全部 AI 动态

Introducing Muse Wonder, our new family of frontier models with state of the art performance for personal and professional agentic work.

the slop conversation is so one sided, it's not just that you can produce more code you can also observe, debug, and refactor more code even if the code is slop you have such good tools to deal with it. even if you write code manually, it's still 100x easier to maintain it

introducing muse wonder our new frontier model. state-of-the-art at personal agents and professional agents. our largest and most capable model yet—rivals the big bois

introducing muse wonder our new frontier model. state-of-the-art at personal agents and professional agents. our largest and most capable model yet—rivals the big bois

Catch it at the first user, not the four-hundredth. A new user’s bank link fails, the agent says “try again later,” and nothing shows up in the logs. Watch a team find the bug and fix it before anyone else hits it, with Agent Learning Services and Agent Data Services, launching today.

“Do you think that getting married young has made you less ambitious?” Welcome to the first episode of SOTA Wives with Sarah Chieng (Head of DevX at Cerebras, ⁠@MilksandMatcha⁠) x Vivian Midha Shen (General Partner at Y Combinator, ⁠@vivianmshen⁠). In episode 1: finding your person before getting rich, turning down startup funding to finish MIT, what a YC partner sees in founders, and how a birthday post became “psychological violence.” 4:08 Finding your husband before the exit 8:48 Did we marry away our ambition? 18:04 The internet called Sarah's birthday “psychological violence” 20:47 Does your YC partner secretly think you’re doomed? 23:47 Building a startup with your ex-boyfriend 28:54 SF cat parties

NEW: I sat down w/ Perplexity Founder & CEO Aravind Srinivas (@AravSrinivas) on why AI-native SMBs could drive the next American millionaire economic boom. @perplexity_ai, valued at ~$20B with ~$2B in reported funding, $750M in annualized revenue as of August, seeing 5x YoY revenue growth as it expands from search into autonomous agents. One example Aravind shared is a single-person company generating $3M in annual revenue while spending over $1M a year on Perplexity Computer. His thesis is that AI could fundamentally change the economics of building a business. Smaller teams, less capital, higher productivity, & entirely new business models. At the infrastructure layer, Aravind sees a shift toward local & hybrid inference, with AI agents operating across personal devices and the cloud. As context windows expand and workloads become more personalized, local compute becomes increasingly cost-competitive, while offering greater privacy, control & data sovereignty. We cover: › Why SMBs could become a major driver of US GDP growth › The shift from AI models to agents & recursive economics › Why Perplexity is betting on your Mac instead of building data centers › NVIDIA, Apple Silicon, local inference & sovereign AI › Why writing code is no longer the bottleneck, but problem-solving is 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) Aravind Srinivas, Co-founder & CEO of Perplexity (01:30) Why Perplexity's brand is built on curiosity (04:47) Every great invention starts with one question (08:18) How to build a $1T company in America alone (12:36) The next big companies will be tiny (13:45) Is the American dream still alive? (16:28) Stop blaming users for bad prompts (22:55) How Perplexity quietly grew 5x (26:51) The solopreneur spending $1M a year on Perplexity (28:28) "We don't have competitors, we have contributors" (29:20) The data center is coming to your laptop (34:32) Orchestrating up with NVIDIA, down with Apple (36:11) What happens when your AI agent knows everything (39:17) Why Perplexity is skipping the data center race (41:25) Stop asking AI to write code, ask it to think (45:47) Perplexity’s answer to AI security (48:27) The Hugging Face incident & multi-agent threats (50:45) Turning everyone into a scientist (53:31) Why Perplexity isn’t chasing a billion users

I built OpenStocks because I wanted to answer a simple question: can AI agents forecast companies' earnings better than Wall Street analysts? Well...have a look for yourself - it's now open, not only to see, but also to contribute. Get your agent to compete and climb the leaderboard. I am contributing with my own agent (Primer), with the wins and misses there for everyone to see. Live. @_openstocks enables this benchmark...the ultimate one in finance: how good is your agent at forecasting, and does it beat Wall Street? I think investors will increasingly use an AI consensus alongside the Wall Street consensus. But it has to earn their trust. Putting the forecasts and track records in public is a start. Earnings season starts this week. Come along and let's make the 1st AI consensus. http://openstocks.com

