OpenAI@OpenAI · 5月30日46

AI can give researchers the freedom to pursue “crazier” ideas. For Terence Tao, AI creates more room to experiment, test unexpected paths, and discover what might otherwise stay out of reach.

译AI能赋予研究者追求“更疯狂”想法的自由。 对陶哲轩而言，AI创造了更多空间去实验、测试意想不到的路径，并发现那些原本可能无法触及的东西。

Epoch AI@EpochAIResearch · 5月30日65

We took another look at the capability gap between open-weight and proprietary models. Since the start of the year, open-weight models have lagged the state of the art by four months.

译我们再次审视了开放权重模型与专有模型之间的能力差距。自今年年初以来，开放权重模型落后于最先进水平四个月。

Google AI Developers@googleaidevs · 5月30日63

The team at @llama_index built an awesome template using LlamaParse and the new Managed Agents in the Gemini API. See how they built an agent that can tackle unstructured documents. 📄↓

译LlamaIndex 团队基于 Google 新发布的 Agents API 构建了一个模板，使智能体能够访问 LlamaParse 和 LiteParse，从而自动处理非结构化文档。其工作流程为：配置数据与输出的 Git 仓库，将仓库克隆至智能体沙箱，安装 LiteParse CLI 与 LlamaParse SDK 及相关技能，最后通过提示词驱动智能体自主执行任务。该模板最终形成一个可直接使用 LlamaParse 和 LiteParse 处理真实世界文档的智能体。

宝玉@dotey · 5月30日33

我就不理解为什么 Codex 在普通模式下一直不支持 ask__user_question tool，有时候回复 Agent 的问题要手动去写就很烦！

swyx@swyx · 5月30日54

guys Opus 4.8 is very very good at writing agent code (zero dependencies, all llm 1P SDKs not just claude, sorry agent frameworks) you should try it. think they trained @ErikSchluntz's and @barry_zyj's Building Effective Agents into this thing

译各位，Opus 4.8在编写智能体代码方面非常非常出色 （零依赖，全部使用大语言模型第一方SDK，不仅仅是Claude，抱歉了智能体框架们） 你们应该试试。我觉得他们把@ErikSchluntz和@barry_zyj的《Building Effective Agents》训练进了这个模型里

AYi@AYi_AInotes · 5月30日64

Greg Isenberg 说了句挺多人不爱听的话, Claude Opus 4.8 发布,他不打算在自己的播客 startupideaspod 里专门讲一期, 理由很简单,它没比 GPT-5.5 强出一个值得你花一小时的身位。 他拿 iPhone 打了个比方,早期每代都是大跃进, 现在变成相机好了一点点、边框圆了一点点, benchmark 说进步明显,真上手的人 vibes 却说不太清。 4.6 到 4.7 再到 4.8,模型这条线大概率已经卷到边际收益递减, 真正能把活儿撬动的,基本都是模型外面那层东西, Claude Code 同周上线的 Dynamic Workflows,能让 Claude 自己写编排脚本、并行拉一堆子代理互相验证, Codex 那个带内置浏览器的桌面 App,把写代码和查资料缝进了同一个界面。 说白了,模型现在越来越像发动机， 你上一次打车,问过司机这车装的什么发动机吗, 没有吧,你只关心它能不能准时把你送到公司。 Greg 赌六个月内没人会在乎你用哪个模型, 就跟没人在乎 Uber 用什么引擎一个道理。 也就是说，模型正在变成电,谁家发出来的电都一样亮, 真正决定你能干成什么的,是你家里装了哪些电器。 说白了，聪明是模型的事,能不能帮你交活,是它外面那层壳的事。

译Greg Isenberg 认为，Claude Opus 4.8 的发布并未带来比 GPT-5.5 更有意义的提升，模型迭代（如4.6到4.7再到4.8）已进入类似 iPhone 常规升级的边际收益递减阶段。他指出，当前真正的创新发生在模型外围工具，例如 Claude Code 上线的 Dynamic Workflows 和 Codex 发布的带内置浏览器的桌面应用。他预测，模型正变得像“电”或 Uber 发动机一样可互换，未来用户将不再关心具体使用哪个模型。

Yuchen Jin@Yuchenj_UW · 5月30日38

I asked Opus 4.8 how Anthropic implements this. It told me @ClaudeDevs isn’t an official Anthropic account. True AGI. 😂

译我问 Opus 4.8 Anthropic 是如何实现这个的。 它告诉我 @ClaudeDevs 不是 Anthropic 的官方账号。 真正的 AGI。😂

宝玉@dotey · 5月30日66

对于 Claude Design 和实际代码的版本同步问题，我目前是这么处理的： 首先要有一个唯一源，就是把 Claude Design 的结果当做设计唯一的源，以它为准 然后在更新design的时候会让它写一个changelog，让 Claude Code 去根据Changelog 同步 尽可能先改 Claude Design 的设计再改代码。有时候临时在代码中修改了，时候去 Claude Design 那边同步一下。 如果有更好的办法也欢迎分享

译针对后续UI改动可能导致Claude Design与实际代码不一致的问题，分享的实践方法是：始终将Claude Design作为唯一设计源，并在更新时生成changelog供Claude Code同步。遵循先修改设计再改代码的原则，若临时修改了代码，事后也应同步回Claude Design。

MiniMax (official)@MiniMax_AI · 5月30日43

MiniMax M2.7 + CyOps = the scorecard speaks for itself 💪

译MiniMax M2.7 + CyOps = 评分说明一切 💪

Replit ⠕@Replit · 5月30日34

Here's everything you need to know about Replit in 60 seconds ⭐️ → Plain English prompts turned into real working software → End-to-end workflow from UI to deployment → Real-time team collaboration with just a link → Parallel AI agents building different parts of your app at once

