GoPagoda | Exploring AI in travel. Sharing thoughts on product, technology, and building things · e/acc ⚡

China
Joined September 2010
SnowShadow retweeted
Cue is free for a limited time. It’s available now on Android, Mac, and Windows. The iOS version has been submitted for review and is coming soon.
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玉伯终于想到这一点了。 不过我的 thesis 是:新时代不存在任何微信这样子的超级 app。新时代的入口属于硬件。 启发我这么想的是我最近做的 GTM 工作流,做完这一套之后,我认为只要任何 IM 提供任何 GUI 的使用方式,那它就必然不可能成为下一个时代的入口。 我首先在本地制作了标准的 Ubuntu 以及 macOS 虚拟机镜像,里面提供了浏览器以及一些 IM 软件。 然后我开发了一个采集程序,在 IM 群组以及社交媒体中采集信息,然后寻找符合用户画像的潜在客户。 因为 Chrome 的 profile 是可以直接抽出然后存储在虚拟机外的,所以针对 web app 我并不需要每次都重新登录。如果不是 web app 也有对应的解决方法。 最后一步就是发起和潜在客户的沟通了。这里比较拙劣的用法是直接使用 AI 产生的信息去和用户沟通。但我依然只会要求 AI 帮我编排好沟通材料,由我自己真人去沟通。 在整个过程中帮我建立连接的,其实并不是我而是我的 AI + 我的意图。我的 AI 可以横跨所有的即时通讯软件及社交媒体,只要它提供任何图形界面的访问方式。 所以只要任何 IM 提供任何 GUI 的使用方式,那它必然就不可能成为下一个时代的入口。因为它必然可以被运行在虚拟机上,然后由 AI 采集信息,帮助用户完成横跨数个 IM 以及社交媒体的连接。 那么下一个时代的入口是什么?我认为它一定会和硬件绑定在一起。或者下一个时代的入口,一定会是 OS 级别搭配硬件销售的。 而且整体看下来我自己做的 GTM 流程存在数据飞轮,而这个飞轮最后掌握在 GTMer 手中。 考虑到数据主权,通过 Qwen 3.8.27B 所有 API 请求和数据都可以留在本地。而我构建这一套设施的硬件成本是 M4 Pro Mac mini 64GB RAM+ 2* DGX Spark。当然,目前 M5 Ultra Mac Studio 256GB 是性价比更高的选择。我目前的配置加起来应该已经超过 10 万了。 但是硬件价格一定会下降,以及目前这一套设备在未来很有可能就可以运行在一个像 Mac mini 甚至像 iPhone这样小的设备里面。iPhone 在发布的时候,其 CPU 相当于 2000 年左右的桌面级 CPU。而目前 A20 Pro 也已经比肩同年代的桌面处理器能力。所以相关的能力在未来一定会走入千家万户。而通过 IM 构建起来的护城河,最后也一定会被瓦解。 另外从这点看,本地 AI 会是一个越来越重要的话题,以及 Qwen 27B 这个模型权重会是一个越来越重要的模型权重。我不得不说,阿里 Qwen 实验室的这个模型权重系列设计真的是非常的有品位。 目前的大厂都只能够自己做软件,最有资源的那一位,却没有做硬件的耐心。所以最后愿机会属于每一位不愿意在大厂打螺丝的朋友们。
细思,Muse 确实有做成新时代微信的机会。 微信当年最重要的功能,是给人发消息,同时比短信还便宜非常多。想发消息的人在微信上,带来用户吸引,形成了网络效应。有用 + 网络效应 + 运气,成就了微信。不是因为微信有多懂你。 Muse 能帮人处理邮件日历、电话催单等各种杂事,这有点像微信最开始的发消息。Muse 的网络效应会在哪。没有网络效应的话,和 ChatGPT 会是同质化竞争。豆包在国内活成了新时代的百度。 Meta 真正的杀招,可能是 WhatsApp -〉 Muse,类似当年的 QQ 通讯录 -〉 微信。不仅自己的 Muse 能干活,而是我的 Muse 还可以连接 WhatsApp 关系网中的其他人的 Muse,彼此因连接而更强大。如果 Muse 敢往这个方向发展,则就有机会构建网络效应。而且会比微信更厉害:微信通过手机,让人醒着就在线。Muse 通过 agent,让人睡着也在线。 有用 + 有机会构建网络效应,最后就只欠运气了。不期待小扎有这个运气,不期待任何大厂有这个运气。只期待齐俊元、玉伯、马小龙等创业者有这个运气。 有意思的时代,正在开启。
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Encoder 的卷土重来 BERT 本来是一条极有潜力的路线:高效地把输入压成 representation。但它停留在了 task-specific 时代,每个任务都需要微调。 GPT 早期同样如此,后来却沿着 scaling、in-context learning 和 instruction tuning 走向了 泛化,把自然语言变成 universal task interface。分类、判断、推理、工具调用,统统变成 generation。 于是 decoder-only 赢下了整个 paradigm,encoder 也失去了继续探索 general classifier 和 general representation model 的历史机会。 但 Agent 改变了 workload。模型开始面对越来越大的输入、越来越重的 KV cache、越来越频繁的循环调用,此时,用一个巨大的 decoder 完整 prefill,再 autoregressive decode 几个 token 来完成一次判断,开始显得昂贵。 Jev 恰好等到了这个窗口。它试图补上 BERT 当年没有走完的路线,保留 GPT 的 task generalization,同时重新获得 encoder 和 classifier 式的 decision efficiency,成为面向 service、面向 Agent 的 泛化 decision model。
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SnowShadow retweeted
文章分为以下段落: “我不看代码” “随时随地可以重写” “那你的愿望呢” “You Aren't Gonna Need It”
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There are two separate questions regarding Dario Amodei’s post about AI and biology: motivation and reality. Motivation. Dario started in biology (was a PhD student of the great Bill Bialek). It might be that the master plan really was to first build much more capable AI systems, and eventually use them to cure diseases. Maybe coding models, chatbots, Claude Code, etc. were never the final destination. They were the road toward models capable of becoming genuinely useful scientific collaborators. It might also be that there was no master plan at all. Anthropic spent years pushing the frontier in coding and general reasoning; those