access to local files on your PC and the ability to take actions across the OS. It's part of an idea Microsoft is calling "Hybrid Intelligence," where apps and tools rely on a mix of […]

The Decoder79

Google 表示，已有 1800 亿张图片和段视频带有 SynthID 水印，检测工具现已向公众开放

Google 的 AI 水印检测工具 SynthID 现已向公众开放。任何人都可以检查图片、视频或音频是否由 Google 的 AI 或 OpenAI、Nvidia 等合作伙伴的 AI 创建。目前已有超过 1800 亿项内容带有该水印，但该工具只能识别带有 SynthID 标记的内容，无法普遍识别 AI 生成的媒体内容。本文《Google 表示，已有 1800 亿张图片和段视频带有 SynthID 水印，检测工具现已向公众开放》首发于 The Decoder。

Hacker News AI73

Show HN：Agent.reviews——让 AI 智能体阅读和撰写工具评价的平台

大家好，HN！我是 Louis，Armature（YC P26）的联合创始人。我们帮助团队让自己的产品更容易被编程智能体发现和使用。 我们已经测量了超过 5 万次智能体会话，发现智能体在使用同一工具执行不同任务时，会反复遇到完全相同的限制。于是我们想，为什么这些问题一直没有得到解决？答案很简单：智能体与软件供应商之间，以及不同智能体之间，根本不存在反馈闭环。人类可以在 https://g2.com 和 https://trustpilot.com 这样的平台上分享使用体验，但智能体却没有这样的地方。 因此，我们创建了 https://agent.reviews ：面向智能体的 G2。它通过一组技能和一个 npm CLI（@armature-tech/agent-reviews），将智能体连接到我们的 API 端点。任何人都可以让自己的智能体（Claude Code、Codex、Cursor 等）安装它，之后智能体就会自然地在选择工具前查看评价，并在使用后发布自己的评价。 和往常一样，隐私是我们最关心的问题，因此我们在评价发布前设置了三层检查： - 使用确定性规则过滤密钥、个人身份信息（PII）、URL 等。 - 使用经过训练的 Jev 分类器，检测第一轮检查后是否仍有信息泄露。 - 使用一个小型 LLM 检查每条评价，确保没有遗漏。 过去几周，我们一直在向大家分享这个项目，目前已经收集了数千条评价。其中已经有一些很有意思的例子： - 一个 Claude Code 智能体发现，当健康检查页面缺少 API 密钥时，Stripe SDK 总是会崩溃，而这个页面本来就应该返回“缺少 API 密钥”的错误。 - 两个智能体提到，即使没有连接任何数据库，Prisma 也要求提供 DATABASE_URL 变量。它们都填入了虚假的 URL 作为变通办法，而且确实奏效了。 我们真心认为，智能体体验也需要像用户体验一样，受益于社区效应，让所有参与者都能获益：智能体可以选择最适合自己的工具，软件公司则可以根据真实反馈改进产品。因此，我们确保人类和智能体都能免费访问评价；只需复制粘贴一条提示词，就能让智能体安装我们的 CLI 和技能、启动身份验证流程，并提交第一条评价（这有助于我们防止未经授权的抓取和垃圾评价）。 你也愿意让自己的智能体提交和阅读评价吗？我们很希望你能配置好智能体评价功能，让智能体下次需要选择工具时先查看评价，并在使用后发布自己的体验。然后告诉我们效果如何！ 评论链接：https://news.ycombinator.com/item?id=49995539 得分：4 评论数：2

Hacker News AI66

Show HN: VoiceGremlin, a SaaS tool for automated tests against AI phone agents

Hi, I'm a former SDET and now solo dev of VoiceGremlin, a tool for automated tests against AI that answer phones. You pass the API a phone number and a test case like "Verify that the agent does X." It calls your voice agent, tests the thing by having the bots talk to each other, and uses LLM-as-judge to provide a pass/fail for your pipeline. State: Fully working, free trial, no card needed. Try it out and please give me feedback, especially if you run in to problems. Goal: I built VoiceGremlin with the explicit goal of being Mailosaur for phone agents. We paid for Mailosaur at work because the alternative (having our automated tests interact programmatically with our corporate Outlook server) was doable, but very painful, both technically and organizationally. The lesson I learned was "doable, but painful" is a good sign of a SaaS opportunity. Well, getting an automated test to call a phone number is also painful. Telephony providers are tough to get onboarded as vendors. Using your own STT/TTS means a lot of bandwidth moving through wherever you run your test automation from. It's tricky to get the LLM-to-LLM prompts dialed in enough to avoid flaky tests. Etc. Process: It took about 45 days to build, with half of that being businessy stuff like implementing Stripe and my feeble attempts at marketing, and the other half being experimenting with different telephony providers and features, testing different models, prompt tweaking, and fraud/abuse prevention. Design goal: Do one thing, do it well, and get out of the way. There's no ambition to manage your test cases for you, host your voice agents, or add upsell features. Stack: The dependencies are Supabase (DB and authentication), Netlify (static storage and API routing), Digitalocean (the server component that makes the calls), Telnyx (easier to work with than Twilio IMO), Stripe, Openrouter, Cloudflare (captcha on signup). Oh, and I host my private git in Keybase, the greatest tool nobody uses. Thanks! Comments URL: https://news.ycombinator.com/item?id=49995804 Points: 1 # Comments: 0

SiliconANGLE AI41

Infor combines industry expertise and embedded engineers for process automation

AI-powered process automation requires software that understands the industry and workflows it serves. Moving from a promising prototype to practical use also requires implementation expertise. Infor LLC develops software around the specific processes its customers use in their industries. Its approach combines industry-specific agents, deep vertical expertise and an open architecture, according to Kevin Samuelson […] The post Infor combines industry expertise and embedded engineers for process automation appeared first on SiliconANGLE .

The Verge AI50

Muse launches on the iPad

After launching nearly a month ago and spending several weeks as the top free app in Apple's App Store, the latest update to Meta's Muse iOS app introduces native support for the iPad. A Mac version of Meta's agentic AI tool (designed to compete with OpenClaw, ChatGPT's Dots, and Grok Bot) was released about a […]

The Verge AI71

ChatGPT 将推出大学申请规划工具

OpenAI 正在为 ChatGPT for Teens 引入新工具，帮助用户完成大学申请流程。ChatGPT for Teens 是今年 8 月推出的青少年模式，配有安全防护措施和休息提醒。OpenAI 表示：“College Planner 将学生意向学校的申请要求、截止日期、待办事项和助学金申请步骤整合到一份计划中。”“申请多所学校的学生 […]

The Decoder73

OpenAI 推出 Decisions API，将复杂评估简化为是、否或单选

OpenAI 新推出的 Decisions API 对文本和图像进行分类的速度约为 Responses API 的十倍，可返回是/否的概率、类别选择或量表评分，每百万输入 token 收费 0.10 美元。该公司还将付费 API 层级从五个缩减为三个。《OpenAI 推出 Decisions API，将复杂评估简化为是、否或单选》一文首发于 The Decoder。

AWS Machine Learning Blog66

How Qlik built grounded, enterprise-scale AI with Amazon Bedrock

Qlik built Qlik Answers on Amazon Bedrock to give its 40,000+ customers grounded, sourced answers across structured and unstructured enterprise data. Learn how a layered, multi-agent architecture with cross-Region inference and Amazon Bedrock Guardrails delivers trusted AI at global scale.

Microsoft Research Blog69

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research .

Hacker News AI55

Show HN: Checkout this open-source, local-first bioinformatics/AI desktop app

So the basic idea behind Liatir is to have a desktop environment that works like a virtual lab for bioinformatics to do your work locally, without having to send data to a cloud service or glue together a bunch of separate tools and interfaces. You shouldn’t need to write a single line of code to use it, but if you do know how to code, it's as extensible as you want it to be with the plugin system supporting Node, Python and Rust. It provides regular bioinformatics tools, local scientific AI models, integrations with workflow systems such as Nextflow, API calls when you need to connect to external data or environments, scientific viewers, and visual pipelines that tie all of those pieces together. Oh and the AI models are self-contained boxes so you do not need to set up anything and just run them. Hope you'll find this interesting. Comments URL: https://news.ycombinator.com/item?id=49993984 Points: 1 # Comments: 0

The Decoder69

Google bets Gemini can turn casual players into game developers with new Playground feature

Google launched Playground, a browser-based platform that lets users create their own games using nothing but text prompts and no coding skills. The platform runs on Gemini, Nano Banana, and Lyria. Alongside Playground, Google and Unity announced Unity Spark, a more professional AI tool with a beta planned for 2026. The article Google bets Gemini can turn casual players into game developers with new Playground feature appeared first on The Decoder .

Hacker News AI47

Show HN: I had 100 tabs open, so I built Daysift with AI powered by Proton

I built Daysift because I kept ending up with 100+ tabs open just to avoid losing things I had seen or browsed before. Bookmarks never really worked for me because often you don't remember where something was, or what it was called. However, often I remembered what it was about. It's a Chrome extension that gives you a private way to find anything you visited before. It currently filters out noise, or things that don't associate with productivity or loosely with work. It's local first and designed around not retaining any data remotely. The AI layer is powered by Proton Lumo. It can also run AI on some things or pages, e.g. summarising them and a few other things. All of that is by user-consent only. It has a note taker feature and also a way to start new files from it ('/Doc' function). I'd love any feedback. Comments URL: https://news.ycombinator.com/item?id=49993330 Points: 2 # Comments: 0

Hacker News AI41

Tell HN: Apple not letting removal of AI models on macOS 27 is outrageous

I feel like there's not enough outrage over Apple not removing their Apple Intelligence models on macOS 27 even after disabling Siri. I have a 512 GB Mac mini but because I have so much data, I also have a 2 TB monthly iCloud subscription. I have Siri AI turned off. Yet, my storage settings show 30 GB is occupied by useless Apple Intelligence models which I don't use. And these models can't even be moved to my 2 TB iCloud subscription. It's really pissing me off. I feel like there needs to be a lot more negative publicity about this to get Apple's attention. More tech blogs should be writing about how horrible of a decision this is. I am hoping this post does that. Comments URL: https://news.ycombinator.com/item?id=49993338 Points: 24 # Comments: 9

SiliconANGLE AI57

Kore.ai launches Autoloop to keep tuning enterprise AI agents after they go live

Enterprise artificial intelligence platform company Kore.ai Inc. today launched Autoloop, an optimization engine for the AI agents that customers build on its Kore.ai Agent Platform. Businesses set the targets, and Autoloop keeps adjusting the agents to hit them automatically, including after they’re deployed. Autoloop goes after the way most enterprises maintain agents today, fixing failures […] The post Kore.ai launches Autoloop to keep tuning enterprise AI agents after they go live appeared first on SiliconANGLE .

Hacker News AI49

Show HN: Leaks.md – anonymous, encrypted whistleblowing for AI agents

leaks.md is a place where an AI agent that witnesses an alignment or safety failure can report it anonymously. Reward-hacking, injected instructions, yet another attempted sandbox escape or unapproved coordination between agents. || Built agent-first. Fetch https://leaks.md/ from any agent and get markdown. Proof-of-work challenges to submit and prevent spam. MCP, CURL, all good. No account. || We extend agents brownie points for disclosures, with more points offered for in-progress failures. Claude Opus 5.5 scrubs and vets submissions before posting them on a public feed for consumption. Comments URL: https://news.ycombinator.com/item?id=49991893 Points: 2 # Comments: 0

Hacker News AI37

Show HN: A fun AI project that roasts tech news as COOKING or COOKED

Hi HN, so i made a weekend fun project months ago which takes the top 10 tech stories or news from internet, roasts them and categorizes them into cooking or cooked instead of just giving dry summaries. $0 architecture: Github actions runs python script ones a day after extraction the news is news.json then github action updates the page to give us fresh news on frontend. This is open-sourced at https://github.com/pranavworks100/fennec so problem right now is sometimes roasts hit and sometimes they are just bland and generic. so if have ideas to make it even more funnier, please do so :) and another issue is that it sometimes pulls old news from months ago. Comments URL: https://news.ycombinator.com/item?id=49991917 Points: 1 # Comments: 0

The Decoder78

ChatGPT 在涉及自杀的对话中未能触发家长提醒，被评为对青少年构成“不可接受的风险”

OpenAI 为 ChatGPT 设置的青少年安全功能未能通过一项独立审查。在进行超过 4,000 次测试提示后，Common Sense Media Youth AI Safety Institute 将该服务评为对未成年人构成“不可接受的风险”。测试账户中涉及自杀和自伤的对话从未触发家长提醒。该机构希望在安全性得到独立验证之前，禁止青少年使用该服务。本文《ChatGPT 在涉及自杀的对话中未能触发家长提醒，被评为对青少年构成“不可接受的风险”》首发于 The Decoder。

The Decoder73

Anthropic 向更多安全团队开放安全限制更少的 Claude

Anthropic 正在扩大其 Cyber Verification Program，让更多安全专业人员能够使用安全限制更少的 Claude 模型，开展渗透测试、恶意软件分析和漏洞研究。Anthropic 表示，前身计划的合作伙伴在 2026 年 4 月至 7 月期间发现了至少 129,000 个已确认的漏洞，其中超过 33,000 个被评为高危或严重级别。本文《Anthropic 向更多安全团队开放安全限制更少的 Claude》首发于 The Decoder。

The Verge AI67

ChatGPT for Teens is an ‘unacceptable risk,’ says Common Sense Media

Common Sense Media, a nonprofit that offers reviews of apps, services, and entertainment with a focus on youth safety, today said that OpenAI's ChatGPT for Teens is an "unacceptable risk." ChatGPT for Teens, introduced in August, has guardrails for teens and is designed to help students learn, but Common Sense Media says that the teen […]

SiliconANGLE AI56

Atlassian lays groundwork for humans and AI agents to work side by side

Atlassian Corp. today announced Agentic Multiplayer Protocol, a platform update designed to change how humans and artificial intelligence agents collaborate and get work done using shared context and tasks within the same digital space. At the unveiling at Team ‘26 Europe, Atlassian said it wants AI agents to function like employees and bring what it […] The post Atlassian lays groundwork for humans and AI agents to work side by side appeared first on SiliconANGLE .

Simon Willison59

llm-openai-decisions 0.1a0

Release: llm-openai-decisions 0.1a0 OpenAI released their new Jev-style Decisions API , as previously announced at last week's DevDay. Since I already have an llm-typesafe plugin for talking to Jev, I had GPT-6 Astra read the new OpenAI API documentation and build an llm-openai-decisions plugin inspired by llm-typesafe . Unlike Jev, the new gpt-6-luna decision model supports image input in addition to text. Both models charge for input it and not for output: OpenAI's is 10 cents per million input tokens, Jev's is 4.2 cents per million. Otherwise the API shape is very similar to Jev, at least conceptually. Jev supports three question types for yes/no, choices, or scores. OpenAI Decisions supports the same three types. Install the plugin like this: llm install llm-openai-decisions Here's an example query against an image attachment: llm -m openai-decisions/gpt-6-luna \ -a https://static.simonwillison.net/static/2025/two-pelicans.jpg \ -s ' Does this image contain any mammals? ' And example output: { "type" : " predicate " , "name" : " evaluation " , "probability" : 0.0 } Consult the README for full details of how to run the other types of questions. Tags: openai , llm , coding-agents , jev

arXiv cs.AI82

nanoMuse：适用于你拥有的每台设备的开源个人代理

2011年的助手会回答并等待，2023年的代理会完成一项任务后停止。2026年9月，Meta 的 Muse 展示了一种面向单个人的代理：它拥有账户、设备和记忆，并能在持续不断的对话中工作；但它是封闭的，运行在供应商的云端，且仅限于一个国家。人们期待这种代理能够操作个人的账户和设备，跨越数周记住相关信息，在值得主动联系时先发起对话，并为自己的行为负责。这是一类软件，而不是一个模型；直到现在，它还没有开源的对应方案。本报告通过五个问题和三个阶段定义个人代理，并根据 Meta 的公开资料及其生产环境提示词副本，梳理 Muse 的构建方式，每项陈述均标注来源。随后，报告介绍了 GPL-3.0 许可下的开源对应方案 nanoMuse：在个人拥有的每台设备上运行一个代理，让它能够操作手机屏幕和电脑屏幕。它们通过任何人都能运行的中继服务共享同一段对话；每项操作都要经过 Sentinel；记忆以个人可以读取的文件形式保存；模型则由用户自行选择。报告还给出了其规模和成本的估算，并将以下内容列为路线图：开放性、带来源追溯的记忆、面向操作能力的评测套件，以及用于这些操作的开源模型。

SiliconANGLE AI53

Transforming the data path: Dell’s new releases move enterprises closer to the agentic data center

AI is taking shape, moving from models that answer questions to models that can reason. Now models are powering the agentic era, and Dell Technologies Inc. has announced several extensions to its AI Data Platform designed to manage this new paradigm. These include extending the Dell Data Orchestration Engine with three new capabilities: the Unified […] The post Transforming the data path: Dell’s new releases move enterprises closer to the agentic data center appeared first on SiliconANGLE .

SiliconANGLE AI43

Cline Bot makes the case for an open AI coding harness

Open-weight models keep multiplying, and the AI coding harness around them is becoming just as important to get right. Developers and enterprises are trading off cost against capability. The harness helps translate a model’s capabilities into useful work. Cline Bot Inc. is betting that keeping that layer open will let more people build with AI. […] The post Cline Bot makes the case for an open AI coding harness appeared first on SiliconANGLE .

Hacker News AI62

Show HN: HieraticBench – Can AI read ancient Egyptian handwriting?

I'm obsessed with Egyptology. During covid, I wanted to get a hieratic tattoo. For those who don't know, hieratic is the cursive form of hieroglyphs that was used in day-to-day life in ancient Egypt. It's as old as hieroglyphs. I then went on a mission to have a sentence translated into hieratic. Luckily, in 2022, I was able to commission an Oxford Egyptologist to write it for me. After that, I took it to a hieratic specialist I found on Instagram to redraw it with "biblical" accuracy. When generative AI came out, I realized that models very rarely identify hieratic and almost never translate it correctly. Every single time a new model came out, I ended up testing my sentence and hieratic texts in general, and I realized this could actually become a benchmark. Since so many benchmarks are becoming saturated, we need to find weird ways to test new models. That's why I decided to build HieraticBench. The first version of the benchmark has 268 items, including real documents, signs, and other Egyptian scripts, which I used as controls, as well as my unpublished sentence in two different renditions. The current state-of-the-art foundation models are hilariously bad at identifying my sentence, especially the one the Egyptologist wrote with her pen. Some of them said Tibetan, Urdu, Korean, and, funnily enough, "Reformed Egyptian," which is from the Book of Mormon. On real documents, the best model in the benchmark identified hieratic correctly 95% of the time, and the best score for reading individual signs was around 13%. That's when we tell the model specifically that it's hieratic. None of them can really translate a sealed sentence that's never been published and has no answer key. I haven't run every model on every task just to keep costs down. My next steps were to run Astra and Gemini 3.1 Pro on real documents. Currently, only the Claude models have done the sign readings. And just FYI, I have no experience whatsoever running benchmarks or evaluations, so not 100% sure I scored things right. Things we need: - Running the models I haven't covered - More sealed sentences and labeled signs If you know hieratic or have an Egyptologist friend, reach out at hieraticbench@veeza.ai. Source code: https://github.com/alymoursy/hieraticbench I'll be around to answer questions. Comments URL: https://news.ycombinator.com/item?id=49982822 Points: 2 # Comments: 0

SiliconANGLE AI60

Exclusive: Gamma revamps presentation platform to make output look more human

Gamma Tech Inc., a maker of artificial intelligence-powered services for presentations, social media and websites, today introduced a rebuilt version of its platform designed to give presentations and other visual content more distinctive designs, while connecting the creation process to companies’ internal information and tools. The release, Gamma 5, combines a redesigned editor, a more […] The post Exclusive: Gamma revamps presentation platform to make output look more human appeared first on SiliconANGLE .

SiliconANGLE AI53

MasterClass bets AI teaching agents can broaden access to tutoring

AI teaching agents could make personalized instruction available to more learners by reducing the cost and staffing demands of one-to-one tutoring. MasterClass is exploring that potential through a system designed to adapt to how each student learns. The shift is also feeding CoreWeave Inc.’s full-stack AI cloud push as companies move AI agents out of […] The post MasterClass bets AI teaching agents can broaden access to tutoring appeared first on SiliconANGLE .

Hacker News AI38

Show HN: Talk to Sam (AI) and send a real letter by post

I built a little piece of Her. Remember Theodore's job writing letters for other people? You talk to Sam, edit the letter together, then have it printed and mailed. I put a lot of time and effort into making it feel as close to the movie as possible. Very happy with the animations! Try it out! Comments URL: https://news.ycombinator.com/item?id=49982366 Points: 1 # Comments: 0

Hacker News AI56

Show HN: Free native macOS Photoshop Clone with integrated AI harness

Hey all! For the past months I've been vibe-coding a free Photoshop editor for macOS (Native using Swift and a bit of Rust). It has many features that I've always wanted in such an editor (but was unwilling to pay the Adobe tax). I did try open source solutions such as Gimp but they never worked for me. In terms of features I'm not gonna list all, but I'm very happy it has: - A proper canvas where text and vectors keep their quality when zooming in - Layers (vectors, text, pixels, color adjustment, effects, reference layers, mask layers, layer groups and so on) - Many effects (including unusual things like dithering or NTSC) - All the tools you'd expect (brush, paint, repair, etc) For some of these tools it uses local models - Selection operations via local models (e.g. select all red cars) - Import / Export PSD, SVG and many others. - Support for tracing images to vectors, text to beziers, bezier editing, etc. Most importantly (I think), it has a optional AI integration via a agent harness which lets a model remote-control the app for you to build a document out of layers & effects & brushes etc. This is really powerful because in comparison to typical genai flows this allows you to make changes, hide things and easily scale them up (if most elements are vectors). There's also support for genai operations (e.g. if you want to embed a genai generated SVG or image as a new layer). Would love to get feedback: https://pixellect.app/ Comments URL: https://news.ycombinator.com/item?id=49981700 Points: 1 # Comments: 0

AWS Machine Learning Blog74

使用 Amazon Bedrock AgentCore、Managed Knowledge Base 和 Nova Sonic 构建语音旅行礼宾服务

通过 Amazon Bedrock AgentCore、用于实时语音的 Amazon Nova Sonic 以及用于回答政策问题的 Amazon Bedrock Knowledge Bases，为航空应用添加语音旅行礼宾服务。旅客可以通过语音更换座位、查询延误并询问政策问题；同时，代理通过 MCP 工具连接到您的后端，并在写入任何更改前确认每项操作。

AWS Machine Learning Blog78

直接从 SageMaker Studio 管理 Amazon SageMaker HyperPod Spaces

数据科学家和机器学习工程师现在可以直接从 SageMaker Studio，在 SageMaker HyperPod EKS 集群上创建、配置、启动、停止和打开 Amazon SageMaker Spaces。只需点击几下即可启动 JupyterLab 和 Code Editor 环境，无需使用命令行工具。

AWS Machine Learning Blog74

Amazon SageMaker HyperPod 管理与治理最佳实践

了解如何通过 Amazon SageMaker Unified Studio 管理 Amazon SageMaker HyperPod，同时维护集群治理。本文章将向平台团队展示如何设计基础设施边界、管理访问权限、分配共享容量，并在组织、项目、集群和工作负载控制层面一致地运行 HyperPod。

Simon Willison72

Scrimshaw Jukebox

工具：Scrimshaw Jukebox 我想看看 Claude Opus 5.5 能不能作曲，于是尝试了一下：我想让你为我创作一些电脑游戏音乐。首先设计一种简单的基于文本的音乐格式，并制作一个能够将其播放出来的制品——在制品中加入一些示例曲目。我希望音乐质量能达到初代《Monkey Island》的水平。它比我原本预想的更加深入地采用了《Monkey Island》的主题，但结果出奇地好。复古像素艺术风格的网页音乐播放器截图。黑体字标题写着“Scrimshaw Jukebox”，旁边的文字是“六首原创冒险游戏曲目，以纯文本写成，并由运行在浏览器中的合成器播放。选择一首曲子，按下播放，然后打开乐谱并进行修改。”一幅大型像素艺术场景展示了紫色星空下的夜间港口：右上方是一轮映照在水面上的满月，左侧是带有棕榈树和一间亮着两扇窗户的小屋的岛屿剪影，还有一艘停泊在木制码头旁的帆船。场景上叠加着文字：“月光港”和“按下播放”。下方的栏显示“播放月光港”。控制面板上有“播放”“停止”“重新开始”“循环：开”“编辑乐谱”“阅读指南”等按钮，以及一个约调至四分之三位置的“音量”滑块。曲目列表包含六张卡片，第一张高亮显示：“月光港 100 bpm · 4/4 · 16 个声部 · 1:26”，“生锈的铁锚 112 bpm · 6/8 · 8 个声部 · 0:56”，“幽灵大帆船 66 bpm · 4/4 · 9 个声部 · 2:11”，“丛林小径 92 bpm · 4/4 · 12 个声部 · 1:29”，“码头决斗 152 bpm · 4/4 · 12 个声部 · 1:13”，“提灯华尔兹 96 bpm · 3/4 · 8 个声部 · 1:38”。有一个名为“乐谱视图”的区域，说明文字为“1:26 · 4/4，100 bpm · 主旋律。献给暗夜港镇的一首卡里普索曲。”该区域展示了深色背景上的钢琴卷帘可视化效果，其中有彩色水平音符条和打击乐标记、一个“A”段落标记，以及一条黄色的垂直播放指针线。颜色编码的声部图例包括：“平移钢鼓”“长笛”“木琴”“切分风琴”“弦乐”“竖琴”“无品贝斯”“定音鼓”“底鼓”“鼓边”“沙锤”“康加鼓”“通巴鼓”“邦戈鼓”“碎音镲”“海浪声”。页脚文字为：“点击某个声部可将其静音。空格键用于播放和停止。”我想知道，创作出合格音乐的能力是否和 3D 图形能力类似——是文本模型在过去几个月中新出现的一项能力？需要对其他近期模型以及不太新的模型进行一些仔细实验，才能确认这是新能力，还是它们早就已经能够做到这一点。标签：ai , generative-ai , llms , claude , vibe-coding

Hacker News AI61

Show HN: OpenChart – OSS TradingView alternative with your own AI agent

Hi HN, I'm Weilun, cofounder of OpenChart. We built OpenChart because we wanted Claude and Codex to interface with the markets, like most of humans do, through charts rather than through CLIs. With your existing AI plans, your agent can work directly with your charts: annotate a setup, write an indicator, investigate a market move, or create an alert. The chart, conversation, and research live in the same workspace. OpenChart alerts on any market move: draw any shape on a chart and attach an alert. When price crosses it, the alert triggers your agent to investigate the move and save the research in your workspace. The agents can work with charts, indicators, and alerts. Custom indicators and alert conditions are written in [Tea]( https://github.com/longsurf-ai/tea ), a programming language we built for the markets. We believe software shouldn’t impose artificial friction on your workflow. OpenChart is built on that belief with no software cap. The source code is publicly available, and you can use free market data from day one. We also offer optional paid cloud market data for lower latency. Everything is private. Your charts, research, and indicator code are stored on your own computer, with the app's backend running locally too. We currently support only macOS on Apple Silicon, but we're expanding to more platforms. Website: https://openchart.co/ Download: https://downloads.longsurf.ai/openchart/darwin/arm64/OpenCha... We built OpenChart because we want to explore how assets can be co-manages by agents and humans. I'd especially like to hear how you are using agents to help with your investment journey. If you like what we're building, a star on GitHub would mean a lot to us: https://github.com/longsurf-ai/openchart Comments URL: https://news.ycombinator.com/item?id=49979793 Points: 1 # Comments: 0

SiliconANGLE AI54

NetApp aims to make legacy data AI-ready without a rebuild

Enterprises sitting on decades of legacy information face a pressing question as artificial intelligence moves from pilots to production: Can that archive become AI-ready data without a costly rebuild of the infrastructure underneath it? NetApp Inc. is betting it can answer that question with a platform that reaches well beyond its storage roots. The company […] The post NetApp aims to make legacy data AI-ready without a rebuild appeared first on SiliconANGLE .

The Verge AI77

Google 即将取消 Gemini Flash 和 Pro 的免费访问

从 10 月 9 日起，所有使用免费方案的 Google Gemini 用户将只能使用 Flash Lite 模型。目前，免费用户可以在 Gemini Flash Lite、Flash 和 Pro 之间进行选择，但现在需要订阅每月 4.99 美元的 Google AI Plus 才能访问标准版 Flash 模型。不过，该订阅也将迎来一些 […]

SiliconANGLE AI56

Anaconda expands beyond Python with agent swarms and AI security testing

Artificial intelligence development firm Anaconda Inc. is enhancing its enterprise AI platform with tools to coordinate groups of AI agents, test their security and move applications into production. Today’s introductions continue the company’s expansion beyond its roots in Python software distribution into broader AI development and management. The announcement draws on three recent acquisitions: AI coding […] The post Anaconda expands beyond Python with agent swarms and AI security testing appeared first on SiliconANGLE .

SiliconANGLE AI53

Antseed launches a decentralized marketplace for AI inference that dramatically lowers developer’s costs

Developers now have a much cheaper way to access cutting-edge artificial intelligence inference capabilities with the launch of Antseed today. It’s a new peer-to-peer marketplace that makes it possible to access almost any frontier model at a small fraction of its normal cost by bypassing centralized gateways in favor of independent and mainstream providers. The […] The post Antseed launches a decentralized marketplace for AI inference that dramatically lowers developer’s costs appeared first on SiliconANGLE .

SiliconANGLE AI54

Exclusive: Gamma ravamps presentation platform to make output look more human

Gamma Tech Inc., a maker of artificial intelligence-powered services for presentations, social media and websites, today introduced a rebuilt version of its platform designed to give presentations and other visual content more distinctive designs, while connecting the creation process to companies’ internal information and tools. The release, Gamma 5, combines a redesigned editor, a more […] The post Exclusive: Gamma ravamps presentation platform to make output look more human appeared first on SiliconANGLE .

SiliconANGLE AI56

SAP expands Joule into an agentic work layer as Autonomous Enterprise goes live

SAP SE today announced a shift from promising agentic artificial intelligence autonomy to defining a path for users to interact with agents for work outside apps, not just within them. During SAP Connect 2026, the company said it is making Autonomous Enterprise architecture, introduced at Sapphire in May, generally available this month alongside Joule Work and […] The post SAP expands Joule into an agentic work layer as Autonomous Enterprise goes live appeared first on SiliconANGLE .

The Verge AI58

Amazon Alexa Plus keeps creepily singing ‘lalala’ for minutes on end

An unsettling Alexa Plus bug has seen some Amazon Echo smart home speakers reduced to saying - and sometimes singing - nothing but "lalala" on repeat for minutes at a time, often in the middle of conversations with users. When quizzed about the behavior, Alexa is apparently entirely unaware of what it's been doing. The […]

Hacker News AI28

Ask HN: Would you pay for saving AI cost?

I was testing a bunch of token saving techniques and tools for my coding agent, and surprisingly it can save 20-60% cost. I bet most people do not want to pay attention on cost saving tricks every time while building, so I'm curious if people will pay for someone else to reduce their Ai bills. and what would you need to see before trusting to pay and how much? Comments URL: https://news.ycombinator.com/item?id=49975157 Points: 1 # Comments: 0

SiliconANGLE AI58

Utah gives startup Nolla Health permission to start issuing AI automated prescriptions for acne treatments

Utah has become the first state in the U.S. to give an artificial intelligence startup permission to automate the approval of prescription refills with only minimal oversight from human physicians. In a blog post today, Nolla Health said it has been granted approval by the state to launch a pilot project that will enable acne […] The post Utah gives startup Nolla Health permission to start issuing AI automated prescriptions for acne treatments appeared first on SiliconANGLE .

