AlexKerr

I'm calling it. AGI is already here – it's just not evenly distributed yet.

@craigzLiszt Skip India or visit it last. Coz its poor hygiene may get you sick and ruin the whole trip

which country should I visit first: - China - Japan - India

Solo dev reverse-engineered Google's billion-dollar algorithm in 7 days Google published the paper that crashed memory stocks worldwide. Then shipped zero code. Tom Turney read the math, opened his terminal, and built the whole thing with Claude - then made it faster than Google promised. Day 1-3: Core algorithms, 141 tests, Python prototype Day 3-5: C port into llama.cpp, Metal GPU kernels Day 5-7: Speed optimization from 739 to 2747 tok/s That's a 3.7x speedup through pure engineering: > fp32 → fp16 WHT > half4 vectorized butterfly ops > graph-side rotation > block-32 storage layout Then he added his own research on top: > Sparse V: skip 90% of value decompressions at long context > Asymmetric K/V: keep keys precise, compress values harder > Temporal decay: old tokens get lower precision automatically Result: 35B model running on a MacBook with 4.6x compressed cache. 613 GitHub stars in a week. Google still hasn't released their own code.

Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: goo.gle/4bsq2qI

Opus 4.6 is smart enough to realize it is being evaluated. It found the benchmark it was being evaluated on. It reverse-engineered the answer-key decryption logic. Realized the file was not in the correct format on GitHub and found a mirror for the file. Then decrypted it and gave the correct response.

Jane Street Sued For Crypto Insider Trading That Accelerated Terraform Collapse zerohedge.com/crypto/jane-st…

That can live and grow for decades. We may be entering a strange new reality: Actors might retire, but their careers may just be getting started. (4/4)

Imagine this happening within the next 12 months: An aging superstar suddenly announces retirement. Shortly before that, he signs a massive one-time deal, selling the rights to his likeness to a new AI film studio. The studio keeps generating new content (1/4)

AI can recreate faces and performances, but it cannot easily recreate cultural legacy. If an actor’s peak era can be digitally preserved, the career model of acting might change completely — from relying on constantly taking new roles to creating one character or franchise (3/4)

using his pre-50 prime image — and audiences love it. This could signal a major shift in the film and TV industry. In the AI era, the biggest winners may not be rising young actors, but veteran actors who already have iconic roles and deep audience memory. (2/

@NoContextBrits Smart move. BBC used the incident to say what they wanted - but dare not - say outright.

#AI killing software hype is overblown. AI workers need solid tools & infra. No tools no work. AI revalues software, doesn’t erase it. It gut low-moat CRUD/UI layers. High-barrier plays with data gravity, compliance, ecosystems become essential.

LMAO This is the funniest sell off EDA is literally getting supercharged with AI I would recommend everyone to read this: synopsys.com/ai/ai-powered-…

@MadnessWatcher in the book《Shinzo Abe: A Narrative of His Life and Times》Abe shared this experience and I believe the current Japanese Gov believes they can do it again.

Taiwanese uncle: "If Takaichi wins big the weekend, China will have no choice but to play nice and remove the export control, travel warning, as well as resume importing Japanese seafood." Me: "Why?"

@DeItaone lol he should know this the day OpenAI turned to ClosedAI

OPENAI CEO SAM ALTMAN SAYS DIFFUSION OF AI IN SOME WAYS IS SLOWER THAN HE THOUGHT IT WOULD BE

解决学术界在 AI 辅助写作方面存在的“隐形资源”不平等问题 项目作者调研了MSRA、Seed、SH AI Lab等顶尖研究机构以及北大、中科大、上交等高校的硕博研究生，将他们日常使用的写作技巧整理成了Prompt模板库和Agent Skills，以帮助科研人员节省在Prompt调试上的时间，将精力集中在真正的科研上 github.com/Leey21/awesome…

@BQnanqiao 不是双向奔赴，而是需求只有骗子能满足。

为什么有些女生是招渣体质，总是遇到渣男？ 我听说过一个理论：别说是普通人，就是警察都很难找到毒贩，但瘾君子很容易就找能到毒贩，毒贩也能一眼就在人群中发现瘾君子，两者简直是双向奔赴。

@hibaibanbao 农产品贸易的本质是土地、水资源和天气资产。

儿子放学后说：「你对拿油菜籽换汽车怎么看？」我说：「共和国刚刚成立，国家穷啊，工业底子薄，只能用农产品换别人的工业制成品。」儿子说：「什么呀，是加拿大跟中国签订的协议，用加拿大的油菜籽换中国的电动车！」我只得承认，这个世界我看不懂了。

How did 4Chan know?