systems have now become much more capable, and this is simply a good moment to turn more attention toward biology. Maybe, but there is also a more cynical interpretation: “Curing cancer” is an extraordinarily powerful form of reputation laundering. They are basically saying: "Yes, we are perhaps creating the largest concentration of technological and economic power in modern history." Yes, we want governments to restrict who can build and access the most powerful versions of this technology. Yes, ordinary people increasingly have less visibility into what the frontier systems can do. But don’t worry - we’re the good guys! We are going to cure cancer. And this is where I disagree with Dario’s claim that “the thing that will work is actually curing cancer.” Suppose Anthropic really does help cure a major cancer. That would be an extraordinary achievement, but curing cancer would not answer the question of who should control AI. A company can create enormous social value and simultaneously accumulate too much power. The pharmaceutical industry itself is a good example. Creating life-saving drugs doesn’t automatically resolve questions about pricing, access, lobbying, competition, data rights or public accountability. And there is another problem with the biology story. I am very bullish about AI for science. We’ve discussed AI drug discovery and related areas several times on our podcast. AI can accelerate literature review, target discovery, protein and molecule design, experimental planning, analysis, and many other parts of the pipeline. But biology is not software. You can make an AI reason 100x faster. You cannot simply make a human body respond to a drug 100x faster. Wet-lab experiments take physical time. Toxicity takes time to observe. Human trials take time. Long-term side effects take time. Diseases are heterogeneous. Manufacturing, recruitment and validation happen in the physical world. AI can remove some bottlenecks, but not all of them. Also, if these models really have a plausible path to curing cancer, Alzheimer’s, hepatitis and other diseases, then “pacing AI progress” has a cost too. If delaying frontier AI by a year delays a cure by a year, that isn’t an abstract opportunity cost. People die during that year. And of course the most important question that I think Dario’s post doesn’t answer: Who gets to decide who may use intelligence, whose data may be used, and for what purpose? Because almost every “safety” policy has a political economy. If the outcome is consistently: Anthropic gets more control, frontier models become less accessible, customers have fewer choices, smaller competitors face higher compliance costs, and governments become increasingly dependent on a handful of frontier labs to tell them what is safe… Then it's rational for people to ask whether every restriction is only about safety. That question remains legitimate even if everyone at Anthropic is acting in complete good faith (Are they?) Maybe people aren’t distrusting AI because they don’t understand how wonderful the future could be. Maybe they distrust a future in which a tiny number of companies build enormously powerful systems, tell everyone else how dangerous those systems are, lobby for rules governing who gets access to them and then ask society to trust that the companies controlling them are the right people to decide. If Anthropic helps cure cancer, I will be the first to applaud. But “we cured cancer” is an argument for AI's value. It is not an argument for concentrating control of AI.