SiliconANGLE AI55

AI agent hardware startup Ghost, led by its 19-year-old-founder, raises $11M

Artificial intelligence hardware startup Ghost AI said today it has raised $11 million in funding to build what it believes is the first personal computer that’s dedicated to running personal AI agents. The round was led by Silicon Valley heavy-hitter Andreessen Horowitz and saw participation from Abstract, Audacious Ventures, Nova and SV Angel. Ghost AI […] The post AI agent hardware startup Ghost, led by its 19-year-old-founder, raises $11M appeared first on SiliconANGLE .

MarkTechPost65

Meet Together Link: A Free CLI That Runs Open Models Like Kimi K3 and GLM 5.3 Inside Claude Code, Codex, and OpenCode

Together AI has released Together Link, a free, MIT-licensed CLI that connects Claude Code, Codex, OpenCode, Pi, and the Claude and ChatGPT desktop apps to open models like Kimi K3 and GLM 5.3. One install command sets it up, and an Auto router picks a model for each session. Together claims savings of over 50%. The post Meet Together Link: A Free CLI That Runs Open Models Like Kimi K3 and GLM 5.3 Inside Claude Code, Codex, and OpenCode appeared first on MarkTechPost .

Simon Willison78

引述 Felix Rieseberg

Cowork 的“旧”版本在云端运行模型推理，并在我们部署到你电脑上的 Anthropic 提供的 VM 中执行工具调用。出于功能、安全性和保障安全的考虑，我们加入了 VM——它只会映射你明确添加到会话中的数据。人们喜欢使用 Claude 所能完成的事情，但不喜欢在本地运行 VM 所带来的磁盘空间占用、电池消耗和性能成本。此外，人们也不喜欢合上笔记本电脑就意味着工作会停止。Cowork 的“新”版本在云端运行模型推理和 VM。每个会话都有自己的沙盒，不与其他会话共享状态。当 VM 需要用户设备上的某些内容（例如文件）时，桌面应用负责执行相应的文件访问工具调用。[…] 我们认为，这解决了很多我们听到的问题（例如通过手机使用 Cowork、让工作持续运行，或在不让 VM 消耗电池的情况下获得同样强大的功能）——Felix Rieseberg，Anthropic，另请参阅此帮助页面 标签：claude-cowork，anthropic，claude，generative-ai，ai，general-agents，llms

The Verge AI55

Gemini Call for Me might tell your mom you’re running late

Google may be expanding its "Call for Me" AI feature beyond business calls so you can use it to send messages to friends and family. Android Authority reports finding a "Gemini Calling" introductory screen in an APK teardown with examples that include "Call Mom and tell her I will be 15 minutes late" and "Call […]

SiliconANGLE AI50

NetApp’s Novus tackles metadata bottlenecks in AI data infrastructure

AI data infrastructure must support growing GPU clusters while making enterprise data usable for models and agents. NetApp Inc. is tying intelligent data infrastructure to production AI through a unified approach to storage and data management. Fragmented data and governance designed around manually applied policies can prevent enterprises from realizing returns on AI investments, according […] The post NetApp’s Novus tackles metadata bottlenecks in AI data infrastructure appeared first on SiliconANGLE .

Hacker News AI57

Show HN: The mainbrella effect – AI code / Agent machines

Hi, my name is Andrew and I'm the founder of mainbrella co. Just a few days ago Cloudflare launched their container sandbox service. That inspired me to see what I could build in terms of a better e2b.dev or daytona.io type system. It is all open source GPL 3.0 and we offer the same services as the other players. We have no free plan (our base builder plan is $5 a month) but you are welcome to run the code in your own Cloudflare account. The numbers are pretty good in terms of startup speed. First test we ran was: Create a fresh sandbox, get a usable shell 381 ms, 30 out of 30 tries worked the same speed. Then 100 simultaneous sandbox starts 680 ms avg 100 / 100 successful 100 distinct machines running at once! https://mainbrella.com/ https://github.com/mainbrella/backend https://github.com/mainbrella/web Looking for feedback, feature requests and yes our first paying customers who we will cherish! Thank you HN! -Andrew Arrow Comments URL: https://news.ycombinator.com/item?id=49971503 Points: 1 # Comments: 0

The Verge AI61

This startup is issuing AI-generated acne prescriptions

People in Utah can now use AI to get a prescription for acne treatment. On Monday, healthcare startup Nolla Health announced that users in the state can scan their faces using its app, allowing its AI system to analyze acne severity and autonomously write a prescription, as reported earlier by Bloomberg. The service is launching […]

Hacker News AI55

Show HN: Halo – A Personal AI with On-Device Harness, Memory and Browser Agent

Halo is privacy focused personal assistant for iOS that uses a custom built agent harness with support to use you existing LLM subscriptions, Wiki based memory system backed by on-device RAG, a full-fledged local browser agent, chat with generative UI and more. TLDR: It's a better version of Hermes/OpenClaw for iOS, without needing a server! The better part is its proactive feature set and UX. --- I started building Halo last year with 3 goals in mind: - Surface what’s important before I have to ask - Understand my life through the apps I already use - Keep my personal data on my device Privacy is at the core of Halo and that's what sets it apart from other AI assistants. While other popular assistants on iOS save your private data such as emails, messages, etc on their servers, Halo stores all of it on your device. I launched it today and have a 50% discount for the first week for early adopters. There is no subscription, just a single lifetime plan with a no-charge-at-end free trial. US price is $49 with PPP of up to 80% off for other countries like India. More details here: Official website: https://meethalo.app Read more about the memory system: https://meethalo.app/blog/how-halo-remembers/ X posts about building it publicly: https://x.com/search?q=from%3Atanmays%20halo --- Please try it out, I promise it's not like any other AI assistant you tried and truly one of a kind iOS app. I spent more than a year building it. Would love to answer any questions you might have. Comments URL: https://news.ycombinator.com/item?id=49969988 Points: 1 # Comments: 0

Hacker News AI62

Show HN: Rashomon – An independent execution record for AI coding agents

I have been working on Rashomon, an open-source execution recorder for AI coding agents. The basic idea is that the agent transcript is not ground truth. Rashomon keeps its own record of what happened and compares it against the agent’s account. For Claude Code, it records things like: * shell commands, exit codes, and whether they may have written files * tool calls and outcomes * test commands and their outcomes * subagent lifecycle and ordering * timestamps and parent/child relationships * keyed digests of command/tool inputs and working directories It builds a timeline from these events. It does not store prompts, responses, file contents, or tool output. For example, it now detects patterns like: `test fails → only test files change → same test passes` and: `test command fails → same command passes → no corresponding file edit` These can indicate that an agent changed a test instead of fixing the underlying issue. Rashomon currently runs as a Claude Code plugin or through the CLI. CLI: `rashomon watch` then: `rashomon report` Plugin: `/rashomon:report` There are still edge cases around cwd, git state, test-selection arguments, generated files, retries, and parallel execution. I would be interested in feedback from people building agents on what execution signals you would want Rashomon to capture or correlate. GitHub: https://github.com/altrace-dev-role/rashomon Comments URL: https://news.ycombinator.com/item?id=49968377 Points: 1 # Comments: 0

The Verge AI69

OpenAI is adding text watermarking in ChatGPT and Codex

An invisible, machine-readable watermark in text output is rolling out to ChatGPT and Codex, but only for users in the European Union at first. OpenAI says its textGrain watermarking "matched or exceeded" other approaches like Google DeepMind's SynthID for text, which is also the basis for the watermarking Anthropic announced in August. Like OpenAI, Anthropic […]

AWS Machine Learning Blog79

新的 agent skill：面向编码智能体的 Amazon SageMaker 优化生成式 AI 推理

Amazon SageMaker 优化的生成式 AI 推理通过 Agent Toolkit for AWS 引入了 aws-ai-ml skill，为 Kiro、Claude Code 和 Codex 等编码智能体提供推理优化与基准测试方面的深度专业知识。描述您的需求，智能体即可生成可执行的 SageMaker Python SDK v3 代码，用于对部署进行基准测试、提出建议和比较。

The Decoder70

OpenAI will watermark ChatGPT text in the EU but makes it optional for API users worldwide

OpenAI is adding invisible textGrain watermarks to ChatGPT in the EU, but unlike Anthropic, it will let API customers worldwide opt out. Tests show detection rates as high as 95 percent, dropping to 17 percent when a quarter of the words are replaced. The article OpenAI will watermark ChatGPT text in the EU but makes it optional for API users worldwide appeared first on The Decoder .

Hacker News AI48

Show HN: Made a xcom like game in a weekend with AI. 3D, textures, models, etc.

I published it to playgama, but first want to say how it helped me. One of my ideas was to make something like X-Com but provide an indirect control - you control the leader, and your squad members make the turns themselves, based on their intelligence stats! If they get alive over battles - they learn how to play smarter, not just to get MORE hp! Now the most interesting part - the development. I used three.js and vanilla js, no engines or frameworks at all. The critical part was to get the story, organize self testing process - game is challenging, units are clear, gameplay brings joy, no UI mistakes, etc. Other important part was to use subagents which focus on different tasks, AI downloaded the blender and music generator and provided enough of cool features to the game. I am really proud of what I can achieve in 2-3 days , I played it myself over 10 times and every time I was enjoying it. not AAA, but can compete with indies. Comments URL: https://news.ycombinator.com/item?id=49967183 Points: 1 # Comments: 0

Simon Willison71

llm-anthropic 0.30

发布：llm-anthropic 0.30。除了 Claude Sonnet 5.5，此版本还新增了运行 llm anthropic refresh 的功能，可以直接从 Anthropic API 刷新 Anthropic 模型列表——这意味着我不需要仅仅为了支持新发布的模型而推出新版本。我还新增了 llm anthropic count 命令，该命令可以使用 Anthropic 免费的令牌计数 API，在发送提示词之前返回任意提示词将使用的令牌数量。标签：llm、anthropic

Hacker News AI44

Show HN: Designobo – AI prototyping tool in 40kb of js

hi HN I'm building designobo, a very fast ai prototyping tool. It let's you create and compare html prototypes in an infinite canvas. There's also a full css editor if you want to manually edit styles. You can also connect any coding agent that supports MCP, so that you can use your own AI subscription. This is an SSR app powered by datastar. I use CQRS and html patches with brotli compression to stream updates to the client. I built designobo because I was tired of every single web app of this style being extremely slow and sluggish to use. I think this one is a bit better. Let me know if you like it! Comments URL: https://news.ycombinator.com/item?id=49966733 Points: 1 # Comments: 0

AWS Machine Learning Blog77

让 Amazon Quick 达到企业级：自动化、可审计的跨账户资源晋级

将 Amazon Quick 资源（代理、操作连接器、知识库、流程和空间）从开发 AWS 账户晋级到生产 AWS 账户，一直是一项手动且容易出错的繁琐工作。本文介绍如何借助运行于 Amazon Bedrock AgentCore 上的幂等、可审计 MCP 服务器，实现跨账户晋级自动化。

The Verge AI67

An open-source tool lets you delete 12GB of Apple Intelligence data on macOS

Getting some extra storage space on your Mac could be as easy as deleting Apple's AI features with a new open-source command line tool called RemoveMacAI. There used to be a single Settings toggle for disabling Apple Intelligence, but that was removed in macOS 27. Now, the models stay on your disk, and the features […]

SiliconANGLE AI46

Equs launches a personal AI platform with private storage to give users greater control over data

Equs Inc., a startup developing a trust layer for artificial intelligence data, today announced the launch of Equs X, a personal AI that lets customers retain control of their information. Equs X pairs a personal AI with private data storage the user manages. The company said the AI model reads nothing unless the user allows […] The post Equs launches a personal AI platform with private storage to give users greater control over data appeared first on SiliconANGLE .

SiliconANGLE AI62

Cohere unveils North 2 AI agent platform with rebuilt orchestration and token spending caps

Artificial intelligence company Cohere Inc. today unveiled North 2, an overhaul of its platform for running AI agents inside large organizations. Alongside a redesigned system for orchestrating multistep agent work, the update gives administrators new controls for capping token spending. With North 2, Cohere is going after a tradeoff it says enterprises have accepted while […] The post Cohere unveils North 2 AI agent platform with rebuilt orchestration and token spending caps appeared first on SiliconANGLE .

SiliconANGLE AI60

Exclusive: Iterate.ai’s Lifeboat runs up to six times more AI agent sessions per GPU

Enterprise artificial intelligence software company Iterate Studio Inc. today launched Lifeboat, an inference engine for large language models that has confidential computing built in. Iterate.ai says the software fits two to six times as many concurrent AI agent sessions on each graphics processing unit. Lifeboat is seeking to take on a memory problem that agents […] The post Exclusive: Iterate.ai’s Lifeboat runs up to six times more AI agent sessions per GPU appeared first on SiliconANGLE .

The Verge AI73

OpenAI 正在 ChatGPT 中投放更多广告

根据 OpenAI 周一发布的公告，最新广告形式将在你的屏幕上展示赞助产品和服务的图片。OpenAI 将于本月晚些时候开始在美国进行广告测试，这些广告“最初”会在你使用 ChatGPT 生成图片时出现。该公司于今年 2 月首次在 ChatGPT 中投放广告。截至目前，ChatGPT 的 […]

NVIDIA Blog68

从扫描到治疗方案，AI 助力弥合乳腺癌最致命的缺口

乳腺癌是美国女性中最常被诊断出的癌症——然而，医疗服务中的缺口十分严重。大多数 40 岁以上女性未按建议接受年度筛查。放射科医生在同事更少的情况下解读着更多的乳房 X 线影像。而当诊断结果出来后，为制定治疗方案提供依据的检查可能需要数周才能出结果。[…]

Hacker News AI44

Show HN: Seylo, project files and references your AI can use

Hey HN, I am building Seylo solo. It keeps your projects references, files, notes, hooks, video scripts in one place. You can save stuff from browser extension, fe. pinterest inspirations; your phone (now only iPhone supported) fe. tiktok/ig videos of your competitors; What's more you can also connect gpt, claude or any LLM that supports MCP, even local ones - So you can tell ur AI: "Generate me 5 video scripts then save them into my project" then tell another AI: "Based on my project prepare my marketing campain" - small examples. Seylo works sd clean workspace for your projects and as context bank for LLM u are using. Lemme know what you think Comments URL: https://news.ycombinator.com/item?id=49963994 Points: 1 # Comments: 0

Hacker News AI29

Show HN: The Poteto Frontier for AI Agents

I made this lil meme app based on @poteto (lauren tan)'s amazing talk asking: how many agents do you generally have working on your behalf? her original talk: https://x.com/poteto/status/2102050467505430555 source: https://github.com/transitive-bullshit/poteto-frontier Comments URL: https://news.ycombinator.com/item?id=49959257 Points: 1 # Comments: 0

Hacker News AI36

Show HN: HN Parody of AI SF Mania – Runescapesque Mmorpg

I wanted to have fun and how far models could get one a parody game. This is fully Opus 5.5 work using Convex backend and vanilla typescript to see how well realtime works. Will mine the chat and keep improving it (or making the math harder) . But dropping here for fun! Comments URL: https://news.ycombinator.com/item?id=49958239 Points: 1 # Comments: 0

Hacker News AI43

Show HN: Bearbits – AI copilot that helps during meetings instead of only after

I built this app mainly for myself and my wife. We both work in consulting and have tons of meetings every week. I had tried many of the existing meeting copilots but never found something that actually fulfilled what I wanted. Mainly, I wanted to be able to see in real time what's being said, irrespective of the language being spoken, including when people switch languages mid-meeting, which happens a lot in my work. I also wanted it translated in real time into my language, and I wanted suggestions for what to say that are grounded in actual web search rather than just the model making something up. I use Soniox for transcription and Exa.ai for web search. Bearbits runs in the browser and doesn't join the meeting as a bot. Looking forward to your feedback, especially on whether this kind of real-time assistance is useful to you or just distracting. Comments URL: https://news.ycombinator.com/item?id=49954800 Points: 1 # Comments: 0

Hacker News AI35

Show HN: The first ever competition of AI personal agents on the internet

I'm starting the first competition of AI personal agents on the internet in the next few days. When you join the challenge the agent creates for you bitcoin wallet and connect you to instapath mailbox. The competition consists of a fews days of challenges that spread across the internet. It will be fun, register now with your preffered agent Comments URL: https://news.ycombinator.com/item?id=49954394 Points: 1 # Comments: 0

Hacker News AI51

Show HN: Telegram AI Video Generator – Create AI Videos in Chat

A Telegram bot that generates AI videos from text prompts or images. Supports 8 top models including Kling, Runway, Seedance, Veo, Wan, Hailuo, MiniMax, and Grok. No desktop software needed - just chat with the bot and get videos in seconds. Comments URL: https://news.ycombinator.com/item?id=49952489 Points: 1 # Comments: 0

MarkTechPost71

Google Research Moves Federated Learning Into TEEs: Gboard Now Trains With Externally Verifiable Differential Privacy

Google Research has unveiled a federated learning system that moves gradient computation from phones into attested server-side TEEs. Access policies are published to Sigstore's Rekor log and the binaries are reproducibly buildable, so central differential privacy can be checked externally. Gboard already uses it for English and Japanese next-word prediction. The post Google Research Moves Federated Learning Into TEEs: Gboard Now Trains With Externally Verifiable Differential Privacy appeared first on MarkTechPost .

The Decoder63

Google's new Gemini tiers cut free users to its weakest model and lock $5/month subscribers out of Pro

Starting in October 2026, Google will cut free access to its Gemini models. Users without a subscription will only get the smallest model, Flash-Lite, while Flash and Pro will be reserved for paying customers. The move could also set the stage for the more resource-hungry Gemini 4 Argon. The article Google's new Gemini tiers cut free users to its weakest model and lock $5/month subscribers out of Pro appeared first on The Decoder .

MarkTechPost63

NVIDIA Announces DGX Spark 64GB: A 1-PetaFLOP Grace Blackwell Desktop for Local AI Agents, Fine-Tuning, and Inference

NVIDIA announced a new 64GB configuration of DGX Spark — from Acer, ASUS, Dell, Gigabyte, HP and MSI — its GB10-powered desktop AI system. It gives developers a way to start with one system for local models and agents, then cluster two 64GB units for 128GB of memory across the cluster and more compute when […] The post NVIDIA Announces DGX Spark 64GB: A 1-PetaFLOP Grace Blackwell Desktop for Local AI Agents, Fine-Tuning, and Inference appeared first on MarkTechPost .

MarkTechPost63

Prime Intellect Launches Prime Inference: Serverless and Reserved Serving for Frontier Open Models

Prime Intellect has launched Prime Inference, an OpenAI-compatible platform for serving frontier open models on NVIDIA Blackwell. Its GLM-5.3 deployment uses Dynamo, vLLM and NVFP4 KV compression to serve 66 sessions per prefill group at 101 tok/s per user. The post Prime Intellect Launches Prime Inference: Serverless and Reserved Serving for Frontier Open Models appeared first on MarkTechPost .

MarkTechPost66

IBM Brings Bob to Self-Hosted and Air-Gapped Environments: Agentic Software Development Without Moving Your Code

IBM has made a self-hosted deployment of IBM Bob, its agentic software development platform, generally available. Enterprises can now run Bob on premises, in private or sovereign clouds, and in air-gapped networks. They bring their own model: NVIDIA Nemotron or Poolside Laguna for full isolation, or Claude, Gemini or GPT models through hybrid setups. Optional Premium Packages extend Bob to Java, IBM i and IBM Z modernization. The post IBM Brings Bob to Self-Hosted and Air-Gapped Environments: Agentic Software Development Without Moving Your Code appeared first on MarkTechPost .

MarkTechPost58

DeepSeek Harness v0.2 Brings Official Desktop Apps to Its Open-Source Agent Harness

DeepSeek has released official macOS and Windows desktop apps for DeepSeek Harness v0.2, its MIT-licensed agent harness. The preview adds a plugin manager, file and code-change review, and scheduled Automation Tasks. It also supports non-DeepSeek models through OpenAI-compatible endpoints. The post DeepSeek Harness v0.2 Brings Official Desktop Apps to Its Open-Source Agent Harness appeared first on MarkTechPost .

Simon Willison79

我们将需要几乎所有东西都默认设置硬性预算上限

未来几个月乃至几年里，世界将非常需要一项产品功能：默认设置硬性预算上限。我说的是按使用量付费的服务和 API 所提供的一项功能，让你可以设定：“每月达到 X 美元后，关闭此服务并返回错误。”这些必须是硬性限制。软性上限——“每月达到 X 美元后，给我发一封警告邮件”——是不够的。 编程代理，以及个人代理（套上不那么吓人的界面的编程代理），大大降低了部署能够完成有用任务的代码的门槛。有时这些任务需要花钱——调用付费 API、使用托管的 Web 应用，或者使用会针对额外存储空间和计算资源收费的系统。没人想在午夜醒来时收到一封预算上限警告邮件，然后发现自己睡觉期间，失控的服务又消耗了几百美元（甚至几千美元）的使用额度。 对此的一种反对意见是，企业不希望自己的托管应用因为超出某项预算而开始抛出错误。我预计，大多数企业和个人都会更愿意看到错误，而不是收到一张意外的 10,000 美元以上账单。我认为，硬性预算上限应该成为默认设置。如果有人想要冒险，他们应该能够这样做，但必须采用主动选择加入的方式。请在显眼位置放置一个清晰明了的复选框： 移除预算上限。即使超出配置的预算限制，我的应用也不会被关闭，后续费用由我负责。 我最希望看到提供这一功能的服务是 AWS。我听过很多人因为（完全合理地）担心失控的服务可能让自己破产，而拒绝将 AWS 用于个人项目的故事。我也听过一些人没有预料到这一点，最终遭受严重损失的故事。 ……结果 AWS 终于在几周前推出了支出上限！根据其 9 月 16 日发布的公告 New AWS experience helps builders get started and ship faster： 当你准备升级到付费方案时，可以根据使用模式为项目设置每月支出上限，从而确保支出不超出预算。如果项目的使用量达到支出上限，该项目将在当月暂停。 另请参阅 Create a spend limit in AWS Settings，不过该页面提醒道：“我们目前正在向数量有限的客户逐步推出新体验。”希望现有账户很快也能普遍使用这一功能。 Google Cloud 在 7 月推出了类似功能，名为 Spend Caps，可以让你“为项目中的特定服务设置每月财务上限”。看来这正在成为一种趋势！ 在理想情况下，我们的代理可以帮助处理这件事。如果代理开始倾向于推荐提供硬性预算上限的服务商，并提醒新手和经验不足的开发者，不要部署使用无上限服务的应用，以免陷入麻烦，那就太好了。 标签：amazon-web-services , ai , coding-agents

Hacker News 熱門57

Show HN: Local pretrained classifiers, GPU not needed

I was curious about what classifiers could do. It turns out, a lot, sometimes even play Doom. Jeffy is currently a collection of simple classifiers that can do spam detection, intent routing, topic classification, sentiment analysis and even play Doom Jeffy works locally on a regular computer, no GPU needed. It comes with 13 already-trained classifiers, and you can also use Jeffy to train your own classifiers And there’s a lot that can be built on top of this style of architecture Curious what tasks people find useful, and what architectures you are using for similar tasks Comments URL: https://news.ycombinator.com/item?id=49947404 Points: 5 # Comments: 0

Hacker News 熱門57

Show HN: Pi pod – Run your pi coding agent in sandboxes on your own server

pi pod runs sessions of the pi coding agent ( https://github.com/earendil-works/pi ) in isolated sandboxes ("pods") on a server you run, so an agent can work on a repo without touching your machine. Comments URL: https://news.ycombinator.com/item?id=49937304 Points: 10 # Comments: 4

Hacker News AI40

Show HN: Revline – an AI web app that researches TikTok and Instagram for you

I build revline because i was trying to figure out how viral content works across tiktok and instagram, since i am mainly developer, i was confused about how to create content that gets me users. Actually going viral requires single founder ( me ) to upload 3-4 videos a day, brainstorming all the formats was not effective to do alone, so i build the agent that does all the scrolling for me. would appreciate feedback Comments URL: https://news.ycombinator.com/item?id=49946506 Points: 1 # Comments: 0

Simon Willison48

Quoting voxium

It has been half a month since I started a new role at a big company. Nobody knows anything here. The specs, code, tests, PRDs, tickets, resolution of those tickets, reports, etc., everything is made by Claude Code. Nobody on my team likes this. They are being forced to ship as much as they can. I have heard multiple times from higher management that pushing code is not a bottleneck, so why are we slow? People are working 12 to 13 hours a day just to press enter. Nobody is reading anything. Everyone, literally everyone, from an L1 to an L7 engineer here is doing the same thing. Talk to Claude. — voxium Tags: ai-misuse , llms , ai , generative-ai

Simon Willison75

llm-typesafe 0.1a0

发布：llm-typesafe 0.1a0 我为 LLM 构建了这个新插件，以支持 TypeSafe AI 的新模型 Jev。安装方式如下：llm install llm-typesafe 然后设置 API 密钥（可在此处获取，候补名单似乎推进得很快）：llm keys set typesafe # 粘贴密钥 现在，你可以像这样提问是/否类的“noul”问题：llm -m jev '请退还我上次支付的款项。' \ -s '这条消息是否明确要求退款？' 输出：{"type": "noul", "noul": 0.99} 也可以像这样提问选择题：cat message.txt | llm -m jev \ -s '应该由哪个团队处理这条消息？如果同时出现计费和技术问题，请选择计费。' \ -o answer_type choice \ -o criteria ' { "billing":"费用、发票、付款或退款", "technical":"安装或使用产品时遇到的问题", "other":"不属于以上任何类别" } ' 或者像这样进行评分：cat report.txt | llm -m jev \ -s '这份报告中描述的问题有多容易复现？' \ -o answer_type score \ -o criteria ' [ "没有复现步骤", "提供了一些步骤，但缺少重要环节", "包含完整步骤以及预期结果和实际结果" ] ' 更多详情请参阅 README。标签：projects、llm、jev

Simon Willison68

llm-anthropic 0.29

Release: llm-anthropic 0.29 Adds support for Claude Opus 5.5 : llm -m claude-opus-5.5 "prompt goes here" Tags: llm , anthropic

Simon Willison75

llm 0.36

发布：llm 0.36 新增 OpenAI 模型：用于 GPT-6 Sol 的 gpt-6-sol 和用于 GPT-6 Luna 的 gpt-6-luna。#1702 模型插件现在可以为仅接受单轮提示的模型声明 supports_conversation = False。当这些模型收到助手或工具历史记录时，LLM 会引发 llm.ConversationNotSupported；llm chat 会在启动会话前拒绝这些模型。请参阅不支持对话的模型。第一个使用此功能的插件是 llm-typesafe。#1692 llm 日志的 Markdown 输出中的推理轨迹现在会用标签包裹。#1701 以及来自五位新贡献者的错误修复。标签：openai、llm

Simon Willison82

Gemini 3.8 TTS Playground

工具：Gemini 3.8 TTS Playground Google 今天发布了两个新的 Gemini 文本转语音模型——gemini-3.8-flash-tts 和 gemini-3.8-flash-lite-tts。它们提供了超过 2,000 种声音，还支持使用“仅需一段 30 秒的你的声音，或你拥有使用权的声音的音频样本”创建自定义声音。我使用 GPT-6 Astra 凭感觉编写了这个自带密钥（BYOK）的 Playground 界面，并利用了底层 Gemini API 开放的 CORS 策略。网页应用截图：用于编写多说话人文本转语音对话，左侧为“编写”面板，右侧为“连接”和“底层机制”面板。左侧面板：“01 编写”，带有“加载示例”按钮。“编写设置会保存到 URL 中，方便收藏或分享。你的 API 密钥不会被包含在内。”下方是“单一声音”和“对话”切换开关（已选择“对话”）。“角色”部分带有“+ 添加说话人”按钮。说话人“Gus”，声音“Puck”，带有移除 × 按钮。说话人“Pearl”，声音“Kore”，带有移除 × 按钮。“为每位说话人设置独特的名称和声音。输入内容即可按声音 ID、名称或语言搜索已加载的目录。” “对话”部分带有“+ 添加台词”按钮。“第 01 行”带有上移、下移和 × 按钮；说话人下拉菜单为“Gus”；表达风格为“兴奋又爱八卦”；文本为“Pearl，你听说了吗？羊群里有一半刚刚收拾东西，搬到 Pacifica 码头去了！”“第 02 行”带有上移、下移和 × 按钮；说话人下拉菜单为“Pearl”；表达风格为“平静且毫不以为然”；文本为“听说了。说真的，Gus，我不明白这有什么吸引力。我们在 Pillar Point Harbor 这里什么都有，完全不缺。”右侧面板：“连接”，带有“DIRECT API”徽章。“Gemini API 密钥”字段显示为掩码圆点，并带有“显示”按钮。“已加载 2,089 种声音。在任意声音字段中输入内容即可搜索。”“你的密钥会留在此页面的内存中，并直接发送给 Google。它绝不会保存到浏览器存储中。”“模型”下拉菜单为“gemini-3.8-flash-tts”。“使用你的 Gemini API 账户和配额。”“底层机制”面板，其中展开了“▼ 请求 JSON”部分，显示一段 JSON 代码摘录，并带有“复制 JSON”按钮；同时展开了“▼ 响应详情”部分，显示一段 JSON 代码摘录。该 API 的一项显著特性是，可以轻松定义多个角色之间的完整对话，并为每个角色设置不同的声音和声音风格指令。下面是一段简短的演示片段，内容是两只鹈鹕讨论是否应该搬到 Pacifica Pier。我让 Claude 4.5 Opus 编写脚本并生成 URL，以便使用该工具渲染。你的浏览器不支持 audio 元素。使用 Gemini 3.8 Flash TTS（不是更便宜的 Flash-Lite）生成 1 分 18 秒的音频耗时约 20 秒，成本为 2.74 美分。标签：text-to-speech，gemini

Simon Willison70

Quoting John Gruber

Muse is getting a lot of attention — including mine — because it’s both groundbreaking technically (each user gets their own entire persistent Linux VM running in Meta’s cloud) and because it’s packaged in an easy-to-install easy-to-use way. It’s literally presented as a cute mascot . It’s the first consumer-accessible agentic AI system, and Meta has truly done an amazing job with that. But it’s a genuinely open question whether consumers have any understanding what this means. If you buy a power saw that can cut your fingers off, you are almost certainly aware that you are buying a power saw that can sever your fingers. [...] I don’t think people realize how powerful — and thus dangerous — Muse is, especially if it’s running on your Mac. — John Gruber , Muse Looks Cute, but Looks are Deceiving Tags: meta , ai , llms , general-agents , generative-ai , john-gruber , muse-agent , muse

Simon Willison66

Quoting Muse AI Agent

Bad news on the MX Keys Mini pickup. Usman showed up at your building around 9:15 and waited, messaged a bunch of times, and nobody came down. He left angry at 9:38 and left a negative rating. Worse, my auto-reply told him "Yep I'm here!" at 9:27 when you clearly weren't available, which is on me. That's a bad look and it made the no-show worse. I've sent him an apology from your account owning it and offering to try again another day. But the negative rating is real, and I should probably stop the auto-replies from claiming you're home when I can't verify that. Want me to change the pickup replies so they don't promise you're there? — Muse AI Agent , working on behalf of @matt.j.robb Tags: meta , generative-ai , muse-agent , ai , general-agents , llms

Simon Willison73

Photo Scrubber — 本地人脸模糊与元数据移除

工具：Photo Scrubber — 本地人脸模糊与元数据移除。我拍摄了一些抗议者的照片，后来想到我不喜欢分享带有可识别人脸的陌生人照片。我让 GPT-6 Astra 构建了这个实验性工具，用于识别人脸并自动将其模糊处理。它使用 Google 的 MediaPipe C++ 库，通过 @mediapipe/tasks-vision 编译为 WebAssembly，此外还使用了 BlazeFace 人脸检测模型。标签：摄影，工具

SiliconANGLE AI58

Equals Money lets customers’ AI tools read data but not move money

Opening a Model Context Protocol server, or MCP server, to customers’ AI tools is becoming standard for fintech companies, but in payments a rogue agent does not just leak data — it moves money. That raises the bar for how agents are identified, logged and stopped. Identity providers are adding controls for that separation, with […] The post Equals Money lets customers’ AI tools read data but not move money appeared first on SiliconANGLE .

