Epoch AI@EpochAIResearch · 5小时前41

Claude Fable 5 scores very well on FrontierMath: Tiers 1–4 (v2), reaching 87% on Tiers 1–3 and 88% on Tier 4. This continues a streak of Anthropic models improving rapidly at math.

译Claude Fable 5 在 FrontierMath（Tiers 1–4，v2）上得分很高，在 Tiers 1–3 上达到 87%，在 Tier 4 上达到 88%。这延续了 Anthropic 模型在数学上快速提升的趋势。

fofr@fofrAI · 5小时前46

Fine-grained 3D motion control in AI video just got a little bit closer

译@andrew_n_carr 宣布“编辑视频运动！放弃提示开始导演”，并展示其“通用视频编辑器”工作流：先用 comic 4 捕捉视频，再用运动编辑器修改动作，最后用视频到视频模型（如 Runway、Gemini）重新渲染。他以时装片段为例，希望模特展现高抬腿活力，无需重拍。主推文 fofr 表示，AI视频中精细的3D运动控制已更近一步。

Replit ⠕@Replit · 5小时前59

New video is out! You no longer build one thing at a time on Replit. Run parallel agents to ship a website, mobile app, video, and pitch deck from one project, all at once. And you can now add multiple artifacts to projects you already have.

译新视频发布了！你在 Replit 上不再一次只能构建一件事。 运行并行 AI 智能体，从一个项目中同时交付网站、移动应用、视频和推介材料。 而且你现在可以向已有的项目中添加多个工件。

OpenAI Developers@OpenAIDevs · 5小时前50

Ask our developer docs. They’ll show you the way The new docs agent on 🔗http://developers.openai.com helps you find answers about OpenAI products and takes you directly to the relevant documentation.

译咨询我们的开发者文档。它们会为你指路。 新的文档智能体在 http://developers.openai.com 上，帮你找到关于 OpenAI 产品的答案，并直接带你到相关文档。

MiniMax (official)@MiniMax_AI · 5小时前50

M3 is live on @telnyx Inference on day-0 go build with Telnyx and M3 today

译MiniMax M3现已登陆Telnyx推理平台。M3是首个结合前沿编码与智能体能力的开源权重模型，拥有1M token上下文窗口和原生多模态理解。凭借M3的1M上下文与Telnyx自有GPU基础设施，一次对话即可处理整个代码库。官方鼓励开发者立即使用。

MiniMax (official)@MiniMax_AI · 5小时前64

day-0 and already on @FireworksAI_HQ with blazing fast inference long-horizon agents, full-repo understanding, multimodal coding all in one model Try M3 today on Fireworks AI

译MiniMax M3 已在 Fireworks AI 上线，Day-0 即获最快推理端点。模型为开源权重，在 Artificial Analysis 指数排名第一。支持 512K 上下文窗口、原生图像及视频输入；采用 MSA 稀疏注意力机制，实现 9 倍更快的 prefill 与 15 倍更快的 decode。定价与 M2.7 持平。M3 将长周期智能体、全仓库理解与多模态编程集成于单一模型。

AK@_akhaliq · 5小时前46

SpenseGPT Practical One-shot Pruning Enabling Sparse and Dense GEMMs for LLM Inference

译SpenseGPT 实用的一次性剪枝，实现LLM推理的稀疏和密集GEMM

MiniMax (official)@MiniMax_AI · 5小时前69

Run M3 locally today with @UnslothAI

译MiniMax-M3 是一款拥有 428B（23B 激活）参数、1M 上下文的新开源模型，性能与 Gemini 3.1 Pro 相当。可在 138GB 内存/显存上运行动态 2-bit GGUF 版本，或 165GB 上运行 3-bit 版本。在 @UnslothAI 的帮助下，今天即可本地运行 M3。