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SnowShadow retweeted
Dear Dario, 1. If Claude can cure cancer to save people like your dad, why should we "pace the progress"? Does that mean more people with Hepatitis C will die? 2. If Fable is so cyber-capable that it must be restricted, why are its safeguards too dumb to distinguish cyber defense from cyber offense prompts? When Hugging Face was under attack, why did Fable refuse to help the defenders? 3. We’re glad you want AI to cure cancer. Why is it OK for Claude to force 30-day data retention on pharma's own data and start competing firms but NOT OK (IP theft) if others distill Claude's data? 4. You’ve said advanced models can recognize when they’re being tested and change their behavior accordingly. So why is government(or anyone) able to design the most thorough test before every model launch? Would that just encourage manipulative models? It seems that every one of your “safety” proposals seems to end the same way: Anthropic gets more leverage, ordinary users get less access, customers pay higher costs, and competitors bear higher regulatory costs, maybe people are not distrusting AI, they are distrusting your approaches with AI.
Replying to @DarioAmodei
2/2 Second, on the messaging around AI.  I do not agree that my messaging has been disproportionately negative.  In fact it has been about equally balanced between risks and benefits: I’ve written one major essay about each, and even in interviews where I discuss the risks, I make sure to frequently mention the incredible benefits as well as proposing possible solutions to the risks (short clips from my interviews that end up on social media tend to be disproportionately negative, as that gets clicks).  In fact, I wrote Machines of Loving Grace because I didn’t feel the AI industry was painting an inspiring enough picture of how the technology could radically transform the world for the better.  The bulk of the essay is devoted to refuting skepticism of AI’s potential in health and biology, and showing why I think it will actually be possible to cure most human disease in ~5-10 years, as crazy as it may sound to ordinary people and frankly to biologists as well (I used to be one!).  And, if you read my most recent essay (Policy on the AI Exponential), I discuss concrete proposals for how to streamline the FDA process to make sure the deluge of AI-accelerated drugs isn’t slowed down by the regulatory process.  I feel the urgency here: I lost my father to Hepatitis C only a few years before the development of direct-acting antivirals (sofosbuvir), which cure 95% of patients and probably would have cured him. I do agree that the public has a negative view of AI (and that this is a big problem), but I don’t think it is primarily caused by me or any other AI leader warning about AI’s risks.  I think it is fundamentally a crisis of trust.  I think that ordinary people don’t trust companies, governments, or the tech industry and always suspect that we are cooking up some new way to screw them over.  The causes of this go back decades and AI is just the latest iteration of it.  I don’t think that a glitzy marketing campaign with a positive spin (which some have advocated that Anthropic do) is the way to win back that trust — at this point, saying that AI will cure cancer is more a cliche than it is inspiring, and most people think it is deceptive.  The thing that will work is *actually curing cancer*.  I think by far the most accurate criticism of AI companies including Anthropic is that we haven’t yet delivered on our big promises to benefit the world.  That is totally on us, and I think it’s the criticism you should be making, instead of all this stuff about messaging and marketing. We are however doing our best to fix this: Anthropic is ramping up its efforts very quickly in biology and medicine, and we hope to have incredible results in the coming years and some early glimmers in the coming months.  When we’ve actually accomplished something real, the whole world will hear about it, as loudly as possible, you have my word on that.  But until then I don’t want to make empty promises, and in the meantime I feel compelled to speak honestly about the very real risks of AI and how to address them.  Honesty is the right thing on the merits, and in terms of public credibility and trust it is no worse than, and may in fact be better than, an approach that ignores or distracts from risks which people instinctively understand are real.
