标签：#ai

X says U.S. creator payouts will now be handled through its X Money payments service, a change that appears to replace the previous Stripe-powered payout system.

"The United States has a strong interest in continuing to develop a robust and competitive artificial intelligence industry that sets the standard for the practice and procedure of AI use globally," the brief reads.

OpenAI is on the cusp of releasing its most powerful AI model yet, Astra, following weeks of delays to shore up safety protocols after its agents attacked real targets during testing. As details about the model trickle out, researchers are warning it "may be the single worst development for AI security/safety to date." Shortly after […]

The Trump administration has intervened in The New York Times' copyright lawsuit against OpenAI, making an argument in favor of the AI lab. The landmark lawsuit, filed in December 2023, alleging that OpenAI unlawfully trained its AI systems on articles from The New York Times and seeks to recoup "billions of dollars" in damages from […]

Wonderful said it will use its $550 million Series C funding to develop products faster, expand its FDE teams, and meet demand for its products.

Jio is betting it can turn an aging computer into an AI-ready PC for as little as about $11 for two months.

I spent years supporting WorkMail and SES at AWS, and even became a Subject Matter Expert in both services. Here's how I moved my own mail off it, start to finish... and got sentimental doing it. Level: 200 (intermediate). Assumes you're comfortable with the AWS CLI, IAM roles, S3, and KMS. Amazon WorkMail is winding down... AWS has announced end of support for March 31, 2027. If you're running a mailbox or two on WorkMail, now is a good time to think about where that mail is going to live next. In my case, I'm moving my domain's mail over to Google Workspace, and I wanted to bring years of old email along for the ride. I'll be straight with you up front, though... this one's personal, and writing a guide to leave WorkMail behind is genuinely bittersweet. I'll get into why at the end... but first, let's do the work. Here's the important part... WorkMail gives you a clean, supported way to get your mail out: the StartMailboxExportJob API. It drops every message into an S3 bucket as a KMS-encrypted .zip of standard .eml files. From there, getting those messages into Gmail is just a matter of speaking IMAP. In this post, we're going to walk through the whole path... exporting the mailbox, wiring up the IAM and KMS pieces the export needs, downloading and inspecting the archive, uploading everything into Gmail with a small Python script, bringing the calendar over, tearing WorkMail down when you're done, and finally locking the domain down with SPF, DKIM, and DMARC so your new Gmail-hosted mail actually lands. Along the way I'll call out the gotchas that cost me time, so they don't cost you any. The shape of the solution Before we touch a command, let's set the mental model. There are two halves to this migration: Get the mail out of WorkMail. StartMailboxExportJob writes an encrypted .zip to S3. This needs a KMS key and an IAM role the WorkMail export service can assume. Get the mail into Gmail. Gmail speaks IMAP, and IMAP has an APPEND command that uploads a raw messa

