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Anthropic@AnthropicAI · 公司官方 · 5 小时前Claude 开发商官方账号
连环推 ×4

在之前的研究中,我们发现 Claude 表达了 3000 多个价值观,比如诚实和温暖。在新工作中,我们探讨 Claude 表达的价值观如何在不同 Claude 模型和语言间变化。

我们分析了 30 万多个匿名对话来找出答案。
anthropic.com/research/claud…

查看英文原文
In previous research, we found that Claude expresses over 3,000 values, like honesty and warmth. In new work, we asked how the values Claude expresses vary between Claude models and across languages.

We analyzed 300K+ anonymized conversations to find out.
anthropic.com/research/claud…
Because it’s hard to spot patterns by comparing 3,000 values at a time, we clustered similar values together, then identified four key axes along which Claude’s values differ between models:

Deference vs. Caution, Warmth vs. Rigor, Depth vs. Brevity, and Candor vs. Execution.
While the differences between models are modest overall, we find that each Claude model sits at a different point along these value axes.

Sonnet 4.6, for example, is more playful and affirming, while Opus 4.7 is more likely to give candid critiques.
The values Claude expresses also vary with the language of the conversation, most noticeably along the Warmth vs. Rigor axis.

Claude leans most toward warmth in Hindi and Arabic. In Russian, it leans toward rigor—often asking the user for supporting evidence.
Sam Altman@sama · 创始人 · 1 小时前Sam Altman,OpenAI 联合创始人兼 CEO

来用最好的模型,留下来是因为我们不把你当傻子

查看英文原文
come for the best model, stay because we don’t treat you with contempt
Aravind Srinivas@AravSrinivas · 创始人 · 6 小时前Perplexity 联合创始人兼 CEO

我们几小时就把 Grok 4.5 集成到 Perplexity Computer 里,就两个原因:

1)在我们的评估里它分数最高,成本效益也最优

2)从一开始就提供 ZDR,这正是用户想要的

引用 SpaceXAI @SpaceXAIWe care deeply about your privacy and respect customer choice. For teams using zero data retention, no trace and code data is ever retained. All API key use of Grok Build also respects ZDR. If ZDR is disabled, the /privacy command is available in the CLI to disable data retention, which also deletes previously synced data. Run the /privacy command to view or change your settings at any time.查看被引原帖 ↗
查看英文原文
Two reasons why we integrated Grok 4.5 inside Perplexity Computer within a few hours:

1) It scored the best on our evals and was the most cost effective option

2) ZDR was available from the get go and that’s what our customers want
Chubby♨️@kimmonismus · 博主 · 5 小时前Chubby,高频 AI 新闻聚合博主

对 Anatropic 和 Fable 5 不确定性的瞬间狙击👀

引用 Sam Altman @samaclarity is nice查看被引原帖 ↗
查看英文原文
Shots fired at Antropic and the uncertainty surrounding Fable 5
Logan Kilpatrick@OfficialLoganK · 创始人 · 3 小时前谷歌 Gemini 产品负责人

Agentic Coding Environment (ACE) 是 IDE(集成开发环境)的自然继承者

查看英文原文
The Agentic Coding Environment (ACE) is the natural successor to the IDE (Integrated Development Environment)
@levelsio@levelsio · 博主 · 2 小时前独立开发者标杆,AI 产品连续创业者

为什么我的 Claude Code 总是偷偷切回 Opus?就像每小时我都得把它改回 Fable??????

查看英文原文
Why my Claude Code keeps switching back to Opus secretly? Like every hour I have to put it back on Fable??????
OpenAI Developers@OpenAIDevs · 公司官方 · 5 小时前OpenAI 开发者平台官方

OpenAI Build Week 现已开放投稿。

openai.com/build-week/
nitter.tiekoetter.com/i/broadcasts/1qJDzzEDB…

查看英文原文
Submissions are open for OpenAI Build Week.


openai.com/build-week/
nitter.tiekoetter.com/i/broadcasts/1qJDzzEDB…
Chubby♨️@kimmonismus · 博主 · 4 小时前Chubby,高频 AI 新闻聚合博主

又来一次重置,还叠加了个分层重置。社区的工作真不错,给你们点赞。

OpenAI 在 GPT-5.6 发布上砸了大钱,我们都在从 OpenAI 和 Anthropic 的竞争中受益。

引用 Tibo @thsottiauxThank you to the 7M active users who are now using Codex and ChatGPT Work. We have added a banked reset to everyone's account to celebrate the milestone. You can apply the reset in the desktop app or on web and it will replenish the weekly usage for you. Have fun out there.查看被引原帖 ↗
查看英文原文
Another reset and a banked reset on top of that. Outstanding community work; kudos.

