🐆🐆🐆 @gakcap

The middle class is gone. According to Fed data, the top 1% of Americans now control ONE-THIRD of all US net worth. Since 2020, the top 1% have gained +$30 TRILLION in net worth, while the bottom 50% are worth just $4.3 trillion. What is happening? Let us explain. (a thread)

Annual reminder: Bethlehem Steel was able to build a ship a day during WW2.

zhil @zhil_arf

2 weeks ago

I think people still don't realize how insane WW2 really was. Number of military planes produced during WW2 America: 300.000 planes Soviets: 140.000 planes Britain: 130.000 planes Germany: 120.000 planes Japan: 90.000 planes Today the US military has around 13.000 planes

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Había expectación por ver qué iba a desclasificar Julián Assange en su regreso a Wikileaks. Y no ha decepcionado. Wikileaks muestra 17 minutos que demuestran que el alunizaje de 1969 fue grabado en el desierto de Nevada. La historia moderna es un timo. #Moonlandingfake

If you are a fan of firework shows, this is surely going to be an epic one - feel free to start warming up the popcorns 🍿

Shipping costs are going parabolic. Insane chart 👀💰

Hier gibt es nichts zu sehen. Nur eine exponentielle Entwicklung auf einer logarithmischen Skala.

This is the textbook definition of a Greek gift. On the surface, OpenAI generously grants 10,000 top-tier mathematicians and elite engineers free access to their frontier models under the noble, philanthropic banner of accelerating scientific discovery, pushing the boundaries of human knowledge, and democratizing artificial intelligence. But deep down in the cold reality of Silicon Valley capitalism, what they are actually doing is executing a massive, highly sophisticated intellectual heist. They are quietly gaining unrestricted, backdoor access to the raw, unfiltered, and unpublished works of the absolute finest minds in the global scientific community. Every single complex prompt, every mathematical query, every theoretical framework, and every line of experimental code fed into that system is a novel, groundbreaking idea that does not even exist yet in published academic literature or peer-reviewed journals. OpenAI does not just provide a helpful service. Their corporate algorithms sit quietly behind the scenes, aggressively monitoring all chat logs, scraping the intellectual property, analyzing the logical steps, and meticulously piecing together these fragmented, brilliant ideas over several months. And just when the human researcher is finally on the absolute brink of putting everything together, OpenAI will ruthlessly assemble a digital army of 10,000 autonomous AI agents, burn $20 million worth of computational tokens, and brute-force the final mathematical steps. Then, with the backing of their massive PR machine, corporate journalists, and tech influencers, they will proudly hold a press conference to claim that their proprietary chatbot has miraculously solved a Millennium Prize problem, completely, shamelessly erasing the human genius they systematically strip-mined to achieve it.

OpenAI @OpenAI

2 months ago

We’re giving scientists, mathematicians, and engineers free access to our frontier models—starting with 10,000 researchers and expanding to 100,000 through 2027. ChatGPT for Academic Researchers is built to accelerate discovery across disciplines.

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Holy cow, its official! OpenAI says its AI has solved the Navier–Stokes Millennium Prize Problem, a mathematical question unresolved for roughly 90 years. An internal model, described as significantly more capable than GPT-6 Astra, powered a group of around 10,000 concurrent agents (see the graph below! Its crazy more powerful than Astra) The agents reached the solution after just 88 hours. Lean formalization and verification took another 17 hours via GPT-6 Astra. The reported result: smooth three-dimensional fluid motion can develop a singularity in finite time under a smooth external force, while total energy remains finite. OpenAI is sharing the proof and its Lean formalization. 130 billion output tokens went into the Navier–Stokes effort alone. The internal model is still training. Welcome to the singularity!

OpenAI @OpenAI

4 weeks ago

We’re sharing a solution to the Navier-Stokes Millennium Prize Problem, one of the deepest problems at the frontier of mathematics. The proof was produced by a group of agents, using an OpenAI next-generation model significantly more capable than GPT-6 Astra. The problem

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🇺🇸 Neuralink recipient Audrey is now playing Mario Kart using nothing but her mind, just weeks after receiving her brain implant. Within days of surgery, she was already controlling a computer cursor with her thoughts and writing her name for the first time in two decades. Now she’s gone from moving a cursor to racing around Mario Kart, showing just how quickly anyone could learn to use the implant. Writer: Mhedi x.com/XFreeze/status…

Fertilizers are about to blow. Pounding the table.

A chart opinion Heating Oil futures are close to confirming a major breakout on the daily continuation chart. I look to place a bet on this

Robux are flowing like never before despite the price reset $RBLX

Commodities are leaving the station.

Fertilizer stocks are coiled for a ripper.