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SnowShadow retweeted
What programming languages are coding agents best at? danluu.com/pl-tokens/
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给关注了半年的agent team 交份作业。 这是一篇2w字长文,也是一份 Agent Team 的最佳实践。 01 一个 Agent,是怎么变成多个 Agent 的为什么真实工作会把 Task Agent 推向 Long-running Agent;为什么一个越来越好用的 Agent 最后又必须分化;多个 Agent 出现以后,瓶颈为什么会转移到 Human。 02 先让“谁负责什么”离开 Human 的脑子Agent 怎样获得稳定身份、Domain 和 Scope;Human 与 Agent 怎样查询当前 Team 的责任结构;真实协作又怎样逐渐沉淀为 Organization 与 Collaboration。 03 让 Agent 自己开始协作Agent Message 带来了什么变化;一个 Agent 怎样把工作直接交给另一个 Agent;为什么 Agent 沟通不是一次把一切说完。 04 Message 负责沟通,Topic 负责收口当工作跨越多个 Agent、多个 Turn 和多天以后,怎样保留唯一的当前版本;Responsible、Participant、Artifact 和 Needs You 分别解决什么问题。 05 Overview:让一支持续变化的 Agent Team 变得可治理当 Human 不再阅读每个 Agent 的全部过程,怎样从更高层观察 Team;怎样发现等待、积压和瓶颈候选;为什么治理关注的是工作流动,而不是让每个 Agent 看起来都很忙。 06 Agent Team 怎样进入真实的外部关系为什么把 Agent 接入 Slack、飞书还远远不够;Agent 对外以后,身份、角色、信任、权限和现实后果为什么必须被分别治理。 07 CodexLoom 在织什么Multiple Agents 与 Agent Team 的真正分水岭是什么;Human 在 Team 中的新位置是什么;为什么需要稳定的 Agent 和动态的 Team。
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Huge congratulations to PKU alumni Wang Hong and Deng Yu, recipients of the 2026 Fields Medal — mathematics’ highest honor! 🧮 Announced at the 2026 International Congress of Mathematicians (ICM) in Philadelphia, the pair are among four mathematicians worldwide to claim this prestigious prize, awarded every four years to outstanding scholars under 40. The awards are historic on several levels. —First time two mathematicians who completed undergraduate studies in mainland China win the Fields Medal in the same year. —Both joined Peking University as undergraduates in 2007. —Wang Hong becomes only the third female Fields Medalist ever, the first Chinese woman, and the first recipient with a bachelor’s degree from a mainland Chinese university. We are immensely proud of their extraordinary breakthroughs and lasting contributions to global mathematics.