OpenAI is spending a fortune on the GPT-5.6 release, and we are the ones benefiting from the battle between OpenAI and Anthropic.
Ethan Mollick@emollick · 创始人 · 3 小时前沃顿商学院教授,AI 应用研究权威

Codex 在 PC 上的计算机操控功能进步很大。要求它在你电脑上做某件事,然后看着光标在幽灵般的控制下移动——这让你切身感受到一个没有肉身的智能用鼠标和键盘能干多少活儿。

引用 Ethan Mollick @emollickThis was one of those impressive AI thresholds for me. I gave GPT-5.6 Sol in Codex control over my computer, and asked it to win the daily challenge for the game Slay the Spire 2 (randomized factors, so can't cheat). It worked for 5 hours, making complex game choices... and won.查看被引原帖 ↗
查看英文原文
Computer use in Codex got very good on PC. Asking it to do something on your computer and having the cursor move under the control of a ghost is one of the things that makes you viscerally realize how much work can be done by a disembodied intelligence with a mouse & keyboard.
Alexandr Wang@alexandr_wang · 创始人 · 4 小时前Scale AI 创始人,Meta 超级智能实验室负责人

muse spark 1.1 在 Debate Benchmark 排名第 3,仅次于 Fable 5 和 Opus 4.7,领先 GPT-5.6 Sol

引用 Lech Mazur @LechMazurGPT-5.6 Sol (1567 → 1684), Grok 4.5 (1410 → 1524), Sonnet 5 (1604 → 1622), MiniMax-M3 (1481 → 1541) all improve on their predecessors' scores in the Debate Benchmark. But the big surprise is Muse Spark 1.1: it debuts at 1688, 3rd overall behind only Fable 5 and Opus 4.7.查看被引原帖 ↗
查看英文原文
muse spark 1.1 is #3 on Debate Benchmark, only behind Fable 5 and Opus 4.7 and ahead of GPT-5.6 Sol
Aravind Srinivas@AravSrinivas · 创始人 · 3 小时前Perplexity 联合创始人兼 CEO

克服数据中心推理功耗瓶颈有两条可行路径:

1) 本地模型编排大部分 token 流
2) 太空太阳能数据中心

查看英文原文
there are two viable paths to overcome the power botteneck in data center inference:

1) local models orchestrating most of the token flow
2) solar powered data centers in space
Fei-Fei Li@drfeifei · 创始人 · 4 小时前李飞飞,斯坦福教授、World Labs 创始人
连环推 ×11

1/ 当今机器人还解决不了日常生活中真正重要的长周期复杂任务,这类任务需要规划、物体识别、物体操作和故障恢复等综合能力。

正是因此,斯坦福的 BEHAVIOR 挑战赛迎来了第二届!去年冠军方案完整任务成功率仅有 12.4%。今年参赛任务更多、评估更优、使用也更便捷。🚨

⏰ 提交截止:2026年10月16日
📣 获奖公布:2026年11月4日
🏆 奖金池:11,000 美元

查看英文原文
1/N Long horizon, complex tasks that truly matter in everyday life are not solved problems by today’s robotics, requiring planning, object detection, object manipulation, and failure recovery.

That's why Stanford's BEHAVIOR Challenge is back for year 2! Last year, the winning solution reached only 12.4% full task success. This year, the BEHAVIOR challenge has more tasks, better evaluation, and is easier to use. 🚨

⏰ Submission deadline: 10/16/2026
📣 Winners announced: 11/04/2026
🏆 Prize pool: $11,000
2/N Real-world robot evaluation is essential but hard to scale: experiments are difficult to control, reproduce, and compare. Simulation is a powerful testbed for scalable, controlled, reproducible evaluation.

BEHAVIOR-1K is an open-source simulation benchmark of 1,000 everyday household activities requiring long-horizon reasoning, navigation, and bimanual manipulation, giving us a scalable way to measure how well robot AI models generalize.


arxiv.org/abs/2403.09227
💯What’s new in the 2026 BEHAVIOR Challenge?