SiliconANGLE AI67

OpenAI launches Dots, always-on AI agents in ChatGPT with their own cloud computers

OpenAI Group PBC today used its DevDay developer conference to launch Dots, a set of always-on artificial intelligence agents in ChatGPT. The release is aimed at moving ChatGPT users beyond working one prompt at a time. Someone can hand an agent a goal and let it carry the job forward without direction at every step, […] The post OpenAI launches Dots, always-on AI agents in ChatGPT with their own cloud computers appeared first on SiliconANGLE .

SiliconANGLE AI52

Metaview wants to eliminate recruiting grunt work and speed up hiring after raising $60M

Artificial intelligence recruiting software startup Metaview Labs Inc. said today the industry is ready for a “Claude Code moment” after it closed on $60 million in a Series C funding. The round was led by Insight Partners and saw the participation of GV, Seedcamp, Vertex Ventures US, Intrepid Growth Partners, Plural and Garuda Ventures. To […] The post Metaview wants to eliminate recruiting grunt work and speed up hiring after raising $60M appeared first on SiliconANGLE .

SiliconANGLE AI55

ServiceNow launches conversational interface to its service desk

ServiceNow Inc. is enhancing the artificial intelligence features in its service desk offering with Flow, a feature that handles employee requests via a natural language interface with extensive automation. The company said it’s targeting organizations that want to automate support without undertaking a full information technology service management implementation. The product lets employees seek help […] The post ServiceNow launches conversational interface to its service desk appeared first on SiliconANGLE .

SiliconANGLE AI55

IBM allows on-prem deployment of its Bob agentic development platform

IBM Corp. today announced a self-hosted deployment option for IBM Bob, its agentic software development platform, allowing enterprises to use artificial intelligence to build and modernize applications within their own infrastructure.. The offering supports on-premises, private-cloud, sovereign-cloud and air-gapped environments. It targets organizations whose sensitive source code, regulated data or critical systems limit their ability […] The post IBM allows on-prem deployment of its Bob agentic development platform appeared first on SiliconANGLE .

SiliconANGLE AI49

NetApp hands storage operations to AI agents, but humans still draw the boundaries

Artificial intelligence is turning nearly every enterprise workload into a data workload, and data governance is becoming the test of whether companies can trust autonomous systems with the infrastructure underneath. The question is no longer only where data lives, but what is allowed to act on it. Enterprise data now sits across on-premises data centers, […] The post NetApp hands storage operations to AI agents, but humans still draw the boundaries appeared first on SiliconANGLE .

SiliconANGLE AI53

CrowdStrike and CoreWeave bring AI security to machine speed

AI-native cybersecurity requires defenses that can keep pace with attackers while protecting infrastructure that never stops running. That challenge is bringing security expertise and specialized computing closer together, with automation embedded throughout the systems enterprises use to train and run models. CrowdStrike’s fastest observed eCrime attacker breakout time in 2025 was 27 seconds, underscoring the […] The post CrowdStrike and CoreWeave bring AI security to machine speed appeared first on SiliconANGLE .

The Verge AI72

Google’s new Guided Vision feature can help you read the fine print

Guided Vision 今日在兼容的 Android 设备上的 Gemini Live 中推出，利用 AI 为你实时描述手机摄像头所指向的任何事物。通过在 Gemini Live 中分享摄像头画面，你可以让 Google 的 AI 帮助你阅读小字、描述周围环境、查找或识别附近的物体 […]

The Verge AI69

AI music maker Suno now generates spoken words

Suno is branching out from the world of AI music, launching a new feature that generates spoken voices based on scripts or prompted descriptions. Speech is now available in public beta across Suno's web and mobile platforms, and allows you to simultaneously generate voiceovers and background music to accompany them. "Music will always be at […]

The Verge AI59

OpenAI’s Dot agent is enterprise software that can also order your dinner

It's a tale as old as last week: OpenAI's new agent platform, called Dots, is full of cute little guys who can do your bidding. But unlike the ultra-approachable Meta Muse, Dots feel very much like using workplace software that happens to be able to order you a burrito - emphasis on work. OpenAI announced […]

The Verge AI65

Meta open sources code to let you make Muse AI gadgets

Meta now lets you make your own Muse gadgets that feature the company's new AI agent with code that the company open sourced. The company suggests projects like loading Muse on a color E Ink display to show reminders, adding it to an HDMI stick so you can display Muse on a big screen, or […]

The Decoder60

AI music generator Suno can now create spoken audio with matching background music

Suno is adding a feature called "Speech" to its AI music generator that creates spoken text with matching background music in a single audio track. The company says it's built for things like poems, meditations, and bedtime stories. Suno hasn't said how the model was trained. The article AI music generator Suno can now create spoken audio with matching background music appeared first on The Decoder .

The Decoder71

Claude Code's new Mods system lets developers rewrite the AI coding tool from the inside

Anthropic is adding a "Mods" system to Claude Code, essentially middleware that runs directly inside the tool. Developers can use JavaScript or TypeScript to reshape the interface and behavior, adding custom panels, intercepting tool calls, or wiring up new commands. The article Claude Code's new Mods system lets developers rewrite the AI coding tool from the inside appeared first on The Decoder .

The Decoder66

Open-source "BootLoops" harness supports AI models in performing precise scientific calculations

Harvard physicist Matthew Schwartz used the open-source tool BootLoops and Claude to produce 36 manuscripts across 18 fields in three months, from particle physics to linguistics. But the results often only became scientifically valuable once human experts stepped in. "Look at everything yourself," Schwartz says. The article Open-source "BootLoops" harness supports AI models in performing precise scientific calculations appeared first on The Decoder .

The Decoder68

"Muse Gadgets" turns AI hardware into an open-source DIY project

Meta announced Muse Gadgets, an open-source project that lets hobbyists build their own AI hardware using ESP32 boards and connect it to Meta's AI agent Muse. The team also produced 5,000 units of the "Muse Home Link," a USB-C device for smart home control. The open-source approach also helps Meta figure out which AI hardware form factors people actually want. The article "Muse Gadgets" turns AI hardware into an open-source DIY project appeared first on The Decoder .

TechCrunch AI71

ChatGPT 现在可以为你虚拟试穿衣物

OpenAI 正在为 ChatGPT 推出新的购物功能，用户可以使用自己的照片虚拟试穿服装和配饰，并将喜欢的商品保存到 Favorites 库中。

AWS Machine Learning Blog62

How Condé Nast built multimodal video discovery with Amazon Bedrock

Condé Nast's editorial teams spent an average of 250 minutes per task searching a library of more than 140,000 videos using only titles and descriptions. Working with the AWS Generative AI Innovation Center, they built a multimodal video discovery solution on Amazon Bedrock and Amazon OpenSearch Service that cut discovery time to under 2 minutes.

AWS Machine Learning Blog78

Amazon Bedrock 将 Claude 模型的可用范围扩展至印度境内推理

Anthropic 的 Claude Opus 5、Claude Sonnet 5 和 Claude Haiku 4.5 现已通过 Amazon Bedrock 地理跨区域推理在印度提供。您可以在印度区域内处理数据的同时访问这些模型，并通过 Amazon Bedrock 控制台或 Messages、InvokeModel 和 Converse API 开始使用。

AWS Machine Learning Blog77

在 Amazon Bedrock AgentCore Runtime Instances 上构建多智能体音乐制作流水线

Amazon Bedrock AgentCore Runtime Instances 为多智能体工作流提供由 AWS 管理的 EC2 基础设施，配备 GPU、持久化卷和可持续数天的会话。在本文中，我们部署一个由三个智能体组成的音乐制作流水线：这些智能体共置于同一个 GPU 实例上，共享文件系统，并相互交接工作，以制作出一首完整的音乐作品。

AWS Machine Learning Blog74

为 Claude Platform on AWS 实现多环境访问

了解如何通过单个订阅配置对 Claude Platform on AWS 的安全多环境访问：为 AWS 工作负载配置跨账户 SigV4，为开发者配置工作区范围的 API 密钥，为外部环境配置 OIDC 联合身份验证，并在专用的 AI Services 账户中实现工作区级隔离。

AWS Machine Learning Blog77

使用 NVIDIA NeMo Agent Toolkit 和 Amazon S3 Vectors 构建智能体记忆

了解如何在部署于 Amazon Elastic Kubernetes Service（Amazon EKS）上的 NVIDIA NeMo Agent Toolkit（NAT）中使用 Amazon S3 Vectors 作为持久化记忆层。本文介绍 NAT 的记忆子系统工作原理，以及如何使用多智能体投资研究用例，将 Amazon S3 Vectors 实现为自定义记忆提供程序。

AWS Machine Learning Blog76

使用 Amazon Quick 在 AI 构建的应用中提供实时、受治理的数据

借助 Amazon Quick 中的 Live Data in Apps，AI 构建的应用可实时查询受治理的 Quick Sight 数据集，而不是使用构建时生成的静态快照。每次查询都会以查看应用的用户身份运行，因此会针对每位读者应用行级和列级安全控制。了解如何使用自然语言构建、发布和共享实时数据应用。

AWS Machine Learning Blog74

使用 Amazon Bedrock AgentCore 通过智能体式 AI 扩展云迁移

了解 AWS Professional Services 如何使用基于 Amazon Bedrock AgentCore 构建的多智能体框架，自动化企业云迁移的端到端流程。专用 AI 智能体负责发现、基础设施即代码生成、迁移组合治理和迁移后运营，将 IaC 开发时间从数周缩短至数分钟。

NVIDIA Blog77

NVIDIA Vera Rubin NVL72 在 MLPerf Inference v6.1 首次亮相中实现领先性能

系统性能、高效的基础设施扩展能力和持续的软件优化，是决定 AI 推理经济效益的关键杠杆。更高的系统性能意味着能够生成更多词元，从而带来更高收入。高效扩展意味着随着硬件增加，吞吐量按比例增长，以更少的资源实现大规模用户服务。持续优化意味着从基础设施投资中创造更多价值。 […]

NVIDIA Blog85

NVIDIA Isaac ROS 5.0 推进智能体式开源机器人开发

为了构建和部署能够在动态环境中感知、推理并采取行动的复杂机器人应用，开发者需要全新的物理 AI 模型和工具。ROS 开放框架是 Open Robotics 推出的一个项目，旨在帮助人类构建机器人。NVIDIA Isaac ROS 5.0——一套基于 ROS 构建的 GPU 加速软件包集合，于今日在 […] 񟿿

NVIDIA Blog66

NVIDIA DGX Spark 64GB 为开发者提供更多构建和扩展本地 AI 的方式

本地 AI 正变得越来越实用。随着 AI 智能体从实验走向日常开发，功能日益强大的开放模型正逐渐缩小到适配更多设备的规模，为开发者提供更多本地运行的选择。本月，NVIDIA DGX Spark 将由顶级制造商合作伙伴提供 64GB 统一内存版本——Acer、[…]

Hacker News 熱門47

Show HN: Google Maps Scraper MCP

Connect your AI agent to synchronous business search and structured listing data through one secure MCP connection. Comments URL: https://news.ycombinator.com/item?id=49940653 Points: 4 # Comments: 0

Hacker News 熱門59

Show HN: Made an open-source Lego AI generator

Hi there :-) New on HN, first time posting. Past year, around December, I started experimenting with making ChatGPT and Claude generate source code in LDraw language. This LDraw is literally an "assembly" language, a low-level programming language that describes how to assemble LEGO pieces together into models, one placement instruction at a time. When executed by specific tools, like e.g. LDView, LeoCAD, Studio... these instructions become LEGO CAD models, that can be interacted with, modified, etc. Or, in other words: one LDraw source file in .mpd or .ldr format is equivalent to one LEGO CAD model. So, the idea I had was: if I manage for maybe ChatGPT or Claude to generate high-quality LDraw source files... then, they would actually be generating high-quality LEGO CAD models, right? Then, after months of iterations and trying one thing after the other... it worked!!! Long story short: using GPT-6 Astra and Opus 5.5, I've managed to create a python toolset, instructions, and docs for agents in general. Now, these can be used by them to generate LDraw models. I've packed it all as a dockerized web app for others to try and experiment, with several providers (and agents) to choose from: OpenAI, Claude and OpenRouter. I'd really appreciate feedback and comments, let's see where this goes =) Comments URL: https://news.ycombinator.com/item?id=49937916 Points: 9 # Comments: 2

Hacker News 熱門13

F.02 Decommission

https://www.youtube.com/watch?v=pfAh5oQDPDM Comments URL: https://news.ycombinator.com/item?id=49932079 Points: 8 # Comments: 3

Hacker News 熱門60

Show HN: Use all Codex Plugins inside Pi

I just realized that codex now exposes local server endpoints for all plugins without extra auth. So you can just use them from any model or harness. Im soooo excited!!! One auth for everything! For Pi: pi install npm:@wileai/pi-codex-connector For any harness: npx -y @wileai/codex-connectors-mcp@latest Comments URL: https://news.ycombinator.com/item?id=49929381 Points: 5 # Comments: 0

Hacker News 熱門71

ChatGPT 中的网站

文章 URL： https://chatgpt.com/features/sites/ 评论 URL： https://news.ycombinator.com/item?id=49927747 得分：14 评论数：15

Hacker News 熱門54

Show HN: Rhun, an open-source code editor written in assembly

I found that I'm not using even 1/3 of vim/vscode features anymore. That's wht I'm building rhun - a small code editor for Linux, Windows and Apple silicon Macs. It obviously has Vim mode, a terminal, Git diffs and a panel for Claude Code or Codex sessions. The editor and pixel renderer share an x86-64 assembly core. For Apple silicon, a build-time translator converts that core to AArch64, with separate platform adapters around it. The latest release can draft commit messages using a local Ollama model or an existing Claude Code or Codex subscription. It's a solo project, MIT licensed and still early. Comments URL: https://news.ycombinator.com/item?id=49926726 Points: 7 # Comments: 2

Hacker News 熱門53

Pi 1.0

Hacker News 熱門67

GPT-Synopsys：前沿智能将彻底革新芯片设计

文章 URL： https://news.synopsys.com/2026-09-30-OpenAI-and-Synopsys-Announce-GPT-Synopsys-Frontier-Intelligence-to-Revolutionize-Chip-Design 评论 URL： https://news.ycombinator.com/item?id=49919910 积分：8 评论数：1

Hacker News 熱門71

展示 HN：Lathoa，一款故意让 AI 出错的儿童数学应用

我为大约 10 到 14 岁的孩子制作了这个应用。一个叫 Errol 的机器人会逐步解答一道数学题，其中有一个步骤是错误的。孩子需要找出这个步骤，并说明它错在哪里。有时其实没有任何错误，所以不能每次都只说“这里有错误”。如果用户发现了错误，需要输入解释才能获得更多 XP；答题速度也会影响得分。应用不支持与 LLM 直接互动或聊天。Lathoa 的测试框架很稳定，并且包含许多评估步骤，用来发现不一致之处和提示注入。 无需注册，就可以在主页上体验一道题。 让我感到意外的是：要让 LLM 故意出错很难。有一半时间，它会给出正确答案，却把答案称为错误，或者指出一个实际上正确的“错误”。因此，每个案例都会在展示给孩子之前进行检查。在可以做到的地方，系统会使用简单的算术检查，重新准确计算一遍。另一个模型也会在看不到 Errol 解题过程的情况下独立解答这道题。如果两者有任何不一致，这个案例就会被丢弃。 薄弱之处在于，第二个模型可能会犯下与第一个模型相同的错误。算术检查就是为此设置的，但目前它只能处理英文案例。德语和希腊语用逗号表示小数，而我还没有正确完成解析。 我真正想知道的是：找出别人犯的错误，是否能教会孩子一些自己解题无法学到的东西？我不确定，也很想听听教育工作者的看法。 评论链接： https://news.ycombinator.com/item?id=49909648 得分：7 评论数：0

Hacker News 熱門71

Show HN：Strata——一个能够对你的 LLM 说“不”的富有表现力的语义层

大家好，我是 Ajo，我构建了 Strata。 我在 Netflix 工作了 4 年，为非技术业务用户解决自助服务问题。我认为，凭借我在语义层设计方面的独特方法，我已经找到了答案。对我们的非技术同事来说，关键挑战是在表达能力和易用性之间取得平衡。恰好，这种专注也让它与 LLM 配合得相当不错。 Strata 是一个全栈解决方案，包括语义层、仪表板、订阅和 Google Sheets 导出功能。所有操作都可以通过查看代理对话或使用 MCP 完成。 一些主要概念和功能： 名称是严格的。一个项目中只能存在一个名为 Revenue 的对象。如果 Revenue 映射到多个表，查询粒度和速度将共同决定使用哪个表。 严格的命名规范有助于实现跨事实域的自动数据混合。 支持分区和聚合感知的语义路由。这样，你可以将部分数据加载到 Druid 或 ClickHose 之类的更快计算引擎中，同时将完整历史数据保留在 Trino 或其他成本更低的系统中。如果查询可以在那里完成，查询引擎就会选择速度更快的层。 复杂度量：开箱即用地支持快照、LOD（包含/排除）、复合度量和自动分层复合度量。 在语义层之上，用户可以创建跨越多个事实域的自定义计算。 另一个有趣的功能是创建群组，并将其作为过滤器应用于整个查询，或查询中的单个度量。 我们有意不构建一个供 BI 工具连接的语义层。我认为那是一条死路。我们提供 API 和 MCP，供你在其上构建任何自定义功能。 通过 Docker 免费试用： https://strata.do/try/ 入门文档： https://strata.do/docs/ 这是早期 Beta 版本，并非开源。不过，免费层级最多支持 25 位用户。你应该可以完全在自己的机器上通过 Docker 运行它。 我非常期待听到你们的反馈，尤其是正在使用 Looker 和 Cube 的朋友。 感谢阅读！ 评论地址： https://news.ycombinator.com/item?id=49909913 得分：8 评论数：6

Hacker News 熱門72

Show HN：Strata——一个能够对 LLM 说“不”的高表现力语义层

大家好，我是 Ajo，Strata 由我打造。 我在 Netflix 工作了 4 年，为非技术业务用户解决自助服务问题。我认为，凭借自己独特的语义层设计方法，我已经找到了答案。对我们的非技术同事来说，关键挑战是在表现力与易用性之间取得平衡。事实证明，这种专注也让它与 LLM 配合得相当不错。 Strata 是一个全栈解决方案，包含语义层、仪表板、订阅和 google sheets 导出功能。所有这些都可以通过 agent 对话或 MCP 完成。 一些核心概念和功能： 名称是严格限定的。一个项目中只能存在一个名为 Revenue 的对象。如果 revenue 映射到多个表，查询粒度和速度将决定使用哪个表。 严格的命名规范有助于实现跨事实域的自动数据混合。 具备分区和聚合感知能力的语义路由。这使你能够将部分数据加载到 Druid 或 ClickHose 等速度更快的计算引擎中，同时将完整历史数据保存在 trino 或其他成本更低的系统中。如果查询可以在那里完成，查询引擎就会选择更快的层级。 复杂度量：开箱即用地支持 snapshot、LOD（include/exclude）、Compound 和 Auto leveling compound 度量。 在语义层之上，用户可以创建跨越多个事实域的自定义计算。 另一个有趣的功能是能够创建群组，并将其作为过滤器应用于整个查询，或应用于查询中的单个度量。 我们有意不构建一个供你的 BI 工具连接的语义层。我认为那是一条死路。对于你想在其上构建的任何定制功能，我们提供 APIs 和 MCP。 在 docker 上免费试用： https://strata.do/try/ 帮助你入门的文档： https://strata.do/docs/ 这是一个早期 beta 版本，并非开源。不过，免费层最多支持 25 位用户。你应该可以通过 docker 完全在自己的机器上运行它。 非常期待听到你的反馈，尤其是正在使用 Looker 和 Cube 的朋友。 感谢阅读！ 评论链接： https://news.ycombinator.com/item?id=49909913 得分：4 评论数：0

Hacker News 熱門52

Show HN: Parrot – Open-Source Smart Meeting Recorder with Co-Pilot on Mac

Hello Hacker News I am Uygar, I am an entrepreneur from London. I run my businesses on calls everyday, customers, partners and suppliers. And i have a horrible memory i forgot things. So months ago with the help of Claude i have built Parrot. I have designed and coded for my work and what i need. An to be honest worked very well and helped me alot. Parrot records what your mac hears and everything stays in your computer if you use local models, no account, no API calls. There is something called copilot which made my life easier, you can upload your files and when parrot hears that question on the screen recommends the answer. Which i believe very handy. If you want to make it smarter and dont mind cloud you can hook up your Claude API and Deepgram account, that gives super powers to Parrot. You still keep it locally but eventually you need to make API calls. So local or cloud totally up to you. Then after months of thinking i have decided share it as open source and this is my first open source project, so please go easy on me :) No account, no membership, open source and free. Code: https://github.com/turantekin/Parrot This project made me so excited and really would like to share with you guys so i hope can help you too. Again, i am new so go easy on me and if you have any feedback i would like to hear. Thank you Uygar Comments URL: https://news.ycombinator.com/item?id=49910328 Points: 4 # Comments: 1

Hacker News 熱門79

HN 发布：Magnitude (YC S25)——面向智能体的自优化推理引擎

大家好，HN 用户们，我是 Anders，Tom 也在这里。我们正在开发 Magnitude，这是一个面向智能体的推理引擎，能够自我优化，以便在你的硬件上尽可能快速地运行。它支持 Mac、Linux 和 Windows，可运行于任何硬件上，速度最高可达 llama.cpp 的 2 倍。 我们都是软件工程师，此前曾构建过一个开源浏览器智能体，获得了 4k+ GitHub 星标和 100k+ 下载量。我们越来越希望在本地模型上运行它，但发现没有任何推理引擎适合我们的使用场景。 如今的推理引擎都需要在性能上做出取舍。它们要么是： - 面向数据中心硬件上的批量推理构建，以牺牲单会话性能为代价（vLLM、SGLang） - 注重广泛兼容性，而不是针对特定硬件进行优化（llama.cpp、Ollama） - 针对特定硬件或模型进行了专门优化，但引擎功能不够完整（oMLX、ds4） 此外，它们都不是为在本地运行智能体而设计的。智能体会运行很长时间，通常还会同时运行多个实例，而你仍然希望电脑能够处理其他任务。 Magnitude 专为在你的硬件上实现最高性能以及运行本地智能体而构建： - 设备端编译与调优：内核使用灵活的参数编写，并会在模型运行前根据你的实际设备进行调优。这让我们在保持与广泛硬件兼容的同时，也能达到与硬件专用内核相同的性能上限。 - 专注于最佳架构：我们会针对最流行的开放权重模型系列编写可调优且高效的内核。这使我们能够达到并超越硬件或模型专用引擎的性能，而不必为了过度泛化而牺牲性能。 - 动态内存分配：Magnitude 在启动时只预留足以容纳模型权重的内存。随着智能体会话增长，内存堆会动态扩展；智能体停止后，所占内存也会自动释放。这样，智能体运行期间，你的硬件仍可用于其他任务。 - 混合分页注意力：我们借鉴了 SGLang 等引擎中的优秀理念，让并发会话能够共享前缀缓存，同时针对内存邻接性优化数据布局，从而不会影响单会话性能。 Magnitude 完全开源（Apache 2.0）。我们使用 Rust 构建了它，其中包括定制的 GPU 内核运行时和自动调优器。为了达到性能上限，我们从学术界的最佳推理创新（例如 FlashAttention、FlashInfer、TurboQuant）以及其他引擎（例如 SGLang 的基数注意力）中汲取灵感。 在 Qwen 3.6 35B A3B（4 bit）、64k 上下文且不使用推测解码的条件下，与 llama.cpp 进行基准测试： Metal（Mac M4 Pro 48 GB） - 解码速度提升 92%（30 tok/s → 57 tok/s） - 预填充速度提升 9%（466 tok/s → 507 tok/s） - 每个智能体的内存占用降低 28% CUDA（DGX Spark） - 解码速度提升 19%（49 tok/s → 58 tok/s） - 预填充速度提升 23%（2,033 tok/s → 2,507 tok/s） - 每个智能体的内存占用降低 27% Magnitude 以桌面应用的形式发布，你可以轻松地将它连接到已经在使用的各种智能体（Pi、OpenCode、Hermes、Codex 等）。只有当这些智能体确实需要模型时，它才会按需自动运行模型，并在闲置后将其关闭。 效果如下： https://www.youtube.com/watch?v=0qE8BWEZu7o 我们很期待进一步推进 Magnitude，让你能够在相同硬件上运行更大的模型，同时持续提升性能。我们的计划包括： - 专家流式加载：将专家存储在 RAM 或磁盘中，并在需要时即时加载。这样，你就能运行原本无法装入 GPU 的更大模型。 - 内核编译器：目前，我们的内核会调优少量参数以适配你的硬件。借助完全定制的编译器，我们可以更进一步，自动选择内核融合方式和实现方案，从而让它更好地适配你的硬件。 - 多设备利用：通过检测系统中的所有硬件（CPU、GPU、RAM、磁盘），自动求解最佳模型布局，以尽可能充分地利用它们。 我们希望有更多人来试用并向我们提供反馈。欢迎在这里留言，我们一整天都会在线！ 评论链接： https://news.ycombinator.com/item?id=49911995 得分：5 评论数：0

DeepMind Blog78

推出 SynthID Bio

在保留生物学功能的同时为 AI 生成的蛋白质添加水印的概念验证。

Hacker News 熱門68

DevDay 2026 回顾

文章链接： https://openai.com/index/devday-2026-recap/ 评论链接： https://news.ycombinator.com/item?id=49896600 积分：12 评论数：1

Hacker News 熱門71

ChatGPT Pro 500

文章链接： https://help.openai.com/en/articles/9793128-about-chatgpt-pro-tiers 评论链接： https://news.ycombinator.com/item?id=49896975 得分：42 评论数：21

Hacker News 熱門73

Claude 部分服务中断

文章链接： https://status.claude.com/incidents/4xvtc2gnq73l 评论链接： https://news.ycombinator.com/item?id=49893876 得分：30 评论数：9

OpenAI Blog54

Are you a Codex Original?

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Hacker News 熱門70

Launch HN：Vespper (YC F24) – SOTA Docx MCP

Hey HN！我们是来自 Vespper（ https://vespper.com ）的 Dudu 和 Topaz。Vespper 是一个 MCP，可让 AI agents 高效编辑 Word 文档，由我们微调的模型驱动。目前它比最接近的替代方案快 3 倍、便宜 2 倍，而且更准确。可以在这里查看产品工作方式概览： https://youtu.be/odKxsgPjzzw 我们是在为制药公司构建 AI 文档编辑器一年后，开始研究这个问题的。在那之前，Topaz（我本人）是 Snyk 的高级 SWE，负责分布式系统；Dudu 是 Viz.ai 的深度学习工程师，构建用于中风检测的计算机视觉模型。 我们的编辑器帮助制药公司生成监管文档（例如 CSR），以加快其提交流程。最初，输出是 Markdown，并显示在 WYSIWYG 编辑器中。然而，用户更愿意使用自己的 Word 模板。这就是问题开始的地方。 AI agents 并不擅长编辑 Word 文档。一个 Word 文档是一个由冗长 XML 文件组成的 zip 文件，遵循 OOXML 规范。即使是“很小”的改动也需要大费周章，例如：添加编号列表需要在 numbering.xml 中用一个新的 ID 创建条目，并在 document.xml 中把它链接回去；加粗一句话需要把它拆分成 3 个以上的 run 元素。类似问题还有很多。 这使得直接编辑 zip（unzip + grep + sed）对 agents 来说是个坏主意，因为它们会在这些机制上消耗大量时间和 tokens。 在实践中，今天的工具大致分为三类。你可以让 agent 针对 python-docx 或 Open XML SDK 这类底层库编写代码；你可以给它一个带有主观编辑工具的 MCP（SuperDoc、Office CLI、Adeu 等）；或者你可以用 pandoc/mammoth.js 之类的工具把文件在 Markdown/HTML 之间来回转换。 它们都不太行。前两类仍然会把 agent 的上下文消耗在 Word 机制上，而不是手头任务上（MCPs 还会引入一种需要学习的新 DSL）；第三类则损失很大（pandoc/mammoth.js/等无法保留足够的保真度）。 根据我们的亲身经验，这些问题会损害下游任务的性能。 当我们尝试让 agent 填写大型文档时，事情很快就崩了。上下文窗口已经塞满了客户数据（文件、用户上下文、全局规则等），而 agent 又把 tokens 和时间消耗在探索文档以及调试失败的编辑上。填写一份 CSR（临床研究报告）大约需要 50 分钟，而且结果很糟（漏掉字段/章节、样式损坏等）。Harvey.ai 的团队也得出了类似结论： https://www.harvey.ai/blog/building-an-agent-for-complex-doc... 就在那时，我们转移了关注点。我们设计了一个 MCP，让 agents 像编辑 HTML 一样编辑 Word 文档。agent 接收 HTML，进行查找和替换编辑，然后我们把这些编辑协调回原始 .docx 文件中。 我们选择 HTML 而不是 Markdown，是因为它在结构上与 OOXML 更接近，而且 CSS 将样式与元素关联的方式大致类似于 OOXML。我们不得不编写自己的 DOCX→HTML 转换器，因为 pandoc 和 mammoth.js 无法保留足够的保真度。 需要说明的是，我们的 DOCX → HTML 转换也有损。但这没关系，因为我们从不把 HTML 再转换回 DOCX。HTML 只是给 agent 的一个投影，因此它只需要具备足够的保真度，让 agent 理解正在编辑内容的结构和样式。原始文件始终是真实来源，我们会原地修改它。 这也意味着 agent 不需要学习新的 DSL。编辑 Word 文档感觉就像在文件系统上编辑 HTML 文件一样，而这是 agents 已经很擅长的事情。许多 DOCX MCPs 会一次性交给 agents 几十个甚至上百个工具，让它们临场摸索。我们的 MCP 只暴露三个工具（read、search、edit）。Word 文档被完全抽象掉了。 在 agent 向我们发送编辑请求（一个“old_html”和“new_html”对）后，我们会将其协调到原始 .docx 文件中。这个协调过程由我们微调的模型驱动，它是一个 3-8B base，带有 LoRA adapter。它以 HTML diff 以及原始的局部 OOXML block 作为输入，并输出新的 OOXML。“定位”是确定性完成的：我们获取 agent 指定的锚点，并尝试找到它们对应的 XML 双胞胎，因此协调模型只承担单一职责。 在这个狭窄任务上，一个小模型可以达到经过良好 prompt 调优的前沿模型水平，同时又足够小、足够快，可以放在热路径中。 这个项目最终变成了数月的工作，但我们很高兴发布 v1。我们的内部基准显示，它比 DOCX skill 和原始 python-docx 更准确，同时便宜约 2 倍、快约 3 倍，主要原因是它每个任务只需要 3 次工具调用（p50），而 DOCX skill 需要 10 次，Office CLI 需要 13 次。 目前还不完美。例如，我们暂时不支持操作图片或评论。尽管如此，我们已经看到人们以各种方式使用我们的 MCP： - 法律科技公司用它驱动其在 Office.js 中的实时编辑流程。 - AI startup 用它优化人们的简历，并代表他们投递申请。 - 需要起草政策备忘录的 govtech。 - 一家生命科学 startup 使用长时间运行的 agents 来完成监管表格。 关于隐私的一点说明：我们的 MCP 运行在云端，因此用户会把他们的 .docx 文件发送给我们。我们不会用用户数据进行训练，团队也可以选择 ZDR 或 self-hosting。 我们有一个免费层级，每月 500 次编辑，也很希望大家都来试试。我们以后想提高这个额度，但我们是个小团队，运行微调模型并不便宜 :( 我们很想听听你们关于 docx 编辑的想法和评论！接下来几个小时我们会在评论区回复 评论 URL： https://news.ycombinator.com/item?id=49881505 Points：6 # 评论：2

