Ahmed Alharbi

The research behind this is wild. Your kitchen sponge has the same density of bacteria as human stool. German scientists found 54 billion bacterial cells per cubic centimeter inside used sponges in 2017. Yours is sitting right next to your sink. Sponges are the perfect home for bacteria. They are wet, warm, full of food bits, and never fully dry between washes. Across all 14 sponges, the team found 362 different types of bacteria. The most common species include strains that can make people sick. In 2011, the public health group NSF International swabbed 30 things in 22 American homes. The dirtiest object in the entire house was the kitchen sponge. It was dirtier than the toilet seat. 75% of the sponges tested positive for the kind of bacteria that includes Salmonella and E. coli. Microwaving does not clean the sponge. The 2017 study found microwaved sponges had higher amounts of the smelliest, most harmful bacteria. Heat kills the weak strains. The strong ones survive and refill the sponge with no competition for space. A 2021 Norwegian study compared kitchen sponges to dish brushes. In brushes, Salmonella was wiped out within three days because the bristles dry out between uses. In sponges, bacteria climbed to about a billion cells per sponge. The lead researcher told CNN that one kitchen sponge can hold more bacteria than there are people on Earth. Three things actually work. Switch to a dish brush, because brushes dry fully between uses while sponges stay wet for hours. Replace your sponge every one to two weeks. Never leave it sitting wet in the sink. Norway and Denmark already do this by default, but most other countries don't. The detergent is fine. Your sponge is the problem.

This 100MW data center in UAE is the largest solar powered datacenter in the world. There are currently 1,300 data centers in the world that are bigger than this one, but this one is the largest solar powered one. That’s 10 square kilometres of solar panels you can see. The datacenter itself is 0.02 square kilometres, so a solar powered datacenter is ~500x larger than a data center using any other form of power. A five hundred times larger site. UAE has some of the highest solar irradiance anywhere on Earth, it is an inhospitable desert. Averaging 9.7 hours of sunlight per day with average irradiance above 2,200 kWh/m^2. If you build this somewhere else, you need more solar panels because your irradiance will almost certainly be lower. Even if the world had an infinite supply of free solar panels, solar power will not be free. Anyone who has ever done major capital projects, who looks at where data centers need to be in the next 5 years and the next 10 years… we know it aint solar. Sorry. You struggle to even build a train track that’s 100 miles long and 10ft wide anywhere in the West, there is zero chance of build 100 square mile solar farms for GW compute. This is why people are talking about space compute. Deploying into space is one strategy to solve the constraints. But there are faster and more scalable strategies, that get you to mass deployment of multi GW data centers. There are strategies that also allow you to power the 10 billion robots and their newtonian actuators, that immediately follow the inference demand cycle. Step back and look at the full cycle of this industrial revolution… There will be billions of chips, but there will be trillions of actuators. This biggest part of this revolution is the embodiment cycle, and it’s big by a factor of 20 or 50x over the stuff that comes before it. There is no analogy in human history for the scale of this economy, of the demand it will place on energy and commodities. The humans own the Earth, and if you exist inside their legal system, they won’t let you turn the surface of their planet into glass. But they do want your chips and your actuators to serve their needs and desires. There is a way to do all of this, and so it will happen.

«خط ثمانية» اليوم متاح بين أيدي الجميع، للاستخدام الشخصي والتجاري —✍️ حمله الآن من الرابط في الأسفل.

Earlier this year Yann LeCun left Meta because Mark Zuckerberg wouldn't bet the company on JEPA. Last week his group dropped the first JEPA that actually trains end-to-end from raw pixels. 15 million parameters. Single GPU. A few hours. The timing is not a coincidence. For four years Meta has been the house that JEPA built. LeCun published the original paper from FAIR in 2022. I-JEPA and V-JEPA came out of his lab. The architecture was supposed to be the escape hatch from LLMs, the path to robots that actually learn physics instead of hallucinating about it. Every version shipped fragile. Stop-gradients. Exponential moving averages. Frozen pretrained encoders. Six or seven loss terms that had to be hand-tuned or the model collapsed into garbage representations. Meta kept funding LLMs. Llama shipped. Llama scaled. Llama got beat by Qwen and DeepSeek. Zuck spent $14 billion to buy ScaleAI and install Alexandr Wang. The FAIR robotics group was dissolved. LeCun's research kept winning papers and losing the product roadmap. He left, started AMI Labs, and said publicly that LLMs were a dead end. Now the paper. LeWorldModel. One regularizer replaces the entire pile of heuristics. Project the latent embeddings onto random directions, run a normality test, penalize deviation from Gaussian. The model cannot collapse because collapsed embeddings fail the test by construction. Hyperparameter search went from O(n^6) polynomial to O(log n) logarithmic. Six tunable knobs became one. The downstream numbers are what should scare the robotics capex class. 200 times fewer tokens per observation than DINO-WM. Planning time drops from 47 seconds to 0.98 seconds per cycle. 48x faster at matching or beating foundation-model performance on Push-T and 3D cube control. The latent space probes cleanly for agent position, block velocity, end-effector pose. It correctly flags physically impossible events as surprising. It learned physics without being told physics existed. Figure AI is valued at $39 billion. Tesla Optimus is mass-producing. World Labs raised $230 million to sell generative world models. Everyone in humanoid robotics is burning capital on foundation-model pipelines that plan in 47 seconds per cycle. LeCun's group just showed you can do it with 15 million parameters on a single GPU in a few hours. This is the Xerox PARC pattern running again. Meta had the next architecture. Meta had the scientist. Meta dissolved the robotics team, passed on the productization, and watched the exit. Three months later the lab that was supposed to be Meta's publishes the result that resets the robotics cost structure. The paper is worth more than Alexandr Wang.