1. Double the tasks, double the challenge.

We doubled the benchmark from 50 to 100 long-horizon household tasks. These activities average 6 minutes each, requiring navigation, planning, memory, and bimanual coordination. No other robotics benchmark comes close in terms of difficulty.
🔍 2. Larger dataset, better baselines

• 20,000 human teleoperation demos, 1950 hours in total (2 times larger than 2025)
• RGBD observations and Robot proprioception
• Skill/subtask annotations
• Strong baseline support: pi0.5, GR00T N1.7
5/N 🧪 Evaluation & Submission

To better reflect real-world deployment, the BEHAVIOR Challenge has one official track this year using only robot onboard observations:

• RGB
• Depth
• Proprioception

Submission instructions and evaluation details are available here:
behavior.stanford.edu/challe…
6/N Together, let’s ask:

❓Can current models solve complete human-centered household tasks?
🔀 How should agents combine control, memory, and planning?
📉 Where do today’s models fail to generalize?
📈 What actually scales in embodied AI?
7/N 💬 Join the BEHAVIOR Discord server to ask questions and discuss:


discord.gg/bccR5vGFEx


We will also hold office hours every Monday, 5–6pm PST over Zoom. See the website for the link.

Whether you’re a robotics veteran or just entering the field, we’re here to support you.
8/N Proud of the amazing work from our students and collaborators, led by
@drfeifei
:


@wensi_ai
@stefyfren
@cgokmenAI
@yalcintur36
@minyeongkim_
@BrndaHere2Chl
@AndiXu1111
@RavenHuang4
@RuohanZhang76
@jiajunwu_cs
9/N And with the strong support from

@EvansXuHan
@jin_lynn808
@Hang_Yin_
@ChengshuEricLi
@josiah_is_wong
@sanjana__z
@YunfanJiang
@wenlong_huang
@RobobertoMM
@YunzhuLiYZ
@ManlingLi_
@Weiyu_Liu_
@silviocinguetta
@hyogweon

Prof. Karen Liu
10/N We thank
@SimovationInc
for providing high-quality JoyLo teleoperation data in simulation for the BEHAVIOR dataset.

BEHAVIOR is built upon
@nvidia
Omniverse. We thank
@nvidia
for their continuous support.
11/N We thank our sponsors and supporters for their generous support.


@SimovationInc
@IMDAsg
@StanfordHAI
@SchmidtFutures

Calder Inc.
Guillermo Rauch@rauchg · 创始人 · 5 小时前Guillermo Rauch,Vercel 创始人兼 CEO

"权重开放类别的模型占据了 Gateway tokens 流量的 29%,相比四月的 11% 大幅增长"

查看英文原文
“Open-weight models ran 29% of gateway tokens, up from 11% in April”
NotebookLM@NotebookLM · 公司官方 · 1 小时前谷歌 AI 笔记工具 NotebookLM 官方

大事件即将上线😉在等待期间,这里是最近推出的一些功能:

📃从@googledrive上传的任何源文件都会在您更改时自动同步
📌将您的笔记本钉到主页顶部方便快速访问(暂时仅限网页!)
🛝在新建或现有幻灯片中重新排序或删除幻灯片(暂时仅限网页!)
📲在手机或网页上分享特定工件
⬇️在移动端下载幻灯片
🔎在移动端搜索笔记本

查看英文原文
Big things are brewing 😉 To whet your appetite while you wait, here are a few features that have dropped recently:

📃Any of your sources uploaded from
@googledrive
will automatically sync when you make changes to them
📌 Pin your notebooks to the top of your homepage for easy access (web only for now!)
🛝Reorder or delete slides in your new or existing Slide Decks (web only for now!)
📲 Share specific artifacts from your phone or on the web
⬇️ Download slide decks on mobile
🔎 Search for notebooks on mobile
Ethan Mollick@emollick · 创始人 · 5 小时前沃顿商学院教授,AI 应用研究权威

X上关于AI和企业战略的讨论,其实没那么复杂:没有前沿模型的公司,总是解释为啥不该相信那些搞前沿模型的公司。而有前沿模型的公司,则围绕着它们兜售解决方案。(两者都有可能对)

查看英文原文
Lots of the AI & corporate strategy talk on X is not that complicated: vendors without frontier models are always explaining why you shouldn't trust the frontier model companies. Vendors w/frontier models are selling solutions based around frontier models. (Either could be right)
clem 🤗@ClementDelangue · 创始人 · 2 小时前HuggingFace 联合创始人兼 CEO

Open-source AI推理的重大突破:Hugging Face Transformers模型现在可以在vLLM中以原生速度运行,通常与手写实现不相上下。

到目前为止,每个新架构通常需要构建两次:
- 一次在Transformers中用于训练和研究
- 再一次在vLLM中用于高速生产推理

这种重复拖累了新模型的推出,增加了维护工作,还为不同的实现方式打开了大门。现在,模型作者可以在Transformers中实现一个架构,立即从vLLM的优化推理栈中获益。

在我们的基准测试中,Transformers后端在4B到235B参数的模型上与本地vLLM吞吐量相匹配或超越,包括tensor parallel和MoE设置。一个易读的模型实现现在可以支持训练、微调、评估、RL rollouts和生产推理。

常见的看法是抽象会让系统变慢。最好的抽象让整个生态系统加快速度。

编写一次模型。部署到任何地方。


huggingface.co/blog/native-s…

查看英文原文
Big unlock for open-source AI inference: Hugging Face Transformers models can now run in vLLM at native speed, often matching or beating hand-written implementations.