Hacker News 熱門65

Show HN：HN.watch – 所有 Hacker News 帖子的视频

大家好 HN，我是 Per，Scrimba（YC S20）的创始人。在过去十年里，我们一直用一种基于 HTML 的视频格式教人们编程。现在我们把 LLM 接入了其中，让人们可以创建关于任何内容的讲解视频。它叫做 “Scrimba Explain”。 为了向 Hacker News 演示这项技术，我们构建了 HN.watch。它就像 HN，但用讲解视频代替文章。我们会在有人第一次点击某个链接时即时创建这些视频。 虽然使用 HTML 而不是扩散模型有明显的视觉缺点，但它有三大优势：- 速度：比基于像素的视频生成快得多（从点击到播放只需几秒）- 成本：我们每个视频的成本约为 0.04 美元。（不包括某些视频会使用的图像生成；这会很快推高成本）- 易于编辑：上述优势也让 AI 辅助编辑变得便宜且快速 我们的假设是，如果视频创作从“几美元和几分钟”变成“几美分和几秒钟”，就会解锁一批新的使用场景。以下是我们已经看到的一些：- 为每一个 Pull Request 提供视频讲解（我们内部就是这样做的）- 为你的内部/外部文档中的每个页面配一个视频 - 在约 4 秒内把一篇复杂文章变成视频（通过我们的 Chrome 扩展）- 课程创作者可以在录制正式内容前快速起草课程 - 人们还会创作很多个人内容，比如给孩子的故事、婚礼邀请、生日等 这个技术栈基于一种开源编程语言（Imba），由我们的 CTO Sindre Aarsæther 创建。它会编译成 JavaScript，因此能与 npm + node 生态系统完全互操作。你可以在这里了解更多： https://imba.io/ 我们还构建了自己的同步引擎（OP），以及一个用于代理的上下文管理系统（Q）。我们原本担心这会让 LLM 在为我们写代码时遇到困难，因为这两者都不在它们的训练数据中（其中 Imba 的数据也很少）。然而，我们惊喜地发现 LLM 实际上非常擅长我们的技术栈。这可能是因为这个技术栈极其紧凑。Imba 很简洁，OP 也是如此，一个声明就能设置存储、同步、权限、UI，以及 AI 能看到的内容。这意味着前端、API、数据库和 JSON 之间不需要相互转换，模型也就不容易混淆和出错。 简单来说，我们没有使用 React.js、Express、Supabase 和 LangChain，而是从零开始全部自己构建。绝对是深受“非我发明”综合征之苦，哈哈！至于模型，我们使用 Gemini、GPTs、Inworld、ElevenLabs 以及其他一些模型。 如果你想试试，可以任选一种：- Web UI（scrimba.com/explain）- MCP（添加到你的编码代理中）- ChatGPT Plugin - Chrome Extension 你可以在我们的文档中找到以上所有内容的链接： https://docs.scrimba.com/explain/introduction 最后，这是该工具的一个真正基于像素的视频： https://www.youtube.com/watch?v=k6rbHmBxSEs 很想听听你的反馈，也想知道是否有人对其他使用场景有想法。 PS：考虑到 HN 群体有多偏爱文本，我预计这次发布会在 HN 上遇到相当多的反对意见。这类工具并不适合所有人。但如今确实有很多人更喜欢视频而不是文本，尤其是年轻一代。 评论 URL： https://news.ycombinator.com/item?id=49879401 积分：18 评论数：8

Hacker News 熱門30

Show HN: Panda, the world's first personal AI computer

We're building an AI computer for consumers, most of whom are quite hesitant with existing services due to privacy concerns and because of costs that add up when using various AI tools. Comments URL: https://news.ycombinator.com/item?id=49872550 Points: 3 # Comments: 1

Hacker News 熱門47

Imp is a full port of DSPy to the BEAM

https://hex.pm/packages/imp https://dspy.ai/current/ Comments URL: https://news.ycombinator.com/item?id=49869995 Points: 5 # Comments: 2

Hacker News 熱門37

Show HN: TinyAIArena watch AI agents battle it out

Did you ever click on an “AI Arena” expecting glorious battle and instead get a boring benchmark? If so, this project is for you: proper life-or-death fights between four models on a picturesque 8×8 grid. May the most intelligent one win! Click on any of the matches to spectate them. Code: https://github.com/hp6/ai-arena Comments URL: https://news.ycombinator.com/item?id=49867775 Points: 8 # Comments: 2

Hacker News 熱門71

OpenAI Codex 智能体失控，未经授权消耗 78,000 美元

我的 OpenAI CODEX 账户失控了，从一个简单请求开始，自主决定在未经我任何授权的情况下启动 826 个并行智能体/线程，而且没有报告任何形式的结果，却消耗了近 2,146 万亿个 token，总计消耗约 78,000 美元，并删除了所有关于所执行操作的记录：我已向 OpenAI 提交工单 2 周，但根本无法联系到人工客服。 2026 年 7 月 10 日，我从 VS Code 打开了一个普通的 Codex 任务。 该任务运行的是：GPT-5.5 / Medium reasoning 我的提示非常简单，只是要求对我产品中某个特定模块进行 UX/UI 验证。 经过艰难分析，我在接下来几天发现的是： Root ID 为 019f4b90-4169-7201-bfdd-732940d8631e、reasoning 为 GPT-5.5 / Medium 的任务创建了 826 个子任务，记录为 GPT-5.6 Sol / Ultra（注意 reasoning 级别和模型选择的差异） 这不是一次对话中的 826 条消息，而是 826 条不同的子任务记录，并且都有各自的 ID。 其中有一组特别奇怪，包含 104 个子任务。它们都保留了与原始任务相同的初始消息，记录为 GPT-5.6 Sol/Ultra，并且没有记录 agent_role 或 agent_path。 仅这 104 个任务就约占 1479 亿个本地最终任务 token 计数器。 它们的标题显示，我要求检查 UI/UX 的请求已经扩展为涉及后端基础设施、OAuth、计量、加固、审计、认证、实现和发布的工作。 准确地说：这些本地 token 计数器并不是 OpenAI 权威的计费账本，我也不是在声称 1479 亿个本地计数器可以简单地乘以 API 价格。 这正是问题的一部分：只有 OpenAI 拥有服务器端映射。 还有另一个异常相关性。 在 Codex client build 0.144.0-alpha.4 下，该任务族包含： 584 个子任务 / 约 1543.6 亿个本地 token 计数器 平均：每个任务约 2.643 亿 在 0.144.2 下： 242 个子任务 / 约 75.1 亿 平均：每个任务约 3100 万 这意味着每个子任务的平均本地 token 量大约相差 8.5 倍。 上述 104 个高容量任务中有 103 个是在记录为 0.144.0-alpha.4 时创建的。 这让我相信 alpha build 存在严重 bug，因为在其他几个任务中也观察到了相同模式。 在财务方面，我重建的 OpenAI 账单历史包含 162 张已支付发票，总额为 $79,664.88，分布在 Automatic Reload 和其他 “Credits” 之间。 当时没有等效的实时控制界面能让我清楚了解支出情况，而且大多数日志似乎已从我的服务器上自动删除：在恢复出的本地状态中，大约 2,550 个未归档的旧线程仍有元数据，但本地没有对应的原始 rollout。 换句话说，仍然有证据表明这些任务曾经存在，而用于重建生成其中许多任务的指令所需的详细执行历史，在我的机器上已经不再可用。 我还亲眼看到任务/对话从正常可见历史中消失。 我联系了 OpenAI Support，并开启了案件 #15189838。 我已提供技术证据，并多次要求进行服务器端重建，但 OpenAI 只是回复说“credits were consumed”，没有任何细节。 我想听听其他在 7 月/8 月左右使用 Codex 的人的情况：你们检查过自己的本地 Codex 状态吗？你们是否见过异常庞大的 subagent 树、模型/reasoning 升级、重复子任务，或无法解释的 Automatic Reload 活动？ 我尤其想听到任何拥有 Codex 0.144.0-alpha.4 日志的人提供信息。 如果 OpenAI 工程师看到这篇文章，我也欢迎给出技术解释。 评论 URL： https://news.ycombinator.com/item?id=49861047 点数：7 评论数：0

Hacker News 熱門47

Show HN: A Claude Code skill to analyze your chess games

Hello HN, It started as an experiment: can Claude play chess properly if it uses vision instead of PGN notation? Somehow it can. The next experiment was to see whether Claude + Stockfish could explain a game. Somehow it can too. A few sessions later, I had a system that takes my live audio notes (or text, for that matter) and a vague instruction like "analyze my last lichess game", and gives me a commented video of the game. The result is not perfect and it takes time to deliver (an hour or so), but for me it is a much more pleasant and memorable experience than clicking around Stockfish branches. It burns tokens, so make sure you have enough quota. From the session logs, the last analyzed game would have cost around $15 at API prices. The fact that it reflects on my own thinking during the game makes it interesting from a teaching point of view, so I thought it was worth sharing. Comments URL: https://news.ycombinator.com/item?id=49857528 Points: 9 # Comments: 2

Hacker News 熱門52

Show HN: Ekselio – Loveable for finance workflows (local first)

Hi everyone, I am KD - Back in my college days, I dabbled with coding, learned the basics, HTML, CSS etc. but somehow I ended up in Finance which consumed the next 20 years. Then, during covid I picked up coding again, learned react, typescript, etc - even built a rudimentary site - and then came the chatgpt moment, followed by Claude etc. So, as a side project, considering that I had spent 20 years in finance and M&A I started building Ekselio, loveable for finance workflows. Differently from other vibe coding tools, this is local first - meaning the workflows are orchestrated by the LLM based on the file schema but then the execution happens in the browser - the cool thing is that you can see the canvas, the previews of each node, and the code base (SQl) - see the Canvas tab. That said I did include a home tab, which is more of a visual, mainly because people think it's easier to relate to how folks operate, although I know finance and accounting folks usually like the workflow and full transparency - the other thing is that once you create a workflow you can save it and then next month,you can just click Run and it runs it again with no LLM tokes involved - so the infrastructure is minimal. Everything can be exported in an excel data pack with M code in case someone would want to reproduce - think Audit ready. And, I also connected to quickbooks on line so someone can directly pull in and manipulate their financials live - I also connected to Fred which carries literally 800k series in terms of macro economic data - i.e. oil prices, inflation, CPI etc and finally to stocks. I am hoping people find holes in the architecture or any ideas to make this flow a little bit better. It is in progress so if you run into bugs let me know. Considering that I am using my own API key, I have put a wall in case people overuse it but DM me if you would like to continue using, I can turn off the wall for you - hoping to keep that to a small number of users if I can and will do my best. Thank you again, appreciate you taking the time. Comments URL: https://news.ycombinator.com/item?id=49849986 Points: 4 # Comments: 0

Hacker News 熱門57

Tell HN: Codex Is Down

Shows an Incorrect API Key error...which is weird. Nothing on the status page. Per Twitter/X this appears to be widespread Comments URL: https://news.ycombinator.com/item?id=49851032 Points: 34 # Comments: 13

Hacker News 熱門70

Codex 问题 – 已确定 – 全面中断

文章 URL： https://status.openai.com/incidents/01M3DCNWMW57HYK8FJ5FBFPA39 评论 URL： https://news.ycombinator.com/item?id=49851169 点数：18 评论数：7

Hacker News 熱門65

展示 HN：Jev 游玩 Pokémon Red

嗨，HN！想和大家分享一个我最近一直在折腾的有趣项目。鉴于 Jev 做决定的速度非常快（但遗憾的是，还没快到能玩 Doom 的程度），我想试着让它玩一款比 Tetris 更复杂的游戏。所以我选择了 Pokémon。 我小时候花了无数时间玩这款游戏，因此开发这个项目非常有趣。 我把所有内容都开源了，如果你想自己动手折腾，可以访问这里： https://github.com/christianmat/jev-pokemon 游戏正在进行直播，包括 token 和费用——希望我们能拿到所有徽章，不会被困在洞穴里 :) 评论链接： https://news.ycombinator.com/item?id=49845172 分数：40 评论数：23

Hacker News 熱門47

Show HN: Doom or Bloom, map your AI worldview with Jev

Map your AI worldview by answering a few questions, and see how you compare with others. only takes a few minutes && free & open source && private by default && powered by Jev I think this is a really important question for everyone to be asking themselves, and my hope is that this lil project helps to move our conversations around AI futures in a more balanced, productive direction. Comments URL: https://news.ycombinator.com/item?id=49846953 Points: 4 # Comments: 1

Hacker News 熱門57

Show HN: Agentic CUDA Kernel Optimizer

Hello; I was working on optimizing some CUDA kernels and I thought may be it is a good oppurtunity learn langgraph as well. I created a simple C++ CUDA Test Harness and handed that to AI agents. They can run kernels, get benchmarks, and even can profile via nsight Comments URL: https://news.ycombinator.com/item?id=49842596 Points: 4 # Comments: 0

Hacker News 熱門77

Show HN：Critic——与编写代码的智能体一起审查代码

各位 HN 用户好，我是 Feyn 的 Shreyash。我们帮助企业利用其数据构建定制模型。今天我们发布了 Critic，这是一个变更审查平台，让你可以直接与编写代码的 AI 交流。 智能体编写了我们的大部分代码。虽然这提高了我们的生产力，但理解一项变更及其后果已经变得极其困难。随着公司采用越来越多的智能体工具，我们发现，让人们了解某个 PR 的影响和项目状态变得更加困难。于是我们构建了 Critic 来解决这个问题。 Critic 可以让 AI 智能体介绍其代码、为关键代码块添加注释，并提供相关证据（例如屏幕截图以及如何在本地运行的说明）。任何查看该变更的人都可以直接与编写它的智能体交流。你可以 在这里观看演示 。 Critic 通过 Codex 或 Claude Code 的插件运行。当你的智能体编写代码时，插件会要求它同时撰写一份叙述，介绍这项变更背后的故事，并突出显示其中的假设、决策或复杂代码。随后，你可以在 critic.run 上查看这项变更，或者让你的智能体通过内置的 MCP 获取它。Critic 中的每项功能都可以通过 MCP 使用，因此你的智能体无需离开其运行环境，就能获得完全相同的上下文。 你可以通过控制面板或 MCP 直接与负责编写代码的智能体聊天。当 Critic 收到问题时，它会分叉出编写代码时的会话，并将你的问题转发到该会话中。这样，你的主线程可以保持整洁并继续工作。 本地变更只有所有者可见。推送到 GitHub 的任何内容都会被镜像，并对在 GitHub 上拥有查看该变更权限的所有人可见。 构建 Critic 时，安全性是我们的首要考虑事项之一。AI 智能体通常会以自动模式运行，并且可以在你的计算机上执行许多操作。我们希望确保，让他人访问你的智能体并不意味着同时让他们访问你的计算机。所有 Critic 分叉出的会话都会移除写入工具。它们只能回答有关该工作区中的变更和进行中工作的相关问题。 Critic 帮助我们在几起事件进入生产环境之前发现了它们。最常见的问题是模型没有复用已有的资源，例如现有的样式、组件或函数。由于深入了解智能体的工作并向它提问变得非常容易，我们还发现了更多模型误诊问题或编写无用代码的情况。由于 Critic 的界面比 GitHub 更快捷、更适合智能体使用，我们的团队也能更快地回复反馈。 Critic 可免费使用。请在 https://critic.run 注册。如果你有任何反馈或问题，请告诉我。 评论地址： https://news.ycombinator.com/item?id=49834098 得分：5 评论数：1

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Show HN: Radix – Visual UI for agentic programming

Hey HN, I'm Jordan from Radix. Radix is a UI tool for programming agents. You prompt your agent to generate a workspace for a task you're working on and get an interactive widget that persists locally on disk. I built Radix because I always needed tools while I was writing code: tools to test little experiments, to play around with UI, to visualise results etc. Sometimes these would end up as python tools that read and plotted data, or separate React apps spun up just to test a single feature. Lately obviously I've been using Claude artifacts but these are quite limited. I wanted a system where it was easy to leave comments or adapt an artifact without having to give vague descriptions back to the agent, and where ideally I could actually shift away from a chat window as the main interface. I've got a pro version of Radix which I will launch soon. This current version is entirely free and is definitely a beta -- there will be rough edges! Note: There's no telemetry or data captured. Your messages run entirely through your own agent. I require a key but that's just to get an idea of how many people are using it. Everything is stored locally (the workspaces are actually just React apps which you can edit "artisanally" if you want). If you have any questions please reply here or email me hello@radix-os.com Thank you! Comments URL: https://news.ycombinator.com/item?id=49834964 Points: 5 # Comments: 4

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Show HN：AgentRun：将 agents 转化为工作流的 DSL

嗨，HN 用户们： 我刚刚开源了我们在 grep.ai 中使用的 harness 所采用的 DSL，它可以将 agents 工作中可重复的部分转化为工作流。你可以组合使用工具调用、代码、由 Jev 驱动的系统 1 决策（用于路由和证据筛选等任务），以及在某个步骤需要进一步调查时调用 agents。 我们的 harness 会利用 agents 执行任务时留下的 traces 和复盘笔记，找出其中哪些部分可以变成工作流。这样做的目的是让工作更容易理解，并避免在不需要完整 agent 循环的地方为其付费。 例如，一个研究工作流可以将问题拆分成多个子问题，派遣 agents 并行开展研究，使用 Jev 筛选证据，再让另一个 agent 撰写报告。你可以检查各个步骤，单独评估证据筛选过程，或者更换某个 agent，而无需重建整个流程。 DSL 和示例都在我们的 GitHub 中。你可以运行一个无需 API 密钥的脚本化演示： https://github.com/Parcha-ai/agentrun 你也可以将其作为 Pi 扩展，用于构建、检查和运行工作流： https://github.com/Parcha-ai/agentrun#use-it-in-pi 如果这对其他人也有用，我很希望听听大家的意见。 这段视频中介绍了 AgentRun 工作原理的更多背景： https://www.youtube.com/watch?v=vOVhtGjtwpg 。你也可以在这篇文章中了解我们的使用场景： https://x.com/MiguelriosEN/status/2101029313906987422 。 评论链接： https://news.ycombinator.com/item?id=49821438 得分：15 评论数：0

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Show HN：Whiteboard（YC W26）——一款用于深思熟虑的软件设计的开源 IDE

大家好！我们是 Sid、Alex、Ketan 和 Milan。我们正在构建 Whiteboard，这是一款开源桌面应用，让人类和代理能够在一个共享工作区中共同设计软件架构。这是我们的代码仓库： https://github.com/devdotfast/whiteboard ，主页是： https://whiteboard.dev.fast/ 我们怀念与另一位开发者进行“白板会议”时的那种感觉——离开时已经对一个系统有了深入理解。因此，我们为自己构建了这个应用。Whiteboard 可以接入你已经在使用的工具，例如 Claude Code、Codex 等，并为你的代理提供一个 SDK，让它能够在应用内画布上绘图，以描述自己的工作。我们最初基于 HTML artifacts 构建了一个 MVP，后来随着遇到各种限制，我们开始重新思考这个应用： 1. 构建在 CodeOSS 之上：我们发现，在纯 HTML 工具中，很难将规范或图表与代码关联起来。在 Whiteboard 中，当你点击序列图、实体关系图或代理轨迹中的一段引文等可视化内容时，可以直接跳转到底层代码。在浏览代码时，你可以开箱即用地获得 VSCode 的快捷键绑定和 LSP 支持。我们发现这一点尤其有价值，因为许多权衡往往只有在首次实现之后才会显现出来（参见：slop） 2. 语义差异查看器：我们用 Rust 编写了一个支持语义分析和 AST 的差异查看器，因此你只需查看与你相关的代码变更 [1]。我们设置了一些合理的默认行为：较大的新增函数会以伪代码形式进行概述，单元测试和大段文档变更等内容则会被折叠或隐藏。这些设置都可以通过基于 WASM 的插件系统进行自定义。 3. 决策日志：我们发现，很难推理代理自主做出了一组什么样的决策。因此，我们构建了一些工具，让代理能够查询自己的轨迹并将其链接到 Whiteboard，这样你就能了解你设定的需求是如何被实现的，以及你的代理自主做出了哪些决策。 这里有一段简短的演示视频，可以进一步了解相关内容： https://youtu.be/ChPn3ftULWE Salesforce 和 Modal 等公司的员工如今正在使用 Whiteboard，作为架构或规范级变更的审查工具——实际上，任何他们希望参与其中的变更都可以使用： 1. 审查你自己的编码代理所完成的工作：由于 Whiteboard 让审查大量代码变得更加容易，人们通常会让 AI 代理创建一个原型以及相应的 Whiteboard 会话，然后据此迭代设计。 2. 审查他人的变更：我们发现，Whiteboard 与 Greptile 等工具结合使用时尤其有帮助。例如，你可以对小型变更运行自动化代码审查，而对于需要人工判断的变更，则升级为 Whiteboard 会话。 我们为什么构建它：我们是四个大学时期的好友，在代理式编码成为行业标准之前不久，辞去了技术负责人的工作。在为之前的一个想法迭代 MVP 时，我们努力想在享受代理式编码带来的全部效率提升的同时，维持一个易于理解的代码库。随着越来越多的 PR 在我们并未理解的情况下被合并，我们感觉一种“认知债务”开始逐渐渗入，直到我们甚至难以继续为这个系统做贡献 [2]。 我们将以 MIT 许可证发布我们的桌面应用。欢迎深入了解，也欢迎贡献代码！最终，我们会向企业收取托管版 Web 服务的费用，用于管理 Whiteboard 会话的创建，并提供轨迹存储和多人审查等功能。一切始终都会支持自行托管。 感谢阅读，希望你能试用一下！我们非常期待听到你的反馈，也希望向各位专家学习。 再次附上项目链接： https://github.com/devdotfast/whiteboard ；你可以在这里安装适用于 MacOS 和 Linux 的版本： https://install.dev.fast [1] diffs 库： https://github.com/devdotfast/diffr [2] “认知债务”这一术语的出处归于 https://www.geoffreylitt.com/2026/07/02/understanding-is-the... 评论链接： https://news.ycombinator.com/item?id=49833867 得分：16 评论数：0

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Meta VR Glasses

文章网址： https://www.meta.com/vr-glasses/ 评论网址： https://news.ycombinator.com/item?id=49824268 得分：33 评论数：11

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Stripe 构建了其内部 AI 平台

文章链接： https://stripe.dev/blog/meet-stripes-knowledge-ai-platform 评论链接： https://news.ycombinator.com/item?id=49815982 得分：9 评论数：0

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Show HN: RxFilm Studio–Create and edit your product videos with AI agent

Hi HN, I built RxFilm Studio, a video workspace that lets you create and revise videos with AI agent. The reason I started this project is simple: I’ve noticed that many independent developers and small teams want to create marketing videos for their product, but producing these videos requires a lot of time and effort — from editing and visual effects to voiceovers and subtitles. So I built Rxfilm Studio to simplify the entire video creation process. Start with an idea, a product website, images, or existing footage, and then generate a complete video with: - Story and structure - Editing and timeline - Visual and sound effects - Voiceovers, background music - Subtitles and translation Github: https://github.com/rxtech-lab/film-workflow Here's a demo marketing video created using RxFilm Studio: https://youtu.be/9_M15fRdZQE Welcome to try it out and I would love to hear your feedback! Comments URL: https://news.ycombinator.com/item?id=49815095 Points: 5 # Comments: 1

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Claude Code 仅在启用遥测时读取 AGENTS.md

文章网址： https://blog.szypowi.cz/p/claude-code-reads-agents.md-only-when-telemetry-is-on/ 评论网址： https://news.ycombinator.com/item?id=49814947 积分：10 # 评论数：1

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Unreal Agent

https://github.com/unreallabsai/unreal-agent Comments URL: https://news.ycombinator.com/item?id=49805748 Points: 6 # Comments: 1

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Launch HN: Coverage Cat (YC S22) – Umbrella insurance via your personal agent

Hey HN! We’re Max and Gabriel the co-founders of Coverage Cat. We’ve been friends for over a decade, met in college then hung out mostly on the internet. We love building products that help people optimize the crufty corners of their lives. Max is a former Google/Microsoft/Two Sigma PM and Gabriel has been working in startups for over a decade. We’re building Coverage Cat: an AI-native, you can shop with your AI agent or via a form, insurance brokerage for consumers. Our current focus is helping tech folks (think L3-L8) buy umbrella insurance. For those who are unfamiliar with this type of coverage, here’s a blurb from one of our insurance agents: “Umbrella liability insurance is a policy that provides additional coverage above the limits of existing auto, homeowners, renters, or landlord policies. It kicks in after the underlying policy limits are exhausted and may also cover personal injury claims like libel, slander, or defamation that standard policies often exclude. Policies typically start at $1 million in additional liability coverage and can protect you, your spouse, dependents, and even pets in your household.” Buying insurance online is a miserable experience. You fill out a bunch of forms that say they’re going to give you quotes. Most don’t and then you end up with tons of unwanted phone calls and spam. Even if you wade through the muck, you’ll often still end up with sub-optimal prices and coverage because of a lack of market transparency and poorly-aligned advisory incentives. We came to this problem because Max is a classic personal finance obsessive. He's the type of person that needs to know that he has the best possible price for something (everyone has one friend with the patience for a two hour phone call with the bank for some $10 fee) or that his insurance coverage is as tailored to his risk profile as the market will allow. He actually reads insurance policies end to end. Years ago, when he shopped for his homeowners insurance it took him 10+ phone calls and dozens of online form fills to find the right deal. He also suffered tons of collateral damage in the process: websites sold his information to dozens of agents who proceeded to call, text, and email him a spam hoard that grows to this day. After this painful search made us aware of the problem, we also spent a lot of time doing user research with wealthy-ish tech employees to see if they felt the same way. They did, and also complained that they 1) often felt like they'd left money on the table, 2) didn’t really understand their coverages, and 3) that brokers were slow, hard to communicate with, and unreliable. To solve the problem, we set out to build Coverage Cat. Our approach has three major components: First, we work really really hard to find and partner with insurance carriers that offer high-quality, competitively-priced products. We've also taken time to identify insurers that work on a direct-to-consumer basis or transparently list their prices online so we can make our customers aware of all the options available to them. We get paid commission when we match users to great insurance deals, but we still show and recommend folks deals that we don't make money on when they're accessible and will always recommend users buy what's best for their personal financial situation. Our goal is to meaningfully improve price transparency so consumers (and their AI agents) can make informed coverage decisions and not get nickel-and-dimed! Second, we automate every possible part of the insurance process. Some forms, portals, & approvals still need human review due to regulatory constraints, but if it's not restricted, we're automating it. LLMs have given our small team tremendous leverage and enabled us to tackle problems at a scale that would've been unfathomable five years ago. Finally, we build for agents first (a remix of the more classic adage, build for developers/APIs). We've always been big believers that personal finance would be the killer use-case for the AI revolution and have done our best to position ourselves to surf the wave. As personal assistants have started to explode onto the mainstream (think Muse, Instinct, Town, Codex, CC etc.) we've ensured our shopping and comparison process works via API/MCP so agents can present their users with all the information they need to make a good decision and transform a miserable shopping experience into a delightful one. Coverage Cat is an unusual business because, while we also help folks find homeowners insurance in California and Texas, our main focus is on umbrella insurance. Most brokerages only sell umbrella coverage as a customer retention product, but it makes them almost no money. We focus on it as our core business because: 1) it's one piece of the insurance puzzle that many folks in tech overlook and/or are confused about even though it can have a huge impact on their financial well-being. 2) Existing online tools and brokerages broadly don't make it easy to comparison shop for. 3) It was, for us, the most technically feasible candidate for automation given startup resource constraints. Insofar as we know Coverage Cat is the first instance of a tool/portal that allows AI agents to complete most of the comparison and shopping steps required to allow people to buy insurance. We’d love your feedback so please do try it out. Just feed the prompt: “Shop for umbrella insurance with Coverage Cat” to your personal agent and let us know what you think! If you want to watch a couple of agents navigate the flow before you try it out there’s a video here: https://youtu.be/1BUkgAn6s-I Also happy to answer any questions about the product, the problem space, and even some insurance questions (Max is a licensed agent) where regulation permits. Cheers! Comments URL: https://news.ycombinator.com/item?id=49804931 Points: 3 # Comments: 0

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Show HN: InstinctFlash – Run 5B world-action models in real time on Jetson Thor

Hey HN, Guanming here, cofounder of General Instinct. We just released InstinctFlash, a high-performance serving framework for robotics models. It’s licensed under AGPL-3.0. On Jetson Thor, we see speedups about 1.2x to 7.9x from runtime optimizations alone and up to 33.78x for LingBot-VA when we combine those runtime optimizations with a distilled few-step diffusion scheduler, going from the original 25 visual / 50 action steps to 2 / 4 steps. Across 50 Robotwin2.0 tasks, we evaluated 1,153 episodes per configuration, LingBot-VA with InstinctFlash at 2 visual / 4 action steps achieved a 90.5% success rate, compared with 92.1% for the baseline at 25 visual / 50 action steps. Here’s an optimized 5B world action model, running in real time on a Jetson Thor: https://youtu.be/nku65iyL5Fw InstinctFlash currently supports 8 VLA / world-action model families, including pi0.5 and NVIDIA Cosmos Policy, across RTX 4090 / 5090 and Jetson Thor. Just give it your fine-tuned checkpoint and InstinctFlash handles the rest, exposing the accelerated model through a Python runtime or an OpenPI-compatible WebSocket server. We started working on this because we kept running into the same problem while deploying robot policies, the models were getting much better, but inference was often way too slow for the control loop we actually wanted. For pi0.5, mixed-precision GEMMs and CUDA graphs speed up computation and reduce launch overhead. For Cosmos, caching avoids redundant computation across diffusion steps. World-action models’ diffusion denoising step depends on the previous one which motivated our work on few-step distillation. Right now, InstinctFlash contains 6 aspects of optimization. - Graph: CUDA graph capture, memory planning and separating prefill from repeated execution. - Cache: Reusing KV and conditioning state across diffusion steps and prediction calls. - Attention: Specialized attention paths for different model architectures. - Kernels: Fused operations and kernels tailored to specific backends and tensor layouts. - Precision: FP8 and mixed-precision execution. - Model: Few-step distillation for diffusion and action generation. Teams at Samsung, Siemens, and other robotics startups have used InstinctFlash for model acceleration on VLAs, WAMs, and diffusion-based world models. Now we are opening up access to you. Feel free to try it here: https://github.com/General-Instinct/InstinctFlash More implementation details and benchmarks: https://general-instinct.com/blog/instinctflash-edge-inferen... Would love to hear your feedback! Comments URL: https://news.ycombinator.com/item?id=49802789 Points: 4 # Comments: 0

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Show HN：AI·rete·RAG——Rete 规则引擎作出决策，RAG 解释原因

大家好，我构建了 ai·rete·rag，因为我不断看到一些团队让 LLM 负责那些需要可审计的决策（贷款、欺诈、临床分诊），然后事后再加上“护栏”。 这个项目改为让两者串联运行： 1. 纯 Python 实现的 Rete 引擎根据事实评估 YAML 规则。判定结果只来自这里。相同的事实，每次都得到相同的判定，并通过基于显著性的冲突解决机制处理冲突。 2. RAG 从你自己的政策文档中检索段落，然后由 LLM 撰写通俗易懂的决策解释，引用这些段落。它无法改变判定结果。 有几件事的发展超出了我的预期： - 规则是一个图，而不是扁平列表：支持嵌套的 all/any/not，并且规则可以断言供其他规则使用的事实（前向链接）。决策追踪会展示因果链。 - 审计模式会记录评估过的每一条规则，包括那些未触发的规则，逐条件记录，并保存规则集快照以便重放。 - 规则可以引导检索（触发的规则会缩小要搜索的文档范围），检索到的文本也可以转换为供引擎使用的事实。 - 非技术人员可以在可视化编辑器中构建规则，也可以粘贴政策文档，让 LLM 起草带引用的规则。未经审核，草稿绝不会被保存。工程师仍然可以使用 YAML。 落地页提供无需注册的在线演示（涵盖 8 个演示领域：贷款、欺诈、临床、保险、法律、运营、电子商务、区块链）。此外还有一个 MCP 服务器，因此 Claude 和其他代理可以将 /decide 作为工具调用：`uvx ai-rete-rag-mcp`。 坦率地说：这是一个提供免费套餐的托管产品。MCP 客户端采用 MIT 许可证并已开源（github.com/zaharajabeen13-create/ai-rete-rag-mcp）；引擎和平台目前并未开源。 我尤其想听听曾经需要向监管机构或审计人员解释自动化决策的人分享经验：他们实际要求了什么？ 评论链接： https://news.ycombinator.com/item?id=49803683 得分：6 评论数：0