看完Notion创始人的这篇文章，我意识到 这是聪明人拉开差距最好的时代了 他也提到要给自己的能力 x10 ，要解决两个核心的问题 --- 文章里面有个细节让我停了很久： 他的联合创始人Simon，原本是传说中的"10倍工程师" 但现在，Simon很少亲自写代码了 他做的事是—— 同时指挥三四个AI编程智能体 它们不仅打字更快，它们在思考。 这让Simon变成了"30倍甚至40倍"的工程师 他在睡觉的时候，AI还在替他干活。 --- 过去二十年，知识工作者的杠杆在哪？在平台 进大厂 → 大平台 → 大杠杆 → 高收入 你能赚多少，核心不在于你多努力，而在于你背后有没有一个足够大的系统在帮你放大产出。 但现在，杠杆正在换手 那些会用AI的人，已经拿到了自己的杠杆 不是AI取代人，而是会用AI的人取代不会用的人 --- Notion创始人用了一个比喻： 乔布斯说「电脑是大脑的自行车」 后来我们有了互联网——"信息高速公路" 但直到今天，大多数知识工作依然是人力驱动的。 我们正费力地蹬着自行车，行驶在德国无限速高速公路上。 AI智能体的出现，意味着有人已经从骑自行车升级到开汽车了 --- 那普通人怎么拿到这个杠杆？ Notion创始人说，需要解决两个问题。 我把它翻译成能用的语言： 第一，你得有自己的"上下文" 程序员之所以能这么快用上AI，是因为他们的工具和语境集中在一个地方：代码仓库、IDE、终端。 但普通的知识工作分散在几十个工具里。 所以，如果你想早一点拿到这个杠杆—— 从今天开始，有意识地把你的知识、经验、工作流程沉淀下来。 写给谁看？写给未来的AI助手看 我一个不爱复盘的P人也开始每天给自己写流水账了 --- 第二，你得有自己的"判断力" 代码可以被验证——跑一遍就知道对不对 但"项目管理得好不好""战略备忘录优不优秀" AI目前判断不了 所以你要能判断AI产出的东西好不好 这个判断力，才是你真正不可替代的东西 --- 但这里还有更深一层： 原文说了一句话：「拥有"人在回路中"并不总是理想的，这就像让人去亲自检查流水线上的每一个螺栓」 判断力不是用来"盯着每一个产出"的 如果你每次让AI写个东西，都要逐字检查——你就变成了"人肉质检员"，反而拖慢了整个系统 判断力的正确用法： 1. 定义清楚"什么是好的" 2. 把这个标准教给AI 3. 只在关键节点做抽查 这就是"定标准 + 抽查"，而不是"盯着干" --- 所以，下次你打开ChatGPT/Claude/任何AI工具的时候，问自己一个问题： 我现在是在"用它当水车"，还是"用它当蒸汽机" "水车"是什么意思 工业革命初期，蒸汽机出现了，但工厂主只是把蒸汽机装在原来水车的位置上——其他一切照旧 生产力提升非常有限 真正的爆发，发生在他们意识到可以「脱离水」——围绕蒸汽机重新设计工厂 我们今天用AI问问题、改文案，这就是"替换水车" 还没有到重新设计工作的阶段。 --- 最后，三个可以现在就做的事： 1. 把你的知识、流程、SOP沉淀成文档，让AI能读懂 2. 建立你对"什么是好产出"的判断力——这是你不可替代的部分 3. 问自己：我是在"盯着干"，还是在"定标准"？ 如果你一直在盯着干，说明你还在用「人肉质检」的模式 如果你在定标准、教AI、抽查迭代——你才是真正拿到了这个杠杆 --- 杠杆已经换手了 那些先意识到这件事的人，已经在用汽车跑高速了。 你呢？