Here's why we can't win a robot war with China
Last year, I published a deep dive on China's maritime drone industrial base - TLDR: China has way more capability for USVs and UUVs than we do.
They've already done this with DJI and UAS.
In Red's Hellscape: Far More Dangerous Than Blue's I unpack a core tension most people don't realize about the military drone market and ADM Paparo's vision.
INDOPACOM has bet on a Hellscape strategy to slow a PLA attempt to cross the Taiwan Strait - INDOPACOM has not bet that we can win a long-run robot vs. robot war.
Other stakeholders in media, industrial base, government are assuming that betting on hellscape means betting on outdoing China in drone warfare.
This. Will. Never. Work.
@McKinsey published another 🔥🔥🔥 proof point that China has a much stronger supply base for robots - look at this graphic showing China's strength in actuators, electronics, and key sensors...
Just like China will always have more shipbuilding than the U.S. they will always have a better supply chain to win on pure mass in this category.
This doesn't mean we should stand down our efforts to build strategic autonomy through a resilient, domestic industrial base - in fact it simply means we should be extra smart about how and where we do it.
Taiwan is playing the human-intelligence game out in the open. If Chinese insiders are fed up, a secure tip channel gives them a way to talk. That is exactly why Beijing fears free societies with better off-ramps.
https://t.co/QMbLS8Y87S
Una ingeniero de IA de Microsoft acaba de revelar cómo construyen agentes de IA reales en sus equipos usando claude de anthropic.
Se trata de un taller GRATIS de 34 minutos impartido directamente por el equipo de Microsoft, donde muestran el proceso de forma práctica y sin rodeos.
En el taller verás cómo:
→ Conectar Claude Opus 4.7 a un agente
→ Agregar más de 1.400 herramientas MCP ya disponibles
→ Desplegar el agente directamente a producción
Es un enfoque mucho más accionable y orientado a resultados reales que la mayoría de cursos de “vibe coding” que venden por $500 o más.
Si estás construyendo o quieres empezar a crear agentes de IA, este taller te va a servir bastante.
Guárdalo y míralo cuando puedas 📔
This looks like a must-read!
"Macro: The Economic Models That Shape Our World" by Greg Kaplan (available in November).
"In clear and engaging prose, Chicago economist Greg Kaplan demystifies how our everyday behavior, including how we spend and save, connects to the biggest questions in fiscal and monetary policy. Tracing the evolution of modern macroeconomics from Keynes to today, he guides readers through the models that are often used to explain our economy, uncovers their flaws, and reveals what cutting-edge research tells us about managing the economy in good times and bad. The journey culminates in HANK, a macroeconomic model co-created by Kaplan that is more consistent with reality. Macro offers a timely, new framework for understanding and shaping economics in the real world."
https://t.co/IxOWTajiak
ESTA TRADER CHINA USÓ CLAUDE FABLE 5 PARA MONTAR UN BOT DE TRADING
Y grabó este tutorial completo donde muestra cómo replicarlo desde 0
31 minutos. Paso a paso. Gratis.
Subtitulado al español e inglés.
Guárdalo, ya me lo agradecerás 🔖
A bug cost him $200,000 in five days.
One week later, the same bot made $95,000 in a single day.
Nothing changed about the market.
He changed the brain.
His wallet:
https://t.co/ALoRkzemwJ
Copytrade: https://t.co/IPY41UFOdx
The old system missed one corrupted data feed.
The bot kept buying a bad position.
Losses snowballed.
By the end of the week, he was down $200K.
Then Claude Fable 5 arrived.
He asked it one question:
"What killed us?"
It found the bug in minutes.
Then it rebuilt the entire decision engine.
Now the bot searches for tiny probability mistakes that most traders never notice.
A market prices an event at 0.2%.
Fable estimates 1.6%.
Same trade.
Different math.
One of those positions returned 205x.
The result?
5,721 trades in 24 hours.
$95,000 profit.
Not luck.
Not prediction.
Just edge, repeated thousands of times.
My colleagues and I just published a new report on U.S.-China AI competition, taking a holistic view of AI leadership. We argue that the competition is about more than who has the best models and chips. It's a contest of energy, data, talent, capital, industrial capacity, diffusion, and national resilience.
The U.S. remains ahead in frontier model development and advanced compute deployment, but China's deep bench of AI engineers, low-cost models, control over critical nodes in the hardware supply chain, vast energy infrastructure, and aggressive push to diffuse AI make it a formidable competitor.