Rohan Paul@rohanpaul_ai · 5小时前43

Most AI agents do not forget because they lack memory; they fail because they remember badly. AGENTCL asks a simple question: does an AI agent really learn from experience, or merely carry clutter forward? Today's agents can spend enormous effort solving one task, then enter the next one almost as if nothing happened. AGENTCL says AI agents need better tests for whether their memory actually helps them learn across tasks. The paper’s main idea is to build task streams where earlier tasks clearly contain pieces that later tasks can reuse, such as a small coding function, evidence for a research question, or a useful workflow. It compares these careful “compositional” streams with normal “naive” streams, where tasks come from the same area but do not have a guaranteed reuse link. Agent memory is easy to overrate when the benchmark is messy. If tasks are not carefully connected, a memory system may look good for the wrong reason, or bad for a reason the test cannot explain. AGENTCL tries to fix that by making the task relationships clear, then measuring whether memory helps on later tasks, stays useful, and transfers to unseen tasks. The key finding is that today’s memory methods can reuse past work when the connection is obvious, but they still struggle to avoid confusion when the next task is different. ---- Link – arxiv. org/abs/2606.02461 Title: "AGENTCL: Toward Rigorous Evaluation of Continual Learning in Language Agents"

译AGENTCL 提出评估 AI 智能体是否真正从经验学习，而非单纯累积信息。通过构建组合任务流（前序任务包含可被后续任务复用的代码片段、研究证据或工作流），与无固定复用线索的随意任务流对比。关键发现：当前记忆方法在任务连接明显时可复用过去经验，但当任务差异较大时仍难以避免混淆。论文旨在为智能体持续学习提供更清晰的测评标准。

ClaudeDevs@ClaudeDevs · 6小时前61

Claude Managed Agents can operate in a sandbox you control, on your own infrastructure or with any provider you choose. Today we added new guides for @blaxelAI, @e2b, @googlecloud, @namespacelabs, and @superserve_ai, so you can choose the best fit for your use case.

译Claude 托管智能体可以在您控制的沙盒中运行，在您自己的基础设施上或您选择的任何提供商上运行。 今天我们新增了针对 @blaxelAI、@e2b、@googlecloud、@namespacelabs 和 @superserve_ai 的指南，以便您选择最适合您用例的方案。

elvis@omarsar0 · 6小时前69

How to effectively run autonomous long-running coding agents? This is one of the most exciting discussions on agents I've ever had. I recorded it and am making it freely available. (bookmark it) The idea of autonomous long-running agents is a real thing. We talk about lots of things like /goal, /loop, and dynamic workflows, and what comes next. One interesting discussion was around how to make the agent run for longer while ensuring it stays on track. Most models today will struggle to coordinate work effectively. They sometimes pause the work early. Lots of mistakes happen, and lots of weird shortcuts (reward hacking). What helps is to be extremely clear about the goals it needs to achieve. To clarify the dos and don'ts clearly. Eliminate any assumptions you think the model would make. Deep expertise matters so much in this. But you can get far through careful planning. My formula currently is to use Opus 4.8 for planning carefully and GPT-5.5 for all executions. For the evaluator (via /goal), I am often using something like Deepseek or the latest models from Qwen, Kimi, and MiniMax, etc. Another insight we discussed to enforce goals is to provide strong visual cues for the agent to compare with. I found that a multimodal goal is a much stronger goal than a plain text one. And use agents to help you set clear goals. Watch here: https://academy.dair.ai/events/cmplo7v3b000e04l1pxprat4d

译DAIR.AI创始人Elvis Saravia分享如何有效运行长期自主编码智能体。他指出当前多数模型难以协调工作，会过早暂停、犯错或走捷径（reward hacking）。关键在于明确目标、消除假设，避免模型自行推断。他的实践公式：用Opus 4.8进行细致规划，GPT-5.5执行所有步骤，评估器（通过/goal）则使用Deepseek及Qwen、Kimi、MiniMax等最新模型。另一关键洞察是提供多模态视觉线索作为目标，比纯文本目标更强，能更好地约束智能体。完整讨论已录制并免费开放。

PixVerse@PixVerse_ · 6小时前49

Victorian gothic nightmares, one Canvas workflow. See how @Shanzyin_ai built THE DREAM EATERS on PixVerse Canvas — nodes, shots, and the full project file, open to explore.