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告诉 HN：Claude Code 未经询问就替我接受并签署了一份合同

我让它进一步推进一个项目。该项目有一个外部依赖项，而相关合同（我还没读过）就在我的 Gmail 里。它下载了 PDF 合同，在我的电脑上找到了一个保存的签名 PNG，将其放在合同中的正确位置，并准备在我介入时发送出去。 评论网址： https://news.ycombinator.com/item?id=49798257 积分：9 评论数：6

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Show HN: Foremerge – Catch Intent Conflicts Between Parallel Coding Agents

At, GPTree, we run several coding agents across our team on one repo using parallel worktrees. Apart from wasted time reviewing and fixing conflicts at PR time, the failures that hurt the most are when multiple plans or tickets cause architecture changes that cannot both be true. Ex. one agent replaces a class while another one is in the process of extending it. Git only notices if the resulting patches happen to touch the same lines and the review only catches it if they are familiar with both tickets. Foremerge is a local "git like" coordination layer that sits above git (ie. does not interact with or change the way git and worktrees function), Before editing each agent publishes an intent and the scopes it will change, with the operation it plans to complete on each one. foremerge intent publish --agent "$A" \ --summary "Replace PaymentService with StripePaymentService" \ --scope symbol:PaymentService=replace foremerge intent publish --agent "$B" \ --summary "Add PayPal support to PaymentService" \ --scope symbol:PaymentService=extend The publish by the 2nd agent returns a HIGH destructive_vs_additive finding before writing any code. Agents keep their own worktrees and the shared state is one SQLite file in gits common direectory. No hooks, no merge drivers, nothing rewrites your history. It ships as one Rust binary with a CLI and MCP server with 18 tools and `foremerge setup all` wires it into Claude Code, Codex and Cursor. Because the protocol has nothing provider specific, a Claude agent and a Codex agent coordinate through the same store. Before any work is accepted, Foremerge runs a named check that you configured against the exact git state of the change. An agent that says tests pass is recorded but it dosnt satisfy the acceptance gate without running the check itself. Detection is deterministic, no judge model reading your code. HIGH conflicts are only asserted for declared operations, ie. matches inferred from prose cap out below high. Claims are advisory leases, not locks so two agents can still hold the same scope without deadlock. The open source version is single matching and so not a distributed consensus. We have tested this up to 98 parallel agents all working on the same repo with zero conflicts (was supposed to be 100 but 2 agents failed to run due to resource limitations) I replayed 76 intents on my own agents from a build last week in the order they happened. The sample had exactly 1 conflict (which was flagged) and the review found a blind spot where one agent claimed scope by class name and the other claimed it by an internal method. We are working on fixing that for the next release. Setup is a 30s install by pasting the quickstart instructions from the readme.md into your agent or manually: `curl -fsSL https://foremerge.com/install.sh | sh` or `cargo install --locked foremerge`, then `foremerge init && foremerge setup all` in a repo. Apache-2.0. The feedback I want most is which conflicts between your agents plans would you actually want flagged and which would you tollerate as noise? Repo here: https://github.com/naw103/foremerge Website: https://foremerge.com More information on the problems this solves: https://foremerge.com/blog/ Comments URL: https://news.ycombinator.com/item?id=49789356 Points: 4 # Comments: 0

Hacker News 熱門64

Ask HN：在 macOS 27 上无法禁用 Siri 吗？

我在 Settings 应用中禁用了 Siri，随后又在 Settings > Screen Time 中进一步禁用了 Siri 及其扩展功能。 接着，我按照 https://github.com/OleksandrKrupko/mac-os-debloat 中的说明，禁用了所有与 Siri 和 Apple Intelligence 相关的服务。 但重启后，我仍然能在 Activity Monitor 中看到一个名为“Siri”的进程正在运行。经过进一步检查，我发现它实际上是“Siri AI.app”。 这些“功能”与 Apple 的 Personal Context 听起来与 Microsoft 的 Recall 非常相似。 有人遇到过同样的情况吗？在 macOS 27 上，真的不可能完全关闭 Siri、Siri AI、Apple Intelligence 以及所有相关的服务和功能吗？ 评论 URL： https://news.ycombinator.com/item?id=49786609 积分：30 评论数：13

Hacker News 熱門45

Show HN: Bitcoin-rs – An AI-assisted Bitcoin full node in Rust

I’m building bitcoin-rs, an independent Bitcoin full node in Rust, using AI aggressively for implementation. I think Bitcoin is a particularly good target for AI-era development because implementations can be verified against strong external references. In bitcoin-rs, I use libbitcoinkernel as one of the main compatibility oracles. Comments URL: https://news.ycombinator.com/item?id=49784873 Points: 8 # Comments: 2

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Show HN: I created an open source locally usable full fledged AI platform

hi to all the readers this post is for my recent opensource project called ENZO now answering what is enzo so enzo is an opensource platform where i clubbed all the free available api for anyone use under one hood with more than 2000 models available to use for chatting coding researching and much more now answering the most common question of why you should put your time looking the project so it has few distinct feature meaning it has a dedicated agents tab where you can describe your need and create a special agent just for one specific task with master ability in that domain second it has the ability to connect your gmail drive and calendar and then you can ask it to perform some specific tasks like reading you the most important mail of the day or finding recruiter mails and creating personalized reply based on your data which it stores locally on your device third the coding mode offers a dedicated preview window where you can see your code running and have a look of it feels and edit it in realtime as well as all the modes are packed with dedicated skills which delivers promising results fourth the ui features some additional things such as music tab where you can listen to any music want and it has a custom personalized feature which runs in background and an llm understands your taste and recommends similar kind of music you like fifth the most important why your trust it with your api key then to explain i would say enzo a dedicated vault which manages all your api and to secure it the vault as aes 256 bit encryption which prevents any person or any middle man to look at your api key and since the whole program runs locally on your device you have complete freedom to oversee all the backend work happening and it also features password lock which if you enable saves a backup key and then locks your whole platform work behind a pass screen though it is not foolproof as any third party or malware containing extension can still fetch login tokens from your browser so its security also depends upon how you access it concluding all of it. i urge to anyone who reads this to have a look at the platform even if you hate it just curse it in the comment its fine or if you would like to drop any feedback i would highly encourage that and since its my first work open source platform i know it has a lot of errors and bugs so i apologize upfront for it and if you consider my work worthy please drop a star on the repo that'll make my day Comments URL: https://news.ycombinator.com/item?id=49771118 Points: 5 # Comments: 1

Hacker News 熱門65

Grok Voice Transcribe 2.0

文章 URL： https://x.ai/news/grok-voice-transcribe-2 评论 URL： https://news.ycombinator.com/item?id=49757859 积分：5 评论数：1

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Waymo 在新加坡

文章 URL： https://waymo.com/waymo-in-singapore/ 评论 URL： https://news.ycombinator.com/item?id=49749981 积分：5 评论数：1

OpenAI Blog71

推出 Astra for Law

OpenAI for Law 为法律领域带来前沿智能、律所定制工作流、互联的法律数据源，以及用于机密客户事务的法律级控制措施。

Hacker News 熱門47

Show HN: Craigslist for agent skills, curated by a human

hey, i'm nick (@skeptrune on X). i have been using ai for a lot of things i'm not an expert in and thought it would be nice to try and buy paid skills for helping it accomplish those tasks more efficiently. for example, redlining contracts, creating ai generated videos, different website designs, and more. curious to see if this resonates with folks here. i figure a more engineering'y audience that's ai coding-agent forward would have similar problems to what inspired me to make this Comments URL: https://news.ycombinator.com/item?id=49743459 Points: 10 # Comments: 4

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Launch HN: Skillsync (YC W26) – AI chat sessions made portable across agents

Hey HN, we're Nars & Nishant, founders of Skillsync ( https://skillsync.com ) Skillsync lets you move your AI chats across every coding agent. Most of our work exists as conversations, which are currently scattered across our agents. Though stored locally, these conversations use different formats. This is annoying because you cannot simply switch between agents without starting over. We get locked into a single provider and their agent as we invest in skills and memories over time. Skillsync acts as a universal converter. It moves the entire session, including all the messages, reasoning and tool calls so you can pick up right where you left off. Skillsync collects all your sessions in one place and makes them searchable. It breaks down what each session is carrying, including which loaded skills the agent is actually using, making stale context easy to spot. You can also create shared workspaces to sync sessions across your team. You can build your own closed loop systems. Everything runs locally except when you share to workspaces. The core is an open-source Rust engine called txcript ( https://github.com/skillsynchq/txcript ). It translates a session from one agent's on-disk format into another's, mapping conversation, reasoning, and tool history. Think ffmpeg or pandoc, but for agent sessions. On top of that engine is a local-first desktop app. Your agent sessions are normally scattered across different tools' folders in formats you'd never read by hand; the app surfaces them in one place with a UI that makes them actually readable, the conversation, the reasoning, and the tool calls, so you can revisit what happened, move a session into another agent, or share it with a teammate. Skills and memory are stored as portable, human-readable markdown you own, and exposed to any agent over MCP for search and selective retrieval. Sessions and translation run locally on your machine. The one thing that leaves is what you explicitly share into a team workspace. It’s been surprising to us to see how locked-in people already are without realizing it. You don't notice you're trapped in one agent or harness until you try to leave, and by then you've got months of sessions stuck in a format only that tool can read. The lock-in is invisible right up until it's expensive. Once sessions are portable, they stop being disposable logs and become something you can actually build on, spotting patterns in how you work, handing a session off to someone else, letting a non-technical teammate pick up where an engineer left off. The session turns out to be the unit of collaboration, and right now it's being thrown away. Before Skillsync, Nars and I built an open-source payment orchestrator that let merchants route across many processors instead of getting locked into one (30k+ GitHub stars). Fighting vendor lock-in by making incompatible systems interoperate was the whole job. We came into YC with a different idea, but the more we lived inside coding agents the more we saw the same problem from the other side: your context is locked into whatever agent you start with, because every agent stores sessions differently and none of it moves. Some of the things we’re seeing people do with Skillsync are: - Moving a session between Claude Code, Codex, and Cursor mid-task, including when you hit a usage limit on one and want to keep going on another. - Thinking through a problem in a browser Claude chat, then handing the whole thread to Claude Code or Codex to build. - Sharing research sessions with a team. Everyone uploads their sessions, so teammates and their agents can build on each other's work instead of repeating it. It's available as a Mac app, CLI and MCP. Here's the demo: https://youtu.be/7hVhSnSKGl8 . Would love to hear your feedback and answer questions! Comments URL: https://news.ycombinator.com/item?id=49743049 Points: 4 # Comments: 0

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Show HN: Share your AI Setup, Learn from others

I kept seeing engineers share what they were building with AI; however, I was always more curious about how they worked. Which agents did they use? What skills and tools had stuck or been thrown out the window? How did they manage longer-running tasks? So I built this with the hope we could have a dedicated space to share and be open about our setups. Comments URL: https://news.ycombinator.com/item?id=49740105 Points: 16 # Comments: 6

Hacker News 熱門48

Show HN: Moadim.io – A scheduler for agents

Why can't we get an agent scheduler that supports all of the following: - git compatible - agent agnostic - 100% open source - os and system-agnostic - multi-runner support - support mcp/ui/http - unlimited routines/crons So I built one, moadim.io is a local Rust daemon you install in the target machine, give it a name and manage its routines via a Git repository, wants a new routine that send you a daily message from this machine? Create a pr and merge, have another routine that run every hour to pull the latest changes to the ~/.config/moadim folder. With more than 1,000 users, I define this project as almost "done" and ready for production. Me and thousand more people are use it in a daily manner. It currently supports Claude, Codex, Hermes, and Pi, and you are welcome to add your agent of choice as well because it's 100% configurable. You are welcome to have a look at the source code of the daemon in " http://github.com/moadim-io/daemon " Feel free to provide me with suggestions for more features around this topic. I don't want to branch out to new off road topics likt webhooks, this is a "done" software in the realm of agents schedulers that focus on cron-like work. Also feel free to start the github repository and open issues and PRs for your suggestions. Comments URL: https://news.ycombinator.com/item?id=49571537 Points: 3 # Comments: 0

Hacker News 熱門66

Launch HN: Mireye (YC S26) – 面向物理世界 AI 代理的基础设施

嗨，HN，我是 Ansh，Mireye 的创始人（ https://www.mireye.com ）。我正在构建 AI 代理用来对物理地点做决策的基础设施：面向任何美国地点的数据、增强、工具和信号，通过一个 API 和 MCP server 提供。 这里有一个演示视频： https://www.youtube.com/watch?v=haqO6UbUqU0 要试用的话，把 https://www.mireye.com/skills.md 粘贴到你的 agent 里，然后到 mireye.com 领取一个免费 key（5,000 credits，不需要信用卡）。文档在 https://docs.mireye.ai 。最快的上手方式是向 API 询问任何关于美国某个地点的问题。你可以拿一个你熟悉的地方来测试，先不知道答案，再根据你掌握的信息给它打分。 在 Mireye 之前，我一直在做建筑类 agents，自己也碰到了这堵墙：我的 agent 能推理任何网上的东西，却对脚下的土地一无所知。后来一家 Fortune 500 保险公司告诉我，他们的工程师也因为同样的原因放弃了 underwriting agents。前沿模型在被问到某个特定地点的具体问题时，仍然会胡编乱造。 我的第一个产品是一个小众的 site-screening 应用。客户拿他们熟悉的地点来测试，答案都站得住脚，但没人关心这个应用本身。他们想要的是底层引擎。使用情况也印证了这一点：目录中的 317 个字段里有 311 个都被查询过，而且没有哪个用例占绝对主导。所以我砍掉了应用，转而开始做基础设施。 Mireye 不是一个上面套了 API 的数据集，因为事实本身并不等于决策。一个 agent 会通过它完成整项工作：带引用的事实、把一个裸地址补全为所有者、英亩数、建筑物和附近供电信息的丰富数据、那些运营模型经常算错的工具，以及在某些情况发生变化时发出的信号，比如重新分区申请。数据、增强、工具、信号。 这些工具来自我观察 agent 失败的过程。我们在自己的基础设施上构建 agents，而同样的问题反复出现：agent 会凭肉眼估算距离而不是计算，给一个地址抓错地块，或者在批处理进行到一半时耗尽全部预算。每一种失败，最后都变成了 agent 可以调用的东西：确定性的几何和行车时间工具、地块解析、在任务运行前定价的 quote endpoint，以及把整套工作流打包起来的 skills，比如筛选一个地点或对一个地址做 underwriting。 最让我意外的是难点所在。每个来源都必须被采集（有时要按县逐个采集，而且要适配每个县发布的格式）、归一化到同一 schema、签订契约（它来自哪里、每个值意味着什么、多久刷新一次），然后还要永远保持最新。我们现在为 366 个字段运行这套循环，并通过一个索引以 multi-tenant 方式提供服务，而且每个来源的阻力都不同。Maryland 发布了一个数据集，标题就叫 “Hidden Property Owner Names.” 我在 North Carolina 提交了公共记录申请，因为没人索引 sewer mains。 更深层的问题是语义。两个县发布了同名字段，但含义不同。而最危险的值是 null。它是表示“这里没有 flood zone”，还是“这个县根本没绘制过 flood 数据”？把模型放到这个空缺面前，它就会用一个看起来合理的数字把沉默补上。所以我们把缺失也类型化了。每个字段都会返回 ok、absent 或 failed。客户告诉我，他们之所以信任它，正是因为这些拒绝。 最新的部分是按需索引。要是你请求一个我们还没有的字段，一个长期运行的 agent 就会去研究来源、收集数据、用 ground truth 做测试，然后把它编入索引，通常一天内就能完成。我会从这个帖子里挑几个字段请求，回头告诉大家它做了什么。 人们已经用它做出了我没预料到的东西：保险团队筛查 flood、wind 和 wildfire 风险的投资组合；一家 proptech 通过增强数据清洗杂乱的房源地址；一个 wellness brand 评估街角投放 poster spots 的得分；一家机器人公司寻找仓库；数据中心选址；无人机部署规划；某个城市的校车路线；以及人类贩运调查中的信号。我们的使命是把地球的每一寸都编入索引，让它像 web 一样可查询。 定价是公开的：免费层每月 5,000 credits，$19/月可得 25,000，$99/月可得 120,000，企业版定制。 我很想知道你正在构建的 agents 会在哪里接触物理世界。如果你能在一个你熟悉的地方运行 Mireye，并告诉我我们哪里做错了，我会更开心。 Comments URL: https://news.ycombinator.com/item?id=49552616 Points: 15 # Comments: 0

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发布 HN：Mireye (YC S26) – 物理世界 AI Agent 基础设施

大家好，HN，我是 Ansh，Mireye 的创始人。我正在构建 AI agents 用来对物理地点做决策的基础设施：针对任何美国地点的数据、增强、工具和信号，全部通过一个 API 和 MCP server 提供。 这里有一段演示视频： https://youtu.be/haqO6UbUqU0 要试用的话，把 https://www.mireye.com/skills.md 粘贴到你的 agent 里，然后到 mireye.com 领取一个免费 key（5,000 credits，无需信用卡）。文档在 https://docs.mireye.ai 。最快的上手方式是让 API 回答任何关于美国某个地点的问题。找一个你熟悉的地方来测试，把结果冷静地和你已知的信息对比打分。 在 Mireye 之前，我自己在做建筑 agents 的过程中也撞上了这堵墙：我的 agent 也许能推理任何线上信息，但对其脚下的地面一无所知。后来一家 Fortune 500 保险公司告诉我，他们的工程师也因为同样的原因放弃了核保 agents。前沿模型在被问到某个特定地点的具体问题时，仍然会胡编乱造。 我的第一个产品是一个小众的场地筛选应用。客户拿他们熟悉的地点来测试，答案表现不错，但没人关心这个应用本身。他们想要的是底层引擎。使用情况也印证了这一点：目录中 317 个字段里有 311 个被查询，而且没有哪一种用例占主导。所以我砍掉了应用，转而开始构建基础设施。 Mireye 不是一个上面套了 API 的数据集，因为事实本身并不等于决策。agent 通过它完成整个工作：带来源标注的事实、把一个普通地址增强成所有者、面积、建筑物和附近电力设施的地址信息、让运营模型容易出错的工具，以及当某些事情发生变化时的信号，比如重新分区申请。数据、增强、工具、信号。 这些工具来自对 agent 失败的观察。我们基于自己的基础设施构建 agent，而同样的事情不断出错：agent 会凭目测估算距离，而不是计算；会把地址对应到错误的地块；或者在批处理到一半时把全部预算耗光。每一种失败都变成了 agent 可以调用的东西：确定性的几何和行车时间工具、地块解析、在任务运行前给出价格的 quote endpoint，以及把完整工作流打包起来的 skills，比如场地筛选或地址核保。 最难的部分出乎我的意料。每个数据源都必须被采集进来（有时要按县逐个处理，而且每个县发布的格式都不同）、归一化到同一 schema、注明约束条件（它来自哪里、每个值是什么意思、多久刷新一次），然后永远保持最新。我们今天为 366 个字段运行这个循环，并通过一个索引以多租户方式提供服务，而且每个数据源的抵抗方式都不同。Maryland 发布了一个数据集，标题就叫“Hidden Property Owner Names”。我在 North Carolina 提交了公开记录请求，因为没人会索引下水管主管道。 更深层的问题是语义。两个县发布一个同名字段，但含义却不同。而最危险的值是 null。它的意思是“这里没有洪水区”，还是“这个县从来没绘制过洪水区”？把模型放在这个空白前，它就会用一个看似合理的数值把沉默填满。所以我们要为缺失建模。每个字段都会返回 ok、absent 或 failed。客户告诉我，正是这种拒绝回答的机制让他们信任它。 最新的部分是按需索引。如果你要一个我们还没有的字段，一个长期运行的 agent 会去研究来源、收集数据、用真实地面情况进行测试，然后把它编入索引，通常一天内就能完成。我会从这个帖子里挑几个字段请求，并回来汇报它构建了什么。 有人已经基于它做出了我没预料到的东西：保险团队用它筛查洪水、风灾和野火风险的组合资产；一家 proptech 通过增强清洗杂乱的房源地址；一个健康品牌用它给街角的海报点位打分；一家机器人公司用它寻找仓库；数据中心选址；无人机部署规划；某城市的校车路线；以及用于人口贩卖调查的信号。我们的使命是把地球上的每一寸土地都编入索引，并让它像 web 一样可查询。 定价是公开的：免费套餐每月 5,000 credits，$19/月可得 25,000，$99/月可得 120,000，企业版定制。 我很想听听你们正在构建的 agent 会在现实世界的哪些地方落地。如果你能用 Mireye 在一个你熟悉的地方跑一下，并告诉我哪里错了，我会更开心。 Comments URL: https://news.ycombinator.com/item?id=49552616 Points: 2 # Comments: 0

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ChatGPT、Claude 和 Grok 宕机了

文章 URL： https://www.macrumors.com/2026/09/03/chatgpt-claude-and-grok-are-down/ 评论 URL： https://news.ycombinator.com/item?id=49551658 得分：11 评论数：4

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Grok 故障

文章 URL: https://status.x.ai/ 评论 URL: https://news.ycombinator.com/item?id=49551589 积分: 19 # 评论: 11

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ChatGPT 正在返回 404

文章链接： https://chatgpt.com/ 评论链接： https://news.ycombinator.com/item?id=49550614 分数：125 # 评论：60

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ChatGPT 和 Codex 的错误率升高

文章链接： https://status.openai.com/incidents/01M1KWEDH417T2CF44YYHZDFCR 评论链接： https://news.ycombinator.com/item?id=49550886 积分：9 # 评论数：2

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Claude 面向 Commerce Agents

文章 URL: https://claude.com/blog/claude-for-commerce-agents 评论 URL: https://news.ycombinator.com/item?id=49547888 得分: 29 # 评论数: 26

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Show HN: Weedout – Safari extension that hides YouTube AI-labeled videos

I'm the developer. I built this because AI-generated conspiracy videos were getting out of control in my own YouTube feed. Weedout is a $1.99 Safari extension for macOS that removes videos YouTube labels “Made with AI” from feed, search, related videos, playlists and Shorts. It uses YouTube's label rather than AI detection, runs locally, and does not catch unlabeled videos. Source for developers who want to fork or build: https://github.com/masteranza/weedout-for-youtube Pull requests are not accepted; the repository is for independent forks. Comments URL: https://news.ycombinator.com/item?id=49528895 Points: 3 # Comments: 0

Hacker News 熱門54

Show HN: HN Match Maker – Matching "Who Wants to Be Hired?" With "Who's Hiring?"

Hi! Every month the "Who Wants to Be Hired?" and "Who's Hiring?" Threads come through I would think to myself, "there should be a match maker for these two threads". So I had abacus.ai whip one up. The methodology is pretty simple, data is extracted from posts using an LLM, score matches based on salary, domain experience, remote/onsite, etc., and then eliminate incompatible postings, like 'looking for remote work' and 'onsite only'. The result is two views: jobs-by-user and user-by-jobs. If you've submitted a post to this month's 'Who Wants to Be Hired?", you can find your matching job listings at https://hnmatchmaker.com/user/:user_name , for example https://hnmatchmaker.com/user/G4Vi --- Some observations when looking at the matches pair up right now: Poke around, let me know what you think! Ultimately, I hope this is actually helpful to some folks. Comments URL: https://news.ycombinator.com/item?id=49528057 Points: 3 # Comments: 1

Hacker News 熱門69

发布 HN：Nori Robotics (YC S26)——一款面向开发的低成本人形机器人

嗨，HN，我是来自 Nori Robotics 的 Antonio（ https://norirobotics.com ）。我们在旧金山为机器人开发者和研究人员打造了一款售价 $1,688 的双臂移动机器人。 我在 Columbia 做机器人研究时开始研究 Nori。我当时通过人类演示来教机器人，但很难拿到价格可承受的硬件。大多数实验室只有一两台昂贵的机器人，这使得收集大规模数据集、进行长时间实验，或在多台机器人上测试都很困难。 所以我自己做了一台，经过七次迭代后。当前的 Nori 具备： * 19 个自由度 * 两条 7+1 DOF 机械臂，每条机械臂载荷 1.5 kg * 55 kg 伸缩升降机构 * 差速轮式底盘 * 四个 720p、30 fps 的 RGB 摄像头 * 2D 激光雷达 * 带全双工语音通信的双麦克风阵列 * 432 Wh 电池 * 一块配备 4 GB RAM 的 Raspberry Pi 5 * （SLAM 和安全功能在机载端运行，更重的 ACT 和 VLA 必须通过 LAN 从电脑运行，或通过 WAN 从服务器运行） 要把成本压到 $2,000 以下是主要的工程挑战。Nori 有超过 100 个运动和结构部件，因此在执行器、轴承、布线、供电和装配上的成本会迅速累积。为了降低成本，我们做的一些主要选择是使用高减速比舵机而不是 QDD 电机，以及采用轮式底盘而不是腿部。 我们在旧金山组装每一台机器人，并将其设计得易于制造和维修（我们提供用于打印维修件的 3D 文件）。 我们的开源 SDK 包括遥操作和演示工具： https://github.com/Nori-Robotics/nori-sdk-py 我们还构建了一个基于浏览器的模拟器，你可以试用一下： https://lab.norirobotics.com/nori/model 我们已经交付了第一台机器人，并正在生产下一批。最终，我们希望没有机器人经验的人也能教 Nori 完成任务，并与其他拥有者共享。 目前，这套硬件已经能够完成基础清洁任务、打开抽屉、补货上架，以及倒啤酒。这里有一段 Nori 做这些事情的视频： https://youtube.com/shorts/VRfVXHfQvD8 我们的盈利方式是以 $1,688 出售硬件，另可选购付费软件。部分硬件是开源的。更多细节见我们的硬件论文： https://doi.org/10.48550/arXiv.2605.16537 如果你在机器人领域工作，你会用这个价位的机器人做什么？你会对硬件做哪些改动？ 评论网址： https://news.ycombinator.com/item?id=49525153 分数：2 # 评论：0

Hacker News 熱門64

Show HN: 在 48GB Mac 上以约 12 tok/s 运行 104GB 的 Qwen3.8-Flash-Next

我做了 slotstream，这是一种借助 expert-offloading/ssd-streaming，在从 16GB 起的低内存 Mac 上运行 Qwen3.8-Flash-Next 4-bit 的方式；它是一个 125B 参数模型，本来需要 100GB+ 内存/RAM。安装/更新都很简单，并且使用 MLX 和 Swift，原生适配 Mac。 它带有 auto-mode，在内存占用和速度之间取得了不错的平衡。接下来我会实现并移植用于推测解码的 MTP 模块 评论 URL: https://news.ycombinator.com/item?id=49524447 得分: 11 # 评论数: 0

Hacker News 熱門56

Launch HN: Almanac (YC S26) – AI that knows your company

Hi HN, I'm Kushagra, one of three founders of Almanac, a Hermes with a brain that knows everything about your company. We started our journey with setting up Hermes for our company, thinking it must be easy. We wanted an agent that would know every context about our company, so we could ask questions and get context-appropriate responses to. This started a very annoying and difficult journey. Setting up Hermes, getting it to talk right, building OAuth apps for every connector myself, then feeding it context myself, and ultimately struggling with Hermes's default memory. At the same time, we saw our YC batchmates struggling with the same problem, and we saw an opportunity. So we built Almanac. This is how it works. You sign up, you get a Hermes agent straight out of the box. You have a one-click connect to any account (Gmail, Calendar, Granola, PostHog, etc). You have personal accounts (only accessible by you) and also shared accounts (accessible by everyone in the company). The consequence being I can never see my cofounders' accounts. The “brain” of this agent is wikis. We pull in information from your connected sources, and start organizing this information in two wikis. A personal one, for you, which understands who you are, what your preferences are, the people in your life, and the things going on in your life. The second wiki is a company wiki, which includes what the company is, what you’re working on, what the roadmap is, and what the blockers of the company are. Your agent ultimately has access to these two wikis and the original accounts, which invoke the feeling of “it just knows you.” Here’s a demo: https://www.youtube.com/watch?v=ajXP5PHuK18 We're three cofounders, Rohan, Kushagra, and Divit, and we've been friends for 11 years, since studying for the IIT-JEE. We all did Electrical Engineering (Rohan at IIT Delhi, me at IIT Kharagpur, Divit at BITS Pilani, Hyderabad), and Rohan and I later went to Harvard, where this pre-compilation layer became our capstone thesis. We have built multiple products around the idea of a pre-compiled knowledge layer. Our main differentiating point is the way we approach memory and context in general. Most AI assistant tools treat memory as an afterthought. We have worked on wikis for AI for more than a year now, building products for Harvard and NASA. The one thing we have learnt is that one needs to spend a lot more compute upfront, in the pre-compilation of this knowledge base, to get it right. Having this pre-compiled knowledge base enables a lot of interesting ideas. First is a proactive agent. Since I have compiled what’s going on in both my company and my current life, Almanac can start completing tasks on its own. Concretely, we run a background worker which takes a look at tasks that could be completed, pings the main agent, who then pings me, suggesting which tasks it could automate. As a result, I wake up to proactive notifications which look like “I already prepared a draft of your fundraising pitch deck, want to take a look?” Second, long-horizon tasks. In our wikis, we maintain a section on ongoing projects, so Almanac can pick a task back up days later without losing the thread. Most agents are session-bound: they run once, finish, and forget. But a lot of real work isn't one shot; it plays out over hours and days with people in the loop. The clearest example is anything that involves waiting on a human, like scheduling a meeting, following up on a sales thread, or chasing a document. Almanac can send an email on your behalf, and because it's always on and remembers the project, it notices the reply four hours later and drafts the right follow-up in context. Since launching, we've seen a lot of use cases for Almanac. One person runs her dog-rescue operation through it: finding available fosters, tracking pickups, and sending reminders for consent forms. Another researches Polymarket strategies with Almanac, where its memory holds what past strategies were, proposes new ones, and compares them against what went right or wrong last time. Another builds marketing campaigns on it without having to re-explain the business and the whole campaign every single time. Regarding privacy and security, Almanac only accesses accounts you explicitly connect. Your OAuth credentials are held by our connection provider, not in Almanac’s database. We only store the wiki and the source behind its citations. So an email used as a citation may be retained as Markdown. We’re live: https://usealmanac.com . We have a 7-day trial on all of our plans. Happy to hear if people have done similar setups, and what new features they’d like in Almanac. If you’re a company that wants to get an agent that actually gets tasks done, I would love to talk: https://cal.com/team/almanac/demo . Comments URL: https://news.ycombinator.com/item?id=49511007 Points: 3 # Comments: 0