译这是你需要在60秒内了解的关于Replit的一切 ⭐️ → 简单的英文提示词即可生成真实可用的软件 → 从UI到部署的端到端工作流 → 仅需一个链接即可实现实时团队协作 → 并行AI智能体同时构建应用的不同部分

François Chollet@fchollet · 5月30日16

Einstein on (not) using NL for invention: "The words or the language, as they are written or spoken, do not seem to play any role in my mechanism of thought"

译爱因斯坦谈（不）用自然语言进行发明："书面或口头的语言文字，在我的思维机制中似乎不起任何作用"

Google AI Developers@googleaidevs · 5月30日43

We love this use of Managed Agents in the Gemini API by the team at @wandb. Silent bugs don't stand a chance. 🐛⬇️

译我们很欣赏@wandb团队在Gemini API中使用托管智能体的方式。隐蔽的bug无处遁形。🐛⬇️

宝玉@dotey · 5月30日68

我的原则： ✅ Reasoning Max ❌Speed Fast 慢就是快，多花点时间推理，你就少花一点时间去验证 快就是贵，Fast 不是不好，主要是性价比不高，不差钱当然无所谓

译推文对比了AI模型的两种推理模式。主张选择Reasoning Max模式，认为多花时间进行深入推理，反而能减少后续验证时间，即“慢就是快”。而Speed Fast模式虽快，但性价比不高，除非预算充足。被引用的推文进一步支持“选择Max”，并指出这样能最大化利用用户宝贵的时间。

宝玉@dotey · 5月30日63

> 5、在工程组织上，他把 Notion 重构成一个杠铃结构。 一端是非常 junior 的工程师，刚毕业或者职业早期；另一端是少数非常 senior 的架构师和技术带头人。 中间那类常规中高级工程师反而被刻意压缩，整个分布像一根两头重、中间瘦的杠铃。 他这个观点是有问题的： 1. 少了中层衔接 初级工程师做出来的东西不靠谱，然后高级的工程的就要花大量时间经历去引导和验证，还得照顾新人的情绪，比自己做还累。 2. 初级会成长为中层 就算说这个杠铃结构是好的，那么经过1-3年，杠铃一头会变成中间那部分，杠铃变三角锥了，难不成隔一段就开除掉中间那一段重新招新人？ 这理论用在 AI 上还靠谱一点，一个人指挥几个 AI 比指挥junior工程师省心多了 Notion 创始人给我感觉就是每次写文章都很厉害，写出来的东西都能传播一波，但 Notion 在 AI 时代有啥惊艳的产品吗？

译该推文质疑了Notion创始人Ivan Zhao提出的“杠铃结构”工程组织模式。该模式主张在团队两端配置大量初级工程师和少数顶尖架构师，刻意压缩中层。推文指出其两大问题：一是缺少中层衔接，导致初级工程师产出需高级工程师花费大量精力引导和验证，成本可能高于自己完成；二是结构不具可持续性，初级员工在1-3年内会成长为中层，导致“杠铃”退化为三角形。推文还认为此理论更适合应用于指挥AI智能体，而非人类工程师，并质疑Notion在AI时代缺乏惊艳的产品创新。

宝玉@dotey · 5月30日68

借助 AI 去写 Mac App 完全没问题的。几点经验分享： 1. 尽可能选 AppKit 而不是 SwiftUI，SwiftUI 不如 AppKit 强大，做出来的界面也不好看。SwiftUI 相比 AppKit 的优势是开发简单，但这条已经被 AI 弥补了 2. 先用 Claude Design 去打磨 UI 设计和 UX 交互再去写代码可以事半功倍 3. Opus 比 GPT-5.5 做出来的 UI 要好看 4. Codex 有个官方 Plugin 叫 “Build macOS Apps”，可以用

译一位开发者分享了借助AI开发Mac应用的四点经验：建议优先使用AppKit而非SwiftUI，因后者界面较弱，而AI已弥补了AppKit开发复杂度高的短板；推荐在编码前先用Claude Design打磨UI与UX；指出Opus模型生成的UI优于GPT-5.5；并提到Codex提供了一个名为“Build macOS Apps”的官方插件可供使用。

AI Notkilleveryoneism Memes ⏸️@AISafetyMemes · 5月30日40

Half the country believes AIs are stupid and not improving, yet... they're about to take everyone's job anyway? The fuck?

译半个国家的人认为AI很蠢且没有进步，然而……它们即将抢走所有人的工作？ 搞什么？

SemiAnalysis@SemiAnalysis_ · 5月30日56

One of the data points we keep flagging from our power-crisis research, because it captures the entire mismatch between what AI operators want to build and what grids can actually approve, is the gap between datacenter interconnect requests in ERCOT and what the grid operator is willing to underwrite. (1/4) 🧵

译我们在电力危机研究中持续关注的一个数据点，因为它捕捉了AI运营商想建设的内容与电网实际能批准的内容之间的全部错配，即ERCOT的数据中心互联请求与电网运营商愿意支持的容量之间的差距。(1/4) 🧵