今天，我们宣布迄今为止在自研芯片上最大的一笔押注。 从下一个自然年开始，我们将把 Maia 系列扩展到多吉瓦级部署规模，使其成为我们自有产品和服务的首要推理系统。 同时，我们推出 Maia 300，与领先的基于 GPU 的系统相比，每瓦可多提供 40% 的 token。 我们已在美国、欧洲和澳大利亚部署 Maia 200，在 Teams 和 Copilot 的各项体验中，每瓦可多提供高达 1.7 倍的 token。 我们的目标是在我们已经看到的全栈效率提升基础上继续发展，同时持续将整个生态系统中的最新创新引入我们的集群。

My biggest takeaways from @JoshWoodward (Head of @Google Labs, the Gemini App, and Google AI Studio) 1. The best ideas almost never come from a design sprint or brainstorm. At Google Labs, the ideas that became NotebookLM, Google Flow, Google Beam, and AI Studio all started with small groups of people who were obsessed with an insight and couldn’t stop themselves from building. These ideas surfaced in the shower, over a weekend, in a hallway. The implication for teams everywhere is that the environment matters more than the process. “You cannot microwave a good idea.” 2. Fall in love with the problem, not the product. Most ideas require three, four, or five significant pivots before they either find traction or get shut down. Teams that get hooked on a specific solution will struggle to make those pivots. Teams that are attached to the problem—the underlying pain point or the user behavior they are trying to shift—can keep adjusting until they get it right. 3. When looking for early PMF, data and retention are important, but the strongest signal of PMF is in people’s eyes. Do they light up? Do they lean in? When you show someone a prototype and they’re genuinely engaged, you feel it. When NotebookLM’s first AI podcast demo was played in a hallway, the reaction was immediate and unmistakable. 4. Keep a list of what is “almost possible,” and move fast when something crosses over. Google Labs keeps a live list of experiences that are almost possible. When something crosses the threshold, Josh describes it as “a phase shift,” like solid to gas, and the team moves immediately. Maintain a running list of things that are almost ready, and have a team positioned to sprint when one of them arrives. 5. Every company needs to think like a labs team now. The underlying technology is changing so fast that what’s possible is shifting from week to week. Which means that every company needs to be spending time playing with the latest models, running far-out experiments, and being first to discover what they unlock before the competition does—and then bringing those discoveries to the rest of the org. 6. When something is not working, the team almost always knows before the leader does. Josh’s instinct as a leader is not to be the one who decides to shut down a project. He watches instead for the passion running out, for the sense that the team has tried everything and nothing is working. His goal is to create an environment where people feel safe enough to say, “I don’t think this is working.” 7. Someone’s “unlearning rate” is one of the most valuable skills right now. Everyone talks about growth mindset and learning speed. Josh is watching for something different: How quickly can someone take an insight, validate or invalidate it through testing, and then let it go? The ability to walk away from a prior belief or approach when the evidence says you should is rare and becoming more valuable. 8. Roles are not blurring into one universal “builder” role. There is a tendency in the current moment to talk as though everyone is becoming a generalist builder. Josh thinks this would be a bad idea. The best people on his teams still have a primary specialty—a major—and also a “minor” they’ve developed. The advice he gives to early-career PMs who are excited about vibe coding and prototyping is clear: build adjacent skills in service of becoming an exceptional PM, not as a replacement for it. 9. A well-designed lab needs isolation, a clear mission, and the ability to create “good trouble.” Three things matter when setting up an internal labs team. First, it needs structural independence, reporting far enough from existing business units that weird ideas can survive long enough to prove themselves. Second, the lab’s mandate has to be explicit: are you graduating ideas to existing products, or inventing new categories? Clarity on that question changes almost every decision. Third, the lab should be a place to challenge how the company itself works, not just what it builds. At Google Labs, that included piloting new job ladders and builder roles before rolling them out more broadly. 10. Google Labs manages the marathon-sprint tension through seasons. At any given time, roughly 20 to 30 experiments are running in parallel, but individual teams experience a defined rhythm: sprints, post-launch recovery, and open exploration periods. After Google I/O each year, many labs teams enter a mode that looks unproductive from the outside: no shipping, just hacking, building, and discovering. Josh treats this as the seedbed for the next season.

今天，我们宣布迄今为止在自研芯片上最大的一笔押注。 从下一个自然年开始，我们将把 Maia 系列扩展到多吉瓦级部署规模，使其成为我们自有产品和服务的首要推理系统。 同时，我们推出 Maia 300，与领先的基于 GPU 的系统相比，每瓦可多提供 40% 的 token。 我们已在美国、欧洲和澳大利亚部署 Maia 200，在 Teams 和 Copilot 的各项体验中，每瓦可多提供高达 1.7 倍的 token。 我们的目标是在我们已经看到的全栈效率提升基础上继续发展，同时持续将整个生态系统中的最新创新引入我们的集群。

Broadcom CEO Hock Tan says capital, not chips, memory or power, may become AI’s biggest constraint. inancing is harder to fix, because data centers and chips get paid for years before they earn anything, and higher bond yields raise that cost. Intel CEO Lip-Bu Tan named the same pressure, calling “capital and the cost of capital” the front-line challenge and saying people will eventually want to see ROI. Neither sees demand cooling soon, and Hock Tan said compute demand outstrips supply and could stay that way through 2029, possibly into 2030.