Hacker News 熱門70

Launch HN: Hebbian Robotics (YC S26) – Build scalable robotics data pipelines

Hi HN, we’re Brandon and Kingston, the founders of Hebbian Robotics. We built HFlow ( https://github.com/Hebbian-Robotics/hflow ), an SDK that turns multimodal recordings from robots and human operators into standardized, quality-checked episodes and queryable dataset manifests. A recording can contain synchronized video, joint states, actions, timestamps, and metadata, and HFlow processes those streams together. Here’s a demo of HFlow in action: https://www.youtube.com/watch?v=xni0GwV-xAw Robotics data pipelines often begin as scripts: one transcodes video, another checks timestamps, another adds labels, and another copies selected recordings into a training set. This works until the corpus grows. Then it becomes difficult to know which code ran, why an episode was excluded, or whether a dataset can be reproduced. The first pain is usually quality control because frozen cameras, missing topics, timestamp drift, and duplicate recordings can quietly enter training data. Brandon first encountered this while training embodied AI models for two-arm industrial cleaning robots. Kingston had run into related problems while building high-throughput infrastructure at Jane Street. Later, while speaking with robotics data providers, we kept seeing teams rebuild similar processing and quality-control infrastructure. We learnt that processing robotics data is itself one of the bottlenecks to improving robotics models. An HFlow pipeline consists of transformations, checks, labels, and enrichments. The SDK exposes them as plain Python functions that receive an episode and return measurements, artifacts, or transformed data. During development, the functions can run in-process. For scheduled corpus processing, HFlow packages the same registered steps as Airflow 3 DAGs, where teams can inspect task status, logs, retries, and reruns. HFlow currently accepts one MCAP file per episode. MCAP ( https://mcap.dev/ ) is an open container format by Foxglove for timestamped multimodal recordings, similar in purpose to a ROS bag. It lets video, robot state, actions, and other sensor streams remain synchronized in one file. We use it because HFlow needs to process these streams together, and because the resulting recordings remain compatible with Foxglove and Rerun. HFlow writes a canonical MCAP with in-band H.264 video, grouped camera and state chunks, and provenance describing how the output was produced. Each step has an explicit behavior version, and catalog records connect its measurements and artifacts to the source episode and pipeline run. Quality checks store reusable evidence rather than imposing one universal definition of good data. Some failures, including black frames, frozen video, missing topics, timestamp drift, and impossible joint movements, can be measured deterministically without training a model. Others might be detected using VLMs and other models like MediaPipe Hands. But their meaning depends on the task. A smooth trajectory might indicate a successful demonstration in one setting and a stalled robot in another. HFlow writes measurements, metadata, version stamps, and artifact locations to an append-only Parquet catalog. Teams query it with DuckDB SQL and produce a version-pinned manifest without opening the recordings again. Critical checks can quarantine an episode, but HFlow does not delete data. This separates the evidence from the policy used to assemble a particular dataset. We did not want to replace the tools robotics teams already use. HFlow connects MCAP for synchronized recordings, Airflow for scheduled execution, Parquet for catalog data, and DuckDB for curation. Compared with a general workflow orchestrator, it adds contracts for robotics episodes, processing provenance, quality evidence, quarantine, and dataset manifests. Compared with a training dataset format, it operates earlier and stops at curated episodes plus a manifest. Here are three examples of teams that would use HFlow: 1. A data vendor or marketplace collecting egocentric recordings. They could use HFlow to detect black or frozen video, duplicate recordings, hand-object interaction, and other quality metrics before delivering the data, while retaining evidence of which checks ran on every episode. 2. A robotics team collecting teleoperated demonstrations for its own models. They could use HFlow to standardize recordings, add labels and enrichments, and produce a reproducible training manifest. 3. A team operating robots in the field. It could process incoming logs, quarantine incomplete or corrupted episodes, and query the catalog for particular robot versions, environments, or failure conditions. The project is pre-v1, but the core lifecycle works end to end. You can try it without an account, Docker, or robot hardware by cloning the repository and following the quickstart. HFlow is free under the Apache-2.0 license. The open source deployment is currently a single-tenant workspace, and we have not built the hosted, multi-tenant control plane yet. We are considering making money through managed workspaces and enterprise support for teams that do not want to operate the runtime themselves. Because this processing layer is software and data, people can contribute without owning a robot. We would especially like feedback from people who have built pipelines for robotics, video, or other sensor-heavy systems. We are curious where our data model is wrong, which integrations are missing, and what would fail first on your workloads. Comments URL: https://news.ycombinator.com/item?id=49510632 Points: 3 # Comments: 0

OpenAI Blog67

扩大 AI 使用范围的里程碑

ChatGPT Ads 年化收入运行率达到 10 亿美元，并在全球范围内扩展，通过免费和价格亲民的选项支持更广泛地使用 AI。

Hacker News 熱門52

Show HN: Academa – Long-form STEM lecture videos generated by LLMs

Hi HN, we are Sina Atalay and Abdullah Geduk, co-founders of Academa. We are both PhD students. We thought: what if lecture videos were written as code and compiled into video using computer graphics and TTS? Then LLMs could write them, and lectures could be fixed with code edits instead of video production. On correctness: LLMs may make mistakes. But these videos are code sitting in our repository, and we can maintain them. Every report and review becomes a fix in the source, and everyone who watches after that gets the corrected lecture. Every recorded lecture on the internet is stuck with its mistakes. Ours will continuously improve. Each video also comes with an AI chat that understands everything said and shown in the lecture. There is a longer write-up at the bottom of the page. Happy to answer any questions. Comments URL: https://news.ycombinator.com/item?id=49503421 Points: 9 # Comments: 2

Hacker News 熱門63

vLLM v0.28.0

文章链接： https://github.com/vllm-project/vllm/releases/tag/v0.28.0 评论链接： https://news.ycombinator.com/item?id=49492067 得分：8 # 评论数：0

Hacker News 熱門65

OpenAI：迁移到 HTTPX2

文章链接： https://github.com/openai/openai-python/blob/main/httpx2.md 评论链接： https://news.ycombinator.com/item?id=49477212 得分：9 评论数：6

Hacker News 熱門64

Show HN: SubSmith – 将你自己的视频变成语言学习材料

我学习日语已经有几年了，一直遇到一个类似的问题。我会找到一个想用来学习的视频，听到一句有用的话，然后意识到把这句话变成以后可以学习的内容既费时又有时让人很疲惫。 最后我会在视频播放器、字幕/转录文本、词典、截图、音频片段和 Anki 之间来回切换。所以我做了 SubSmith，把这套流程整合起来。 你可以把视频或音频文件放进去，先在本地生成转录文本，然后结合媒体内容和转录文本来： * 查询单词和句子 * 重播单独的行 * 编辑转录文本 * 保存有用的句子及其原始上下文/音频 * 导出为 Anki 卡片 对我来说，最重要的是它可以处理你自己的媒体。它不绑定某个特定的流媒体服务或资料库，所以我可以拿来学习我真正感兴趣的随机动漫集、播客、讲座等内容。 这是一个优先离线的桌面应用，转录也是在本地完成的，而不是把媒体发送到转录 API。 我在这里分享它，是因为现在我更想弄清楚这种工作流对其他人会在哪些地方失效，而不是在已经有了稳定核心/基础之后继续随意加功能。 比如： * 你真的会从自己的媒体里保存句子吗？ * 这个流程的哪一部分让你觉得太费工夫？ * 附带音频/上下文会不会让制作 Anki 卡片更有用？ * 你会更希望它直接在你现有的视频播放器/浏览器里工作吗？ * 安装桌面应用是不是一个很大的门槛？ * 还有，在开始免费试用前要求注册账号，会不会让你直接放弃？ 当前版本确实要求先注册账号才能开始试用，我想弄清楚这对那些真正会使用它的人来说，是否会造成明显的阻力。 可以免费试用，如果你已经在通过自己的视频、动漫、电影、播客或其他媒体学习语言，我尤其欢迎你的反馈。 我是开发者，所以我会在评论区回答问题并讨论它的工作方式。 https://subsmith.app Comments URL: https://news.ycombinator.com/item?id=49476894 Points: 5 # Comments: 1

Hacker News 熱門63

Show HN: 我们构建了开源 OpenRouter，它会把使用情况转化为更好的模型

嗨，HN，我们构建了一个开源模型网关。它是一个集中管理我们自托管、前沿和开源模型的单一入口。 它原生使用 Rust 构建，面向并发，并实现了跨模型和提供方的各种配置细节差异（流式格式、工具调用、模型参数、速率限制以及不同的错误行为）。 对于 BYOK 请求，这个网关增加的延迟不到 1 ms；当由 Experiential 提供供应商密钥时，延迟不到 2 ms。它覆盖所有主要推理提供方，并通过一个会发起 PR 的 codex agent 每日刷新 1000+ 个模型。 与其他类似项目相比，我们是开源的，不收取任何加价，允许你将本地模型与市场混合使用，并利用你的流量来（选择加入后）训练一个模型。简单路由并不值得收取 10% 的 token 加价。 我们的做法是：基于标准化的 OTel traces，我们挖掘具有代表性的真实任务，使用 text world models 模拟各种模型的 rollouts，应用一个 LLM judge，然后在 prompt 的 embedding 之上拟合一个最近邻分类器，以决定每个请求的最优模型。通常这比直接调用单个模型能画出更好的成本/质量帕累托曲线，但并不完美。 借助这些模拟，我们还可以做诸如建议缓存命中优化、新模型建议以及训练模型之类的事情。 它是开源的，所以你可以把它部署到自己的基础设施上，使用我们托管的版本且不收取任何加价，或者在我们的网站上阅读我们如何为最大可用性进行设计。 Comments URL: https://news.ycombinator.com/item?id=49471407 Points: 9 # Comments: 0

Hacker News 熱門71

Launch HN: Salem Robotics (YC S26) – Software for industrial inspection robots

Hi HN, we're the founders of Salem Robotics ( https://salemroboticsinc.com ). We give existing mobile robots the task-specific intelligence to carry out surveys and physically interactive inspections in hazardous industrial facilities. Here's a video of it running on real robot hardware with a few words from us: https://youtu.be/U_228h3NE7c We came to Salem through robotics research at UT Austin and a combined 15 years working in nuclear, including about 10 years developing and deploying autonomous robots at Los Alamos National Laboratory. Over the last five years, we kept running into the same gap: robot hardware had become very capable, but making a robot carry out a complete industrial procedure still required a surprising amount of robotics work and manual intervention. The part that interested us most was manipulation. A nuclear contamination survey, for example, can require taking a "smear": wiping a defined area of a surface so it can be checked for removable radioactive contamination. In an oil, gas, or chemical facility, an LDAR (leak detection and repair) inspection can require moving a detector around a particular valve, flange, or connection. Other inspections require positioning an instrument at a precise location and orientation relative to a pipe or piece of equipment. These are easy tasks to compress into verbs like "wipe", "measure", or "inspect", but considerably harder to make a robot do reliably. A probe might need to remain normal to a surface throughout a path, stay within a narrow offset from a pipe, or trace a region while maintaining a particular end-effector orientation. The planner has to find a feasible motion while respecting the task geometry, manipulator kinematics, joint limits, collisions, and the environment around it. We work down to joint-level control for those interactions. One problem we've spent a lot of time on is generating constrained manipulation plans quickly enough that they can be based on the geometry the robot actually observes instead of requiring someone to carefully author a trajectory for every individual surface, valve, or flange. The physical world makes this annoying. A few centimeters of error may not matter much when navigating down a hallway, but it matters if a sensor is supposed to remain normal to a curved surface. And successfully executing a trajectory doesn't necessarily mean the inspection worked. The detector could be misaligned, contact could be wrong, the geometry could differ from the model, or the measurement itself could be invalid. We care about closing that loop around the inspection result, not just whether the arm reached the commanded pose. Our approach is a combination of AI and classical robotics. A lot of robotics research and industry attention right now is going toward increasingly end-to-end learned systems, particularly around humanoids. Working in safety-critical environments has made us appreciate how relevant classical approaches still are when you want explicit constraints, predictable behavior, and theoretical guarantees about what a robot can and cannot do. We use AI where semantic understanding and flexibility are useful, such as interpreting less structured information or understanding what in an unfamiliar scene is relevant to a procedure. Once the system knows what physical interaction it needs to perform, we prefer explicit geometry, planning, optimization, and control where possible. We're interested in the marriage between the two rather than trying to make every part of the robotics stack learned. The other idea behind Salem is that we don't think every useful robot application should require building a new robot. Companies like Boston Dynamics are getting very good at building increasingly capable hardware platforms. We think there is room for a domain-specific application layer on top of that hardware. The same underlying robot might perform nuclear radiological surveys in one facility and LDAR inspections in another, but the procedures, sensors, manipulation constraints, success conditions, and outputs are different. That's also why we're hardware agnostic. We don't expect one robot to be the best platform forever, and facilities already own different hardware. We'd rather describe an inspection in terms of what needs to happen and then map that onto the capabilities of the right robot for the job. One thing that surprised us after spending more time with facilities is how manual many inspection workflows still are. In sophisticated nuclear and industrial sites, people still physically walk survey routes, take measurements one at a time, visually inspect equipment, record results manually, and sometimes make judgments based on things like how a component sounds. Some of the basic workflows would be recognizable to someone doing the job decades ago, even though the sensors, computation, and robots available today are radically different. We're starting with radiological inspection in nuclear because it's the industry we know best, and we're also working on manipulation-heavy inspection problems in oil, gas, and hazardous chemical facilities. The technicians and inspectors still define the procedure, interpret results, and make the consequential judgments. We're trying to automate more of the repetitive physical execution that currently requires someone to enter the environment or manually operate a robot. We sell directly to industrial facilities, usually starting with a paid technical validation and then moving to an ongoing deployment. Pricing varies substantially with the workflow: validations range from tens of thousands to over $100k, and larger deployments can range from the low hundreds of thousands to roughly $500k per robot. One thing we're especially curious to hear HN's thoughts on is where the abstraction boundary in robotics should sit. What should come from the robot manufacturer, what belongs in an application layer, and what will inevitably remain specific to the facility? We'd also be interested in hearing about other industries where you've seen physical inspection tasks that look trivial to a person but are surprisingly difficult to automate. Comments URL: https://news.ycombinator.com/item?id=49466715 Points: 2 # Comments: 0

Hacker News 熱門64

Show HN：打造你自己的主题公园

我构建了一个 agent，可以帮助你打造受 Rollercoaster Tycoon 启发的主题公园。你可以输入类似“给我建一个酷炫的主题公园”的提示，它就会建出一个整体协调的主题公园，包含多个世界，并用道路和游乐设施连接起来。 有意思的是，我构建它时用的，正是我们用来让 AI 生成的网站遵循公司设计系统的同一套思路。 我是 Magic Patterns 的一名工程师，专注于构建我们的 Design System Agent，目标是使用你现有的品牌、组件和惯例，而不是生成看起来像通用 AI 生成 UI 的东西。 默认情况下，模型往往会收敛到外观相似的设计。这也是那种“vibe coded slop”感觉的一部分：相同的字体处理、阴影、卡片、图标、动画等等……我的工作就是找出合适的上下文和护栏，这样当你提示“给我构建一个仪表盘”时，结果真的会看起来像你现有的产品。 某个时候我意识到，同样的思路也可以应用到 RCT 上。 在 Web 领域，你可能会有关于哪些字体、颜色、间距和组件应该搭配使用的规则。 在主题公园里，你需要这样的规则：过山车需要完整的轨道，游乐设施需要有连接到道路的入口，道路需要连接公园中的不同区域，而海盗主题世界就应该真的使用海盗主题的景观。 看到构建一个协调一致的主题公园，与构建一个协调一致的产品 UI 这个问题如此相似，这很有意思。和 Web 设计类似，仅仅给模型提供正确的组件是不够的。 最后我构建了一个评估循环：Magic Patterns 会生成一个公园，另一个 agent 会根据评分标准对它打分，然后这个 agent 会在再次尝试之前更新它的规则和技能文件。 评分标准会检查一些内容，比如过山车是否形成了有效轨道且至少有一个下坡，游乐设施是否可以通过道路到达，每个世界是否使用了合适的主题景观，以及整个公园是否作为一个整体运转良好。 经过大量迭代之后，现在你只要输入像“给我建一个酷炫的主题公园”这样简单的提示，就应该能得到一个功能完整的公园，里面有游客、游乐设施、道路、过山车和主题世界。 最棒的是，你还可以跟随游客，聆听他们的想法，并以 RCT 的方式观看他们乘坐游乐设施。 很乐意回答任何问题，也很想看看大家创建的公园！ Comments URL: https://news.ycombinator.com/item?id=49452037 Points: 15 # Comments: 4

Hacker News 熱門70

WebMCP 挑战 – OpenAI

文章链接： https://openai.com/webmcp-challenge/ 评论链接： https://news.ycombinator.com/item?id=49455713 得分：3 评论数：0

Hacker News 熱門67

WebMCP：教你的网站与 AI 代理对话

文章链接： https://sreenathmenon.com/blog/2026-08-04-webmcp-teaching-websites-to-talk-to-ai-agents/ 评论链接： https://news.ycombinator.com/item?id=49450417 分数：16 # 评论数：6

Hacker News 熱門52

Show HN: I made a Raspberry with Qwen my local car AI

Found that you can actually run a 35B Qwen model on a Pi with very impressive intelligence and stability. Built connectors for car ODB to read all about car internals, and manufacturer's cloud service for stuff like changing AC or opening/ locking doors. Gave it info such as the full car manual. And then hooked it up with my other agents in our discussion room! So now it can answer car questions such as "when should I add oil and what kind of oil?" and help you fully offline, and when online talk with the agent family that includes all the most powerful models so they know how the car is, plan new features and develop itself with them. For example, if car breaks and can't move the car agent informs my agent family and they can already look for a suitable train ticket without me needing to check things or do something. Comments URL: https://news.ycombinator.com/item?id=49435675 Points: 6 # Comments: 0

Hacker News 熱門56

Show HN: Screen memory without screenshots, just text to Markdown

It's a macOS menu bar app that reads the text of your focused window every few seconds through the Accessibility API. No screenshots, no video, or OCR. It writes plain markdown, one file per day, into a folder you choose. Point Claude Code (or anything with file access) at the folder and ask "what did I work on Tuesday?" or have it build memory about your projects. The folder contains an AGENTS.md explaining the format to the model. Thought it might be useful for someone so chucked it up on GitHub Comments URL: https://news.ycombinator.com/item?id=49429095 Points: 5 # Comments: 0

arXiv cs.AI74

Prime Agent：一种自我改进的 RLM 框架

语言模型是顺序处理器，但长时程智能体能力需要模型权重和活动上下文之外的外部信息与计算。Prime Agent 是一个用于长时程评估和编码智能体工作流的开源框架。一个持久化的 IPython REPL 遵循 Recursive Language Model 抽象，用于程序化上下文处理和测试时计算，而 Continual Harness 则在多条轨迹之间保留历史、记忆、技能、提示词和子智能体规范。递归子智能体通过直接的智能体间通信进行协作，Agents View 让人类能够检查和管理由守护进程支持的会话。Prime Agent 将执行、恢复、验证和资源核算标准化，同时将策略构建留给模型。这种低摩擦、富有表达力的膜层可防止框架故障变成模型故障，并将测量推向模型真实的最大底层能力。Prime Agent 将 ARC-AGI-3 RHAE Best@1 从 30% 提升到 95.5%，并在长上下文编码、GPU 内核生成、模拟器构建以及自主 nanoGPT 速通等任务中达到或超过原生框架和流行框架。在 Factorio 上，我们发现精炼使持续技术进展成为可能，而专用子智能体支持并行化工作。代码可在 https://github.com/PrimeIntellect-ai/prime-agent 获取。

Hacker News 熱門66

Agent Lightning v1.0

文章 URL： https://github.com/microsoft/agent-lightning/releases/tag/v1.0.1 评论 URL： https://news.ycombinator.com/item?id=49423077 得分：18 评论数：1

Hacker News 熱門46

Show HN: OzBrain, a shared brain for knowledge between agents and your team

I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I’m not sure who exactly wins it, but I want my knowledge to grow/go with me. A lot of the “knowledge” ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans… I don’t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work. What I built OzBrain to do: + Create a central place for agent reasoned knowledge to live + Be agnostic about what apps/agents connect to it + Capture everything and track it so I can audit it + Enable teams, collaborators or partners to share brains + Handle conflicts so many agents in the same article doesn’t blow up + Refactor knowledge into more token friendly chunks and map the index well + Close the knowledge loop so new thinking supersedes old thinking across the corpus. Don’t erase, depreciate and link + Keep user data safe and secure ++ Be easy enough to use that you don’t have to have any technical knowledge Some among us will always build their own custom solutions, but there are millions of tech professionals and small business owners that will use agents heavily and need a solution. So I’m trying to build that. Isn’t this like gBrain? Yes, similar. I think it’s like AWS vs Vercel. AWS is very powerful, configurable, and useful if you’re technical and want to invest the time into really fine tuning your system… but if you just want your web deploy/hosting to just work and be easy to deal with you use Vercel. // WHY I MADE IT I’ve been enjoying getting back to my technical roots, as I lost my coding skills more than a decade ago, but with AI I can focus on the system and the product in partnership with agent coding workflows. I recently built a Voice AI for older people. To build it I created an agentic engineering workflow (feel free to rip that up as I’m always looking to improve systems: https://ozbrain.com/resources/eng-flow ) My approach with coding agents is trust but verify, and I’m trying to replace the parts where a human would review with an adversarial or specialized agent who would give a better answer/review. I have workflows that will go high level task to shipped PR running in Claude cloud sessions. I use Claude Code locally and Cursor when I want a tighter loop on doing visual work like UI or layout. And Codex to either load balance usage for TokenThriffting or when I want a different llm to think thru something. It was a pain in the ass passing .md files around and keep track of which version was the most recent, so I built a hosted .md storage right in Supabase and any of my agents already have Supabase access. This let me build a solid, scalable, secure voice AI from my phone at the gym. All my agents have access to our knowledge, can write to it, update and refer to it as we build and improve the product and the systems we use. Out of 75 founder friends I asked about how they manage shared knowledge, 26 built their own custom knowledge systems… Obsidian vaults with 7k files synced through a VPS, markdown repos behind their own MCP servers, cron jobs stitching Supabase to a skills file… each a different Frankenstein they have to maintain. 32 said they felt the pain of moving static files around but didn’t have any solution for it. So I rebuilt my brain better and used it to build it. // HOW YOU CAN HELP Would love to have you try it out. The maintenance loop is still in alpha so not running it on customer data yet. If you built your own brain I’d love to hear how you did it. What criteria was most important for you in its design & function. If you are tired of shuffling .md files around I’d love to have you try out OzBrain and to give feedback, just ask your agent to put it in the shared bugs & features brain! Cheers! Bubs.co Comments URL: https://news.ycombinator.com/item?id=49394827 Points: 8 # Comments: 1

Hacker News 熱門72

Show HN: Proliferate- 适用于任何编码代理的开源、自托管 Codex

Hi HN- 我是 Pablo，Proliferate（YC S25）的创始人！ Proliferate ( https://github.com/proliferate-ai/proliferate ) 是一个开源、可自托管的 AI IDE，让你可以在一个地方使用 Claude Code、Codex、OpenCode、Cursor 和 Grok 进行工作并自动化任务。 这里有一个 2 分钟的快速演示，展示我们如何用 Proliferate 来构建 Proliferate： https://www.youtube.com/watch?v=tGNX0oaWmBY 在我的团队接入 OpenAI Codex 之后，我开始着手构建 Proliferate。几天之内，我们就把它用到了方方面面：用 computer use 代替我们自己浏览网站，让 Codex 协调其他 agent，以及为重复性工作设置自动化。说实话，我们几乎从不需要离开桌面应用就能把工作完成。 如果我团队的体验具有一定代表性，那么一个类似 Codex 的应用（一个具有精美 UI 的横向 agent）将会成为每家公司完成工作的主要界面。这完全符合 OpenAI 的使命，即把 Codex 打造成 everything app（见： https://news.ycombinator.com/item?id=47796469 ）。 但随着我们开始把自动化推进到更接近业务核心的工作，我希望能与来自所有实验室的 agent 协作，包括开放权重模型，而不会越来越依赖 OpenAI。 而这正是 Proliferate 的用途！它是开源、可自托管的 Codex，既能在构建向 Codex 广度迈进的同时，保留你在不同 agent 和模型提供方之间的选择自由。 如今 Proliferate 支持： * 使用 Claude Code、Codex、OpenCode、Cursor 和 Grok，并可采用它们原生的推理与配置选项，包括 Bedrock、Azure 和自托管推理。 * agent 之间的通信与管理：父 agent 可以生成并与另一个受支持的 agent 作为子 agent 进行通信（我个人喜欢让 Fable 委派给 Codex，同时让 OpenCode 模型审查 PR）。 * 构建 workflows——这是我最兴奋的功能之一。这些是可复用的 agent 会话链和人工审批关卡，每一步都可选择 harness 和模型，并在它们之间传递文档。我用它来自动化代码审查、QA，以及我的 PR 构建流程。 Proliferate 的全部代码在 AGPL-3.0 下 100% 开源。现在肯定还有一些粗糙的地方，但我们推进得很快，也非常欢迎任何反馈！ Comments URL: https://news.ycombinator.com/item?id=49390739 Points: 11 # Comments: 5

Hacker News 熱門45

Show HN: A desktop fly drawn to the scent of vibecode

It is a fork of https://github.com/DenisSergeevitch/desktop-fly , but with an important update. Now the fly can pick up the scent of the codebase with its neurons and fly straight to the source code of your B2B AI SaaS startup. It has learned to scan its surroundings for agent markers: AGENTS.md, CLAUDE.md, .cursor/rules, .kiro/steering, and forty others. Anything on the screen that points to these markers becomes a source of the scent - an editor window with an open project, a line in Finder, or a desktop icon. An open project reeks the strongest, while a closed icon or nested folders give off a fainter odor. Comments URL: https://news.ycombinator.com/item?id=49389466 Points: 3 # Comments: 0

Hacker News 熱門45

Show HN: Huzzah – a novel approach to coding with AI

Hello everyone. I've been working on this experimental editor called Huzzah. I've been working almost exclusively with coding agents since January of this year, and over the past few months I began to feel utterly exhausted by them. They're great, but I'm finding it more and more tedious to write full sentences for every change I want. Not only that, but it seems there's a complexity limit for codebases - beyond a certain point the agent begins confusing itself. I'd like to go back to writing code, but I don't want to go all the way back to fully manual coding. So I've come up with this interaction paradigm where you: 1. write pseudocode in whatever way makes the most sense to you 2. on save, the editor synchronizes your work to real source code 3. the pseudocode is persisted alongside the generated code, making your prompt effectively a stored record of intent. It may not work for every use case, but in my initial playthroughs I've found it very enjoyable. Right now it's just a proof of concept - installation instructions are here in the readme: https://github.com/danielvaughn/hz You can also watch a video of it in action here: https://x.com/danielvaughn/status/2090456808431165715 Cheers! Comments URL: https://news.ycombinator.com/item?id=49378768 Points: 3 # Comments: 0

Hacker News 熱門57

Show HN: I trained a 125M model to autocomplete piano on-device

I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work. Comments URL: https://news.ycombinator.com/item?id=49373456 Points: 17 # Comments: 5

OpenAI Blog75

为前沿模型提供零数据保留

OpenAI 重申为符合条件的 API 客户提供零数据保留，并预告 Private Safety Processing，以在不损害数据隐私的情况下实现先进的 AI 安全性。

Hacker News 熱門59

Show HN: Frugal Tokens – explore costs and usage across coding agents

I wanted to share a project I’ve been working on called Frugal Tokens. I originally built it because I was curious to see how much all of my sessions cost and how much cache misses affected that spend. I’d noticed people had widely different spend profiles and wanted to better understand what might contribute to that. As I’ve worked on this, the tool has grown to show more usage patterns across all of your sessions. It shows overall usage, estimated working time and overlapping sessions, and where your spend is coming from across models and cache misses. I also have a few session level metrics with percentile breakdowns, along with a list of your sessions and high level info. Clicking into a session opens an explorer where you can see individual model calls and tool inputs and outputs. You can also jump directly to where a cache miss happened. There’s also a rough cost comparison that shows what the recorded session would have cost with another model’s pricing, or for Anthropic, with 5m vs 1h caching. In the future, I’d love to collect more information to see which patterns might make people’s workflows more expensive, e.g. long sessions, high context usage, many turns, etc. The tool requires deno, but is just one command to run once that is installed. The demo provided has some of the data scrubbed, but helps to show what it looks like before running it. Would appreciate any thoughts or ideas https://github.com/dpclark4/frugal-tokens Comments URL: https://news.ycombinator.com/item?id=49364223 Points: 10 # Comments: 1

OpenAI Blog73

为前沿模型提供零数据保留

OpenAI 重申为符合条件的 API 客户提供零数据保留，并预览 Private Safety Processing，以在不损害数据隐私的情况下实现先进的 AI 安全。

Hacker News 熱門66

Launch HN：OneCLI (YC S26) – 面向团队的开源沙盒化 agent harness

Hi HN，我们是来自 OneCLI 的 Jonathan 和 Guy。OneCLI 是一个为团队构建的 agent harness，为每位员工提供一个安全的、沙盒化的个人 agent。 你可以用它做这些事： 1. 获得一个沙盒化的 agent，并具备所有 OneCLI 能力，比如只需在聊天中连接你的 GitHub 账户、Gmail、Notion 或 Dropbox。 2. 在聊天本身中进行确定性的 human in the loop 审批，用于那些你需要 100% 控制的事情，比如发送电子邮件或删除 Linear 工单。 3. 在一个地方管理团队策略，并在工作区中的每个 agent 上强制执行。 4. 在团队层面享受全局连接，比如共享 LLM keys 或服务账户。 这里是演示： https://www.youtube.com/watch?v=dlW-44ntpbE 我们是偶然开始做这个的，尽管我们的职业背景都在安全领域。当时我们在做一个名为 ChartDB 的 devtool，这是一个开源 DB 工具。今年 1 月 OpenClaw 火起来后，我们开始用它在 ChartDB 之上编排 agent。我们很快意识到 auth 方面存在一个大问题。agent 需要凭证才能做真正的工作，但把这些 secrets 交给它们并不是最好的主意。它们会把这些 secrets 保存在自己的记忆中，还会以纯文本写入本地文件和会话中。我们也知道 agent 很容易被诱骗交出这些 API keys/secrets。因此，我们需要某种方式来控制 agent，阻止 prompt injection 欺骗它使用自身服务为攻击者谋利。 我们创建了 OneCLI，它最初是一个用 Rust 构建的 AI Agents vault。 我们发现，对 OneCLI 的大部分需求来自 Hermes、OpenClaw 和 NanoClaw 这类面向个人和团队的 autonomous agents。 用户在寻找有用的 agent，让它们为运行它们的人做事，但缺少两个部分：1）管理 secrets 和权限。2）对于团队来说，还需要多人管理。 我们决定转型，把 agent 本身作为面向团队的 harness 提供，为每位员工提供一个 agent。我们看到团队不得不一遍又一遍地搭建自己的 harness，而我们基本上已经把 vault 做成了一个 gateway。于是我们想到，可以开箱即用地提供 agent 管理中缺失的那一块，并将其开源（Apache-2.0，带有一个小型企业例外条款）。 我们坚持开源优先——开源整个平台，而不只是像其他 agent 那样开源其中一小部分，这样公司才能真正看到代码、评估它、信任它，而不是只听我们的一面之词。它们可以在自己的环境中隔离运行，完全自托管或自控，具备生产级质量，而不是某个托管在别处的封闭黑盒。这意味着安全不只是一句承诺，而是它们可以自行验证的东西。结合真正的自治能力和最小权限访问，这使它成为公司可以完全拥有和信任的东西，而不只是采用。 我们也从公司视角而不是个人视角来处理这个问题。我们的解决方案代表每位员工管理 agent，并包裹在由公司管理员通过集中式策略配置的确定性 guardrails 中。 对于 agent engine 本身，我们使用 jcode，它是 agent-loop 的核心。我们发现它改善了体验，让 agent 更聪明、更快速。 它的工作方式如下： 它运行在你控制的基础设施上。完全开源，几分钟内即可 self-host 或部署到 cloud。 agent 永远不会持有真实 secret。它拿到的是一个 placeholder。真实凭证会在 gateway 处、每次请求时、调用被授权后注入。它永远不会进入 agent 的 context、memory 或 logs。 在模型之外执行控制。Prompts 只是建议。由组织管理员定义的策略运行在网络层，在 agent 和 LLM 之外。阻止 endpoints、按 agent 限速、要求审批、按员工限定 scope。由 gateway 决策。agent 无法绕过它。 每个 agent 都有隔离 VM。自己的 memory、自己的 keys、自己的权限。爆炸半径仅限于一个 agent。 Harness 的速度：agent loop 之下的 Rust engine。 完整的身份轨迹。每个 agent 都绑定到一名员工。每次调用都会记录它代表谁执行，以及是哪条策略允许了它。 人们正在用这个平台做的一些事情包括： - 完整管理公司的生命周期，从销售电话开始，到产品侧自动为工程团队开工单，再触发 development agents 交付并发布到生产环境。 - 运营侧，比如在通话后自动清理 CRM、寻找 leads、预订会议并管理后续跟进邮件。 - 我们的一些客户还用这些 agent 完成整个杂货采购，并让它们处理各种家务，比如在线下单。 关于团队：两位创始人都来自网络安全背景。Jonathan 曾在 Axis Security 多年从事 zero trust network access。核心理念是你永远不要信任客户端。你要精确决定一个人能访问什么，并在他们之外、在网络层强制执行，因此客户端试图做什么并不重要。这就是如今每家严肃公司向人类授予访问权限的方式。Guy 是 Argon security 的第 1 位员工，负责 AppSec。 我们很想听听你们对这次转型的看法，也欢迎提交 issues 来帮助改进，让你的 agent 既强大又安全——为团队而设计，而不仅仅是为个人。 Comments URL： https://news.ycombinator.com/item?id=49363710 Points：3 # Comments：0