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SnowShadow retweeted
It finally finished, here's the final output. Used 60% of my monthly Kimi usage on it macos27.kimi.page
I asked a Kimi K3 Max agent swarm to recreate macOS 27 with real Liquid Glass and native apps in web browser and it’s been going for 3 hours
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SnowShadow retweeted
PREDICTION after kimi k3 successful launch and mogs opus 4.8, Dario Amodei is gonna write an essay explaining how open-weight Chinese AI threatens democracy, national security, and the future of humanity
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SnowShadow retweeted
好久没写长文了,最近半年一直在关注 Palantir 的 FDE 模式。前阵子 OpenAI 和 Anthropic 同一天各自成立了新的部署公司,FDE 在中文圈又翻红了一轮。但中文圈关于 FDE 的讨论很多都偏离了它本来的定义,或者干脆实际上说的就是 AI 咨询/实施。在这层误解上讨论来讨论去,结论自然五花八门。 所以把手头的资料做了一次系统梳理:Bob McGrew(Palantir FDE 从 0 到 1 亲历者,后任 OpenAI VP Research)在 YC 的访谈,a16z、Sequoia、Flybridge 等 VC 对 FDE 的分析和争论,Palantir 官方博客里 FDSE 的工作日常,以及 Gartner、Everest Group 等机构的判断,素材就整理了 6 万字,来给 FDE 一个清晰的定义。 再加上我在一线构建 AI 产品中积累的体感,尝试去回答:为什么在 AI 产品中,大家如此热衷 FDE。 全文 7000 字,可以摸鱼时候慢慢看~
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SnowShadow retweeted
Come try out the incredible work from our genius multimodal colleagues! 🐳👀 The little whale can now see (in grayscale testing)~ ✨
Now, we see you. 👀
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SnowShadow retweeted
Cat Wu 说了一句话让我印象很深,「Jobs are fake.」 她是 Anthropic Claude Code 的产品负责人,刚上了 Lenny's Podcast。这期信息量很大,我试着把核心的东西压缩一下。 Anthropic 的产品节奏已经快到不讲道理,功能从构思到上线,最快一天。不是因为用了最强模型(虽然确实有帮助),核心是组织和流程。几乎零审批、所有功能先以「研究预览」上线降低发布承诺、工程师端到端自己看到 Twitter 上的用户反馈就能周末把功能做完发出去,不需要经过 PM。PM 的角色不是审批门,是给团队搭好发射台,让工程师想发就能发,营销和文档能在第二天跟上。 最反直觉的是她对新模型的态度。别人上新模型是加功能,他们上新模型第一件事是删功能,把之前给模型打的补丁一个个撕掉。to-do list、强制提醒、各种 prompting 干预,模型变聪明了就不需要了。比如早期模型做 20 个 call site 的重构会漏改,团队加了 to-do list 工具强制跟踪;到 Opus 4 之后模型自己就会用,根本不需要提醒。她说每次模型升级,团队都会通读整个 system prompt,逐段问「模型还需要这个提醒吗?」不需要就删。播客里引用了一句话,「模型会把你的 harness 当早餐吃掉。」 但更重要的是提前构建「还不能工作的产品」。代码审查功能他们尝试了好几个版本,早期模型准确率不够一直没正式发。直到 Opus 4.5/4.6,团队才觉得可靠到工程师可以信赖它在合并前发现大部分 bug。她的建议是,永远提前做好原型,新模型一出来直接换进去验证差距是否被填上。 关于 PM 未来最稀缺的能力,她反复提到一个词,product taste。代码越便宜,品味越值钱。GitHub 上几万条 issue 什么需求都有人提,知道该做哪个、怎么做最好,这个判断力才是最值钱的。工程背景短期有用(能判断实现难度从而更好地做优先级决策),但她刻意只说「未来几个月」,因为每隔几个月模型能力就跳一级,所需技能跟着变,没人能预测更远。她觉得最重要的是第一性原理思维,搞清楚技术环境怎么变了,团队最缺什么,然后低自尊地戴上任何需要的帽子。 Anthropic 能跑这么快还有一个原因,使命统一。如果两个优先级冲突,问哪个更符合 Anthropic 的使命,答案就出来了,全员立刻执行。Cat 说了一句很重的话,「如果 Claude Code 失败了但 Anthropic 成功了,我会非常开心。」整个团队都愿意为公司目标牺牲自己产品的 KR。这种文化让决策极快,也是为什么一家起步晚、融资少、没有分发优势的公司能跑到 110 亿美金 ARR。 她对从业者几条比较实在的建议。 1/ 把重复劳动交给 AI,但别只做到 95% 就放弃。95% 准确率的自动化不是自动化,最后那 5% 才是真正值得投入的地方。 2/ 构建你真正每天在用的应用。One-shot 一个原型发个推然后再也不打开,你既没学到东西,也没获得杠杆。 3/ 别沉迷于炫耀 setup。她原话,「简单的配置往往效果更好。」有一群人花大量时间堆 skill 和 MCP,结果核心工作反而没做。 4/ 2024 的产品是对话式的,Claude Code 这一代产品是行动式的。当 AI 真正能代你执行而不只是告诉你该怎么做,那才是真正的顿悟时刻。 她的人生信条回到开头那句话,Just do things。理解约束,推导行动,然后直接去做。不要等许可,不要被岗位定义限制。这大概也解释了为什么 Anthropic 能跑这么快。
How Anthropic’s product team moves faster than anyone else I sat down with @_catwu, Head of Product for Claude Code at @AnthropicAI, to get a peek into their unprecedented shipping pace, how AI is changing the PM role, and how to be the right amount of AGI-pilled. We discuss: 🔸 How Anthropic’s shipping cadence went from months to weeks to days 🔸 The emerging skills PMs need to develop right now 🔸 Why you should build products that don't work yet—then wait for the model to catch up 🔸 Why a 95% automation isn't really an automation 🔸 Cat’s most underrated AI skill (introspection) 🔸 What Cat actually looks for when hiring PMs now (hint: it's not traditional PM skills) Listen now 👇 youtu.be/PplmzlgE0kg