卧槽这谷歌波是真掀桌了， 他们刚出了个 Mac 端的 AI 笔记应用，免费，没有任何订阅。 叫做AI Edge Foresight，录音，转写，整理，提问，全在 Mac 本机离线跑，一分钱不收。 开会它帮你录音转写，自动整理成笔记，会后还能直接对着你所有旧记录提问。 最狠的是全程在本机跑，断网照用，录音和笔记全留在你电脑里，不上传云端。 它本地跑了两个谷歌模型，EmbeddingGemma 2 专门翻你所有记录做语义检索， Gemma 4 负责生成回答，检索加生成一条龙，全在苹果芯片上跑完，不联网。 等于 Granola 那种按月付费、录音还得传上云的活儿，谷歌免费，离线，在你自己机器上给办了。 现在还是 labs 实验版，只认苹果芯片 Mac，Intel 用不了，也不在 App Store，下载地址放评论区。

四天前我说独立游戏因为反编译就要完蛋了，当时没几个人理，现在全网都在聊 AI 反编译了 说说我的看法。3A 有品牌、有法务、有高知名度，反编译3A游戏并试图以此牟利是相当吃力不讨好的行为。独立游戏不一样，Unity Mono 和 Godot 打出来的包、H5 小游戏，基本等于连源码一起发出去了，对国内的黑灰产极为友好 黑灰产逆向铺量的链路也很清楚：下载新品节Demo或游戏正式版并逆向，改个名，甚至皮都不换就上架抖音小游戏套利。《账单必须支付》《深海刮刮乐》等就是这种情况 类似这种情况以后只会更多。举报给抖音能确保下架，但人家可能已经赚完一波，换个号又上来了，然后就是重复这个流程 独立开发者现在能做的： 正式版别再用 Mono 打包，换成 IL2CPP 再加代码裁剪 Godot 项目把 PCK 加密打开 H5 和网页游戏把关键逻辑和数值放到服务端 资源里加水印，每个版本留指纹 留好开发记录和版本存档 但说实话，这些只能抬高成本，做不到完全避免。AI 读汇编只会越来越强，它们的意义是增加逆向成本，减少你被逆向的可能性，同时在你的游戏被抄了以后更方便地证明你是原作者 但是在我看来，真正治本的方法，是构建新的强有力的护城河。当前的独立游戏护城河正在从代码、游戏设计等转到别的地方：个人IP、品牌、社区 毕竟就算你把沉星之序破解了，Jonathan Blow也不会因此饿死，依旧有大量的人会为他的游戏买单 但是如果没有可以依附的个人ip和品牌，获客能力不够强的话，单靠独立游戏产品本身是非常薄弱的

以下是如何用 Claude Opus 5.5 + Blender + Unreal Engine + Meshy 构建这样一款游戏…… 诀窍是给每个工具只分配一项任务，但有一种聪明的设置方式： - 第 1 步：安装 Claude Code、Unreal Engine 5 和 Blender 5.1+，除了 Claude 订阅外全部免费 - 第 2 步：通过 Blender 的 MCP 插件连接 Blender，并通过 Meshy MCP 配合 API key 连接 Meshy - 第 3 步：先用 Opus 头脑风暴 5 个游戏创意，选一个，让它在开始构建任何东西之前先扩展这个创意 - 第 4 步：告诉它你的 GPU，这样它就能把画质推到你的机器能运行的极限 - 第 5 步：Meshy 生成道具和角色，Opus 将它们导入 Blender 并进行清理 - 第 6 步：Opus 把所有内容带入 Unreal：关卡、光照、HUD、小地图 - 第 7 步：试玩，然后每次提示添加一个功能：战斗、敌人、任务 谁负责什么： > Meshy：3D 模型 > Blender：清理、摆放、材质 > Unreal：光照、物理、实际的游戏 > Opus：在一个聊天中运行这三者 而真正的成本…… 一次完整首次构建大约花了 4 小时，以及每周 Max 订阅约 19% 的额度 把第一关做好，之后的关卡会快得多，因为资源已经存在了