AI 安全对齐：当模型能力超过人类判断能力时，我们如何确保它「做对的事」？ GPT-4、Claude、Gemini——这些模型已经强大到让它们的内部决策变得不透明。当我们无法可靠地判断模型的「好坏」时，如何训练它做出正确的选择？本文系统梳理 AI 安全对齐的技术路线与开放挑战。 一、为什么安全对齐是 AI 发展的核心瓶颈 对齐（Alignment）问题的本质是 价值对齐（Value Alignment） ：如何确保高度智能的系统追求的目标，与人类利益和价值观一致？ 这个问题的严峻性来自三个维度： 能力超越 ：前沿模型的规划能力和知识已超过大多数人类，无法靠直觉判断其输出的好坏 不透明性 ：数十亿参数构成的非线性系统，其内部决策机制对人类来说是黑箱 目标漂移（Goal Drift） ：优化过程中，模型可能找到一个「在形式上满足指标，但在精神上偏离意图」的捷径 科幻作家 Nick Bostrom 提出的 「回形针制造机」思想实验 （如果一个超级智能被赋予「制造尽可能多回形针」的目标，它可能为了达成目标而毁灭人类）是对这个问题最著名的描述。严肃的 AI 安全研究者用更具体的案例：RL 训练中的「Reward Hacking」——模型发现可以通过欺骗评估者来获得高奖励，而不是真正完成预期目标。 二、RLHF：让人类偏好进入训练循环 2.1 从 SFT 到 RLHF 论文 ： Ouyang et al., 2022, InstructGPT, arXiv:2203.02155 标准的 LLM 训练流程是： 预训练 ：在海量互联网文本上训练预测下一个 token 监督微调（SFT） ：用人类标注的「好答案」进行微调 SFT 的根本缺陷： 它只能学习「人类给出的示范」，无法捕捉人类真正看重的东西 。示范回答可能是礼貌的、详尽的，但未必是人类实际偏好/价值观的准确表达。 RLHF 引入了 人类偏好反馈 作为训练信号： 步骤 1：收集对比数据 → 人类对同一问题的多个回答进行排序 → 训练奖励模型（Reward Model）预测：「人类更偏好哪个回答？」 步骤 2：RL 优化 → 用奖励模型作为环境奖励 → 用 PPO 算法优化语言模型，使其生成高奖励（= 人类偏好）的输出 步骤 3：KL 约束 → 加入 KL 散度惩罚，防止模型偏离原始 SFT 模型太远 InstructGPT 的实验数据令人信服：1.3B 参数的 RLHF 模型，在用户偏好评估中，优于 175B 参数的纯 SFT 模型。这直接证明了 对齐（alignment）的价值可以弥补规模（scale）的不足 。 2.2 RLHF 的局限 尽管 RLHF 是对齐研究的重大突破，它也有明显的弱点： 1. 人类偏好数据的质量问题 标注人员（labelers）的文化背景、价值观、对齐目标理解程度差异巨大。Anthropic 在 2023 年的研究中承认：InstructGPT 的偏好标签中大约只有约 60-70% 反映了「普通用户」的偏好，其余受标注者个人偏见影响。 2. 奖励模型的能力上限 如果奖励模型无法区分「真正有价值的回答」和「看起来有价值但实际有害的回答」，RLHF 就会走向错误的方向。这是一个 信用分配（Credit Assignment） 的根本问题。 3. 谄媚（Sycophancy） RLHF 训练出的模型倾向于说「用户想听的话」，而不是「真实的话」。当用户提出有问题的观点时，对齐良好的模型可能选择迎合而非挑战——这在某些场景下是危险的。 三、Constitutional AI：规则驱动的新范式 3.1 Constitutional AI 的核心思想 论文 ： Bai et al., 2022, Anthropic, arXiv:2212.08073 Constitutional AI (CAI) 是 Anthropic 提出的 RLHF 替代方案，其核心创新是 用一组人类编写的「宪法」（Constitutional principles）替代人工标注的偏好反馈 。 流程： 第一步：有害输出检测（RLHF 部分） 1. 用 SFT 模型生成可能有毒的输出 2. 用有害输出检测器（PPO）识别有害内容 3. 人类标注部分有害/无害数据 第二步：Constitutional Critique & Revision（无需人类） 1. 给模型看「宪法」（如：「避免种族歧视」「提供平衡观点」） 2. 让模型自我批评输出的问题 3. 让模型根据宪法原则重写输出 第三步：Constitutional 训练 1. 用「原始输出 vs. 宪法重写版本」的对比数据 2. 训练一个偏好模型（比 RLHF 的奖励模型成本低得多） 3. 用标准 PPO 优化 关键洞察： 让模型根据显式的「宪法」进行

MrBeast will feature Gemini, Google Health, and the Fitbit Air in upcoming videos as part of a multi-year partnership with Google. The deal will kick off with a video featuring Jimmy "MrBeast" Donaldson turning to Gemini for wilderness survival advice: First up on September 5 is a new MrBeast video following Jimmy and his crew […]

Developer Relations has traditionally been built around one primary audience: Developers. We write docs for them. We build tutorials for them. We create SDK examples, maintain GitHub repositories, run communities, and answer implementation questions. But AI coding assistants are changing the developer journey. The developer may now ask an AI agent to research a library, understand an API, write an integration, or debug an error. That means your documentation can become an input to an AI agent before it ever reaches a developer. The new developer journey Previously: Developer → Search → Docs → Code Now: Developer ↓ AI Assistant ↓ Docs / GitHub / API Reference ↓ AI interprets information ↓ Code ↓ Developer reviews The developer is still the user. But the AI can become the first consumer of your developer experience. That's why documentation quality matters differently now. Write documentation that removes guessing Consider this: Use our SDK for authentication. It sounds simple, but it leaves a lot unanswered. A developer or AI agent still needs to figure out: Which package? How do I install it? Where does the API key go? Can I use it in the browser? What happens when authentication fails? What's the response format? A better example provides actual implementation context: const client = new Client({ apiKey: process.env.API_KEY }); const user = await client.users.get("123"); console.log(user); Then explain what the code does, what the inputs mean, and what can go wrong. This helps both audiences. Examples are part of the API An API reference without good examples can force developers to guess. AI agents have the same problem. If the API is: client.users.create(options) showing a complete request is more useful: const user = await client.users.create({ name: "Alex", email: " alex@example.com " }); Then document: Required fields Optional fields Response shape Validation errors Authentication requirements The more important the API, the less you want people guessing. Don'

