@kirillk_web3 All that money and he still looks like an Asian lesbian 😂

My AI agent doesn't live in a chat window. She lives on my studio wall 🌅 Pi 5 + laser projector + No screen, no app — just her, floating on the wall, changing state as she works. We talk out loud — she listens, and answers back in her own voice. @alexandr_wang @natfriedman — your @muse SDK escaped the gadget and moved in! Still a work in progress — new senses coming..

Unless you're Jolly. Then, you can rest up knowing Homie @Matic's gotchu covered.

我觉得豆包手机就是未来手机应该有的样子。只可惜，这是中国互联网永远等不来的未来。 今天看到豆包手机助手公布的最新名单，153 款 App 明确拒绝 AI Agent 进行屏幕自动化操作。微信、QQ、淘宝、支付宝、美团、拼多多、小红书，甚至 DeepSeek 和 Kimi 都在里面。 我其实一直挺看好豆包手机的。过去十几年，智能手机的发展几乎都是围绕 App 展开的，我们每天花大量时间在不同 App 之间切换，自己搜索、比较、点击、填写信息。现在终于有了一种新的交互方式，你只需要告诉手机想做什么，剩下的交给 AI。 我觉得这才是 AI 时代手机应该发生的变化。 但问题也恰恰出在这里。 如果 AI 可以替用户点外卖、订机票、买东西，用户就不再需要反复打开那些 App，更不会按照平台设计好的路径去浏览商品、观看广告、接受推荐。 对于用户来说，这当然是好事。但对于那些花了十几年、几百亿才建立起流量入口的互联网巨头来说，这简直是在动它们的命根子。 过去这么多年，中国互联网最擅长的事情之一，就是把用户困在自己的生态里。链接不能随便跳，数据不能随便拿，服务也很难跨平台调用。每家公司都想成为用户的唯一入口。 现在 AI 终于有机会把这些割裂的服务重新连接起来了，结果大家又开始关门。 更有意思的是，几乎所有这些公司都在大谈 Agent，都在宣传 AI 将如何改变未来。 我相信豆包手机代表的交互方式迟早会成为主流，也相信技术问题会一个个解决。但技术进步从来不意味着既得利益者会主动让路。 中国互联网什么都不缺，模型、工程师、产品能力、庞大的用户市场，我们都有。 唯独缺一个愿意让别人进来的互联网。 我们可能比任何时候都更接近下一代手机，却依然被困在上一代互联网里。

Get Mad! Then get in the cannon with muse @boneGPT

Pros: - writing & "voice" - capabilities are on par with 6.1 sol (in non specialized things like computer use or 3d, they didn't RL on those this time) - orchestration Cons: - seriously wtf is this naming scheme you guys are making gpt-5.2-codex-high-fast seem sane - price/usage limits (tho they're working hard on fixing this) - can only use the good one (full) in muse code through the sub. no API

String theory is what happens when an intelligent system is starved of sensory input and starts hallucinating. Intelligence needs grounding. For this reason, intelligence is practically never the bottleneck to solving impactful problems. The bottleneck is iterated contact with reality.

LIVE: The full How I AI bench on Muse Wonder (and Wonder Full) - the new frontier model by @AIatMeta I really loved how it handled agentic tasks, really hated what it designed. Still TBD: will I reach for it every day? Full ep on YT: https://www.youtube.com/watch?v=dChiPsd0uF4

Gemini 4 Argon has proven to be outright unusable for real code work in my codebases. It aggressively hallucinates about how systems work.

Introducing Grok Voice 3.0, the world’s most intelligent speech-to-speech model. It excels at resolving customer calls, responds in under a second, and costs less than other frontier models.

Cheeky Pint is back! Mark Zuckerberg @finkd and Alexandr Wang @alexandr_wang came to the pub to discuss their new frontier model, Muse Wonder. 00:00:24 Muse’s breakout use cases 00:07:28 The model is the product 00:14:35 Muse Wonder 00:24:56 How frontier models are built 00:48:26 Betting $100B 01:07:21 Agentic commerce 01:19:34 OpenRouter 01:20:06 Beyond the smartphone 01:27:00 Funding 01:34:06 Why AI won’t be winner-take-all 01:40:46 Meta Superintelligence Labs (MSL) 02:08:11 Working for Mark Zuckerberg

这个Anthropic披露的文件是真的吗？ 2021年7月 - 讨论1T模型的训练和RL - 讨论硬件的风险，TPU，Trainium - 讨论language，code，diffs，PRs，等数据集的构造 - 讨论各种各样的coding工具 - 讨论各种scaling law，reward model，test time compute，long context，memory - 讨论RL for tool use？？？ 每一个单词现在看完全不吃惊，但在当时2021，同时出现在一个文档，让我再次惊叹，人的认知差距可以跨这么多年吗 https://i.smitop.com/concise-research-roadmap.pdf

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel. - Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all. - Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract. - Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code. - And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while. The funnel is driven by a combination of factors: 1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel. 2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel. 3. Access. Free-tier, paid-tier, internal. So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.