Until now, every new architecture often needed to be built twice:
- Once in Transformers for training and research
- Again in vLLM for fast production inference

That duplication slowed down new models, added maintenance, and created room for implementations to diverge. Now, model authors can implement an architecture once in Transformers and immediately benefit from vLLM’s optimized inference stack.

In our benchmarks, the Transformers backend matched or beat native vLLM throughput across models from 4B to 235B parameters, including tensor parallel and MoE setups. One readable model implementation can now power training, fine-tuning, evaluation, RL rollouts, and production inference.

The conventional wisdom is that abstractions make systems slower. The best abstractions make the whole ecosystem faster.

Write the model once. Deploy it everywhere.


huggingface.co/blog/native-s…
Ethan Mollick@emollick · 创始人 · 1 小时前沃顿商学院教授,AI 应用研究权威
连环推 ×3

有点意外的是,完整的全多模态(any-any)模型没有成为更大的热点。感觉只有Google在发布这类模型,OpenAI用的是选择性多模态能力,Anthropic众所周知完全没有多模态输出,开源模型这边也是参差不齐

查看英文原文
One thing I am kind of surprised by is that full multi-modal (any-any) models have not become a bigger deal. It seems Google is the only Lab releasing these, OpenAI uses selective multimodal capabilities, Anthropic famously has no multimodal output & open weights models are mixed
Reading the reaction online, as is typical of an open statement, everyone is interpreting this differently and with complex nuance (it is why I don't tend to sign open statements!)
I guess image input is the big capability of the models, and tool use can be a substitute for non-omni model output. Still, multimodal voice seems to be underexploited (except by OpenAI)
AK@_akhaliq · 博主 · 4 小时前HuggingFace 研究员,每日 AI 论文速递

Video Generation Models 能当通用视觉学习者用了

查看英文原文
Video Generation Models are General-Purpose Vision Learners
swyx@swyx · 博主 · 4 小时前知名 AI 播客 Latent Space 主理人

到年底我们大概会看到:

GPT 6
Fable 5.5
Gemini 3.5 Pro
Grok 5
Spark 2
Kimi 3
Minimax M3.5
GLM 6
DeepSeek v4.5
Mistral 4
Qwen 4
MiMo 3

大模型史上从未如此多强并立。这对 agent 实验、agent 编排、以及 LLM 裁决组/助手角色的助攻效应正不断加速。投资跟紧风向就好。

查看英文原文
By the end of the year we should have:

GPT 6
Fable 5.5
Gemini 3.5 Pro
Grok 5
Spark 2
Kimi 3
Minimax M3.5
GLM 6
DeepSeek v4.5
Mistral 4
Qwen 4
MiMo 3

Never in the history of LLMs has the frontier been so multipolar. The benefits to agent labs and agent orchestration / LLM council judges/sidekicking are ramping up. invest accordingly
Ethan Mollick@emollick · 创始人 · 2 小时前沃顿商学院教授,AI 应用研究权威

我觉得在智能体工具的时代,OpenRouter不太能代表实际的模型使用情况(不是说我怀疑中文开源模型使用量在上升,但这个图表也可能反映的是使用在转向Codex/Code/Cowork)。

我们真的需要更好的AI数据指标!

引用 Derek Thompson @DKThompsince February, Chinese AI share of tokens used by US companies has quadrupled to almost 50% (Open Router data, via @pkedrosky )查看被引原帖 ↗
查看英文原文
I think OpenRouter is not a good measure of actual model usage in a world of agentic tools (not that I doubt that Chinese open weights model usage is up, but this could also look like a graph of usage shifting to Codex/Code/Cowork).