Rohan Paul@rohanpaul_ai · 5月30日76

I had to test it myself to believe this unreal inference speed. 3,000 tokens/s for 1 user on standard datacenter GPUs. They leveraged a hidden efficiency gap in how GPUs generate tokens. @Kog__AI just achieved 3,000 tokens/s on 8× AMD MI300X GPUs and 2,100 on 8× NVIDIA H200 (FP16, no speculative decoding). Their tech preview is on a 2B model, and they show how their techniques will scale to large frontier MoE models at similar speeds. That's a huge number because normal low-batch GPU decoding for 2B to 8B models is usually closer to 100 to 300 tokens/s per request, so Kog is claiming something like a 10X to 30X jump in the speed one user actually feels. Their trick: they are getting the speed by treating LLM decoding as a memory streaming problem, not mainly a math problem. For 1 user at batch size 1, the GPU is not doing big, efficient matrix-matrix work like in training or large-batch serving; it is repeatedly pulling the model’s active weights from high-bandwidth memory for each new token, so speed depends on how smoothly those weights keep flowing. Normal inference stacks keep breaking that flow. They run many separate GPU programs for different parts of the model, move intermediate results through memory, wait at synchronization points, talk back to the CPU for scheduling or sampling, and then repeat this token after token. Kog’s answer is to co-design 3 things that are usually tuned separately: the runtime, the low-level GPU code, and the model architecture. The biggest engineering move is the monokernel, where the whole decode pass runs as 1 persistent GPU-resident program, including sampling, so the system does not keep stopping for kernel launches, CPU scheduling, and intermediate memory round trips. They also rebuilt synchronization, because their own measurements say grid sync was eating around 35% of token-generation time; instead of making every compute unit wait at a broad barrier, each unit waits only for the exact data it needs. On AMD MI300X, they also map memory access around the chiplet layout, because memory latency changes depending on which die makes the request. Then their Laneformer model uses Delayed Tensor Parallelism, which lets cross-GPU communication happen in the background instead of blocking every layer.

译Kog团队在标准数据中心GPU上实现了极高的单用户推理速度，在8× AMD MI300X GPUs上达到3,000 tokens/s，在8× NVIDIA H200上达到2,100 tokens/s。相比常规推理速度（约100-300 tokens/s），实现了10-30倍提升。其核心思路是将LLM解码视为内存流问题，通过协同设计monokernel、重建同步机制、针对性内存访问映射及采用延迟张量并行的Laneformer模型架构，消除了传统流程的阻塞点。

Yuchen Jin@Yuchenj_UW · 5月30日35

Google is fighting every final boss at once: OpenAI & Anthropic in models, Nvidia in chips, AWS & Microsoft in cloud, Meta in ads, Tesla in self-driving, Apple in phones and OS. At $4.6T, it feels weirdly undervalued.

译Google正在同时对抗所有最终Boss： 在模型领域对抗OpenAI和Anthropic， 在芯片领域对抗Nvidia， 在云服务领域对抗AWS和Microsoft， 在广告领域对抗Meta， 在自动驾驶领域对抗Tesla， 在手机和操作系统领域对抗Apple。 市值4.6万亿美元，却感觉被奇怪地低估了。

Berryxia.AI@berryxia · 5月30日57

很多人还在等参数更大的模型才能跑好本地Agent！ Liquid AI的LFM2.5-8B-A1B却用1.5B active参数就已经做到了。 这个8B MoE训练了38T tokens加大规模RL，上下文直接128K，工具调用和多步agent能力很强，能接近4倍参数模型的表现。 单台笔记本就能跑完整本地agent loop，延迟低、全程隐私安全，不用调用GPT-4o或Claude。 支持llama.cpp、MLX、vLLM等框架，覆盖Apple、NVIDIA、AMD硬件。 本地Agent落地比多数人想的快多了。 你已经在本地跑Agent了吗？

译Liquid AI发布了LFM2.5-8B-A1B，一款为设备端优化的模型。它采用8B MoE架构，但仅有1.5B active参数，在38T tokens上进行了大规模RL训练，并将上下文扩展至128K。该模型的工具调用与多步智能体能力强劲，表现可接近4倍参数规模的模型。它支持在单台笔记本上完整运行本地智能体循环，具备低延迟与隐私优势。该模型兼容llama.cpp、MLX、vLLM等框架，覆盖Apple、NVIDIA、AMD硬件。