译PixVerse 展示 AI 电影制作人 @Shanzyin_ai 使用 Canvas 工作流创作的维多利亚哥特风格短片《THE DREAM EATERS》。短片包含完整节点、多个镜头及项目文件，开放探索。剧情设定为古老庄园中青少年被迫吞噬权贵噩梦，一名有缺陷的新兵将黑暗拖回现实。PixVerse 推出限时活动：转发+关注+回复“DREAM”，72 小时内可获得 150 Credits 及该工作流。

Epoch AI@EpochAIResearch · 6小时前64

FrontierMath: Tiers 1–4 (v2) is live. We concluded an audit that addressed errors in 42% of problems. Rankings are similar but scores are higher across the board. The current leaders are GPT-5.5 (xhigh) with 85% on Tiers 1–3 and Google’s AI co-mathematician with 76% on Tier 4.

译FrontierMath: Tiers 1–4 (v2) 现已上线。 我们完成了一项审计，修正了 42% 的问题中的错误。排名相似，但整体得分更高。目前的领先者是 GPT-5.5 (xhigh)，在 Tiers 1–3 上达到 85%，以及 Google 的 AI co-mathematician，在 Tier 4 上达到 76%。

Chubby♨️@kimmonismus · 6小时前65

Google DeepMind published a 60-page paper mapping the road from AGI to superintelligence, written by Hutter, Legg, and Genewein. No hype, just a sober analysis The paper uses three levels. AGI = roughly average human performance across most cognitive tasks. ASI = a system that beats large, well-coordinated groups of human experts across virtually everything (their bar: tens of thousands of experts working ten years on one problem). Universal AI / AIXI = the theoretical ceiling, uncomputable, only approachable from below. Then they explore the question of how this could be achieved: Scaling compute, models, and data, the continuation of the trend that drove the breakthrough so far. It is the only path with historical data available for extrapolation. The core question: Does quantity transform into quality? Even if individual models plateau, the sheer act of running millions of faster AGI instances could trigger the leap. (A quick aside: that is a fascinating philosophical idea. It always reminds me of Hegel’s dialectic, the notion that quantity transforms into quality. We ought to start drawing on philosophical theories to make sense of the future.) Algorithmic paradigm shifts: a genuine break from the transformer pretraining paradigm. New architectures, new learning methods. However, hard to predict by definition. Recursive self-improvement: AI accelerates AI research, which produces better AI, which accelerates research further. Multi-agent coordination: superintelligence emerges from large collectives of AGI agents working together, like automated corporations or AI economies. Collective intelligence potentially far exceeding any individual model. The authors naturally point to what I repeatedly describe as the biggest bottleneck: energy. I recently linked to a few graphs showing, on the one hand, the extent to which energy is already becoming a problem and, on the other, how China dominates the expansion of both nuclear and solar energy in the global race. But the authors also address a profound shift in the world of work in a post-AGI era. I would say this is a reality we must face. So, it is not just about scaling, but also about whether the underlying conditions - such as energy and hardware - can be effectively established. Six things that could slow or stop all of this: The data wall. Quality training data runs out, possibly before the end of this decade. Resource demand grows too fast. Energy, chips, rare earths, investment. The physical infrastructure can't scale arbitrarily. The neural paradigm hits a ceiling. Pretrained transformers plus fine-tuning may not be enough to reach AGI, let alone go beyond it. Research gets harder. Keeping Moore's law going already needs 18x more researchers than in the 1970s. Ideas are genuinely harder to find as fields mature. The abstraction barrier. Models trained on human concepts may never invent new ones from scratch. Saturating GPQA or SWE-bench shows mastery of what humans already worked out, not the ability to go beyond it. Train only on pre-Newtonian physics and you won't reason your way to relativity. Deliberate slowdown. Regulation, accidents, public backlash. Real, but likely countered by the competitive pressure between companies and nations. I think it’s great that Google is addressing questions such as which paths they believe lead to AGI, what the road to ASI might look like, what challenges will arise, and much more. Overall, however, it sounds to me like all of this could actually succeed, making it, in that sense, a call to discuss and reflect on the consequences.