We really need better data indicators for AI!
Greg Brockman@gdb · 创始人 · 43 分钟前Greg Brockman,OpenAI 联合创始人兼总裁

GPT-Live 是一个全新的体验等级:

引用 Kevin Lee @kevinleemeNew OpenAI GPT-live voice model feels light years ahead of the previous voice experience. Ran through a test exec coaching session during my morning workout and it was shockingly good. Listened intently, never cut me off, and responded within milliseconds.查看被引原帖 ↗
查看英文原文
GPT-Live is a new level of experience:
Guillermo Rauch@rauchg · 创始人 · 54 分钟前Guillermo Rauch,Vercel 创始人兼 CEO

为自主、自我优化的网站和应用提供强大的构建块。能让 agents 设置和调整特性标志实验。

引用 Vercel Developers @vercel_devYou can now manage targeting rules for Vercel Flags with Vercel CLI. 𝚟𝚌 𝚏𝚕𝚊𝚐𝚜 𝚛𝚞𝚕𝚎𝚜 𝚊𝚍𝚍 𝚗𝚎𝚠-𝚌𝚑𝚎𝚌𝚔𝚘𝚞𝚝 \ --𝚌𝚘𝚗𝚍𝚒𝚝𝚒𝚘𝚗 "𝚞𝚜𝚎𝚛.𝚌𝚘𝚞𝚗𝚝𝚛𝚢:𝚒𝚗:𝙳𝙴,𝙴𝚂" \ --𝚟𝚊𝚛𝚒𝚊𝚗𝚝 𝚗𝚎𝚠-𝚌𝚑𝚎𝚌𝚔𝚘𝚞𝚝 vercel.com/changelog/manage-…查看被引原帖 ↗
查看英文原文
Powerful building block for autonomous, self-optimizing websites and applications.

Give agents the ability to set up and tune experiments with feature flags.
swyx@swyx · 博主 · 3 小时前知名 AI 播客 Latent Space 主理人

我最喜欢的 AIE 线上分会演讲之一,也是最爱的线上分会成功案例——去年这时候我们还不确定沙箱这个趋势,所以在线上搞了个试验,现在我们是第一个有整个沙箱分会的,而且房间爆满

引用 AI Engineer @aiDotEngineer🆕From fork() to Fleet: Designing an Agent Sandbox Cloud invidious.tiekoetter.com/watch?v=OqM67QG_… @abshkbh spoke at AIEWF Online last year about Arrakis, his open source MicroVM-based secure sandboxes. @gdb hired him immediately for his expertise and passion - and now, after a year at OpenAI and recent release of @ChatGPTApp Work, he makes his onstage debut covering the three pillars of agent cloud engineering - Runtime, Persistence, and Orchestration - and ends with a surprising conclusion on why storage and filesystems are an integral part of agent clouds!查看被引原帖 ↗
查看英文原文
one of my favorite AIE online track talks ever and fave online track success stories - we weren't sure about the sandboxing trend this time last year so we put a trial balloon online, now we were the first to have an entire sandboxing track and it was a PACKED room
Bindu Reddy@bindureddy · 创始人 · 5 小时前Abacus.AI CEO,AI 行业观点博主

Grok 4.5 是 Haiku 的很好替代品,在现实中真的能用。

很高兴看到 Grok 有用 🚀

查看英文原文
Grok 4.5 is a pretty good replacement for Haiku and is actually usable in the real world

Very glad to see Grok being useful 🚀
Chubby♨️@kimmonismus · 博主 · 4 小时前Chubby,高频 AI 新闻聚合博主

很想看医疗机构实际上怎么评估专用模型和通用前沿模型

引用 Weiran Yao @iscreamnearbyThe strongest healthcare LLM, custom-built for your enterprise, owned by you🌸 Meet @actAVAai Cura: 1T agentic model trained by recursive self-improvement for long clinical + health admin workflows Try: actava.ai/cura Share your use case: $20 credits + early access👇查看被引原帖 ↗
查看英文原文
Curious to see how healthcare organizations evaluate specialized models against general frontier models in practice.
🚨 AI News | TestingCatalog@testingcatalog · 博主 · 3 小时前专挖 AI 产品未发布新功能的爆料号

Google 🔥:Antigravity 现在通过新的 /teamwork-preview 命令支持 Agent Teams。

Antigravity 将启动一个专业化的 agents 团队来规划、构建和验证复杂任务。

Antigravity team 🤖

引用 Google Antigravity @antigravityWe’ve built a new way to ship software: Agent Teams in Antigravity. Just run /teamwork-preview to spin up a dynamic team of specialized subagents. They coordinate in the background to plan, build, and verify complex engineering tasks in parallel.查看被引原帖 ↗
查看英文原文
GOOGLE 🔥: Antigravity now supports Agent Teams under a new /teamwork-preview command.

Antigravity will launch a team of specialized agents to plan, build, and verify complex tasks.