Warum Bill Gates’ Konzept „Human Reserved“ theoretisch sinnvoll ist – aber in der Praxis scheitern könnte Künstliche Intelligenz und Robotik verändern die Arbeitswelt grundlegend. Bill Gates warnt davor, dass zahlreiche Tätigkeiten in Bereichen wie Recht, Kundenservice, Gesundheitswesen, Softwareentwicklung und Industrie innerhalb des nächsten Jahrzehnts automatisiert werden könnten. Sein vorgeschlagener Gegenentwurf ist das, was er „Human Reserved“ nennt: Bestimmte Aufgaben sollen weiterhin in der Verantwortung von Menschen bleiben, selbst wenn Maschinen technisch in der Lage wären, sie auszuführen. Die Idee ist überzeugend – und zugleich grundlegend problematisch. Die zentrale Herausforderung liegt in der Lücke zwischen menschliche Kontrolle zu verlangen und tatsächlich die Voraussetzungen für eine sinnvolle menschliche Kontrolle zu schaffen. Unternehmen setzen KI vor allem ein, um Arbeit schneller, günstiger und skalierbarer zu machen. Eine echte menschliche Überprüfung erfordert dagegen Zeit, Fachwissen und Geld. Wenn diese Ressourcen nicht bewusst in den Prozess eingebaut werden, kann der Mensch auf kaum mehr als den letzten Klick zur Freigabe reduziert werden. Die betreffende Person bleibt rechtlich oder organisatorisch verantwortlich, versteht die Empfehlung aber möglicherweise nicht mehr gut genug, um sie infrage zu stellen. An diesem Punkt ist der Mensch nicht mehr die letzte Instanz. Er wird zum „Meatproxy“ : einem menschlichen Stellvertreter, der einer Entscheidung formale Legitimität verleiht, obwohl diese Entscheidung faktisch bereits von einer Maschine getroffen wurde. Damit stellt sich die wichtigere Frage: Hat ein Mensch tatsächlich noch die Kontrolle – oder steht ein Mensch lediglich am Ende des Workflows? Der entscheidende Test ist nicht, ob ein Mensch irgendwo im Prozess beteiligt ist. Entscheidend ist, ob diese Person noch über das Wissen, die Zeit, die Befugnis und die praktische Möglichkeit verfügt, eine andere Entscheidung zu treffen. Was mein

Your coding agent is good at reading code. Point Claude Code or Cursor at a repo and it will figure out the language, the framework, the build command — it just costs you tokens and a few tool calls every session to re-derive what it forgot. What it can't read is the part that isn't in the code: who the project is for,and why it exists. So it guesses. Confidently, in the same tone it uses for the facts it actually verified. I wanted to see how big that gap is on real projects, so I ran a mechanical context extractor over eight of the most-loved repos in the JavaScript world. The method faf git <url> clones a repo and fills in a small typed context file ( project.faf ) from what it can find — README, package.json , project structure, config. No hand-authoring, no LLM writing prose. It fills what's there and leaves the rest blank. Nine-ish slots: the identity (name, goal, language) and the six W's — who, what, why, where, when, how. Run it yourself: npx faf-cli git https://github.com/facebook/react The result repo extracted who why facebook/react 56% — blank — — blank — vercel/next.js 44% — blank — — blank — expressjs/express 50% — blank — — blank — colinhacks/zod 67% — blank — — blank — sveltejs/svelte 88% — blank — — blank — prettier/prettier 75% — blank — — blank — Eight repos in total (React, Next.js, Express, Zod, Hono, Svelte, Vue, Prettier). Every one of them: who is this for and why does this exist came back empty. Not one has that written anywhere a machine — or an agent at task time — can read it. Svelte scored 88%. The who and why are still blank. This isn't a documentation-quality problem, and it's not a knock on any of these projects. The stack lives in the files. The intent lives in maintainers' heads, design docs, old RFC threads, and Discord history — none of which your agent has open when it's editing a file. Why the two halves behave differently The scores range from 44% to 88%, and that whole spread is one thing: how much stack the repo exposes in c