Nearly half (47%) of China’s entire non-government workforce is now trapped in the gig economy, a staggering mathematical reality that redefines the country's economic crisis.
While the daily grind of this "flexible" workforce is clear, official data from the National Bureau of Statistics (NBS) and state-linked research exposes the true structural gravity of the situation. Out of China's roughly 730 million total employed citizens, only about 50 million secure stable, coveted public sector and government roles. This leaves a remaining private workforce of 680 million people left to navigate the open market.
When you overlay the China New Employment Forms Research Center's projection that the gig economy will swell to 320 million people this year, the math becomes undeniable. This is no longer just a temporary safety net for displaced workers. It means nearly half of China's entire independent labor force has been systematically locked into precarious survival mode.
With official data proving that almost half of China's private workforce is now confined to the gig economy, do you think this structural shift can permanently absorb the economic slowdown, or is it building a dangerous social and demographic time bomb?
#ChinaEconomy #GigEconomy #FlexibleEmployment #LaborMarket #EconomicCrisis #ChinaJobs
Two math olympiad champions wrote a training manual in 1993 on two old Macintosh computers, and every American kid who has won a major math competition in the last decade learned to think from it.
Their names are Sandor Lehoczky and Richard Rusczyk. The book is called The Art of Problem Solving. Most people in math know it as AoPS.
Since 2015, every single member of the US International Math Olympiad team has been an AoPS student. Not most of them. Every one.
That statistic sounds impossible until you understand what the book actually does.
Lehoczky and Rusczyk were not professors. They were competitors. Lehoczky earned the sole perfect AIME score in 1990 and led the national first place team. Rusczyk was a USA Mathematical Olympiad winner and a perfect AIME scorer in 1989. They had both survived the same brutal selection process the book was designed to train students for.
And the first thing they decided was that almost every existing math textbook was teaching the wrong thing.
School math gives you formulas. You memorize them. You apply them. You pass the test. Then you sit down in front of a real competition problem and the formula does not apply, and you have nothing underneath it.
That is the gap. The gap is not knowledge. It is thinking.
The entire premise of AoPS is that problem-solving is a transferable skill, not a bag of memorized tricks. A student who genuinely understands why a technique works can adapt it, combine it with something else, and deploy it in a context they have never seen before. A student who only memorized the technique freezes the moment the problem looks different.
The book teaches the difference between a formula and a method.
A formula tells you what to compute. A method tells you how to see. The students who win olympiads are not the ones who know more formulas. They are the ones who have trained themselves to look at an unfamiliar problem and recognize its structure. To see that this problem is secretly asking the same question as a problem they solved three weeks ago, just dressed differently.
Rusczyk calls this "learning to read the problem." Not reading the words. Reading what the problem is actually asking underneath the words.
The second thing they built into the book is tolerance for being stuck.
Most students treat confusion as a signal to stop. The book treats confusion as the starting point. Every chapter pushes students past the point where the obvious approach runs out. That moment of running out is not failure. That is where the actual thinking begins.
Lehoczky once described it this way. If you can solve a problem quickly, you are not learning. You are performing. Learning only happens when you are past the edge of what you already know.
The book was written on old Macintosh computers in 1993. Rusczyk launched the AoPS website in 2003. Today the community has over one million users. Thousands of students enroll in AoPS online courses every year. Most winners of every major American math competition are AoPS alumni.
A platform built by two kids who were good at math competitions has become the infrastructure that produces the next generation of mathematicians, engineers, and scientists who are good at thinking.
The formulas you memorized in school will eventually be obsolete.
The thinking you trained will not.
What is one problem in your life right now that you have been avoiding because you do not yet know the right formula to solve it?
A Muslim passenger:
"Excuse me, can you turn off the music?"
The driver:
"Why?"
The passenger:
"Music is haram."
The driver:
"Why is music haram?"
The passenger:
"Because there was no music in the time of prophet Muhammad."
The driver:
"Well, get off then. There were no cars back then either. A camel will come pick you up."
😂🤭😂
SCOOP #NSA is using #Mythos to conduct offensive cyber operations. Anthropic engineers are embedded in the US intelligence agency.
(@CristinaCriddle & me @AsiaLens)
https://t.co/AU3411VmY3