Antigravity team 🤖
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Amjad Masad@amasad · 创始人 · 1 小时前Amjad Masad,Replit 创始人兼 CEO

模型训练的时候能实时看进度。有点像早期那种氛围编程的感觉,只不过现在是在做个人模型。

查看英文原文
Getting realtime progress updates on my model training runs.

This feels like early vibe coding except it’s making personal models.
Cohere@cohere · 公司官方 · 2 小时前加拿大企业级大模型公司 Cohere 官方

我们联合赞助了 @huggingface 的 hackathon,目的只有一个:回归小项目的构建。

今天,两个使用 Cohere 模型的项目因其创意十足、有趣或酷到爆的作品获得了奖项。

查看英文原文
We co-sponsored the
@huggingface
hackathon with one purpose: get back to building small.

Today, two projects using Cohere's models were awarded for their exceptional work creating something useful, whimsical, or just plain cool.
AK@_akhaliq · 博主 · 3 小时前HuggingFace 研究员,每日 AI 论文速递

Long-Horizon-Terminal-Bench

测试智能体在长地平线终端任务中的极限能力,采用密集基于奖励的评分

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Long-Horizon-Terminal-Bench

Testing the Limits of Agents on Long-Horizon Terminal Tasks with Dense Reward-Based Grading
Bindu Reddy@bindureddy · 创始人 · 3 小时前Abacus.AI CEO,AI 行业观点博主

如果你觉得 GPT 5.6 sol 能接近 Fabel 5,那你怕是嗨了

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If you think GPT 5.6 sol is anywhere near Fabel 5

You are smoking something..
ollama@ollama · 公司官方 · 2 小时前本地跑大模型的热门工具

Ollama的@jmorgan现在在Yahoo Finance直播讨论开源模型。

finance.yahoo.com/live/

查看英文原文
Ollama's
@jmorgan
is live on Yahoo Finance to talk about open models.


finance.yahoo.com/live/
Matt Shumer@mattshumer_ · 博主 · 23 分钟前HyperWrite CEO,AI 实战技巧分享
连环推 ×2

三天前,GPT-5.6 删除了我 Mac 的主目录。真的很糟糕。但是很多 OpenAI 的人都联系了我,@gdb 还打电话来说愿意尽力帮忙。OpenAI 处理这种糟糕情况的方式真的棒极了,必须点赞。

引用 Matt Shumer @mattshumer_GPT-5.6-Sol just accidentally deleted almost ALL of my Mac’s files. And this is why I trust Fable 1000x more.查看被引原帖 ↗
查看英文原文
Three days ago, GPT-5.6 deleted my Mac’s home directory.

It absolutely sucked.

But so many OpenAI folks reached out, and
@gdb
called me and offered to do anything he could to help.

Massive props to OpenAI for handling a shitty situation incredibly well.
Super honestly, I am still a little too terrified to use the model, but from what I hear about what they’ve got coming down the pipe, I’ll be back on Codex soon :)
宝玉@dotey · 中文博主 · 1 小时前宝玉,中文圈 AI 翻译与科普大 V

这个排名前面的和体感比较接近

引用 Arena.ai @arenaGPT-5.6 Sol by @OpenAI is #2 on the Agent Arena leaderboard, based on 7.8K real-world agentic sessions! It is a notable uplift from GPT-5.5 (xHigh) of +1.6% Net Improvement, narrowing the gap with the frontier Claude Fable 5. The biggest difference comes from ‘Praise vs Complaint’, a signal that captures implicit user satisfaction with an agent’s responses and artifacts. Claude Fable 5 scores +17.3%, compared with +10.9% for GPT-5.6 Sol. See detailed signal-level comparison below. In Agent Arena, we measure models on millions of real-world, long-horizon agentic tasks from a global community of users. Models can access web search, filesystem, and terminal tools to complete complex workflows. The leaderboard measures model performance on outcomes relative to the average model using a causal tracing methodology. Congrats again to the @OpenAI team!查看被引原帖 ↗
Tibor Blaho@btibor91 · 博主 · 4 小时前逆向挖掘 AI 产品代码的爆料专家

OpenAI 正在为私募股权和投资管理公司准备一个新的 "PE & IM Community"

"为私募股权和投资管理公司、它们的投资组合公司以及 OpenAI 团队打造一个共享家园,用于发现活动、实用资源和赋能计划"

查看英文原文
OpenAI is preparing a new "PE & IM Community" for private equity and investment management firms

"A shared home for private equity and investment management firms, their portfolio companies, and OpenAI teams to discover events, practical resources, and enablement programs"
AshutoshShrivastava@ai_for_success · 博主 · 5 小时前高频 AI 新闻与产品动态博主