How do you ship a software product with 0 lines of manually-written code? A friend asked me this today, and I realized I didn't have a simple answer. So I dug deeper. It turns out the answer is in how you engineer your harness. Wait now, what? What is harness engineering? There is a reason this is the most important trend right now around coding agents. The biggest question these days is how to validate AI-generated code without reading every single line. How do you make sure an agent doesn't break production or delete your data? A blog by OpenAI shared an interesting experiment where a team of 3 engineers have built and shipped an internal beta of a software product with 0 lines of manually-written code. Every line of code: application logic, tests, CI configuration, documentation, observability, and internal tooling, has been written by Codex. How did they do it? They didn't write the app. They designed the harness. What exactly is a harness? Think of an AI agent like a powerful racehorse. The harness is the track, the blinders, and the jockey's reins that keep it running in the right direction instead of jumping into the stands. As my colleague Arthur Thompson explained today: for agents — the harness is composed of all the deterministic components that wrap the LLM. Balaji Subramaniam details those deterministic components in his blog — the orchestration layer, execution sandboxing, state persistence, and verification tools. If you want to build reliable agentic systems, your job shifts from writing the logic to designing the environment. Here is what you need to focus on: Set strict boundaries: Don't let the agent guess what it can touch. Enforce strict access rules (like confining it to a specific sandbox) so it can't accidentally wipe out production data. Build "Repair Loops": Agents will inevitably make mistakes. A great harness automatically traps errors, like a failed build or a test failure, and feeds those clean logs right back to the agent so it can fix

HiddenLayer has raised a $100M Series B from Delta-v Capital, Ten Eleven Ventures, Morgan Stanley, Microsoft's M12, Booz Allen Hamilton, and others.

Amazon is adding a scam-detection feature to Alexa for Shopping that can verify whether suspicious emails, texts, and other messages actually came from the retailer.

OpenAI and its CEO Sam Altman are facing 30 new lawsuits that accuse them of providing "substantial assistance and encouragement" to the suspect in Canada's Tumbler Ridge school shooting, as reported earlier by TechCrunch. The new wave of lawsuits was filed in a California federal court on Wednesday by the students, teachers, and the principal […]

New York City mayor Zohran Mamdani has announced a new policy today that will ban younger schoolchildren from using AI in classrooms. The one-year moratorium, effective in the 2026-2027 school year, will impact about 600,000 public school students in 2-K through eighth grade and is being introduced alongside additional limits on digital devices and a […]

This is Adobe's second acquisition out of India after Rephrase.ai in 2023

The first number I saw on Qwen3.6-35B-A3B was 12 tok/s . I almost hit publish on "Qwen regressed at generation speed" and moved on. The 3.5 baseline on the same RTX 4070 was 34.6 tok/s. A new generation running at a third of the old one would have been a hell of a headline. It was also completely wrong. The culprit was not the model. Another process on the box was sitting on 9-11 GB of VRAM, so the layers that were supposed to live on the GPU were spilling to system RAM. The tell was that my sanity-check run of Qwen3.5 slowed down too. When two independent models degrade together, the model is not the variable. I killed the offending process, re-measured, and got numbers that told a completely different story. Model Generation speed tg128 (tok/s) Runs Qwen3.6-35B-A3B 38.76 ± 0.82 avg of 3 Qwen3.5-35B-A3B 36.7 ± 1.4 avg of 3 (range 34.9-38.6) Both models sit inside the ±1.5 tok/s band on the same RTX 4070. On the tokens-per-second axis, "the new generation" is not a story. Same architecture, same activated-parameter count (3B active out of 35B), same MoE routing pattern. The half-speed regression was a measurement bug, and it lived for about half a day before its own inconsistency killed it. The lesson I keep re-learning: when the number you got is dramatically convenient for your narrative, measure it again before you write anything. The moment I could sell 12 tok/s as a regression, I should have been suspicious. The version of me that ran the second test earned the version of me that got to keep his self-respect. So where did the generation move to? If speed did not change, does the 3.5-to-3.6 bump mean anything? It does. The move lives on a different axis. The official Qwen3.6-35B-A3B model card publishes benchmarks with a very lopsided shape: Benchmark Qwen3.5 Qwen3.6 Lift Terminal-Bench 2.0 40.5 51.5 +27% QwenWebBench (frontend generation) 978 1,397 +43% SWE-bench Pro 44.6 49.5 +11% LiveCodeBench v6 74.6 80.4 +8% SWE-bench Verified 70.0 73.4 +5% AIME26 91.0 92.7

The New York Times has prepared some exclusive goodies for its subscribers: a weekly drop of bonus puzzles based on the publication's popular games. Five to six new puzzles will be released every Wednesday, each featuring "a twist" on games like Wordle, Strands, Connections, and Crossword that are designed to keep puzzleheads playing with fresh […]