you can outsource your thinking but you cannot outsource your understanding

Watch Cursor build a 3M line browser in a week

Anthropic is donating the Model Context Protocol to the Agentic AI Foundation, a directed fund under the Linux Foundation. In one year, MCP has become a foundational protocol for agentic AI. Joining AAIF ensures MCP remains open and community-driven. anthropic.com/news/donating-…

Elon Musk came up with a pretty incredible idea during the Q3 Earnings Call, that no one is really talking about. His words: “Actually, one of the things I thought, if we've got all these cars that maybe are bored, while they're sort of, if they are bored, we could actually have a giant distributed inference fleet and say, if they're not actively driving, let's just have a giant distributed inference fleet. At some point, if you've got tens of millions of cars in the fleet, or maybe at some point 100 million cars in the fleet, and let's say they had at that point, I don't know, a kilowatt of inference capability, of high-performance inference capability, that's 100 gigawatts of inference distributed with power and cooling taken, with cooling and power conversion taken care of. That seems like a pretty significant asset.” So basically, each car has ~1 kilowatt of high-performance AI inference capability, Tesla wouldn’t need to build giant data centers — the fleet is the data center. Tesla could turn their entire fleet into a giant distributed inference network, spread across the world, powered by the batteries and AI in the car already. Mind blown.

What in the F is an AI factory? I had to investigate what the unelected @EU_Commission is talking about today So according to them, it's some data centers (which they call supercomputers) in 6 different EU countries I checked out the most powerful one: Karolina, a Czech data center, it mostly has CPUs though (see pic) not GPUs, so mostly useless for AI The GPUs it does have are 72x 8x NVIDIA A100 GPU, so 576x A100, or equivalent of 240x H100s (H100 is about 2.4x the compute power of A100) So let's compare that: @xAI has 200,000x H100 GPUs So the xAI data center has 800x more compute than the Czech one If we combine xAI, Meta, AWS, etc. it's about 750,000 H100s If we assume the other 5 data centers in the EU are equivalent to the Czech one (which is massive stretch because most of the others seem AI consultacny services, they don't even HAVE chips!), the EU's new "AI factories" have a total of 1,440x H100 GPUs, let's round up to 1,500 to be nice So the EU is trying to compete with 750,000 GPUs with their own 1,500 GPUs, so 500x less?? Correct me if I'm wrong but it's just seems very low impact and another ridiculous idea and burning of EU tax payers money that will end up in local cronies and bureaucrats and will do NOTHING to improve the AI business climate for Europe The best way to improve it is to deregulate, make it super easy and low tax (especially when starting out) to start AI companies in Europe

2024 evals can it count letters 🥺 can it do college stuff 🤓 are its solutions diverse 👉👈 2025 evals has it worked for 30 hours yet 🦾 has it increased gdp 📈 has it discovered novel math 🧮

"the human brain doesn't need tons of training data to do stuff"

I think @cursor_ai is essentially dead. Long live @OpenAI and @windsurf_ai.

I use @windscribecom as my main VPN. It's free up to 15GB/month and the only one with inverse split tunneling on macOS. Highly recommended.

Claude raises the waterline for competence. It handles 99% of general discrete tasks better than the average person - the kind of work that used to require basic professional training but not deep expertise. This creates an interesting split: routine generalist work (basic coding, first-draft writing, common diagnoses) becomes commoditized, while both deep specialists AND high-level generalists (those who synthesize across domains) become more valuable. LLMs compress the middle of the skill distribution. They automate what used to require modest expertise, leaving people to either specialize deeply in narrow fields or become meta-thinkers who connect insights across domains in ways AI can't yet match. We're not just becoming a species of specialists - we're bifurcating. Some go deep into niches, others rise above to see patterns LLMs currently miss. The comfortable middle ground seems to be disappearing.

the problem with the 4D chess theory is that every insane/irrational decision trump makes could be seen as a 4D chess move - when in reality it’s far more likely he’s just chewing on the pieces

Sustainable abundance

The "meaning crisis" revolving around AI will turn out to be a complete nothing burger There is no intrinsic sense of meaning to doing something that is economically valuable. It's 100% cultural storytelling around specific circumstances. Humans find ways to tell stories about anything they do and inject "meaning" into it, be it athletes, entertainers, parents, priests, psychologists, being a friend, etc. This is one of the biggest category errors most successful people tend to make, because they feel they are the top of the "meaning" food chain. but this is, again, cultural. Younger generations will adapt instantly to not working and will easily find meaning in various random things in their lives

this is sad and pathetic

the bubble people in silicon valley live in a bubble. they wake up, drink their mushroom coffee, take their electric bike to a coworking space, and spend the day talking about ai safety, the singularity, and whatever new nootropics they’re microdosing. they believe the biggest risks to humanity are misaligned ai, social media addiction, and the wrong people winning elections. meanwhile, outside the bubble, the real world is still burning. •1 in 10 people on earth doesn’t have enough food to eat. that’s 800 million people waking up hungry every day. •in sudan, over 5 million people have been displaced by war just this year. entire villages wiped out. children killed. no media coverage. •in gaza, more than 27,000 people have been killed in just a few months. entire families erased in seconds. •in haiti, 80% of the capital is controlled by gangs. women are being raped in front of their families. nobody is coming to save them. •in yemen, a child dies every 10 minutes from malnutrition and disease. a war nobody talks about. •north korea has concentration camps the size of cities. people born in them, live in them, and die in them. none of these people care about your ai safety debates. they don’t know what alignment means. they don’t have twitter threads about consciousness. they don’t read longform thinkpieces about the future of the human race. they’re too busy trying to stay alive. the western world has convinced itself that history is over. that things will continue in a straight line, that life will get better, that technology will solve everything. but for most of the world, history isn’t over. it’s still happening. and it still looks a lot like hell.

being technical is a surprisingly small factor in "feeling the agi" there are graybeard phd computer scientists who've never heard of chatgpt and philosophy dropouts at openai unfakeable curosity some min iq are ~all you need to see the machine god before others. i love that.

o3-mini might be the best LLM for real-world physics. Prompt: "write a python script of a ball bouncing inside a tesseract"

I asked o3-mini to "create an autonomous snake game, where snakes compete with each other"

We have to take the LLMs to school. When you open any textbook, you'll see three major types of information: 1. Background information / exposition. The meat of the textbook that explains concepts. As you attend over it, your brain is training on that data. This is equivalent to pretraining, where the model is reading the internet and accumulating background knowledge. 2. Worked problems with solutions. These are concrete examples of how an expert solves problems. They are demonstrations to be imitated. This is equivalent to supervised finetuning, where the model is finetuning on "ideal responses" for an Assistant, written by humans. 3. Practice problems. These are prompts to the student, usually without the solution, but always with the final answer. There are usually many, many of these at the end of each chapter. They are prompting the student to learn by trial & error - they have to try a bunch of stuff to get to the right answer. This is equivalent to reinforcement learning. We've subjected LLMs to a ton of 1 and 2, but 3 is a nascent, emerging frontier. When we're creating datasets for LLMs, it's no different from writing textbooks for them, with these 3 types of data. They have to read, and they have to practice.

Using @OpenAI Operator within Operator has the best error message