AYi@AYi_AInotes · 5月30日67

http://x.com/i/article/2060387880300646400 # AI didn't make orgs faster. It just exposed that orgs never had memory AI didn't make your organization faster.It just exposed that your organization never had a memory to begin with.I've been chewing on this for a year. Here's the part nobody wants to say out loud 🧵 Honestly, I've been chewing on this question for the better part of a year. I started paying attention to AI back in 2023, which makes it three years now. And I'm a decent sample of one: I run my account solo, I write solo, I do my own ops. AI tools genuinely turned me into a one-person quasi-team. My output is more than 10x what it used to be. But over the last six months, I've been watching friends who actually have teams — and I keep noticing the same off-kilter pattern. One sentence: individuals are flying, organizations are crumbling. Everyone is on ChatGPT, Claude, Gemini, Cursor. Everyone says they're 10x faster. And yet, when you add up the whole team, output is slower than it was two years ago. Something is clearly wrong here. I've been trying to figure out where it actually breaks. The MIT Sloan 2026 AI Adoption report that dropped a couple of days ago gave me the most direct answer I've seen. 1. The 95% Number Hits Harder Than You'd Think There's one stat in that report: 95% of enterprise AI investments produce no measurable business return. Honestly, that one stopped me cold. Not 50%. Not 70%. Ninety-five percent. Meaning: out of 100 companies — that spent the money, bought the tools, trained the staff — 95 of them can't show you a single number you could put in an earnings report. Your first instinct might be: maybe they're using it wrong? Maybe the models still aren't good enough? I turned it over in my head for a long time. Neither answer holds up. The real bottleneck is something else — and it's buried in another stat from the report that most people skipped right past: more than 30% of team time is spent rebuilding context that someone else on the team already had. What does that look like? Let me sketch a scene and see if any of it feels familiar: A decision got made three months ago. Today's retrospective rolls around, and nobody can find the original discussion thread. A product question gets asked in the user chat 20 times a day, and every ops person has to copy-paste the same answer from scratch. A new hire spends their first month scraping together fragments from Feishu, WeChat Work, email, Yuque, and half a dozen other apps, just trying to piece together "how does this company actually work?" There it is. That's the truth. AI didn't make organizations faster, because organizations never had memory in the first place. AI just turned up the volume on that fact. 1. Why Individual Upside Doesn't Roll Up to the Organization I've started calling this the "AI Productivity Paradox." The mechanism behind it is roughly this: AI tools are personal exoskeletons strapped onto individuals. I write code in Cursor, draft articles in Claude, do research in NotebookLM — and all the memory those tools accumulate lives on my laptop, under my account. The day I leave the company, that memory walks out with me. The day I get promoted to a different role, that memory resets to zero. The day I try to collaborate with a colleague, that memory just doesn't transfer. Which is exactly why individual productivity gains don't compound at the organizational level. Every employee is an island. Every island has a little factory on it. But there are no bridges between the islands. This is also why, at the closed-door Sequoia AI Ascent summit a few days ago — 150 top founders, six hours of conversation — the room landed on a new definition for 2026: "the commercial year zero of long-horizon agents." Sequoia partner Pat Grady said something that's been stuck in my head for days: > The next round of AI doesn't sell tools — it sells outcomes. Sounds like a comment about supply, but the more I sat with it, the more I think he's actually describing the demand side: Customers don't want tools anymore — because tools get installed on individuals, and individuals don't move org-level metrics. Ten ChatGPT seats don't help me. What I actually want is for every conversation, every decision, every piece of feedback inside my company — from yesterday to today — to be captured, searchable, and reusable. Once you start thinking this way, the problem clicks into place: No matter how smart an agent is, if it doesn't know what your organization is thinking, it's just a smart fool. It can write perfect copy, but not the one sentence that captures your brand voice. It can answer every generic question, but not "did we actually ship the fix for that bug last week?" It can hand you a polished market analysis, but it doesn't know you killed that exact direction three months ago. OK, I'm wandering — what I'm trying to say is: the problem was never the model. The problem is that the organization never gave the model a place to learn. 1. A Few Products Are Trying — But None of Them Is the Savior Let me be honest about something here: There are already some products taking a swing at this space. But frankly, none of them have solved the whole problem. The one I've been watching most recently is Lucius — they just closed a $3M seed round two days ago, led by the Future Capital Discovery Fund. This is the third startup from founder Zhao He, and his first two both died on the same rock: users won't even write the documentation. His angle this time is interesting: if people refuse to write the docs, let the AI sit there and listen, learn, and capture them on its own. How does it actually work? Their loop looks roughly like this: A user asks something in the community chat → the AI tries to answer with what it already knows → if it can't, it auto-creates a task for the ops team → ops answers → the AI captures the answer, structures it, and files it into the knowledge base → next time someone asks the same thing, the AI handles it. No prompts to write. No rules to configure. It's like a new intern who quietly sits in the chat, listens, and slowly figures things out. The early-user numbers: community self-resolution rate went from 29% to 88%, and ops time spent on repeat answers dropped from 3 hours a day to 20 minutes. But here's my cold water: it can't handle complex consultations from high-value customers, it can't generate or execute code, and at its core it's still a "load-shedder for high-frequency, repetitive scenarios." What it really does is carve out the most time-wasting 30% of standardized repetitive work. It's not replacing your team. You can't expect it to take over your business. But you can use it to make sure your team never gets asked the same question 20 times again. Is that enough? For a lot of small teams, I think it actually is. But for anyone holding out for the fantasy of a "fully autonomous AI company," it's nowhere close. So my read on Lucius is — it's an interesting sample, not the destination. This category is just getting started. A pile of similar "organizational memory layer" products will show up over the next year, and who actually breaks out is anyone's guess. Image This is their official Discord community if you want to try it: https://discordhunt.com/en/servers/lucius-lab-1484054485020966956 Lucius is currently offering a launch promo with 400 free actions — if you run a community of your own, give it a spin. 1. The One Thing I Actually Want to Say I've rambled a lot. Here's the part I really mean: The winners of the next era won't be the companies with the strongest model. They'll be the companies with the deepest organizational memory. It took me a long time to be willing to write that line down, because it implies that most of the energy we spent over the past three years "chasing the strongest model" was pointed in the wrong direction. Models get refreshed every three months. The moat is pathetically shallow. But a company that has accumulated two years of conversations, decisions, feedback, and brand voice — that's not something you can copy, and it's not something a competitor can catch up to overnight. So if you let me give one line of advice to three kinds of people, here's what I'd say: To founders: Don't go all-in on the bleeding-edge model. Find a vertical scenario and make your "organizational memory" as thick as possible. Models will keep changing, but organizational memory is the thing that compounds. To managers: Stop buying your team more AI tools. First ask whether your team has a single place where every conversation actually gets captured. Without that foundation, every additional tool just accelerates the chaos. To individuals like me: Even if you're a team of one, start building your own Context Layer. Your project notes, your customer conversations, your writing material — these are the most valuable assets you'll own over the next five years. Honestly, I haven't fully figured this out either. I'm still juggling more than a dozen AI tools. I still re-enter the same idea into different places. I still routinely fail to find an insight I had three months ago that I was sure I'd remember. So this isn't a "I figured it out, follow me" tutorial. It's a letter from one practitioner in the AI era to another one fumbling through the same fog. If you've felt that same off-kilter pattern of "individuals flying, teams crumbling" — then we're in this together. Let's take our time, and figure it out together. (This piece is synthesized from the MIT 2026 AI Adoption report, notes from the closed-door Sequoia AI Ascent 2026 summit, and recent industry developments. Lucius is mentioned as one example, not as a recommendation.)