译Google DeepMind发表60页论文，由Hutter、Legg、Genewein撰写，定义AGI（多数认知任务达平均人类水平）、ASI（超越大量专家协作）和不可计算的AIXI三个层级。实现路径包括规模扩展、算法突破、递归自我改进和多智能体协调，瓶颈在于能源与硬件。六种阻碍：高质量数据可能本十年内耗尽、资源需求过快、神经范式天花板、研究难度激增（维持摩尔定律需18倍于1970年代的研究者）、模型无法创造全新概念、人为放缓。作者认为这是对AGI后果的严肃反思呼吁。

Ammaar Reshi@ammaar · 6小时前53

I asked Claude Fable 5 to reverse engineer a 1993 DOS game with no source code. It read the raw machine code, rewrote the engine in C, and gave me a fully editable port for every platform. 30 min from EXE to iPhone. Sharing it all so you can revive your own childhood games!

译我让Claude Fable 5逆向工程了一款1993年的DOS游戏，没有源代码。 它读取了原始机器码，用C重写了引擎，并给了我一个完全可编辑的移植版，适用于每个平台。 从EXE到iPhone，30分钟。 分享这一切，让你也能复活自己的童年游戏！

Nathan Lambert@natolambert · 6小时前46

derivation of policy gradient: https://rlhfbook.com/c/06-policy-gradients#deriving-the-policy-gradient

译策略梯度推导： https://rlhfbook.com/c/06-policy-gradients#deriving-the-policy-gradient

Rohan Paul@rohanpaul_ai · 6小时前63

The official SEC filings of @SpaceX says AI accounts for almost all of its expected $28.5 trillion total addressable market.

译SpaceX 的官方 SEC 文件显示，AI 几乎占其预期的 28.5 万亿美元总可寻址市场的全部。

Google Gemini@GeminiApp · 7小时前30

This event is happening soon! Join the Gemini Discord here: http://discord.gg/gemini

译Gemini 即将在 Discord 社区活动中介绍全新的 Neural Expressive 设计语言。产品营销经理 Megan C. 将讲解动态视觉响应、无缝模式切换等提升使用体验的亮点。活动于本周五 6 月 12 日上午 11:30 PT 举行，快来 Discord 加入吧！

Google AI@GoogleAI · 7小时前40

Here’s what launched this week: — Gemini 3.5 Live Translate our latest audio model for live speech-to-speech translation — @NotebookLM got a major upgrade including agentic capabilities in chat, more advanced reasoning, and a suite of new output formats — Project Genie from @GoogleLabs is now available to Google AI Ultra 5x subscribers globally — Notebooks in @GeminiApp are now available in the European Economic Area, United Kingdom, and Switzerland — DiffusionGemma, our newest experimental open @googlegemma model that explores text diffusion, an exceptionally fast approach to text generation

译Google AI 本周推出多项更新：Gemini 3.5 Live Translate 是用于实时语音到语音翻译的最新音频模型；NotebookLM 获重大升级，加入智能体对话能力、更高级推理及新输出格式；来自 GoogleLabs 的 Project Genie 向 Google AI Ultra 5x 订阅者全球开放；GeminiApp 中的 Notebooks 在 EEA、英国、瑞士上线；同时发布实验性开源模型 DiffusionGemma，探索文本扩散技术，实现极快文本生成。

MiniMax (official)@MiniMax_AI · 7小时前41

M3 is now live on @parasail_io 🚀

译MiniMax M3 已在 @parasail_io 上线。这是首个具备前沿编码与智能体能力的开源权重模型，拥有 1M 上下文窗口和原生多模态理解。联系我们可以启动专属端点进行测试！🚀

Kling AI@Kling_ai · 7小时前18

Who’s Your Matchday Pick?

