PPE

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This man is getting to serenity level

franchement l'annonce de Huawei hier à Shanghai vient de mettre noir sur blanc ce que j'essaie de vous faire comprendre ici depuis des années pour vulgariser Huawei vient d’annoncer au sympoisum ieee iscas une nouvelle loi physique qui remplace la loi de moore, ils l’appellent la tau scaling law et elle change littéralement le paradigme du semiconducteur mondial, en gros au lieu de continuer à rétrécir les transistors ce qui se heurte à des limites physiques quantiques infranchissables, ils optimisent dorénavant la constante de temps tau à 4 niveaux simultanément et obtiennent des gains de performance équivalents à ce que les américains atteignent avec leur lithographie euv à 200 millions de dollars la machine, sauf qu’eux n’ont pas accès à cette lithographie depuis les sanctions de 2019 la Chine dépasse donc la silicon valley sur son propre terrain et la rend périmée et ce qui se joue réellement est l'exact contraire de ce que Washington imaginait en décidant des sanctions de 2019 en ce sens je crois que très peu de gens ont pris la peine de regarder vraiment les slides de la présentation parce que le coeur de la rupture se cache ailleurs que dans le concept marketing de tau scaling law, il se trouve dans un détail technique que seuls quelques ingénieurs spécialisés ont remarqué (et que je suis parti fouiller haha), il existe visiblement un procédé de collage entre couches de silicium avec un espacement + petit que 2 micromètres, ce qui transforme les fils verticaux reliant les différentes couches d'une même puce en chemins de calcul à part entière, ils maîtrisent là l'intégration en 3 dimensions au sens fort pendant que le reste du monde raisonne encore sur un seul plan horizontal pour moi la meilleure image c'est celle d'un architecte qui construit une tour pendant que ses concurrents continuent d'étaler des maisons individuelles à l'horizontale, intel et tsmc se battent pour graver des transistors toujours plus minuscules parce que leurs lithographies euv les enferment dans cette logique, huawei coupé de ces lithographies depuis 2019 a choisi un autre combat, raccourcir au maximum le temps qu'un signal électrique met pour traverser l'ensemble du système, cette durée qu'ils nomment tau et qu'ils minimisent simultanément au niveau du composant du circuit de la puce et de la machine complète, c'est de la physique réelle présentée dans la conférence ieee la plus sérieuse au monde sur le sujet d’ailleurs les chiffres font réfléchir, allez jeter un coupé d’œil et vous allez voir que la densité de transistors monte de 126 à plus de 400 millions par millimètre carré entre 2024 et 2031, la fréquence des coeurs grimpe de 2,6 à 5 gigahertz, même la performance des systèmes complets fait x125 en 4 ans entre 2026 et 2030 & surtout 381 puces ont déjà été fabriquées en série selon ces principes depuis 2020, autant dire qu'ils ont commencé à changer de paradigme dès la première vague de sanctions américaines, 6 années de travail discret pendant que les analystes occidentaux les croyaient en mode survie mdr ce que tout le monde prenait pour de la résistance était en réalité un virage stratégique médité et mené avec la patience d'un peuple qui voit à 50 ans (la vision à très long terme de la Chine dont je vous parle souvent ) je vous le répète depuis des années ici, les sanctions occidentales accélèrent la politique industrielle et tech de Chine au lieu de la freiner, elles l'obligent à inventer le monde d'après pendant que l’occident reste coincé dans celui d'avant, d’ailleurs pour info même BYD a créé la batterie LFP face au blocage du nickel et il domine désormais le marché mondial de la voiture électrique, deepseek a conçu son architecture multi-head latent attention face au blocage des puces h100 et il a divisé par 10 le coût des grands modèles de langage, Huawei vient de poser logicfolding et tau scaling face au blocage de l'euv et il redessine déjà la trajectoire mondiale du semiconducteur jusqu'en 2031

Matt Levine's column today tells the story of a glitch in Fidelity's systems that resulted in one woman having all her financial accounts deleted, along with any trace that they had ever existed Fortunately, it appears she had the time and fortitude to relentlessly pursue the issue, and ended up getting her account recovered, and was interviewed by the NYT But what happens to those who don't have that luxury? Nightmarish stuff

Massively useful Codex trick for 10x better frontend: You can ask Codex to use Claude as a sub-agent to have Claude handle frontend/design work. Just say “Use claude -p with an excellent, well-scoped, but un-opinionated (UI/UX-wise) prompt anytime you need a design change).”