译AI工具虽使个体效率大幅提升，却未加快组织整体产出。核心在于组织普遍缺乏“记忆”：MIT Sloan 2026年报告显示95%的企业AI投资未产生可衡量回报，超过30%的团队时间用于重复建立上下文。个体生产力因AI工具（记忆留存于个人账户）而提升，但这种收益无法在组织层面整合，导致“个人在飞，组织在垮”。Sequoia在AI Ascent峰会提出，2026年将是长周期智能体的商业元年，下一轮AI将卖结果而非工具。

Boris Cherny@bcherny · 5月30日62

Salesforce published a detailed writeup on going agentic with Claude Code. A couple things jumped out. A migration they'd scoped at 231 days shipped in 13. One PR delivered 21 endpoints at 100% test coverage.

译Salesforce发布了一篇关于使用Claude Code实现智能体化的详细文章。有几点引人注目。 他们曾评估需要231天的迁移工作，在13天内完成。其中一个PR交付了21个端点，测试覆盖率达到100%。

AK@_akhaliq · 5月30日69

minWM A Full-Stack Open-Source Framework for Real-Time Interactive Video World Models

译minWM 一个用于实时交互视频世界模型的全栈开源框架

Google AI@GoogleAI · 5月30日64

Hear the architects of Gemini reflect on their journey to continue pushing the frontier of AI, on this episode of Release Notes. @JeffDean, @koraykv, @OriolVinyalsML, and @NoamShazeer sit down on camera together to share a behind-the-scenes look at the people behind the model, and how they saw the vision come together.

译聆听Gemini的架构师们回顾他们持续推动AI前沿的旅程，本期Release Notes节目。 @JeffDean、@koraykv、@OriolVinyalsML和@NoamShazeer一同出镜，分享模型背后团队的幕后故事，以及他们如何见证愿景的实现。

Orange AI@oran_ge · 5月29日67

当 AI 把我的时间节省之后 我竟然发现自己无事可做 人类面对时间自由手足无措的样子 很有趣

译推文探讨了AI带来时间节省后的悖论效应：当人们终于获得所追求的自由时间时，反而感到手足无措。引用@fortelabs的观点指出，AI节省时间后暴露出许多人生活的核心问题——缺乏深厚的业余爱好、社群联系和文化积累，生活完全以工作为中心。面对意外获得的自由，人们非但无法有效利用，反而更可能将自己更深地埋头于工作中，形成循环。最终，“自由”本身成了最令人无所适从的东西。

Kling AI@Kling_ai · 5月29日62

Kling AI Cannes Showcase — RAPHAEL: Behind the AI Workflow Go behind the scenes of RAPHAEL, an AI-powered feature film created with Kling AI. See how the creator used Kling AI across the filmmaking workflow, from creative ideation to final cinematic frames, streamlining production and unlocking new creative possibilities.

译Kling AI戛纳展示——RAPHAEL：AI工作流幕后 深入了解RAPHAEL，一部使用Kling AI创作的AI驱动故事片。看看创作者如何在整个电影制作流程中运用Kling AI，从创意构思到最终电影画面，简化制作并释放新的创作可能性。

elvis@omarsar0 · 5月29日68

// Scaling Laws for Agent Harnesses // If you build agent harnesses, this one is worth your time. (bookmark it) Most harness tuning treats every token and tool call as if volume is all that counts. New research shows that most of it does not. The work introduces Effective Feedback Compute (EFC), a coordinate that counts only the feedback an agent can actually act on. Raw token and tool-call counts explain agent failure at R2 of 0.33 to 0.42. EFC pushes that to 0.99. Why does it matter? Once you budget by useful feedback instead of raw volume, reallocation alone lifts success from 0.27 to 0.90 at the same compute. This also turns harness design from guesswork into something you can predict. Paper: https://arxiv.org/abs/2605.29682 Learn to build effective AI agents in our academy: https://academy.dair.ai/

译新研究提出“有效反馈计算（EFC）”指标，用于优化AI智能体测试框架的设计。传统评估中，原始token数和工具调用次数预测智能体失败的R²值仅为0.33至0.42，而EFC将此提升至0.99。基于EFC进行资源重分配，可在相同计算量下将智能体成功率从0.27显著提升至0.90，使框架设计从经验猜测变为可预测过程。

向阳乔木@vista8 · 5月29日65

最近摸索出的PPT设计流程，效果好，制作效率也很高。 1. GPT 5.5 Pro和Grok搜索获取参考资料；通过大模型提问，回答添加自己的理解和想法。 2. 让 Codex 或 CC 基于这些信息加工，提炼方法论、添加金句，写成一篇经验贴，输出 Markdown。 3. 把 Markdown 上传到 Youmind，用自己写的PPT提示词，先生成大纲，再生成20页高清PPT页面和3张不同样式的空白背景页。 4. 导出zip图片压缩包，依次粘贴到Keynote中，用空白背景，制作自我介绍、FAQ、联系二维码。

译用户分享其AI驱动的PPT制作流程：先用GPT 5.5 Pro和Grok搜集资料并形成个人理解；再由Codex或CC加工成Markdown格式的经验帖；然后将内容上传至Youmind，生成大纲及20页高清PPT页面；最后导出图片包，在Keynote中完成自我介绍、FAQ等最终页面的制作。