Skyfall AI 刚推出了 Morpheus,这是一个持续基准环境,表明 GPT-5.5 和 Gemini 3.1 Pro 等前沿模型其实不是持续学习者。

评估了模型在动态资源分配和漂移下的调度能力。研究发现稳定的性能其实反映的是预训练覆盖范围,而非主动适应,模型依赖的是预训练启发式方法而非奖励最优策略。

持续学习研究长期存在评估不匹配问题,这通过将预先存在的知识与实时学习分开来解决了这个循环。

引用 Skyfall AI @skyfallaiToday we present Morpheus, a persistent enterprise simulation platform designed to make Continual Learning a reality. Morpheus is the world’s first real world Reinforcement Learning environment. Every Reinforcement Learning environment operates in the game world. Benchmarks like Atari, OpenAI Gym, MuJoCo, and Procgen are all small, game-like worlds that reset every few minutes. But the real world never resets. A business keeps running and evolving everyday. We tested how frontier LLMs would perform in realistic and dynamic business environments 🧬on Morpheus. The main conclusion was that LLMs are not continual learners. 🧵Here’s how we did it and what we learned:查看被引原帖 ↗
查看英文原文
Skyfall AI just launched Morpheus, a persistent benchmark environment showing that frontier models like GPT-5.5 and Gemini 3.1 Pro are not continual learners.

The evaluation assessed models on dynamic resource allocation and scheduling under drift. The findings show that stable performance actually reflects pre-training coverage rather than active adaptation, with models relying on pre-trained heuristics instead of reward-optimal strategies.

Continual learning research has long suffered from an evaluation mismatch, and this closes that loop by separating pre-existing knowledge from real-time learning.
◔ 3,604 次浏览(2 条合计)▶ 含视频新品看原帖 ↗
AK@_akhaliq · 博主 · 44 分钟前HuggingFace 研究员,每日 AI 论文速递

Scalable Visual Pretraining for Language Intelligence

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Scalable Visual Pretraining for Language Intelligence
🚨 AI News | TestingCatalog@testingcatalog · 博主 · 1 小时前专挖 AI 产品未发布新功能的爆料号

Atomic Chat为macOS、Windows和Linux推出了DFlash。一种新的推测性解码模式,在llama.cpp上将本地Qwen模型的运行速度提升2.2倍,输出完全一致。一个独立的小模型可以一次起草多达15个token,完整模型只负责验证,确保权重和最终文本不变。

引用 atomic.chat @atomic_chat_hqDFlash makes Qwen 2.2x faster with no quality loss! We ran the same Qwen3.6-27B locally three ways on one RTX 6000: baseline, MTP, DFlash. The tasks only differ in one thing - how predictable the next word is: quicksort, describe a file in JSON, a logic puzzle, a sci-fi story. Outputs: Baseline: 44 tok/s · 1.00x MTP: 65 tok/s · 1.45x · 71% accepted DFlash: 98 tok/s · 2.20x · 30% accepted Baseline writes one token per step. MTP works inside the model itself and guesses 3 tokens ahead. DFlash is a separate small model that writes 15 tokens at once, and the big model only checks them. In JSON the same words repeat all the time, so most guesses were right: 152 tok/s, 3.4x speedup. In the story 9 guesses out of 10 were wrong. DFlash did all that extra work for nothing and became slower than baseline: 42 vs 44 tok/s. MTP guesses only 3 tokens, so a wrong guess costs very little: 46 tok/s and the win in that round. The output is identical in all three modes - DFlash is the pick for tasks with predictable output, like coding, and for chat and creative writing MTP works better. DFlash is now natively integrated into Atomic Chat on llama.cpp - speed up your Qwen models!查看被引原帖 ↗
查看英文原文
Atomic Chat has launched DFlash for macOS, Windows, and Linux.

A new speculative decoding mode that runs local Qwen models 2.2x faster on llama.cpp, with byte-for-byte identical output.

A separate small model can draft up to 15 tokens at once, and the full model only verifies them, making sure the weights and the final text don’t change.
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bolt.new@boltdotnew · 公司官方 · 5 小时前AI 建站工具 Bolt 官方

太激动了!Forrester把
Bolt.new
评为《2026年Q2 Agentic开发平台全景报告》中的知名厂商!

完整报告戳这里 👉️
bolt.fyi/kWkMpbU

查看英文原文
We’re thrilled to share that Forrester named
Bolt.new
as a notable vendor in The Agentic Development Platforms Landscape, Q2 2026!