My wife (non-technical) built a fully functional iOS/Android app in 6 weeks... and it's made $1.5k on the app store since launch in the first month! She's a stay at home mom with our 2 young children (3 and 1), and she wanted a way to write letters to our children to keep memories of their childhood (we kept using notes app and was all a mess). Thank you @amasad and @Replit for making this possible! Link if you want to try it: apps.apple.com/us/app/from-ma…

The new daycare teacher gave our kid a cupcake today Kid was super happy. He was like “there’s no cupcake at home. Cupcake so yummy” Think we might be fked for the next couple days ☠️🤣

I just got back from SF and I FEEL INSPIRED. I spent 5 days with frontier AI model teams, AI startup founders, and 3 billionaires. My takeaways: 1. I had lunch with 3 billionaires. All of them are buying SaaS companies and rebuilding them agent-first. They were deeply inspired by Bending Spoons and Ryan Cohen's eBay deal. Buy the company, cut the headcount, rebuild the tech, add agents, add features, make more valuable experience, raise prices. 2. The frontier model companies are hungry for usage data from the field. They can see API calls and token counts. They can't see the actual workflows. If you're deep in a niche using these models in ways the model companies haven't seen, that understanding is incredibly valuable. Usage intelligence is the new alpha. 3. Consumer AI is massively underbuilt. Every billboard in SF is either B2B inference infrastructure or vertical agent companies. The entire city is optimized for enterprise. Meanwhile you have companies like Cal AI doing $50M ARR in 18 months as a consumer app. I met with a cool few teams doing consumer AI (@paulscherer / @ekuyda) 4. MCP came up in literally every conversation. The companies exposing their product as MCP endpoints are getting pulled into deals they never pitched for. The ones that aren't are becoming invisible to agents. This is the new SEO. If agents can't find you, you don't exist. Building products for agents is the new zeitgeist in general. 5. Not uncommon for hot seed rounds to be $25-50 million valuations. I saw a Series A at $450 million 6. If I had a dollar every time someone mentioned "forward-deployed engineer" this trip I could have funded a seed round. It's the hottest role in SF right now. The person who sits between the agent and the customer, making sure everything actually works. 7. The mood around open source shifted. A year ago it felt like open source was chasing the frontier models. Now founders are telling me Gemma and DeepSeek are good enough for 80% of what they need at a fraction of the cost. The "which model do you use" conversation is being replaced by "which model for which task." Model loyalty kinda feels dead. 8. Voice agents came up more than I expected. Multiple founders told me voice is the interface for the next billion users. The billion people who will never type a prompt will absolutely talk to one. 9. The Obsidian community in SF is weirdly intense. Multiple founders showed me their vaults unprompted. Like showing someone your home gym. It's a flex now. The quality of your knowledge base (second brain?) is becoming a status symbol among builders. 10. Maybe it was just the people I met but the age of the founders is shifting. I met more founders over 40 this trip than any trip before and more founders under age 21 than ever before. Founders getting older and younger at the same time. 11. I spoke to a lot of fast-growing startups, VCs and frontier models who are hiring content creators right now. 12. The restaurant scene in SF is actually better than it's been in years. Founders are going out more. Alcohol is out, not surprisingly. 13. SF doesn't feel like the only place anymore. We all have access to the same frontier models. We all read the same X feed. A founder in NYC or Lagos is calling the same APIs as a founder in SoMa. So in the past it felt like SF was always lightyears ahead, doesn't feel that way anymore. It's okay not to live in SF and have BIG DREAMS. 14. The coworking spaces in SF are half empty but the coffee shops are packed. People want to be around people. I had a few startup ideas here.... 15. Walking around the Mission I noticed something: the street-level businesses, the taquerias, the barbershops, the laundromats, none of them use any AI at all. 16. I heard the phrase "agent debt" for the first time. Like technical debt but for agents. When you hack together an agent workflow fast and never clean it up, the system prompts conflict, the memory gets polluted, the tools overlap. 6 months later the agent is doing weird things and nobody knows why lol. 17. Met a few people who carry two phones now. One for personal. One that's basically an agent terminal running Telegram or iMessage connections to their agent fleet. It's always amazing to get that dose of inspiration in SF. I FEEL INSPIRED. But I'm so happy to be back home, locked in and building. We're 12-18 months into a shift that will take 15 years to play out. The urgency in every conversation was real. What an incredible time to be building.

Bro wTF $SPCE still going lmao

This is also the case in East Asia, where women’s higher education enrollment rates have surpassed those of men. Because women generally expect their husbands to have a higher level of education and income than themselves, many women choose to remain single. This is bound to further intensify the decline in fertility and the aging crisis.