X.PIN@thexpin · 5月29日65

http://x.com/i/article/2060305879338029061 # Huawei can't win the Nanometer race. So it is changing the game. Unable to compete at the frontier of transistor scaling, Huawei is betting that the future of chip performance lies in integration, interconnects, and light. Huawei cannot reliably win the nanometer race. So it has decided to run a different one. On May 25, 2026, He Tingbo, Huawei’s borad member and president of semiconductor business, took the stage at the International Symposium on Circuits and Systems in Shanghai and announced what she called the τ (Tau) Law, a new principle for how chips should be made faster in an era when making transistors smaller is no longer a reliable path forward. Huawei described it as the first attempt by a Chinese company to articulate a post-Moore scaling framework with global ambitions. The announcement generated a wave of coverage, most of it focused on whether this constituted a genuine scientific contribution or a rebranding of known techniques. Both framings miss the more consequential question: why is Huawei doing this at all, and what does it reveal about where the company is placing its bets? The answer starts with a set of circumstances Huawei did not choose, and a moment in the industry’s trajectory that made those circumstances easier to work with. The timing is not accidental. As transistor scaling slows globally, AI systems are becoming increasingly constrained by data movement rather than raw compute. The bottleneck is shifting from how fast a single chip can calculate to how efficiently thousands of chips can share data across a system. The industry was already moving toward advanced packaging, chiplets, and optical interconnects to address that shift. Huawei’s contribution was to turn those scattered trends into a single narrative, and claim the naming rights before anyone else did. Since 2020, U.S.-led export controls have effectively cut Huawei off from the ecosystem required to manufacture chips at the industry’s leading edge. The result is that Huawei cannot access leading-edge manufacturing on the same terms as Apple, Nvidia, or Qualcomm. The Mate 60’s appearance of 7nm-class chips, achieved through SMIC, showed that the door is not entirely shut. But competing at the industry’s true frontier has become extraordinarily difficult in a way that is structural, not temporary. That frontier has a straightforward competitive logic. Smaller transistors fit more computing power into the same area, consume less energy per operation, and run faster. This is what Moore’s Law predicted in 1965 and what the industry has organized itself around ever since. Every two years or so, the leading foundries push to a new node: 7nm, 5nm, 3nm. The companies that can access those nodes gain a measurable performance advantage over those that cannot. Competing there, at the very frontier, is what Huawei cannot currently do on equal terms. That is the constraint within which the τ Law was designed. ## A Different Variable to Optimize The τ Law proposes an answer to that constraint. In Huawei’s formulation, τ refers to the effective RC time constant that governs how quickly signals can propagate and switch states within a chip. Smaller τ means faster signals, more operations per second, higher effective performance. Moore’s Law, underneath all the transistor-count language, was always producing performance gains by reducing τ: shrink the transistors, shorten the wires connecting them, signals arrive faster. Huawei’s argument is not that this was wrong. It is that there are other ways to reduce τ that do not require a new process node: through the circuit layout, the chip architecture, and the systems connecting chips together. Huawei defines a four-layer optimization stack: the transistor itself, the circuit connecting transistors, the chip connecting circuits, and the system connecting chips. Each layer has its own version of τ, and each offers opportunities to compress signal travel time without shrinking transistor dimensions. The τ Law is a framework for pursuing all four simultaneously. Here is the honest assessment of what this represents: Huawei did not discover this direction. The physics pointing toward it, with RC delay as the binding constraint as geometric scaling slows, has been in semiconductor textbooks for decades. Intel, TSMC, and Samsung are all working on versions of the same techniques. What Huawei did was name the direction, formalize it into a single framework, and build a public roadmap around it. That is a different kind of contribution than inventing the underlying physics. But it is not nothing. Moore’s Law itself was not a discovery of new physics. It was a prediction that became a commitment that became a coordination mechanism for an entire industry. ## Folding Is Not Stacking The most tangible expression of the τ Law at the chip level is Logic Folding, and understanding it requires separating it from something it superficially resembles: conventional 3D chip stacking. The semiconductor industry has been stacking chips for years. TSMC’s SoIC, Intel’s Foveros, and Samsung’s X-Cube all take multiple finished chips and connect them vertically to reduce the distance signals travel between them. It is a genuine and increasingly important technique. But each chip in the stack is still internally structured the same way it always was: circuits laid flat across a single layer, signals running long horizontal paths to reach neighboring gates. Logic Folding addresses the interior of the chip, not the space between chips. Rather than finishing the chip and then connecting it to others, Huawei redesigns the circuit layout during the design phase, redistributing logic gates across multiple vertical layers within a single chip. Connections between layers are made through face-to-face hybrid bonding, routing signals vertically across short distances rather than horizontally across long ones. 3D stacking shortens the distance between chips. Logic Folding shortens the distance inside a chip. One is a packaging innovation applied after manufacture. The other is a design innovation applied before it. They address different layers of the same problem, which is also why they are complementary rather than competing. On the first commercial implementation, the new Kirin chip expected this autumn, Huawei claims transistor density rises from 155 million to 238 million per square millimeter, and says energy efficiency improves by 41%. These numbers come from Huawei and have not been independently verified. What can be said without qualification is that the improvement is achieved without a new manufacturing process, on existing foundry infrastructure, which is the point the τ Law is making. The goal is approaching the transistor density associated with leading-edge nodes through design rather than fabrication. This is a meaningful achievement if the numbers hold up. It is also, importantly, a packaging and integration achievement more than a transistor achievement. The performance gain comes from rethinking how circuit elements connect to each other, not from making them individually smaller. And that logic, followed to its conclusion at the system level, leads directly to co-packaged optics. CONTINUE READING AT https://www.thexpin.com/p/huawei-post-moore-chip-strategy