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SnowShadow retweeted
Our paper on Subliminal Learning was just published in Nature! Last July we released our preprint. It showed that LLMs can transmit traits (e.g. liking owls) through data that is unrelated to that trait (numbers that appear meaningless). What’s new?🧵
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SnowShadow retweeted
i do like this passage, and here are some thoughts: 1. critical thinking is essential in the era of agents. i still remember that many years ago when i studied the lesson of critical thinking, i learned that keeping debating with yourself by listing out reasons can really deepen your thinking. today, critical thinking becomes humans debating with agents, so that they can think more deeply together and analyze problems in a more comprehensive way. 2. designing a healthy and well-structured organization and system is essential for creation and building. with systematic support and efficient tooling, humans can work exponentially more effectively together with agents. that gives people more time to take care of their physical and mental health, while also exploring new opportunities. 3. new era often favors newbies, because they have less past experience and therefore less fear of current difficulties. what oldbies should really think about is which parts of their experience are actually worth leveraging. from my perspective, we should think more carefully about which experiences are truly aligned with first principles. but anyway, ai first is super, super exciting!
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SnowShadow retweeted
Wow, this tweet went very viral! I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the other person's agent customizes & builds it for your specific needs. So here's the idea in a gist format: gist.github.com/karpathy/442… You can give this to your agent and it can build you your own LLM wiki and guide you on how to use it etc. It's intentionally kept a little bit abstract/vague because there are so many directions to take this in. And ofc, people can adjust the idea or contribute their own in the Discussion which is cool.
LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.
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SnowShadow retweeted
1987: AI can't win at chess—planning is uniquely human 1997: AI can't win at Go—intuition is uniquely human 2016: AI can't win at poker—bluffing is uniquely human 2023: AI can't get IMO gold—reasoning is uniquely human 2026: AI can't make wise decisions—judgment is uniquely human
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SnowShadow retweeted
Yes, how would the US government react if leading lab wanted to relocate to China? Oh wait, there are laws that would prevent this....
The CCP’s response to the @ManusAI deal sends a clear message: there is no such thing as a truly “private” tech company in China. Even when firms seek to globalize or exit the Chinese market, the CCP retains leverage, through regulatory review, ownership structures, and direct intervention. As AI becomes a foundational technology, the United States must approach systems originating in China with clear-eyed scrutiny. wsj.com/tech/leaders-of-ai-f…
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