A kid from Singapore who grew up training to be a concert pianist became one of the most important AI researchers alive, quit Google to start a frontier AI lab with 20 people and $60 million, built a model that competed with GPT-4 in a year, then walked away from the unicorn he created and went back to Google to lead the team that just won the International Math Olympiad with an AI. His name is Yi Tay. Almost nobody outside the AI research world knows it. Here is the story. Yi grew up in Singapore. He earned a classical piano performance diploma from Trinity College London in 2012 and almost became a professional musician. He went into computer science instead, did his PhD at Nanyang Technological University, and joined Google Brain as a research scientist. There were almost no Singaporean researchers in frontier AI at the time. He used to say he was on an uncharted path. At Google he became the co-lead of PaLM-2, the brain behind Google's entire AI stack. He invented UL2, a pretraining method now used across the industry. He invented Differentiable Search Indexes. His work shipped inside Google Assistant, YouTube, and Search. When ChatGPT launched in late 2022, Yi made a decision that shocked the research community. He left Google. In 2023 he co-founded Reka with researchers from DeepMind and Meta. The headquarters was in San Francisco, but the team was scattered across Asia, Europe, and the US. They had no big-tech backing. They had 20 people total. They had $60 million in funding. For context, OpenAI had around 600 people working on GPT-4. Google Gemini had 950 co-authors on the technical report. Reka had fewer than 5 people on pretraining. Yi lived nocturnally for 639 days. Five cups of coffee a day. Takeout twice. He gained 15 kilograms. He had a newborn baby. He worked across time zones his entire team was spread across. He built infrastructure from scratch in places Google had taken for granted. In May 2024 Reka Core debuted at number 7 on the LMSYS leaderboard. The only GPT-4 class model on the planet that was not trained by OpenAI, Google, Anthropic, or Meta. A 20-person company with 5 people on pretraining had just shipped a frontier model. Alibaba Cloud, NVIDIA, and Oracle became partners. The company hit a $1.3 billion valuation. Then in November 2024 Yi did something nobody expected. He walked away. He posted a quiet note on his blog titled "Returning to Google DeepMind." After 639 days of building one of the most respected frontier labs outside the big four, he went back to the company he had left. He wrote that he had learned more than he ever thought possible. He did not explain much else. Google made an extraordinary bet on him. They let him build something nobody else in the industry has, a DeepMind lab in Singapore. Yi runs it with Quoc Le. The team focuses on reasoning, reinforcement learning, and post-training for Gemini. It started with a dozen researchers. It now has over 300. Last summer, Yi's team led the effort that won the International Math Olympiad gold medal with Gemini Deep Think. The model solved IMO problems in a live competition, the kind that fewer than a hundred humans on Earth can solve under time pressure. His team also drove the work behind Gemini's ICPC 2025 gold medal. Yi still lives in Singapore. He still plays piano when he has time. He calls himself a global citizen who does not identify with any local AI scene. He has been at Google for nearly 14 years if you count the Reka detour. He says the Singapore lab is just getting started. A pianist from Singapore co-led the model that powered Google AI, left to build a frontier lab with 20 people and beat models trained by armies, walked back into Google, and is now running the team that just taught a machine to win Math Olympiad gold. The most influential AI researcher you have never heard of is sitting in a Singapore office right now, training the next generation of models that think.

i just can't unsee this now ..

It’s not like they got time to go travel for vacation anyway 🤣

lol real. But they only do it because they care 🤣

Tech was one of the last paths into the upper middle class without family money, credential laundering, or nepotism. Now those jobs are disappearing too. That is why the layoff story matters beyond tech.

"I think harder, work harder, have greater courage, and am more active than others." "Before starting any project, I spend an enormous amount of time and effort thinking, analyzing, and planning every detail. When it is time to implement these plans, it may look like I am working off the cuff, or even recklessly, but if I hadn't first sat down and planned everything out, the Hyundai Group of today could never have existed." — Chung Ju-yung

As someone who builds institutional quant systems, this Anthropic lecture on Claude for Finance is the closest thing to an HFT research desk I've ever seen released for free. Bookmark & watch today. It's the most valuable 1 hour in quant AI right now. Then read article below.

An ex-Jane Street trader revealed, How Sam Bankman-Fried behaved on day one of his internship. Four years before FTX. Why Jane Street makes millions of tiny trades instead of one big one. What they did in 60 seconds when their model broke in 2008. Why the offer isn't about math. Almost nobody has seen this interview. Bookmark it tonight. Then read the article below.

been almost a year since i made this tweet and honestly not much has changed here's the state of ai in today's enterprise world: - genai POCs are still failing at scale & in large corps - MCP turned out to be pretty fucking useless - multiagents have been disappointing. enterprise workflows mostly reward deterministic orchestration, not autonomous stategraphs - hallucination still remains a core unsolved issue even with the SOTA models - so does memory. maintaining state over long/cross conversations continues to be a challenge - larger context windows & more parameters haven't really achieved much compared to the last generation - tokens are costing more, not less, as models, architectures, and harnesses have progressed - mid-size CEOs are realizing that replacing engineers with agents isn't the best way forward (agents are costing more than humans) - non-tech megacorp CEOs still don't know what to do exactly & are implementing stupid KPIs such as measuring copilot usage to push AI adoption - consultants who are not rebranding themselves as "forward deployed engineers" are having a really hard time - organic ai adoption is bottom-up and not top-down across corps - using tools like coderabbit has become imperative in fighting the thousands of lines of AI slop even senior engineers are committing every day - nobody seems to be writing code anymore - still doesn't mean code is solved. LLMs are duds on large codebases

The FOMO is real. I feel anxious that I don’t have agents running when I’m asleep 🤣 Such a waste of sleeping time not to have something running lol

I don’t think we talk enough about the devastating impact ai has had on people whose real name is Claude 🤣