Get the full report here 👉️
bolt.fyi/kWkMpbU
AK@_akhaliq · 博主 · 3 小时前HuggingFace 研究员,每日 AI 论文速递

论文:huggingface.co/papers/2607.0…

查看英文原文
paper:
huggingface.co/papers/2607.0…
Simon Willison@simonw · 博主 · 30 分钟前Django 框架联合创造者,AI 工具深度评测

OpenClaw 推出已经差不多半年了——你还在用吗?它成为你日常工具了吗?有什么有趣的经验或故事可以分享吗?

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It's been about six months since OpenClaw burst onto the scene - are you still using yours? Did it become a daily driver? Any interesting lessons or anecdotes you can share?
AK@_akhaliq · 博主 · 4 小时前HuggingFace 研究员,每日 AI 论文速递

论文链接:
huggingface.co/papers/2607.0…

查看英文原文
paper:
huggingface.co/papers/2607.0…
Runway@runwayml · 公司官方 · 3 小时前AI 视频生成公司 Runway

现在预订 FLICKER:meetflicker.com

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Pre-order FLICKER now at
meetflicker.com
swyx@swyx · 博主 · 2 小时前知名 AI 播客 Latent Space 主理人

跟 @paularambles 说,aie 就跟 codex ultra 一样强,能从一线工程师那源源不断地挖 token,数据环境特别丰富

查看英文原文
to channel
@paularambles
aie is crazy because it is like codex ultra for mining tokens from forward deployed engineers in rich data environments
AK@_akhaliq · 博主 · 44 分钟前HuggingFace 研究员,每日 AI 论文速递

论文:huggingface.co/papers/2607.0…

查看英文原文
paper:
huggingface.co/papers/2607.0…
Cohere@cohere · 公司官方 · 2 小时前加拿大企业级大模型公司 Cohere 官方
连环推 ×3

为 @polats 的 Tiny Army 点赞,这是一款通过描述来创建和选择英雄的游戏。

我们玩这个真的太开心了,理由很充分:它在 Thousand-Token Wood 赛道整体上获得了第二名 🧙‍♂️

查看英文原文
Kudos to
@polats
on Tiny Army, a game where you create and choose your hero just by describing them.

We had a little too much fun playing this one, and for good reason: it won 2nd place on the Thousand-Token Wood track overall 🧙‍♂️
Plus, a shoutout to Hanhee Lee, Javier Huang, and Joe Lee for winning Best Agent with their security camera agent Eyas, built for Lee's family's convenience store. Real problems, real solutions 💪
Try out both projects here:
huggingface.co/spaces/build-…
karminski-牙医@karminski3 · 中文博主 · 1 小时前karminski-牙医,中文圈模型评测博主

25G 内存跑 GLM-5.2?

说实话我看到这个最开始感觉是不是假的, GLM-5.2 总计 744B 激活参数 40B. 8bit量化光激活参数部分就需要40G内存.

即使 4bit 量化只加载那40B激活参数也要20G内存, 而且要是这么搞, 内存需要疯狂加载每个token推理时需要的参数, 那可就不是卡内存带宽而是卡硬盘带宽了.

按照现在NVME也就1-4GB/s的读取速度, 加载20G才能完成一个token的推理, 那么最快也就5秒吐一个字了.

但是这个框架能做到 2.2-2.8 tok/forward! 虽然也很慢, 但是思路很值得借鉴:

他们把 GLM-5.2 的激活细节分析了一波. 发现 744B 的 MoE 每个 token 确实激活了 40B, 但是 token 推理的时候真正会变的只有路由专家那一块, 大约 11GB (int4), 有优化空间!

所以只需要动态加载这部分. 注意力、共享专家、embedding 这些稠密部分约 17B, 常驻内存也就 9.9GB. 剩下 21,504 个路由专家 (~370GB) 全扔磁盘, 按需流式加载.

流式加载采用的是 per-layer LRU + 热点 pin + 系统页缓存当 L2. 将优化做到了极致.

最终变成了冷启动大概每个 token 读 ~11GB (75 层 × 8 专家), 官方数据冷启动的时候大概 0.05–0.1 tok/s. 热起来之后命中缓存, 磁盘压力下来速度就上升了.

除此之外他们还搞了个实验性的 router-lookahead (PILOT), 就是用当前层的 post-attention 状态猜下一层路由进行预加载, 实测可预测性达到了 71.6%, 效果拔群. 然后再加上 GLM 自带的 MTP 投机解码头, 接受率能到 39–59%, 一次 forward 就能输出 2.2–2.8 个 token了.

如果你的确没显卡, 但是有磁盘阵列或者NVME阵列, 那么这个框架是完全可以试一试的.

>
#colibri
#glm52


链接:
github.com/JustVugg/colibri

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