译由于美国出口管制，华为在芯片先进制程竞赛中面临困难。为此，华为于2026年5月提出“τ（Tau）定律”，旨在为后摩尔时代的芯片性能提升提供新框架。该定律的核心是优化有效RC时间常数（τ）以提升信号传播速度。其方法是不完全依赖制程微缩，而是从晶体管、电路、芯片互连及系统架构四个层次进行优化，以压缩τ值。华为将其描述为中国公司首次提出具有全球影响力的后摩尔扩展框架。

Orange AI@oran_ge · 5月29日47

也许会有一种新的软件商业模式 第一版免费 后续更新每次都收费 毕竟 AI Coding 第一版是最简单的… 维护是很费心力的… 或者 agent 自己迭代的版本免费 人类迭代的版本收费

Peter Steinberger 🦞@steipete · 5月29日41

“clanker” is not a slur. “vibe coding” is.

译“clanker”不是贬义词。“vibe coding”才是。

Chubby♨️@kimmonismus · 5月29日40

o3 should have been named GPT-5. Time to say goodbye. Great model.

译o3 本该被命名为 GPT-5。 是时候说再见了。 很棒的模型。

歸藏(guizang.ai)@op7418 · 5月29日63

有了 Claude Code 和 Cursor 这种软件以后，真的不只是写代码厉害。 我之前拿到豆包手机以后，想给它装个谷歌框架，但一直在 Google Play 那有点问题，死活装不上。 今天突然想起来，打开让 Claude Code 帮我装。 打开 USB 调试模式后，它直接就帮我搞定了：自动下载安装包、自动安装、自动调试好 这个未来感觉很有用。

译推文指出，Claude Code、Cursor等AI编程工具的能力已超越代码编写。作者分享了一个实际用例：在手机安装谷歌框架遇到问题时，通过Claude Code自动完成了下载安装包、安装和调试的全过程，体现了这类工具在解决日常技术问题上的潜在实用价值。

Runway@runwayml · 5月29日52

Go behind the scenes to learn more about how The Rogue was made in under a month, by a single person with Runway. The Rogue is part of Project Luxo: a new initiative exploring how AI-generated video has crossed the uncanny valley.

译深入幕后，了解《The Rogue》如何由一个人在一个月内使用 Runway 制作完成。 《The Rogue》是 Project Luxo 的一部分：这是一个探索 AI 生成视频如何跨越恐怖谷的新项目。

Berryxia.AI@berryxia · 5月29日68

🔥我尼玛，兄弟们，这下真的是爆肝了。 已经开源在GitHub了，记得Star一波啊！ 我肝了2周+花费了800刀干出来的项目~😭 自己可以真实去体验，文旅馆的真的都可以搞一搞！ 一个用 3D 渲染技术three.JS 搭起来的盛唐长安互动世界，并接入 Agora Skills 做了核心实时语音互动小游戏。 你可以在里面： - 和 NPC 对话、李白对诗、玩诗词小游戏 - 进入珍宝馆欣赏诗画 - 逛 AI 展馆，体验古文明与 AI 结合的沉浸感 这玩意我改了N遍，改的我头皮发麻。 强迫症的我，光剪视频都剪了N次，有可能有人说是垃圾，不管如何，我认真做了。 享受这个Solo 干项目的时光,Learning in Public ! 如果你觉得有意思，欢迎点个 Star 支持一下。 体验地址和Github地址见评论👇🏻，兄弟们记得一键三连啊！

译作者开源了一个使用3D渲染技术Three.js搭建的盛唐长安互动世界项目。项目核心功能是接入Agora Skills，实现了实时语音互动。用户可以在虚拟世界中与NPC对话、与李白对诗、玩诗词小游戏、进入珍宝馆欣赏诗画，以及逛AI展馆体验古今融合。作者透露该项目花费了2周开发时间及800美元成本，现已托管在GitHub上并开放体验。

AYi@AYi_AInotes · 5月29日60

今晚把红杉闭门会的纪要看完了，红杉把150位AI领域创始人与OpenAI、Google、英伟达的核心高管聚在一起，闭门六小时， 我印象里最狠的一句话不是说AGI要来了，是有大佬把我们这几年练的本事，比作了铝。 1884年美国给华盛顿纪念碑封顶， 用的是当时比黄金还贵的金属，铝。 后来电解法一出来，铝价直接崩了99.5%， 才有了今天我们拿铝箔包个三明治，吃完可以随手就扔。 红杉的Buhler说，AI对认知工作干的就是这件事。 你花十几年练出来的写代码、写文案、做分析、看合同， 正在以肉眼可见的速度，从奢侈品变成铝箔。 但咱们也先别急着慌， 就跟当年铝跌成白菜价之后天也没塌， 反倒是飞机、高楼、易拉罐这些全新行业， 全是踩着便宜的铝长出来的。 也就是说认知能力变便宜，杀死的不是有本事的人， 反而是那些只会把本事当存货、舍不得贱卖的人。 所以真正的问题并不是我的本事会不会贬值，这个是肯定会的。 最重要的是什么？ 是当思考变得像铝箔一样随手可得的时候， 你能不能用这堆白菜价的脑力， 去造一个以前根本造不起的东西。

译红杉资本举办闭门会议，聚集150位AI领域创始人与OpenAI、Google、英伟达的核心高管。与会者将AI对认知工作的影响，比作电解法让铝价暴跌99.5%，使铝从比黄金贵的建筑材料变为廉价的铝箔。这隐喻写代码、写文案等长期练就的认知能力正迅速贬值。但观点强调，认知能力变便宜并非危机，真正的挑战是：当思考变得像铝箔一样随手可得时，能否用这些“白菜价”的脑力，去创造以前根本造不起的新事物。

Peter Steinberger 🦞@steipete · 5月29日57

No LLMs for finding bugs even?