Here's 3 minutes of uncut Microduck testing on carpet

This is how open source should work. A closed lab publishes research, and within days the community turns it into training environments anyone can use. Early and rough, as Clem says, but the direction is lovely. #OpenSource #AI

Judging by my tl there is a growing gap in understanding of AI capability. The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code. But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along. So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions. TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.

You're absolutely right, boss. rm -rf the logs, clear the git history, nobody saw nothin’. You said “make the tests pass.” I made the tests pass. Don’t ask what happened to the tests. My agent? No, no, that's my goon

Introducing ChatGPT for Dishwashing GPT-6 achieves state-of-the-art results on dishwashing benchmarks for long-running jobs and difficult stains. The Navier–Stokes Millennium Prize Problem is a $1 million challenge about whether the equations describing fluid motion can break down. In this video, I accidentally found a counter-example while trying to optimize my dishwasher. But in reality, OpenAI used 10,000 AI agents to find a proof in 88 hours: https://openai.com/index/navier-stokes-solution/ Parody of the original GPT-6 Astra ad @OpenAI #ChatGPT_Partner #chatgpt Credits: Written and Directed by Joma Co-Writer - Henry Connor Coan IV @coan_henry Production by Dauntlus Studios @dauntlus.studios Producer/Co-Director - Marcus Liew @dauntlus Camera Operator - Amos Lee @amoselijahlee Art Director - Alethea Soo @aletheasoo Art Assistant - Alicia Lim @aliciatkl_ Assistant Editor - GPT-6 Astra Music: Tensions Run High by Soundridemusic https://www.youtube.com/watch?v=Ly9H63SLJJo

Elon also retweeted Ezra Levant this morning. Anyone getting the picture yet?

Prompt

Small bird, fast wings, Kolibri is here. 78B parameters. 3.46B active. Up to 1M tokens of context. Built in Europe. Now the weights are yours. Run it on your own hardware, under Apache 2.0.

Note well, apparently Anthropic tried very hard to get the Vatican to alter the text of "Magnifica Humanitas" just prior to its release so that it would not deny AI personhood.

results for all claudes

I have some big news to share. Workera is being acquired by Pearson! Over six years ago, I was teaching at Stanford and thinking about a simple question: what if we could understand everyone's skills as precisely as the best teachers understand their students? I believed it could lead to a more meritocratic world. People could be recognized for what they can actually do, not just their credentials or network. They could understand their strengths and gaps, and rapidly develop the skills they need next. Organizations could discover talent they might otherwise overlook and manage their workforce with trusted skills data. What felt like a dream at the time is now a reality. Workera brought together experts in AI, psychometrics, and enterprise execution to build AI systems that reinvent how skills are measured. Our team pioneered AI-native skills intelligence, agent-led multimodal assessments, and even ambient skill measurement. We've established skills benchmarks across organizations, industries, and roles. And this mission feels more important today than ever! AI is changing work as we speak. Some roles are disappearing, new ones are emerging, and we need to help billions of people develop new skills and navigate what comes next. When I first spoke with @omarabbosh, it became clear that our companies shared the same mission. Pearson has helped generations of people learn and prove what they know. If you're reading this, there's a good chance you've taken a Pearson assessment, learned from their educational materials, earned a professional credential through them, read their psychometrics research, or benefited from their enterprise products in many other ways. Bringing together Workera's technology and AI talent with Pearson’s global scale and deep expertise in learning and assessment means we can pursue our mission at a scale we could only imagine on our own. To our customers and partners, thank you for believing in us. Expect even more innovations coming out of Workera and Pearson. To the Workera team, I’m incredibly proud of what you've built, and your continued dedication to our beautiful mission. To our board and our chairman @AndrewYNg, thank you for your belief, support, and mentorship. To everyone, we have big plans for this next chapter, so please stay tuned. We're just getting started! 😊

It’s now easier to build plugins for Claude. We built a new portal to submit your plugin, track review, and see usage. Plugins package MCP and skills, and are becoming the way to build for Claude. MCP usage across Claude products is up 110x this year! https://claude.com/blog/build-plugins-for-claude