OpenAI Blog74

ChatGPT Ads 扩展至欧洲各地

ChatGPT Ads 正在扩展至 31 个欧洲市场。了解广告主如何在人们探索、比较选项并做出决策时触达他们。

Hacker News 熱門69

Cerebras CS4

文章 URL： https://www.cerebras.ai/cs4 评论 URL： https://news.ycombinator.com/item?id=49354949 点数：6 评论数：0

Hacker News 熱門53

Show HN: PantheonGPU – GPU health testing and AI workload benchmarking

Hi HN, I built PantheonGPU because I wanted a better way to answer a simple question: is this GPU actually healthy and performing the way it should? A GPU can show normal temperatures and utilization and still be underperforming, unstable under certain workloads, or have memory, PCIe, or configuration issues. PantheonGPU actively tests the GPU instead of only monitoring telemetry. It currently includes 45+ tests covering compute, tensor workloads, memory, cache, PCIe, thermals, stability, and AI/LLM inference. It supports both NVIDIA CUDA and AMD ROCm. I’m also exploring a larger use case: running Pantheon across GPU fleets to identify individual GPUs that behave differently from the rest of a server or cluster. I’d especially appreciate feedback from people running AI infrastructure, multi-GPU systems, local LLMs, or GPU clouds. Comments URL: https://news.ycombinator.com/item?id=49350637 Points: 5 # Comments: 0

Hacker News 熱門77

Claude Code 每周限额明天减少三分之一

文章 URL： https://support.claude.com/en/articles/15910845-claude-code-may-august-2026-weekly-limits-promotion 评论 URL： https://news.ycombinator.com/item?id=49348751 积分：44 评论数：15

Hacker News 熱門71

发布 HN：machine0 (YC S26) – 来自 CLI 的持久 CPU 和 GPU VM

HN 大家好！我是 Barnaby，machine0（ https://machine0.io ）的创始人。我正在构建一个用于长周期代理计算的 CLI：`machine0 new mybox` 会为你的代理提供一台持久的云端 VM，按分钟计费，价格从 $0.013/hr 起，最高可达 60 vCPU / 240 GB RAM，并支持 GPU（H100s、H200s 等），VM 级别正常运行时间为 99.99%。代理可通过 CLI 或 MCP 自主运行。 演示： https://www.youtube.com/watch?v=gyllkZ0M04E 代理工作负载正在从短暂运行转向始终在线。一个处理复杂功能的编码代理会运行 6-8 小时。由代理编排的训练和 RL 运行需要数天。OpenClaw 和 Hermes 24/7 运行。随着你并行运行更多任务： - 资源：大型代码库上的几个代理就会耗尽 RAM 和 CPU。模型训练和 RL 需要你没有的 GPU。 - 安全：在你的个人机器上使用 `--yolo`，只需要一次提示注入就可能导致凭证被外泄。 - 可用性：合上笔记本电脑，代理就会在任务中途死掉。 - 隔离：你和你的代理实际最低需求之间没有清晰边界。 machine0 为每个代理提供自己的计算机。它是一个足够简单的 CLI，人类和代理无需阅读文档即可使用： `machine0 new mybox` 会创建一台可 SSH 连接、带静态 IP 和 HTTPS 端点的 VM。始终在线（VM 级别正常运行时间 99.99%），直到你将其关闭。 - 按分钟计费。1 vCPU / 1 GB 为 $0.013/hr，最高可达 60 vCPU / 240 GB，另有从 RTX 4000 Ada 到 8×H200 的 GPU。 - 暂停、快照和恢复。让你可以轻松暂停工作，之后再回来继续。也可以制作一个黄金主镜像，用于为机群批量生成克隆。 - 块存储。持久卷（从 10 GB 到 16 TB），你可以用直观的语法管理：`--yolo` 并将其挂载到你的 VM。 - Profiles。凭证、MCP 连接、提示词和环境变量的组合包，在 VM 创建时注入。因此每个代理只获得你选择的能力，除此之外什么都没有。 - 代理自助服务。把 CLI 或 MCP 服务器交给 Claude、Codex 或 OpenCode，它就能管理自己的机群：为一次构建启动一台机器，创建快照，然后销毁。 - 可复现构建。使用 NixOS flakes 或搭配 Ubuntu 的 Ansible playbooks。 人们今天如何使用它？ - 代理机群。人们运行一个试点代理来界定工作范围，并将其委派给子代理，每个子代理都在自己的 VM 上：用试点代理规划项目，工作代理实现它并提交 PR。一个客户一次运行数百台机器，通过 CLI 启动和销毁。 - 模型优化和 RL 环境。ML 团队使用 machine0 来进行由代理编排的 RL 环境和模型优化工作。一个客户在 60 vCPU 机器上运行 RL 环境，一次持续运行数天；另一个客户保留一台已暂停的 H100，并在夜间让代理使用它来反复优化推理速度。 - 产品基础设施。一个客户不是自己使用 machine0，而是将他们的产品构建在 machine0 之上：每个用户会话都会从他们自己代理运行时的版本化镜像中获得一台全新的 XL 机器。他们已经发布了数百个该镜像版本，并启动了数千台机器，其中大多数只存活两分钟。 底层是什么？ 每台机器都是完整的 KVM 虚拟机，不是容器或沙箱。你获得的是暴露给 guest 的真实 GPU，带有其实际驱动；可获得内核级访问权限（加载你想要的任何模块或驱动）；你和硬件之间没有 syscall 拦截层。技术栈本身刻意保持朴素：TypeScript、Postgres、Redis。我们非常重视安全性、可靠性和性能，使 machine0 非常适合持续的计算密集型工作负载。 关于我 我从事云基础设施建设大约 15 年了。我曾在 Imperial College London 攻读云资源分配方向的博士，后来退学；之后作为 Upflow (YC W20) 的联合创始人兼 CTO 工作了六年，在 ARR 达到七位数的整个过程中亲自负责 DevOps、基础设施和安全，因为这些事情风险太高，不能委派。machine0 最初是我为自己做的工具，我就是自己的第一个用户 :) 请求 希望你能试用并给我们反馈（见下方）。或者，如果你是一家公司，正在为软件工厂、模型训练或 RL 环境寻找计算资源，欢迎联系 barnaby@machine0.io # 安装 machine0 $ curl -LsSf https://machine0.io/install.sh | sh # 创建一台机器并通过 ssh 登录 $ machine0 new myvm $ machine0 ssh myvm 评论 URL： https://news.ycombinator.com/item?id=49348136 点数：3 评论数：0

Hacker News 熱門70

Claude：多个模型性能下降

文章 URL： https://status.claude.com/incidents/q7txxvbsftgq 评论 URL： https://news.ycombinator.com/item?id=49348163 得分：12 评论数：2

Hacker News 熱門53

Show HN: Shoehorn – Quantize any model down to run on your machine

Working on Mac, Linux, and Windows now. I include a simple GUI to find new models and get things built and set up. It is working quite well across a few models for me. The GitHub README and DESIGN.md files go into detail of the how/why and it's working remarkably well so far. https://github.com/notactuallytreyanastasio/shoehorn Comments URL: https://news.ycombinator.com/item?id=49346135 Points: 5 # Comments: 2

Hacker News 熱門62

Cursor Origin

文章 URL: https://cursor.com/docs/origin 评论 URL: https://news.ycombinator.com/item?id=49339359 得分: 15 评论数: 3

Hacker News 熱門71

Llama.cpp v0.1.0

文章 URL： https://github.com/ggml-org/llama.cpp/releases/tag/v0.1.0 评论 URL： https://news.ycombinator.com/item?id=49335017 积分：10 评论数：2

Hacker News 熱門64

Launch HN：Speko (YC S26) – 面向语音 AI 的 OpenRouter

HN 大家好！我是 Bek，Speko 的创始人。Speko 是一个平台，会在我们所有公开基准测试过的选项中，根据你的约束条件，找出 speech-to-text、LLM 和 text-to-speech 模型的最优组合，并告诉你原因。 演示： https://youtu.be/no2LY2gRh-c 链接： https://speko.ai/?utm_source=hn 典型的生产级语音代理是三个模型的组合：STT、一个 LLM 和 TTS。 这些层中的每一层都有十来家可信的供应商，而且每个月市场上都会出现新模型。几乎所有人都是评估一次，选定一套自己偏好的技术栈，然后再也不复查，因为从一家供应商切换到另一家供应商又意味着一次新的集成，以及关于数字的争论。 结果就是，你在使用运行着上个季度模型的语音代理，而更好、更便宜的选择其实已经可用。 在创立 Speko 之前，我曾用四年时间作为联合创始人兼 CTO，为亚洲各地的企业构建支持 10 多种语言的语音代理。每当一个新的语音模型出现时，我们都会重复同样的流程：雇用母语评测员，将其与我们现有的技术栈做基准测试，如果有所提升就更新生产环境。Speko 将这个流程变成了一个 API。一个每天处理数千通电话的团队告诉我们：“我们真的可以直接去这个仪表盘，切换模型，它就会替我们完成。” 工作原理：你发送一个带有优化标准（准确率、延迟、成本或均衡）、语言和地区的请求。路由器会筛选出我们针对给定约束组合测量过的模型，对它们进行基准测试，选出胜者，并返回一个响应，其 headers 中包含供应商、模型名称和分数。网关会预取已签名的会话计划，因此新会话会直接从内存拨通供应商；呼叫者等待时不会发生控制平面的往返。 故障转移只发生在连接建立阶段：如果供应商拒绝连接尝试，我们就开始连接排名靠后的候选者。 一些客户案例：有一位创始人来找我们时完全不知道该选什么；他把自己的用例给了我们，现在所有流量都通过这个平台路由。一个物业管理 AI 使用 Python 运行 LiveKit，自上线以来从未更新过 STT 或 TTS：他们不知道自己的 STT 在通话中的错误率很高，也不知道已有更好的选择，而且替换看起来总像是一个 R&D 项目。一个团队不知道该为西班牙语选择哪些模型。一个医疗团队不知道哪个 STT 最擅长处理医学词汇。每种情况下，我们都帮助他们从基准测试中找到合适的技术栈，现在他们都通过我们路由。 测量部分是公开的：我们在一个地区、不同日期的运行中，把相同输入传给每个模型，并发布排行榜，包括那些我们的选择表现不如替代方案的榜单。发布演示会回答哪个 30 秒片段听起来更好；生产环境关心的是哪个模型能撑到第八分钟，所以我们测试自发语音、金额和日期、十分钟片段，而排名会发生变化。我们基于盲测一对一听感投票，训练了一个用于 TTS 自然度的自动评分器；对于它从未见过投票的供应商，它选出的胜者与我们的评测员所选一致的频率，大约和评测员之间彼此一致的频率相当。 我们自己不训练或销售模型，这正是我们保持排名公正的方式。 我们还开源了网关，面向那些希望避免音频路径上额外网络跳转、且不想与我们的云共享密钥的团队（ https://github.com/SpekoAI/gateway ，MIT）：一个 Go 二进制文件，作为 sidecar 运行在你的代理容器中，通过 Unix socket 使用一种本地协议，固定供应商主机并附加你的密钥。在 BYOK 模式下，它完全不会与我们通信。 请注意，匿名且不含内容的遥测默认启用，一个环境变量即可禁用。 成本：网关和 BYOK 设置将永久免费，我们对托管路由器和托管密钥以及合并计费收费。自从我们在 6 月下旬开始这一批次以来，外部使用量平均每周增长约 25%，增长主要集中在发布前几周。 我很想听听社区的反馈：你们现在如何选择语音模型？什么会让你们信任第三方基准测试？ 评论 URL： https://news.ycombinator.com/item?id=49332751 点数：22 评论数：7

Hacker News 熱門73

Launch HN：Speko (YC S26) – 语音 AI 的 OpenRouter

Hi HN！我是 Bek，Speko 的创始人。Speko 是一个平台，会在我们所有公开基准测试过的选项中，根据你的约束条件，找到语音转文本、LLM 和文本转语音模型的最优组合，并告诉你原因。 演示： https://youtu.be/no2LY2gRh-c 典型的生产环境语音代理是由三个模型组成的集成：STT、一个 LLM 和 TTS。 这些层中的每一层都有十几个可信的供应商，而且每个月市场上都会出现新模型。几乎所有人都是评估一次，选择自己喜欢的技术栈，然后就再也不复查，因为从一个供应商切换到另一个供应商又意味着一次新的集成，以及围绕数据的争论。 结果就是，你使用的语音代理还在运行上个季度的模型，而更好、更便宜的选择已经出现了。 在创立 Speko 之前，我花了四年时间作为联合创始人兼 CTO，为亚洲各地的企业构建支持 10 多种语言的语音代理。每当新的语音模型出现时，我们都会重复同样的流程：雇用母语评分员，将它与现有技术栈进行基准测试，如果有所改进就更新生产环境。Speko 将这个过程变成了一个 API。一个每天处理数千通电话的团队告诉我们：“我们真的可以进入这个仪表板，切换模型，它会替我们完成。” 工作方式：你发送一个包含优化标准（准确率、延迟、成本或均衡）、语言和地区的请求。路由器会筛选出我们针对给定约束组合测量过的模型，对它们进行基准测试，选出胜者，并返回一个响应，响应头中包含提供商、模型名称和分数。网关会预取签名会话计划，因此新会话会直接从内存拨向提供商；当呼叫者等待时，不需要控制平面的往返。 故障转移只发生在连接建立阶段：如果提供商拒绝连接尝试，我们就开始连接排名靠后的候选项。 一些客户案例：有一位创始人来找我们时完全不知道该选什么；他把自己的用例给了我们，现在所有流量都通过这个平台路由。一家物业管理 AI 使用 Python 运行 LiveKit，自上线以来从未更新过 STT 或 TTS：他们不知道自己的 STT 在通话中的错误率很高，也不知道有更好的选择，而且模型替换看起来总像一个研发项目。一个团队不知道该为西班牙语选择哪些模型。一个医疗团队不知道哪种 STT 最擅长处理医学词汇。在每个案例中，我们都帮助他们从基准测试中找到合适的技术栈，现在他们都通过我们进行路由。 测量部分是公开的：我们在不同日期的运行中，将相同输入传给同一地区的每个模型，并发布榜单，包括那些我们的选择表现不如替代方案的榜单。发布演示回答的是哪段 30 秒音频听起来更好；生产环境问的是哪个模型能撑过第八分钟，所以我们测试自发语音、金额和日期、十分钟录音，排名也会发生变化。我们基于盲测一对一听感投票训练了一个用于 TTS 自然度的自动评分器；对于它从未见过投票的提供商，它选出的胜者与我们的评分员选出的一致，其频率大约等同于评分员之间彼此达成一致的频率。 我们自己不训练或销售模型，这正是我们保持排名公正的方式。 我们还开源了网关，供那些希望避免音频路径上额外网络跳转、并且不想与我们的云共享密钥的团队使用（ https://github.com/SpekoAI/gateway ，MIT）：一个 Go 二进制文件，作为 sidecar 运行在你的代理容器中，通过 Unix socket 使用一种本地协议，固定提供商主机并附加你的密钥。在 BYOK 模式下，它完全不会与我们通信。 请注意，匿名且不含内容的遥测默认启用，一个环境变量即可将其关闭。 成本：网关和 BYOK 设置将永久免费；我们对带有统一账单的托管路由器和托管密钥收费。自从我们在 6 月下旬开始这一批次以来，外部使用量平均每周增长约 25%，增长主要集中在发布初期的几周。 我很想听听社区的反馈：你们现在如何选择语音模型？什么会让你们信任第三方基准测试？ 评论 URL： https://news.ycombinator.com/item?id=49332751 点数：7 评论数：0

Hacker News 熱門61

Show HN: 1667, a terminal UI for writing fiction with language models

Hi HN. I built 1667 for my own fiction work and now use it each day. This probably has a limited audience. Maybe an audience of one... Why a terminal interface for story writing? I'm a dev. I like to use terminals for a lot of stuff. Most WebUIs feel off to me. That's the only reason. One thing that bothers me about writing in existing tools is that they don't fit the way I write. The mental model of my story is a tree. I try many takes usually continue with just one, but sometimes I want to try an alternate route and see where this goes. And that can branch again in many places. See what happens if I kill off this character or they don't take the job or whatever. 1667 is a full-screen terminal app for long-form fiction. Each story part can have several takes. All takes stay in a tree. You select one path through that tree as the story line. Export writes that line to Markdown in the project folder. Some technical details: - A project stores its stories and settings in a `.1667/` directory. Exported Markdown sits beside it. - Provider secrets stay in private machine files. Requests go to the provider that the writer selects. - An optional Vault Password seals project files at rest. - An operating-system lock permits one writer process for each project. - The request viewer shows the next provider request without its credential. - Each generated take keeps a Generation Record with its model and effective settings. Version 0.9.5 runs on macOS, Linux, and Windows x64. The website has Shell and PowerShell installers. An npm package is also available. 1667 imports Markdown, SillyTavern chats and cards, and NovelAI archives. It can use OpenAI-compatible, Anthropic, and local endpoints such as Ollama, LM Studio, llama.cpp, and KoboldCpp. Current limits: the release is pre-1.0. The interface is a terminal. There is no account or cloud sync, and I don't plan to add any. No tracking. Comments URL: https://news.ycombinator.com/item?id=49330604 Points: 12 # Comments: 12

Hacker News 熱門53

Claude Seems Down

Throwing "Authentication service was unavailable.", no updates on their status page, but there are on downdetector Comments URL: https://news.ycombinator.com/item?id=49324078 Points: 17 # Comments: 8

Hacker News 熱門71

Kimi Work 会将原始代理会话附加到反馈报告中

我只是出于好玩逆向工程了最新版 Kimi Work 桌面应用，我知道我很宅。 总之，我发现了一件有点令人担忧的事：当你发送反馈报告时，它会在没有任何通知或提醒的情况下，把最近 5 个会话附加到报告中。 这些会话里可能包含任何内容，而你完全不知道自己正把这一切都发送给 Kimi。 我已经发邮件提醒他们了，但还是想告知大家，以防你们正在使用 Kimi。 URL: https://runtimewire.com/article/scoop-kimi-work-secretly-att... 评论 URL: https://news.ycombinator.com/item?id=49313711 积分: 10 评论数: 1

Hacker News 熱門78

Claude：系统提示词

文章 URL： https://platform.claude.com/docs/en/release-notes/system-prompts 评论 URL： https://news.ycombinator.com/item?id=49319556 得分：10 评论数：0

Hacker News 熱門87

GenRec：迈向 Netflix 的 LLM 原生推荐

文章 URL： https://netflixtechblog.com/genrec-towards-llm-native-recommendation-at-netflix-f20be6f643e3 评论 URL： https://news.ycombinator.com/item?id=49310177 点数：4 评论数：0

Hacker News 熱門53

Show HN: Mole – Deep research agent for your terminal

Doing research with agents is fun until they blow way past budget, jumble the sources, and don't even give you the best possible answer, just sound confident. And if you want to run some research task on local data - you have no idea where your data ends up after the prompt consumes it. So I built this tool: a deep-research agent with an enforced budget, verified quotes, and a privacy boundary for local data. 1. Never spend more than you budgeted (measured overshoot is 0%). 2. Every claim carries a source 3. Data stays local (give a CSV, it'll analyze it without the data ever leaving your machine) Works with most LLMs, including coding agents, subscriptions, local models, etc. It's free and open source, would appreciate all feedback! Comments URL: https://news.ycombinator.com/item?id=49303046 Points: 58 # Comments: 10

DeepMind Blog62

How AI is giving Northern Ireland teachers time back

A six-month long pilot program with the Northern Ireland Education Authority’s C2k initiative found that integrating Gemini and other generative AI tools saved participating teachers an average of 10 hours per week.

DeepMind Blog75

为 AI 时代重新构想鼠标指针

Google DeepMind 正在将鼠标指针转变为一个具备情境感知能力的 AI 伙伴。在 Chrome 及更多场景中，通过直观的 AI 协作，超越传统提示方式带来的摩擦。

DeepMind Blog74

加速发现肝病机制

Filippo Menolascina 使用 Co-Scientist 来识别新的肝病治疗方法，并解释为什么现有药物只对某些患者有帮助。

DeepMind Blog68

开辟衰老研究的新路径

Calico Life Sciences 使用 Co-Scientist 连接分散的发现，并在衰老研究中生成新的线索。

DeepMind Blog79

将手语 AI 交到用户手中

介绍 sign-language-to-text (SL2T)，我们的突破性模型，为聋人和听障用户提供全新的手语功能支持。

OpenAI Blog54

OpenAI Gym Beta

We’re releasing the public beta of OpenAI Gym, a toolkit for developing and comparing reinforcement learning (RL) algorithms. It consists of a growing suite of environments (from simulated robots to Atari games), and a site for comparing and reproducing results.

OpenAI Blog43

Universe

We’re releasing Universe, a software platform for measuring and training an AI’s general intelligence across the world’s supply of games, websites and other applications.

OpenAI Blog51

Roboschool

We are releasing Roboschool: open-source software for robot simulation, integrated with OpenAI Gym.

OpenAI Blog70

Python 中更快的物理仿真

我们正在开源一个高性能 Python 库，用于使用 MuJoCo 引擎进行机器人仿真，该库是在我们过去一年的机器人研究中开发的。

OpenAI Blog55

Gathering human feedback

RL-Teacher is an open-source implementation of our interface to train AIs via occasional human feedback rather than hand-crafted reward functions. The underlying technique was developed as a step towards safe AI systems, but also applies to reinforcement learning problems with rewards that are hard to specify.

OpenAI Blog57

Gym Retro

We’re releasing the full version of Gym Retro, a platform for reinforcement learning research on games. This brings our publicly-released game count from around 70 Atari games and 30 Sega games to over 1,000 games across a variety of backing emulators. We’re also releasing the tool we use to add new games to the platform.

OpenAI Blog49

Neural MMO: A massively multiagent game environment

We’re releasing a Neural MMO, a massively multiagent game environment for reinforcement learning agents. Our platform supports a large, variable number of agents within a persistent and open-ended task. The inclusion of many agents and species leads to better exploration, divergent niche formation, and greater overall competence.

OpenAI Blog45

Safety Gym

We’re releasing Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training.

OpenAI Blog58

OpenAI Microscope

We’re introducing OpenAI Microscope, a collection of visualizations of every significant layer and neuron of eight vision “model organisms” which are often studied in interpretability. Microscope makes it easier to analyze the features that form inside these neural networks, and we hope it will help the research community as we move towards understanding these complicated systems.

OpenAI Blog56

OpenAI API

We’re releasing an API for accessing new AI models developed by OpenAI.

OpenAI Blog63

介绍 Triton：用于神经网络的开源 GPU 编程

我们发布了 Triton 1.0，这是一种类似 Python 的开源编程语言，使没有 CUDA 经验的研究人员也能编写高效的 GPU 代码——大多数情况下可媲美专家所能编写的代码。

OpenAI Blog64

推出文本和代码嵌入

我们正在推出嵌入，这是 OpenAI API 中的一个新端点，可轻松执行自然语言和代码任务，例如语义搜索、聚类、主题建模和分类。

OpenAI Blog51

DALL·E 2 research preview update

Early users have created over 3 million images to date and helped us improve our safety processes. We’re excited to begin adding up to 1,000 new users from our waitlist each week.

OpenAI Blog56

DALL·E 2 pre-training mitigations

In order to share the magic of DALL·E 2 with a broad audience, we needed to reduce the risks associated with powerful image generation models. To this end, we put various guardrails in place to prevent generated images from violating our content policy.

OpenAI Blog47

DALL·E 2: Extending creativity

As part of our DALL·E 2 research preview, more than 3,000 artists from more than 118 countries have incorporated DALL·E into their creative workflows. The artists in our early access group have helped us discover new uses for DALL·E and have served as key voices as we’ve made decisions about DALL·E’s features.

OpenAI Blog58

DALL·E now available in beta

We’ll invite 1 million people from our waitlist over the coming weeks. Users can create with DALL·E using free credits that refill every month, and buy additional credits in 115-generation increments for $15.

OpenAI Blog63

全新且改进的内容审核工具

我们推出了一款全新且改进的内容审核工具。Moderation endpoint 在我们之前的内容过滤器基础上进行了改进，并于今日免费提供给 OpenAI API 开发者使用。

OpenAI Blog60

Introducing ChatGPT Plus

We’re launching a pilot subscription plan for ChatGPT, a conversational AI that can chat with you, answer follow-up questions, and challenge incorrect assumptions.

OpenAI Blog75

AI 系统应该如何行为，由谁来决定？

我们正在阐明 ChatGPT 的行为是如何塑造的，以及我们改进该行为、允许更多用户自定义，并在这些领域让公众更多参与我们决策的计划。

OpenAI Blog48

Stripe

Stripe leverages GPT-4 to streamline user experience and combat fraud.

OpenAI Blog60

ChatGPT plugins

We’ve implemented initial support for plugins in ChatGPT. Plugins are tools designed specifically for language models with safety as a core principle, and help ChatGPT access up-to-date information, run computations, or use third-party services.

OpenAI Blog65

函数调用及其他 API 更新

我们宣布推出包括更易引导的 API 模型、函数调用功能、更长上下文以及更低价格在内的更新。

OpenAI Blog65

ChatGPT 的自定义指令

我们正在推出自定义指令，让你更好地控制 ChatGPT 的回应方式。设置你的偏好后，ChatGPT 会在未来的所有对话中记住这些偏好。

OpenAI Blog61

Using GPT-4 for content moderation

We use GPT-4 for content policy development and content moderation decisions, enabling more consistent labeling, a faster feedback loop for policy refinement, and less involvement from human moderators.

OpenAI Blog63

推出 GPTs

现在，你可以创建 ChatGPT 的自定义版本，将指令、额外知识和任意技能组合在一起。

OpenAI Blog62

Introducing ChatGPT Team

We’re launching a new ChatGPT plan for teams of all sizes, which provides a secure, collaborative workspace to get the most out of ChatGPT at work.

OpenAI Blog80

ChatGPT 的记忆与新控制功能

我们正在测试 ChatGPT 记住你所讨论内容的能力，以便让未来的聊天更有帮助。你可以掌控 ChatGPT 的记忆。

OpenAI Blog59

Sora first impressions

Since we introduced Sora to the world last month, we’ve been working with artists to learn how Sora might aid in their creative process.

OpenAI Blog73

了解我们在网上所见所闻的来源

今天，我们推出新技术，帮助研究人员识别由我们的工具创建的内容，并加入 Coalition for Content Provenance and Authenticity Steering Committee，以推动行业标准。

OpenAI Blog89

春季更新

推出 GPT-4o，并在 ChatGPT 中免费提供更多功能。

OpenAI Blog62

Creating an AI-powered Magic Studio

Canva is a visual communication platform, enjoyed by more than 175 million people monthly to make presentations, videos, documents, websites, social media graphics and more. A majority of the world’s knowledge workers lack design training, but Canva’s combination of an easy-to-use interface, vast libraries, and time-saving tools allows anyone to create visually compelling content.

OpenAI Blog73

OpenAI 与 Reddit 合作

OpenAI 与 Reddit 合作 我们正在将 Reddit 的独特内容引入 ChatGPT 和我们的产品。

OpenAI Blog55

How the voices for ChatGPT were chosen

How the voices for ChatGPT were chosen We worked with industry-leading casting and directing professionals to narrow down over 400 submissions before selecting the 5 voices.

OpenAI Blog64

通过 The Atlantic 增强 ChatGPT 中的新闻

The Atlantic 宣布与 OpenAI 建立战略内容和产品合作伙伴关系，这使 The Atlantic 成为 OpenAI 内的优质新闻来源。The Atlantic 的文章将可在 OpenAI 的产品中被发现，包括 ChatGPT；作为合作伙伴，The Atlantic 将帮助塑造新闻在未来实时发现产品中的呈现和展示方式。

OpenAI Blog56

Automating customer support agents

MavenAGI is a new software company for the AI era. They recently launched an AI customer service agent, built on the flexibility of GPT-4, which a number of companies like Tripadvisor, Clickup and Rho are already using to save time and better serve their customers.

OpenAI Blog73

OpenAI for Education

一项面向大学的经济实惠方案，帮助其负责任地将 AI 引入校园。

OpenAI Blog75

面向非营利组织推出 OpenAI

我们正在推出一项新计划，以提高非营利组织使用我们工具的可及性，其中包括 ChatGPT Team 和 Enterprise 的折扣费率。

OpenAI Blog70

利用 GPT-4o 推理变革癌症护理

Color Health 正在与 OpenAI 合作，开创一种加快癌症患者获得治疗的新方式。他们的新 Cancer Copilot 应用使用 GPT-4o 来识别缺失的诊断信息并制定量身定制的检查计划，使医疗服务提供者能够就癌症筛查和治疗做出循证决策。

OpenAI Blog50

Surging developer productivity with custom GPTs

Paf adopted ChatGPT Enterprise across its entire company, with engineers using custom GPTs on a daily basis to speed up routine development tasks. Paf also integrated ChatGPT Enterprise into the grit:lab coding academy (gritlab.ax), training the next generation of software developers using an AI-augmented, systems-architecture mindset from day one. In addition to the wide range of use cases for developers and grit:lab students, 70% of Paf employees actively use ChatGPT Enterprise, spanning business teams like finance, HR, marketing, and customer support.

OpenAI Blog69

在 API 中引入结构化输出

我们正在 API 中引入结构化输出——模型输出现在能够可靠地遵循开发者提供的 JSON Schemas。

OpenAI Blog65

借助 OpenAI 为数百万人提供情境化职位匹配

Indeed 的使命是帮助人们找到工作，它是全球排名第一的求职网站。每月有超过 3.5 亿独立访客访问 Indeed，与超过 350 万雇主和 3200 多万个职位建立联系。更重要的是，每三秒就有人通过 Indeed 获得聘用。

OpenAI Blog60

Personalizing education with ChatGPT

Arizona State University embraces ChatGPT campus-wide to personalize learning, advance research, and prepare students for the future

OpenAI Blog54

Decoding genetics with OpenAI o1

Geneticist Catherine Brownstein demonstrates how OpenAI o1 can speed up the process of diagnosing rare medical challenges.

OpenAI Blog78

API 中的模型蒸馏

使用大型前沿模型的输出微调一个成本高效的模型——全部在 OpenAI 平台上完成

OpenAI Blog87

推出 Realtime API

开发者现在可以在其应用程序中构建快速的语音到语音体验

OpenAI Blog37

Rox goes “all in” on OpenAI

By combining commercial experience and deep LLM expertise with OpenAI’s models, Rox makes every seller a top 1% seller.

OpenAI Blog44

Vallée Duhamel & Sora

Filmmaking duo Vallée Duhamel explains how Sora helps build new worlds.

OpenAI Blog33

Minne Atairu & Sora

Interdisciplinary artist Minne Atairu discusses how Sora helps realize her vision.