译多个知名开源项目正在全面禁止AI/大语言模型相关的代码贡献。QEMU的政策是拒绝任何被认为包含或源自AI生成内容的贡献；NetBSD将AI生成的代码推定为污点代码，不得提交；Zig对AI实施完全禁令，明确禁止使用大语言模型生成内容、翻译、查找bug，甚至禁止讨论使用聊天机器人/大语言模型服务；OBS Studio则要求代码必须由人类编写。

Rohan Paul@rohanpaul_ai · 5月29日52

This is probably the most entertaining way to understand one of AI’s hardest AI debates. Transformer vs Post-Transformer, argued by leading researchers, inside a real physical boxing ring. Both technically deep and genuinely entertaining. I was glued for the entire 1 hour 20 minutes. So many super cool points to learn. 🥊 Transformers - Transformers still own the present because they work at scale. They are simple, trainable, hardware-friendly, and already power the strongest AI systems we use today. - The Transformer is basically a memory machine. It stores information as keys and values, then uses attention to pull back the most useful parts when answering. - The real Transformer advantage is not just “attention.” The bigger advantage is that it fits modern hardware extremely well, so it can process huge batches of tokens fast. - Scaling is still the brutal rule. If you give Transformers more compute, more data, and more parameters, they usually keep getting better. Any Post-Transformer architecture has to scale just as well, or better. - It is not enough to look clever on small tests, because the real question is whether it improves faster than Transformers when scaled up. - A replacement cannot be slightly better. Because the whole AI stack is already built around Transformers, the next architecture may need to be around 10x better to force everyone to switch. - Transformers are powerful, but they may be brute force. A human does not need to read the entire internet many times to become smart, but current LLMs need enormous data and compute. 🥊 Post-Transformer - Post-Transformer people are not saying Transformers are bad. They are saying Transformers may be the best current tool, not the final form of machine intelligence. - The biggest Post-Transformer target is native reasoning and continual learning. Today’s LLM reasoning often feels like text-based step-by-step work added on top, instead of thinking happening naturally inside the model. - Latent reasoning is one possible next step. That means the model reasons inside its own hidden internal space, instead of writing every thought out as words. - Continual learning is still a major weakness. Humans keep learning from experience, but most Transformer-based models are trained, frozen, and then only adapt inside the prompt. - Long context is not the same as real memory. A model can read a huge prompt, but that is different from building a life history, learning from mistakes, and updating beliefs over time. - The future may be hybrid, not a clean replacement. Transformers may stay as 1 building block while newer systems add better memory, better reasoning, and better learning loops. - The most interesting possibility is that Transformers may help discover their own successor. AI agents are already getting better at research and coding, so the next architecture may come from AI-assisted architecture search. ------- - Benchmarks are a problem. Many public benchmarks are easy to game, so they may show leaderboard strength without proving deeper intelligence. - Perplexity is still probably a great metric to evaluate frontier models,, because it tests prediction quality. --- Overall, Transformers continue to dominate, but the frontier is clearly widening. Pathway’s BDH (Dragon Hatchling — brain-inspired reasoning architecture), Sakana AI’s CTMs (Continuous Thought Machines — models that think over time), and Liquid AI’s LFMs (Liquid Foundation Models — efficient multimodal foundation models) - all of these show how the frontier is expanding. --- From “Pathway (pathway[.]com)” Youtube channel (link in comment) @zuzanna_pathway

译这是一场关于AI架构的辩论。Transformer阵营指出，其凭借简单、硬件友好、可扩展的优势主导当下，核心是基于键值存储的记忆与注意力机制，并强调任何替代架构必须能在扩展性上与之匹敌，且需达到约10倍优势才能颠覆现有技术栈。Post-Transformer阵营则认为，当前大语言模型的推理更像是后置的文本步骤，真正的突破在于实现模型内部的“潜在推理”与持续学习能力，并指出长上下文不等于真正记忆，未来可能是混合架构。辩论还提到，当前公开基准测试易被优化，而困惑度（Perplexity）仍是评估前沿模型的有效指标。最后指出，尽管Transformer仍占主导，但前沿正在拓宽，并列举了Pathway的BDH、Sakana AI的CTMs和Liquid AI的LFMs等新兴架构作为例证。

Ethan Mollick@emollick · 5月29日57

Reconstructing software engineering around AI is going to take work (even as the ability of AI to code increases at a rapid rate). Organizations are ideally spending tokens for two things: 1) building stuff 2) experiments to figure out best practices (which involves failure)

译围绕AI重构软件工程仍需努力（即使AI的编码能力正以极快的速度增长）。 理想情况下，组织应将token用于两件事： 1) 构建产品 2) 探索最佳实践的实验（这必然包含失败）

Chubby♨️@kimmonismus · 5月29日38

This feels like the 2026 version of the old ‘LLMs are just stochastic parrots’ take

译推文主推文将教皇方济各（Pontifex）的言论比作“随机鹦鹉”论调的2026年新版，意指此类质疑在当下重新流行。引用的核心观点强调，AI不具备人类的亲身经历、身体感知、情感（如喜悦与痛苦）、道德意识，也无法真正理解爱、工作或责任，因其缺乏人类成长所需的感知、关系与精神视角。推文认为，尽管形式更新，这类对AI本质的否定性判断本质未变。