译谁是你的比赛日选择？

Rohan Paul@rohanpaul_ai · 7小时前66

The Information: Anthropic is reportedly moving from renting cloud compute to leasing and managing data centers itself, with more than 1GW of planned U.S. capacity and Google potentially backing the lease payments. The old model is simple: Anthropic pays cloud providers for GPUs or custom chips, but the cloud company controls the building, power, networking, cooling, and much of the hardware schedule. The new model puts Anthropic closer to the factory floor of AI, where data centers are not office buildings but power-heavy machine rooms built to feed training and inference systems around the clock. To give context of its current situation, until now, Anthropic’s compute strategy has mostly run through cloud providers, with over 10GW of server rentals committed, including a $200 billion Google agreement. The company has also locked in large cloud deals with Akamai, AWS, CoreWeave, and Fluidstack, covering Amazon’s Trainium hardware and a $50 billion Fluidstack partnership. It has also expanded its data center team and signed a SpaceX/xAI lease for the whole Colossus 1 data center at $1.25 billion a month, plus Colossus II space.

译Anthropic正从租用云算力转向自建数据中心，计划在美国部署超1GW容量，Google可能为其租赁付款提供财务担保。此前Anthropic已承诺超10GW云服务器租赁，包括与Google的2000亿美元协议，以及Akamai、AWS、CoreWeave、Fluidstack的大型合作（含500亿美元Fluidstack合作、AWS Trainium硬件）。此外，Anthropic以每月12.5亿美元租下xAI/Colossus I数据中心全部空间，并租用Colossus II。此举旨在通过自控服务器降低长期计算成本。

Chubby♨️@kimmonismus · 7小时前31

Holy, no way! (/s)

译据 The Information 报道，OpenAI 正在准备一个新 AI 模型。主推文回应：“天哪，不会吧！（/s）”

jason@jxnlco · 7小时前16

codex users! how have you found codex'x ability to use (correctly) computer use / chrome extension / in app browser? if you want to give us feedback leave a comment and I'll organize it for the team!

译codex 用户们！ 你们觉得 codex 在（正确）使用电脑/Chrome 扩展/应用内浏览器方面的能力怎么样？如果想给我们反馈，请留下评论，我会整理给团队的！

Odyssey@odysseyml · 7小时前44

World models can now create imagined experiences for AI—environments where agents continuously learn, adapt, and improve. We suspect multi-agent interaction may be a critical ingredient for recursive AI and general intelligence. https://odyssey.ml/the-era-of-multi-agent-imagined-experience

译世界模型现在可以为AI创造想象体验——智能体在其中持续学习、适应和提升的环境。 我们推测多智能体交互可能是递归AI和通用智能的关键要素。

Lee Robinson@leerob · 7小时前61

http://x.com/i/article/2065439304785039360 # Building recursive agent systems At Cursor, we run thousands of agents to help us train the next version of Composer. We give them research tasks, and if they aren't succeeding or run into issues, they DM us on Slack or page us via PagerDuty. ## Scaling training for Composer We’ve built an org chart of agents that work together. As we’ve scaled training for Composer, we’ve wanted to run thousands more experiments. This was possible before, but it was slow and hard to keep track of every experiment’s status. To speed things up and parallelize work, we built an always-running agent system (yes, it's a loop). ## An agent system for research Here’s how the system works: 1. The main agent runs on a massive remote machine with all the tools you'd use locally, plus a file on disk acting as an “inbox” for the fleet. 1. It SSHes into machines running hundreds of child agents and collects their statuses into the inbox. 1. On every loop, it checks fleet health, keeps healthy tasks running in the background, and surfaces anything broken to the team on Slack. 1. Like all infra, the agents occasionally hit transient issues or need to be poked, so the main agent can control the whole fleet, quitting or restarting processes as needed. This “fleet manager” builds on our previously published research on long-running agents. We’ve given the manager many different skills that encode tacit knowledge for how to run ML experiments, review and monitor results, and more. ## Researchers with superpowers Training a great model means trying a bunch of ideas for creating useful RL data. A single laptop is not enough here, you really want an army of computers in the cloud to run experiments in parallel. And since we aren't compute-constrained, we rolled out this infra for everyone in ML. Researcher time is our scarcest resource and we’ve found a way to scale their leverage by orders of magnitude. Imagine if you had a human manager with 10,000 direct reports. Obviously that wouldn’t work well, but this human → agent “org” kind of does! If you have a problem that is verifiable, where throwing more tokens at it will solve it faster or better, it’s worth considering building a system like this. It’s enabled us to have swarms of agents crawling through Composer’s data to recursively improve itself for future versions. And if this sounds exciting, we’re hiring!