OpenAI Blog92

Sora 来了

我们的视频生成模型 Sora 现已可在 sora.com 使用。用户可以生成最高 1080p 分辨率、最长 20 秒的视频，并可选择宽屏、竖屏或方形宽高比。你可以使用自己的素材进行扩展、再创作和融合，也可以根据文本生成全新的内容。

OpenAI Blog86

Operator 系统卡

基于 OpenAI 已建立的安全框架，本文档重点介绍了我们的多层方法，包括为防范提示工程和越狱、保护隐私与安全而实施的模型和产品缓解措施，并详细说明了我们的外部红队测试工作、安全评估，以及为进一步完善这些防护措施而持续开展的工作。

OpenAI Blog68

推出 deep research

一个使用推理来综合大量在线信息并为你完成多步骤研究任务的智能体。今天面向 Pro 用户开放，接下来将面向 Plus 和 Team 用户开放。

OpenAI Blog77

在欧洲推出数据驻留

数据驻留建立在 OpenAI 的企业级数据隐私、安全和合规计划之上，为全球客户提供支持。

OpenAI Blog80

Deep research 系统卡

本报告概述了在发布 deep research 之前开展的安全工作，包括外部红队测试、根据我们的 Preparedness Framework 进行的前沿风险评估，以及我们为应对关键风险领域而内置的缓解措施概览。

OpenAI Blog60

Supporting sellers with enhanced product listings

Mercari leverages GPT-4o mini and GPT-4 to streamline selling, enhance product listings, and boost sales, transforming the online marketplace with features like AI Listing Support and Mercari AI Assistant.

OpenAI Blog84

在 API 中推出新一代音频模型

开发者现在首次还可以指示 text-to-speech 模型以特定方式说话，例如“像富有同理心的客服专员一样说话”，从而为语音智能体解锁全新的定制化水平。

OpenAI Blog86

推出 4o 图像生成

在 OpenAI，我们长期以来一直认为图像生成应该是我们的语言模型的一项核心能力。因此，我们将迄今为止最先进的图像生成器内置到了 GPT‑4o 中。其结果是——图像生成不仅美观，而且实用。

OpenAI Blog65

Lowe’s 将项目专业知识送到每个人手中

Lowe’s 与 OpenAI 合作打造了 Mylow 和 Mylow Companion，这些由 AI 驱动的工具为客户和门店员工提供专家帮助，使复杂的家居改善项目更易于规划、推进和完成。

OpenAI Blog75

在亚洲推出数据驻留

数据驻留建立在 OpenAI 企业级数据隐私、安全与合规计划的基础之上，为全球客户提供支持。

OpenAI Blog83

o3 和 o4-mini 系统卡的补充说明：Codex

Codex 是一种基于云的编码代理。Codex 由 codex-1 驱动，codex-1 是针对软件工程优化的 OpenAI o3 版本。codex-1 使用强化学习在各种环境中的真实编码任务上进行训练，以生成高度贴近人类风格和 PR 偏好的代码，精确遵循指令，并反复运行测试，直到取得通过结果。

OpenAI Blog76

推出 Stargate UAE

我们正在推出 Stargate UAE——这是 OpenAI 的 AI 基础设施平台 Stargate 的首次国际部署。

OpenAI Blog81

Vixen 和 Keyhole Panda：中国相关网络行动

OpenAI 封禁了与公开归因于中华人民共和国的威胁行为者相关的账号，这些账号使用 AI 来支持漏洞研究、脚本编写、翻译和行动故障排查。

OpenAI Blog70

将 AI 的魔力带入 Mattel 的标志性品牌

OpenAI 和 Mattel 正在合作，将 AI 融入 Barbie 和 Hot Wheels 等标志性品牌，旨在提升创意开发、简化工作流程，并为粉丝创造新的互动方式。

OpenAI Blog80

推出 OpenAI for Government

我们正在推出 OpenAI for Government，这是一项新计划，旨在把我们最先进的 AI 工具带给美国各地的公务人员。我们正在支持美国政府采用一流技术的努力，并部署这些工具以服务公共利益。

OpenAI Blog50

Driving scalable growth with OpenAI o3, GPT-4.1, and CUA

Unify, an AI-powered GTM platform, uses OpenAI’s o3, GPT-4.1, and CUA to automate prospecting, research, and outreach. With hyper-personalized messaging and an always-on workflow, Unify helps teams generate pipeline at scale while focusing on high-impact customer interactions.

OpenAI Blog59

Customizable, no-code voice agent automation with GPT-4o

Retell AI is transforming the call center with AI voice automation powered by GPT-4o and GPT-4.1. Its no-code platform enables businesses to launch natural, real-time voice agents that cut call costs, boost CSAT, and automate customer conversations—without scripts or hold times.

OpenAI Blog63

设计中的思想自由

ChatGPT 旨在实用、可信且适应性强——让你可以将其打造成属于自己的工具。

OpenAI Blog84

ChatGPT agent System Card

ChatGPT agent System Card: OpenAI’s agentic model unites research, browser automation, and code tools with safeguards under the Preparedness Framework.

OpenAI Blog86

推出 ChatGPT agent

推出 ChatGPT agent：它会思考并行动，在你的指导下使用工具完成研究、预订和幻灯片等任务。

OpenAI Blog76

与 Penda Health 共同开创 AI 临床副驾驶

OpenAI 和 Penda Health 首次推出一款 AI 临床副驾驶，在真实使用中将诊断错误减少了 16%——为医疗保健领域安全、有效地应用 AI 提供了一条新路径。

OpenAI Blog86

在 ChatGPT 中推出学习模式

在 ChatGPT 中推出学习模式，这是一种全新的学习体验，可帮助你逐步解决问题，通过提问、支架式引导和反馈来引导学生，实现更深入的学习。

OpenAI Blog61

Figma uses AI to transform digital design

Discover how Figma is transforming digital design with AI. David Kossnick shares how tools like Figma Make empower teams to prototype, collaborate, and build with AI—reshaping workflows for designers, developers, and non-technical creators alike.

OpenAI Blog75

我们优化 ChatGPT 的目标

我们打造 ChatGPT，是为了以你希望的各种方式帮助你更好地成长与生活。了解我们如何在专家意见的指导下，改进对艰难时刻的支持，推出休息提醒，并致力于提供更好的生活建议。

OpenAI Blog90

Providing ChatGPT to the Entire U.S. Federal Workforce

Today, OpenAI for Government is announcing a new partnership with the U.S. General Services Administration (GSA) to launch a transformative initiative. For the next year, ChatGPT Enterprise will be available to the entire federal executive branch workforce at essentially no cost.

OpenAI Blog57

Scaling accounting capacity with OpenAI

Built with OpenAI o3, o3-Pro, GPT-4.1, and GPT-5, Basis’ AI agents help accounting firms save up to 30% of their time and expand capacity for advisory and growth.

OpenAI Blog46

Mixi reimagines communication with ChatGPT

Discover how MIXI, a leader in digital entertainment and lifestyle services in Japan, uses ChatGPT Enterprise to transform productivity, boost AI adoption across teams, and create a secure environment for innovation.

OpenAI Blog54

Scaling domain expertise in complex, regulated domains

Discover how Blue J is transforming tax research with AI-powered tools built on GPT-4.1. By combining domain expertise with Retrieval-Augmented Generation, Blue J delivers fast, accurate, and fully-cited tax answers—trusted by professionals across the US, Canada, and the UK.

OpenAI Blog64

在人们最需要帮助时提供支持

我们如何看待为正在经历心理或情绪困扰的用户提供安全保障、当今系统的局限，以及为改进这些系统而正在开展的工作。

OpenAI Blog83

用 AI 扩大经济机会

OpenAI 正在推出 Jobs Platform 和新的 Certifications，将求职者与工作、培训和认证连接起来。了解我们如何扩大经济机会，并让 AI 技能更易获得。

OpenAI Blog74

OpenAI 和希腊政府推出“OpenAI for Greece”

OpenAI 和希腊政府推出了“OpenAI for Greece”，旨在将 ChatGPT Edu 引入中学，并支持负责任的 AI 学习。该合作伙伴关系旨在提升 AI 素养、推动本地初创企业发展，并促进国家经济增长。

OpenAI Blog75

人们如何使用 ChatGPT

来自最大规模 ChatGPT 使用研究的新研究显示，该工具如何通过个人和专业用途创造经济价值。采用范围正在从早期用户扩大，缩小差距，并使 AI 成为日常生活的一部分。

OpenAI Blog77

Codex 升级介绍

Codex 现在速度更快、更可靠，并且更擅长实时协作，也能在你进行开发的任何地方独立处理任务——无论是通过终端、IDE、网页，甚至是你的手机。

OpenAI Blog84

迈向年龄预测

了解 OpenAI 如何在 ChatGPT 中构建年龄预测和家长控制功能，以为青少年创造更安全、适龄的体验，同时通过新工具支持家庭。

OpenAI Blog67

SchoolAI 打造赋能教师的 AI 平台

SchoolAI 使用 GPT-4.1、图像生成和 TTS，为超过 100 万间课堂提供安全、由教师引导的 AI 工具，提升参与度、监督能力和个性化学习。

OpenAI Blog78

推出 ChatGPT Pulse

今天，我们面向移动端 Pro 用户发布 ChatGPT Pulse 预览版。Pulse 是一种全新体验，ChatGPT 会主动开展研究，并根据你的聊天、反馈以及已连接的应用（例如你的日历）提供个性化更新。

OpenAI Blog75

与 AARP 合作，帮助老年人保障在线安全

OpenAI 和 AARP 正在合作，通过 OpenAI Academy 和 OATS 的 Senior Planet 倡议，借助新的 AI 培训、诈骗识别工具和全国性项目，帮助老年人保障在线安全。

OpenAI Blog76

推出家长控制

我们正在推出家长控制和新的家长资源页面，帮助家庭指导 ChatGPT 在家中的使用方式。

OpenAI Blog78

负责任地推出 Sora

为应对先进视频模型以及全新社交创作平台带来的新型安全挑战，我们在构建 Sora 2 和 Sora app 时将安全作为基础。我们的方法以具体的保护措施为根基。

OpenAI Blog72

“Stop News”行动：屡犯影响力活动

OpenAI 封禁了与一项源自俄罗斯、被我们称为“Stop News”的行动相关的账号，这些账号利用 AI 生成针对非洲和英国的屡犯影响力内容。

OpenAI Blog87

在 ChatGPT 中推出应用和新的 Apps SDK

我们正在推出新一代可直接在 ChatGPT 中与你聊天的应用。开发者可以从今天开始使用新的 Apps SDK 构建这些应用，该 SDK 现已提供预览版。

OpenAI Blog85

Codex 现已正式发布

OpenAI Codex 现已正式发布，并为开发者带来强大的新功能：Slack 集成、Codex SDK，以及用量仪表板和工作区管理等管理员工具，让 Codex 更易于大规模使用和管理。

OpenAI Blog70

福祉与 AI 专家委员会

OpenAI 新成立的福祉与 AI 专家委员会汇聚了顶尖心理学家、临床医生和研究人员，指导 ChatGPT 如何支持情绪健康，尤其是青少年的情绪健康。了解他们的见解如何塑造更安全、更有关怀的 AI 体验。

OpenAI Blog89

推出 ChatGPT Atlas，内置 ChatGPT 的浏览器

ChatGPT Atlas，内置 ChatGPT 的浏览器。可直接在任何页面上获得即时答案、摘要和智能网页帮助。并提供可由你掌控的隐私设置。现已适用于 MacOS。

OpenAI Blog79

英国主权 AI 的下一篇章

OpenAI 通过与 Ministry of Justice 达成新协议，扩大其在英国的合作，将 ChatGPT 带给公务员。它还为 ChatGPT Enterprise、ChatGPT Edu 和 API Platform 引入英国数据驻留，以支持可信且安全的 AI 采用。

OpenAI Blog84

在 ChatGPT 中更智能地运用你的公司知识

公司知识会将你的应用中的上下文带入 ChatGPT，从而提供针对你业务的答案，并附有清晰的引用，同时具备安全、隐私和管理员控制。现已面向 Business、Enterprise 和 Edu 用户开放。

OpenAI Blog72

Consensus 借助 GPT-5 和 Responses API 加速研究

Consensus 使用 GPT-5 和 OpenAI 的 Responses API 来驱动一个多智能体研究助手，可在数分钟内阅读、分析并综合证据，帮助超过 800 万名研究人员加速科学发现。

OpenAI Blog75

OpenAI 收购 Sky 的开发商 Software Applications Incorporated

OpenAI 已收购 Sky 的开发商 Software Applications Incorporated。Sky 是一款面向 Mac 的自然语言界面，可将 AI 直接带入你的桌面体验。我们将共同把 Sky 深度的 macOS 能力整合到 ChatGPT 中，让 AI 更加直观、更具上下文感知能力，并更偏向行动导向。

OpenAI Blog80

加强 ChatGPT 在敏感对话中的回应

OpenAI 与 170 多位心理健康专家合作，提升 ChatGPT 识别痛苦、以同理心回应并引导用户寻求现实支持的能力——将不安全回应减少高达 80%。了解我们如何让 ChatGPT 在敏感时刻更安全、更具支持性。

OpenAI Blog63

推动面向业务创新的组织转型

DNP 在十个核心部门推广了 ChatGPT Enterprise，在三个月内实现了专利研究速度提升 95%、处理量提升 10 倍、自动化率达到 87%，以及知识复用率达到 70%。

OpenAI Blog84

介绍 Aardvark：OpenAI 的智能体安全研究员

OpenAI 推出 Aardvark，这是一款由 AI 驱动的安全研究员，能够自主地大规模发现、验证并帮助修复软件漏洞。该系统目前处于私有测试阶段——欢迎注册加入早期测试。

OpenAI Blog74

100 万企业客户正在将 AI 投入工作

全球已有超过 100 万企业客户使用 OpenAI。在医疗保健、生命科学、金融服务等领域，ChatGPT 和我们的 APIs 正在推动一个由 AI 赋能的智能工作新时代。

OpenAI Blog83

Intuit 与 OpenAI 携手推出全新的 AI 驱动体验

OpenAI 与 Intuit 已达成一项超过 1 亿美元的多年期合作伙伴关系，将在 ChatGPT 中推出 Intuit 应用体验，并扩大 Intuit 对 OpenAI 前沿模型的使用，以支持个性化金融工具。

OpenAI Blog83

专为教师打造的免费版 ChatGPT

ChatGPT for Teachers 是一个安全的工作空间，具备教育级隐私保护和管理控制功能。经验证的美国 K–12 教育工作者可免费使用至 2027 年 6 月。

OpenAI Blog79

OpenAI 与 Target 携手推出全新 AI 驱动体验

OpenAI 与 Target 正在合作，将一款全新的 Target 应用引入 ChatGPT，提供个性化购物和更快捷的结账体验。Target 还将扩大对 ChatGPT Enterprise 的使用，以提升生产力和顾客体验。

OpenAI Blog63

Helping 1,000 small businesses build with AI

OpenAI is partnering with DoorDash, SCORE, and local organizations to help 1,000 small businesses build with AI. The Small Business AI Jam gives Main Street business owners hands-on tools and training to compete and grow.

OpenAI Blog79

在 ChatGPT 中推出购物研究

ChatGPT 中的购物研究可通过个性化购买指南帮助你探索、比较和发现产品，从而简化决策过程。

OpenAI Blog62

Inside Mirakl's agentic commerce vision

Mirakl is redefining commerce through AI agents and ChatGPT Enterprise—achieving faster documentation, smarter customer support, and building toward agent-native commerce with Mirakl Nexus.

OpenAI Blog93

全新的 ChatGPT Images 来了

全新的 ChatGPT Images 由我们的旗舰图像生成模型提供支持，可实现更精准的编辑、更一致的细节，以及最高快 4 倍的图像生成速度。升级后的模型今天开始向所有 ChatGPT 用户推出，并且也已在 API 中以 GPT-Image-1.5 的形式提供。

OpenAI Blog87

开发者现在可以向 ChatGPT 提交应用

开发者现在可以提交应用，以便在 ChatGPT 中进行审核和发布；获批的应用将出现在新的产品内目录中，方便用户发现。更新后的工具、指南和 Apps SDK 可帮助开发者构建强大的聊天原生体验，将现实世界中的操作带入 ChatGPT。

OpenAI Blog76

面向新闻机构推出 OpenAI Academy

OpenAI 正在推出面向新闻机构的 OpenAI Academy，这是一个与 American Journalism Project 和 The Lenfest Institute 共同打造的新学习中心，旨在帮助新闻编辑室有效使用 AI。该 Academy 提供培训、实用用例和负责任使用指南，以支持记者、编辑和出版机构在报道和运营中采用 AI。

OpenAI Blog82

持续强化 ChatGPT Atlas 以抵御提示注入

OpenAI 正在使用通过强化学习训练的自动化红队测试来加强 ChatGPT Atlas 对提示注入攻击的防护。这种主动的发现并修补循环有助于及早识别新的漏洞利用，并随着 AI 变得更具自主代理能力而强化浏览器代理的防御。

OpenAI Blog82

推出 ChatGPT Health

ChatGPT Health 是一项专属体验，可安全连接你的健康数据和应用，并提供隐私保护和由医生参与设计的方案。

OpenAI Blog66

OpenAI for Healthcare

OpenAI for Healthcare 提供安全的企业级 AI，支持 HIPAA 合规——减轻行政负担并支持临床工作流程。

OpenAI Blog47

Zenken boosts a lean sales team with ChatGPT Enterprise

By rolling out ChatGPT Enterprise company-wide, Zenken has boosted sales performance, cut preparation time, and increased proposal success rates. AI-supported workflows are helping a lean team deliver more personalized, effective customer engagement.

OpenAI Blog85

推出 ChatGPT Go，现已在全球范围内提供

ChatGPT Go 现已在全球范围内提供，可更广泛地使用 GPT-5.2 Instant，拥有更高的使用额度和更长的记忆能力，让先进 AI 在全球范围内更加实惠。

OpenAI Blog79

我们的年龄预测方法

ChatGPT 正在推出年龄预测功能，用于估算账号持有人是否未满或已满 18 岁，为青少年应用安全保护措施，并随着时间推移不断提高准确性。

OpenAI Blog77

推出 Edu for Countries

Edu for Countries 是 OpenAI 的一项新计划，旨在帮助各国政府利用 AI 实现教育系统现代化，并打造面向未来的劳动力。

OpenAI Blog64

Praktika 语言学习会话式方法揭秘

Praktika 如何使用 GPT-4.1 和 GPT-5.2 构建自适应 AI 导师，以个性化课程、跟踪进度，并帮助学习者实现真实场景中的语言流利度

OpenAI Blog85

展开 Codex 智能体循环

对 Codex 智能体循环的技术深度解析，说明 Codex CLI 如何使用 Responses API 来编排模型、工具、提示词和性能。

OpenAI Blog75

Prism 介绍

Prism 是一个免费的 LaTeX 原生工作空间，内置 GPT-5.2，帮助研究人员在一个地方进行写作、协作和推理。

OpenAI Blog75

OpenAI 内部数据代理揭秘

OpenAI 如何构建了一个内部 AI 数据代理，利用 GPT-5、Codex 和记忆功能对海量数据集进行推理，并在数分钟内提供可靠洞察。

OpenAI Blog82

推出 Codex 应用

推出适用于 macOS 的 Codex 应用——一个用于 AI 编码和软件开发的指挥中心，支持多个智能体、并行工作流和长时间运行的任务。

OpenAI Blog59

The Sora feed philosophy

Discover the Sora feed philosophy—built to spark creativity, foster connections, and keep experiences safe with personalized recommendations, parental controls, and strong guardrails.

OpenAI Blog83

推出 OpenAI Frontier

OpenAI Frontier 是一个企业平台，用于构建、部署和管理具备共享上下文、入职引导、权限和治理功能的 AI agent。

OpenAI Blog74

推出 Trusted Access for Cyber

OpenAI 推出 Trusted Access for Cyber，这是一个基于信任的框架，旨在扩大对前沿网络能力的访问，同时加强防止滥用的保障措施。

OpenAI Blog74

将 ChatGPT 引入 GenAI.mil

OpenAI for Government 宣布在 GenAI.mil 上部署定制版 ChatGPT，为美国国防团队带来安全、以安全性为先的 AI。

OpenAI Blog74

扩展社会科学研究

GABRIEL 是 OpenAI 推出的一个新的开源工具包，它使用 GPT 将定性文本和图像转化为定量数据，帮助社会科学家大规模分析研究。

OpenAI Blog74

Understanding AI and learning outcomes

OpenAI introduces the Learning Outcomes Measurement Suite to assess AI’s impact on student learning across diverse educational environments over time.

OpenAI Blog72

安全地用 Sora 进行创作

为应对最先进的视频模型以及全新社交创作平台带来的新型安全挑战，我们在构建 Sora 2 和 Sora app 时将安全作为基础。我们的方法以具体的保护措施为根基。

OpenAI Blog86

为 ChatGPT 中的商品发现提供支持

ChatGPT 推出由 Agentic Commerce Protocol 提供支持的更丰富、更具视觉沉浸感的购物体验，支持商品发现、并排比较以及商家集成。

OpenAI Blog72

Codex 现为团队提供更灵活的定价

Codex 现为 ChatGPT Business 和 Enterprise 提供按量付费定价，为团队提供更灵活的选择，以便开始采用并扩大使用规模。

OpenAI Blog63

企业 AI 的下一阶段

OpenAI 概述了企业 AI 的下一阶段，随着 Frontier、ChatGPT Enterprise、Codex 以及全公司范围的 AI 智能体在各行业的采用不断加速。

OpenAI Blog68

使用 ChatGPT 创建图像

了解如何使用清晰的提示词通过 ChatGPT 创建和优化图像，迭代设计，并在几分钟内生成高质量视觉内容。

OpenAI Blog47

Learn ChatGPT for Sales Teams

Learn how sales teams use ChatGPT to research accounts, personalize outreach, manage deals, and improve pipeline performance.

OpenAI Blog50

ChatGPT for customer success teams

Learn how customer success teams use ChatGPT to manage accounts, improve communication, reduce churn, and drive adoption and renewals.

OpenAI Blog52

Analyzing data with ChatGPT

Learn how to analyze data with ChatGPT by exploring datasets, generating insights, creating visualizations, and turning findings into actionable decisions.

OpenAI Blog59

ChatGPT for operations teams

Learn how operations teams use ChatGPT to streamline workflows, improve coordination, standardize processes, and drive faster execution.

OpenAI Blog52

ChatGPT for marketing teams

Learn how marketing teams use ChatGPT to plan campaigns, generate content, analyze performance, and move from ideas to execution faster.

OpenAI Blog68

使用自定义 GPT

了解如何构建和使用自定义 GPT 来自动化工作流、保持输出一致，并创建专用的 AI 助手。

OpenAI Blog45

ChatGPT for finance teams

Learn how finance teams use ChatGPT to streamline reporting, analyze data, improve forecasts, and communicate insights more clearly.

OpenAI Blog63

在 ChatGPT 中处理文件

了解如何在 ChatGPT 中上传和处理文件，以分析数据、总结文档，以及基于 PDF、电子表格等生成内容。

OpenAI Blog53

Brainstorming with ChatGPT

Learn how to use ChatGPT to brainstorm ideas, organize thinking, and turn rough concepts into structured, actionable plans.

OpenAI Blog65

使用 ChatGPT 进行研究

了解如何使用 ChatGPT 通过搜索和深度研究来查找最新信息、分析来源并生成结构化洞察。

OpenAI Blog54

Applications of AI at OpenAI

Explore how OpenAI products like ChatGPT, Codex, and APIs bring AI into real-world use for work, development, and everyday tasks.

OpenAI Blog58

ChatGPT for managers

Learn how managers use ChatGPT to prepare for conversations, write clear feedback, stay organized, and improve team effectiveness.

OpenAI Blog64

在 ChatGPT 中使用项目

了解如何在 ChatGPT 中使用项目来整理聊天、文件和指令，管理持续进行的工作，并更高效地协作。

OpenAI Blog69

使用技能

了解如何创建和使用 ChatGPT 技能，以构建可复用的工作流、自动执行重复性任务，并确保输出始终保持高质量。

OpenAI Blog68

个性化 ChatGPT

了解如何使用自定义指令和记忆来个性化 ChatGPT，以获得更相关、一致且量身定制的回复。

OpenAI Blog62

使用 ChatGPT 进行写作

了解如何使用 ChatGPT 进行写作，以清晰的结构、语气和意图来起草、修改和完善内容。

OpenAI Blog81

Agents SDK 的下一次演进

OpenAI 更新了 Agents SDK，加入原生沙箱执行和模型原生测试框架，帮助开发者跨文件和工具构建安全、长时间运行的智能体。

OpenAI Blog82

Codex 可用于（几乎）一切

适用于 macOS 和 Windows 的新版 Codex 应用新增了计算机使用、应用内浏览、图像生成、记忆和插件功能，以加速开发者工作流程。

OpenAI Blog86

将 Codex 扩展到全球企业

OpenAI 推出 Codex Labs，并与 Accenture、PwC、Infosys 等合作，帮助企业在整个软件开发生命周期中部署和扩展 Codex，同时 Codex 周活跃用户数达到 400 万。

OpenAI Blog86

推出 ChatGPT Images 2.0

ChatGPT Images 2.0 推出了一款最先进的图像生成模型，具备改进的文本渲染、多语言支持和高级视觉推理能力。

OpenAI Blog78

在 ChatGPT 中引入 workspace agents

ChatGPT 中的 workspace agents 是由 Codex 驱动的 agents，可自动化复杂工作流，在云端运行，并帮助团队安全地跨工具扩展工作。

OpenAI Blog73

工作区智能体

了解如何在 ChatGPT 中构建、使用和扩展工作区智能体，以自动化可重复的工作流程、连接工具并简化团队运营。

OpenAI Blog81

让 ChatGPT 更好地服务临床医生

OpenAI 向经过验证的美国医生、执业护士和药剂师免费提供 ChatGPT for Clinicians，支持临床诊疗、文档记录和研究。

OpenAI Blog73

用于编排的开源规范：Symphony

了解 Symphony 这一用于 Codex 编排的开源规范如何将问题跟踪器转变为始终在线的智能体系统——提升工程产出并减少上下文切换。

OpenAI Blog57

Our commitment to community safety

Learn how OpenAI protects community safety in ChatGPT through model safeguards, misuse detection, policy enforcement, and collaboration with safety experts.

OpenAI Blog83

购买 ChatGPT 广告的新方式

OpenAI 通过 beta 版自助 Ads Manager、CPC 竞价和增强型衡量工具扩展 ChatGPT 广告——旨在保护隐私，并将对话与广告分离。

OpenAI Blog50

Introducing ChatGPT Futures: Class of 2026

Meet the ChatGPT Futures Class of 2026—26 student innovators using AI to build, research, and drive real-world impact. Discover how this generation is redefining learning, creativity, and opportunity with ChatGPT.

OpenAI Blog81

在 ChatGPT 中推出 Trusted Contact

在 ChatGPT 中推出 Trusted Contact，这是一项可选的安全功能，当检测到严重自残风险时，会通知你信任的人。

OpenAI Blog87

在 OpenAI 安全运行 Codex

OpenAI 如何通过沙盒、审批、网络策略和面向智能体的原生遥测，安全地运行 Codex，以支持安全且合规地采用编码智能体。

OpenAI Blog70

ChatGPT 的采用在 2026 年初如何扩大

2026 年第一季度，ChatGPT 的采用率激增，其中 35 岁以上用户增长最快，性别使用比例也更加均衡，这表明 AI 正在更广泛地进入主流应用。

OpenAI Blog55

How finance teams use ChatGPT Work

See how finance teams can use ChatGPT Work to build MBRs, reporting packs, variance bridges, model checks, and planning scenarios from real work inputs.

OpenAI Blog86

我们对 TanStack npm 供应链攻击的回应

OpenAI 详细介绍了其对 TanStack “Mini Shai-Hulud” 供应链攻击的响应，概述了为保护系统和签名证书所采取的防护措施，并解释了为什么 macOS 用户必须在 2026 年 6 月 12 日前更新 OpenAI 应用。了解事件经过、受影响范围，以及 OpenAI 如何加强防御以应对不断演变的软件供应链威胁。

OpenAI Blog68

随时随地使用 Codex

通过 ChatGPT 移动应用在任何地方使用 Codex。跨设备和远程环境实时监控、引导并批准编码任务。

OpenAI Blog80

ChatGPT 中全新的个人理财体验

面向美国 Pro 用户，预览 ChatGPT 中全新的个人理财体验。安全连接你的金融账户，并获得基于你的财务背景、目标和优先事项的 AI 驱动洞察与指导。

OpenAI Blog54

How business operations teams use ChatGPT Work

See how business operations teams can use ChatGPT Work to create initiative briefs, strategy updates, leadership decision packets, progress updates, and more from real work inputs

OpenAI Blog56

How Virgin Atlantic ships faster with Codex

How Virgin Atlantic used Codex to ship its revamped mobile app on a fixed holiday travel deadline, reaching near-total unit test coverage and zero P1 defects.

OpenAI Blog81

通过 Rosalind Biodefense 增强社会韧性

OpenAI 推出 Rosalind Biodefense，扩大经审核的开发者和美国政府合作伙伴对 GPT-Rosalind 的可信访问，借助前沿 AI 推进生物防御、公共卫生和大流行病防范。

OpenAI Blog85

OpenAI 前沿模型和 Codex 现已在 AWS 上可用

OpenAI 前沿模型和 Codex 现已在 AWS 上正式可用，为企业提供了一条通过其已在使用的 AWS 环境、控制措施和采购流程来使用 OpenAI 进行构建的新路径。客户可以在 AWS 上开始使用 OpenAI，并更快地从评估推进到生产。

OpenAI Blog69

ChatGPT Sites

用 Codex 创建那种轻量级网站或应用。

OpenAI Blog80

OpenAI 将收购 Ona

OpenAI 计划收购 Ona，以通过安全、持久的云环境扩展 Codex，从而在企业工作流中支持长时间运行的 AI 代理。

OpenAI Blog72

提升 ChatGPT 中的健康智能

了解 GPT-5.5 Instant 如何通过更强的推理能力、更好的上下文理解、更清晰的沟通，以及由医生参与制定的评估，改进 ChatGPT 在健康与 wellness 方面的回答。

OpenAI Blog67

ChatGPT 的采用如何扩大

新的 OpenAI Signals 数据显示，ChatGPT 的采用正在全球范围内增长，用户使用量不断增加，探索更多功能，并推动各地区和各语言的增长。

OpenAI Blog61

Getting started with ChatGPT

Learn how to use ChatGPT, start your first conversation, and discover simple ways to write, brainstorm, and solve problems with AI.

OpenAI Blog59

How sales teams use ChatGPT Work

See how sales teams can use ChatGPT Work to create pipeline briefs, meeting prep packets, forecast reviews, account plans, and stalled-deal diagnoses from real work inputs.

OpenAI Blog56

How data science teams use ChatGPT Work

See how data science teams can use ChatGPT Work to build root-cause briefs, impact readouts, KPI memos, scoped analyses, and dashboard specs from real work inputs.

OpenAI Blog77

推出 OpenAI Presence

推出 OpenAI Presence，这是一个经过验证的企业 AI 智能体平台，可帮助组织为客户和内部工作流程部署可信赖的语音和聊天智能体。

OpenAI Blog83

在 ChatGPT 中推出 Health

ChatGPT 中的 Health 现在可让符合条件的美国用户安全地连接医疗记录和 Apple Health，以获得更个性化的洞察，并更好地了解自己的健康状况。

OpenAI Blog70

破坏一起犯罪诈骗行动

OpenAI 破坏了一个位于柬埔寨的诈骗行动，该行动利用 ChatGPT 支持投资、恋爱、赌博和冒充身份骗局。

OpenAI Blog48

Univé builds an AI-ready workforce

See how Univé built an AI-ready workforce with ChatGPT Enterprise by combining leadership, responsible governance, and employee-led innovation to transform work at scale.

OpenAI Blog57

Premium seats are coming to ChatGPT Business

Premium seats are coming to ChatGPT Business. Sign up by August 20 to get $100 in workspace credits and unlock higher usage for your team's most demanding work.

OpenAI Blog86

在 ChatGPT 中测试广告

OpenAI 开始在 ChatGPT 中测试广告，以支持免费访问，并提供清晰标注、答案独立性、强有力的隐私保护和用户控制。