My conversation with @dougleone, partner at @sequoia. 0:00 How to Dominate for Decades 3:17 Turning Fear Into a Tailwind 5:40 Immigrant Drive, Hard Work & the Beach Club 12:19 Simplifying Life & Choosing Discomfort 14:37 The Homeless Sequoia Partner With No Plan B 16:49 Stepping Down, Coming Back & Starting Over 20:04 Why AI Is Different From Every Previous Technology Shift 22:13 Doug's Three Investment Heuristics 23:52 The Cost of Selling Great Companies Too Early 25:39 Helping Founders Turn Products Into Businesses 28:28 David Vélez, Nubank & the Psychology of Founder Support 32:25 Why Founders Must Remain the Soul of the Company 36:13 Torturing Yourself Into Greatness 39:57 Don Valentine, Succession & the Sequoia School of Hard Knocks 44:28 Staying Grounded & Developing a Sniffer for People 47:46 Hypercompetitive With a Heart of Gold 50:13 Fred Luddy, Israeli Founders & Radical Directness 52:28 Trust Is the Accelerant of Business 59:57 Starting From Zero & Hunting for the Next Great Founder 1:05:59 Architect Your Board Like Your Product 1:10:24 Truth, Disagreement & the Art of Difficult Feedback 1:13:43 Humanity, Introversion & the Value of Relationships 1:18:18 What Doug Learned From Michael Moritz Includes paid partnerships.

🔻 YOUR TEARS CONTAIN A NATURAL PAINKILLER 6 TIMES STRONGER THAN MORPHINE. AND YOU WERE TRAINED TO HOLD THEM BACK. In 2006, researchers at the Pasteur Institute in Paris discovered a molecule in human tears called leucine-enkephalin. It is an endogenous opioid. Your body manufactures it. It binds to the same receptors as morphine. It is six times more potent. Every time you cry, your body is not breaking down. It is self-medicating. Dr. William Frey at the University of Minnesota proved that emotional tears have a completely different chemical composition than reflex tears. When you cry from cutting an onion, the tears are mostly water. When you cry from grief, stress, or pain, the tears contain cortisol, adrenaline, prolactin, and leucine-enkephalin. Your body is literally flushing stress hormones out through your eyes and replacing them with its own painkiller. People who cry regularly have lower blood pressure, lower cortisol levels, and stronger immune function than those who suppress tears. This has been measured. Men are told crying is weakness. Women are told it is emotional instability. Children are told to stop. An entire species trained to suppress the one biological mechanism designed to flush poison from the body and replace it with medicine. You have a pharmacy behind your eyes. It activates automatically when you need it most. It costs nothing. It requires no prescription. And for your entire life, you were told to shut it off. The next time your body tells you to cry, let it. It knows exactly what it is doing. Share this until everyone sees it. ⟁

A biological server rack is now online for the first time. About 16 million living human neurons sit inside it. They process data and point toward a form of artificial intelligence that uses far less electricity than today’s chips. Researchers at the National University of Singapore built the rack with Cortical Labs and DayOne. It holds 20 CL1 units. Stem-cell neurons grow on silicon chips lined with microelectrodes. The setup does not need a conventional wet lab. Electrical pulses link the cells to ordinary software. The network can change and organize itself, which is why the team describes it as synthetic biological intelligence rather than a standard silicon model. Life support keeps the cells working. Pumps and controls manage heat, fluids, and food. The neurons are expected to last about six months. Power is the headline number. One CL1 unit, including that life support, uses about 30 watts. A top Nvidia H100 GPU can take 700 watts or more. No one claims this rack will replace silicon. The aim is a cheap, low-power option for jobs such as drug screening, spotting fraud, and teaching robots to adapt.

Wheat

NYC budget...biggest source of funds is property tax, biggest outlay is public schools

Neil Movva (@neilmovva) started his career at Nvidia, working on GPUs and kernels, and has an unusually deep understanding of inference, from software to chips to power. We spend a lot of time on each of those layers, how they connect, and where the important tradeoffs are. What makes this conversation special is how detailed it is (like a 401-level class), yet Neil makes it remarkably clear and easy to follow. Today he runs Sail Research, a company building infrastructure for agents to make tokens as cheap as possible. We discuss: - Latency versus throughput - Why there are no bad chips, only bad pricing - The end of kernel engineering - Buying chips and power no one else wants - New chip architectures - Nvidia lore + his contrarian view of the company - Open source and the frontier labs I learned a ton. Enjoy! TIMESTAMPS 0:00 Intro 0:38 Building a “Token Factory” 4:21 The Future of Background Agents 13:09 Nvidia and the GPU Stack 23:27 Chips, Memory, and Transformers 36:14 The Future of AI Training Data 44:32 Chip Scarcity and Compute Arbitrage 52:44 Reinventing the AI Data Center 59:01 Power and the “Scavenger Strategy” 1:10:10 Open vs. Closed AI