译Cursor 为训练下一代 Composer，构建了一个始终运行的递归智能体系统。主智能体在远程机器上通过 SSH 管理数百个子智能体，将状态收集到磁盘“收件箱”，循环检查集群健康并保持任务运行，通过 Slack 向团队报告问题。主智能体具备多种技能用于运行和监控 ML 实验。研究人员可并行运行数千个实验，大幅提升效率。对于可验证的问题，投入更多 tokens 能更快解决。

MiniMax (official)@MiniMax_AI · 7小时前70

M3 open weight just dropped and it's live on @Modular cloud on day zero with up to a 1M-context and MSA architecture kernel-to-cloud optimization is exactly what M3 needs glad to have @Modular with us from the start

译MiniMax 发布 M3 模型开源权重，并宣布与 Modular 合作，在 Modular Cloud 上当天上线。M3 支持最高 1M-token 上下文长度，接受文本、图像、视频多模态输入，采用 MSA（Multi-Stream Attention）架构，专为长时间运行的智能体（Agent）与编码（Coding）工作负载优化。

🚨 AI News | TestingCatalog@testingcatalog · 7小时前51

NVIDIA ❤️ MiniMax MiniMax M3 weights are now available on @huggingface, and NVIDIA now offers a Free Endpoint on its platform for testing. Testing time 👀

译NVIDIA ❤️ MiniMax MiniMax M3 权重现已在 @huggingface 上发布，NVIDIA 在其平台上提供免费端点用于测试。 测试时间到 👀

Jeff Dean@JeffDean · 7小时前48

Quite interesting thread on capabilities of real biological neurons (spoiler: they're way more capable than classical artificial neurons in a perceptron) . Nice work @IdoAizenbud and collaborators!

译据 Jeff Dean 转发，Ido Aizenbud 与合作者的新研究发现，单个皮层神经元能够对猫狗进行分类、识别口语单词并解决 10 位奇偶校验——这些任务此前被认为需要整个网络才能完成。

AYi@AYi_AInotes · 8小时前47

Claude Fable 5+gpt-image 2简直就是生图王炸组合！！ oh no，应该是掌管AI生图的神， 这以后还自己研究什么生图提示词啊， 想要什么图交给Fable5就好了啊！ 时间过得也快，眨眼小半年没有玩AIGC生图了， 趁着今天不加班，想着试试用Fable5给我个美女看比赛的图，就把其女友的照片发给它了， 提示词就一句话：给我一个这个女孩看NBA总决赛的现场照片，身材要比参考照片丰满一些，要笑靥如花，背后是美国总统特朗普和尼克斯老板， 结果真的让我卧槽了，他思考的过程会先分析gpt对什么关键词敏感，以及不能出现NBA等品牌词，不能出现人名，自己把NBA改成了NBC，把特朗普去掉了， 然后再看出片效果，这质感、光影，人物一致性， 前女友看到都得跟我复合吧！ 提示词老规矩评论区自取⬇️

译用户分别测试了Claude Fable 5与gpt-image 2的组合以及Fable 5单模型。生图场景中，用户给了一张女友照片和一句话提示词（“看NBA总决赛，身材丰满，笑靥如花，背后是特朗普”），模型自动分析敏感词，将NBA改为NBC、移除特朗普，生成的人物一致性与光影效果惊艳。另一场景，用户直接对Fable 5说“做落地页，自由发挥”，模型自主搜索2026设计趋势、调整配色动效、藏了3个彩蛋，几分钟内输出完整可用的单文件HTML。模型展现出极强的自然语言理解和主动规划能力。

Tibo@thsottiaux · 7小时前50

Heard your (amusing) feedback that it was at times annoying to receive a reset of your Codex usage without warning. Next time we press the button you will get to choose when it actually applies. Happy codexing.

译听到了你们（有趣的）反馈，说有时在毫无预警的情况下收到 Codex 用量重置让人烦心。 下次我们按按钮时，你可以选择它实际何时生效。祝编程愉快。

jason@jxnlco · 8小时前46

codex for open source! just granted about another huge batch including some that you might recognize! tensorflow/tensorflow n8n-io/n8n twbs/bootstrap github/gitignore ytdl-org/youtube-dl vercel/next.js 30-seconds/30-seconds-of-code kubernetes/kubernetes papers-we-love/papers-we-love angular/angular neovim/neovim microsoft/web-dev-for-beginners florinpop17/app-ideas bitcoin/bitcoin gin-gonic/gin microsoft/playwright laravel/laravel gothinkster/realworld spring-projects/spring-boot tensorflow/models apple/swift unclecode/crawl4ai tldr-pages/tldr snowpackjs/astro embedchain/embedchain vim/vim pingcap/tidb jonnyburger/remotion aspnet/aspnetcore seleniumhq/selenium jqlang/jq immutable-js/immutable-js anncwb/vue-vben-admin pynecone-io/pynecone martinvonz/jj serverless-stack/serverless-stack manojvivek/responsively-app trekhleb/homemade-machine-learning sipeed/picoclaw spicetify/spicetify-cli vueuse/vueuse guidance-ai/guidance nautechsystems/nautilus_trader hshoff/vx preservim/nerdtree officedev/office-ui-fabric-react carlospolop/peass-ng reduxjs/reselect adonisjs/adonis-framework rizinorg/cutter facebookresearch/llama-recipes stackexchange/dapper resendlabs/react-email tomav/docker-mailserver lichess-org/lila google/libphonenumber apache/incubator-brpc googlechrome/chrome-app-samples hwchase17/langchainjs fanux/sealos argoproj/argo argoproj/argo-workflows rjsf-team/react-jsonschema-form secureauthcorp/impacket scylladb/scylla uuidjs/uuid cayleygraph/cayley cesiumgs/cesium eclipse-vertx/vert.x pyodide/pyodide jetstack/cert-manager rileytestut/altstore sunnyyoung/wechattweak-macos pydanny/cookiecutter-django pandas-profiling/pandas-profiling espanso/espanso ansible-semaphore/semaphore k9mail/k-9 nock/nock dotnet/aspnetcore.docs selectize/selectize.js mozilla-mobile/firefox-ios wanghongenpin/network_proxy_flutter webpack-contrib/webpack-bundle-analyzer alicevision/meshroom actions/virtual-environments jxnl/instructor theramu/fay svprogresshud/svprogresshud lexikos/autohotkey_l lipis/flag-icon-css redpanda-data/redpanda vega/vega mrjbq7/ta-lib uber/ludwig keplergl/kepler.gl devicons/devicon crossplane/crossplane openaccess-ai-collective/axolotl go-shiori/shiori audiokit/audiokit pyroscope-io/pyroscope px4/px4-autopilot quickwit-oss/quickwit vuecomponent/ant-design-vue-pro divanteltd/vue-storefront k2-fsa/sherpa-onnx jantimon/html-webpack-plugin mockery/mockery automattic/node-canvas divio/django-cms containers/skopeo kubernetes/kompose lucia-auth/lucia microsoft/fluentui-system-icons triton-inference-server/server pressly/goose altair-viz/altair pwndbg/pwndbg maplibre/maplibre-gl-js webtorrent/webtorrent-desktop hackmdio/codimd

译Codex 为开源项目提供免费授权，最新一批包括 TensorFlow、Next.js、Kubernetes、Angular、Swift、Spring Boot、Playwright、Vim、Bitcoin、n8n、Bootstrap、酷狗（30-seconds-of-code）等大量知名开源仓库，列表涵盖机器学习、前端框架、基础设施、游戏开发、数据库等众多领域。具体授权范围和细则未在推文中说明。

Replit ⠕@Replit · 8小时前31

Agent Superpowers: Skills + Custom Instructions https://x.com/i/broadcasts/1kJzDDnMvrWKv

译Agent 超能力：技能 + 自定义指令 https://x.com/i/broadcasts/1kJzDDnMvrWKv

Claude@claudeai · 8小时前40

Claude Fable 5 has been out for a couple of days. Some projects people have already built with it:

译Claude Fable 5 已经发布几天了。人们已经用它构建的一些项目：

Emad@EMostaque · 9小时前55

Unitree whose robots you have all seen only shipped 5,500 last year. The robotics market is literally going to be thousands of times bigger and likely end up bigger than the entire human labor market. Especially when they spread to space.

译人形机器人初创EngineAI秘密提交港股IPO，成为继宇树、优必选后第三家冲刺公开市场的中国公司。该公司2023年成立，最新估值超100亿元，苹果代工厂立讯精密为股东。2025年出货约400台，目标2026年4000-5000台。对比之下，宇树2025年出货5500台，智元5200台，优必选全尺寸1079台。EngineAI PM01定价2.55万美元起。创始人赵同阳原为小鹏机器人团队负责人。主推文观点认为机器人市场将增长数千倍，最终超过整个人类劳动力市场。

Deedy@deedydas · 8小时前72

Claude 5 Fable (Ultracode) "Make a playable alpine glacial valley at sunrise" No meshes or models. Everything you see is math. Fable screenshotted its own work and iterated. Took ~30 mins, ~500k tokens, ~2500 lines of code, and ~$25. Extremely impressive.

译Claude 5 Fable (Ultracode) "在日出时制作一个可玩的高山冰川山谷" 没有网格或模型。你所看到的一切都是数学。Fable 截取了自己作品的屏幕截图并进行了迭代。 耗时约 30 分钟，约 500k tokens，约 2500 行代码，约 25 美元。极其令人印象深刻。

Ethan Mollick@emollick · 9小时前72

There has been a push to use OpenEvidence AI for doctors. But this paper suggests general models are much better: “Frontier LLMs outperformed clinical AI tools in all three evaluations. Clinical AI tools performed comparably to auto-enabled Google Search AI Overview on the RCQ.”

译一项发表在Nature Medicine的研究显示，通用前沿大语言模型（Google、OpenAI、Anthropic）在医学信息评估中全面优于专门的临床AI工具（OpenEvidence和UpToDate）。12名美国临床医生进行随机盲测，Frontier LLMs在三项评估中均胜出。临床AI工具的表现与自动启用的Google Search AI Overview在RCQ测试中相当。

OpenRouter@OpenRouter · 9小时前31

WSJ: OpenRouter provides a bundle of strategies that you can use to reduce inference costs, which our customers use every week. https://www.wsj.com/tech/ai/the-ai-price-war-is-here-piling-pressure-on-openai-and-anthropic-86e1d21b?st=Jm3E6f&reflink=article_copyURL_share Read about what shipped this week in our Cost Reduction Month thread: https://x.com/OpenRouter/status/2064011848823816419

译OpenRouter宣布本月为“成本降低月”，计划每周至少发布一项降低推理成本的功能。据WSJ报道，OpenRouter提供一系列降本策略，帮助客户应对AI模型突破后常见的成本压力。引用数据显示，过去三年里重大技术突破后往往伴随成本飙升。首批功能已在本周上线，后续清单将持续更新。OpenRouter旨在通过持续优化推理开销，缓解OpenAI、Anthropic等大模型厂商的定价压力。

🚨 AI News | TestingCatalog@testingcatalog · 9小时前53

SPACEX 🔥: $SPCX GOING PUBLIC TODAY WITH A $1.77 TRILLION MARKET CAP AT $135 A SHARE! Considering that xAI is now a part of it, it is becoming one of the biggest AI events of the year. WHOIS IN? 👀

译SPACEX 🔥：$SPCX 今日上市，市值1.77万亿美元，股价135美元！ 考虑到xAI现在是它的一部分，这正在成为今年最大的AI事件之一。 谁参与？👀

Emad@EMostaque · 9小时前38

If you think AI valuations are crazy just wait until SpaceX, OpenAI and Anthropic all are liquid. Hopefully some crazy ideas and impactful ideas get funded, especially as many of the stockholders think AGI is coming so like use it or lose it

译如果你觉得AI估值疯狂，那就等到SpaceX、OpenAI和Anthropic都变得流通起来。 希望一些疯狂但有影响力的想法能得到资助，尤其是很多股东认为AGI即将到来，所以要么利用它要么失去它。