How To Prompt @HowToPrompt__

Economists mathematically proved that AI will cause something worse than mass unemployment. They call it a "distribution-and-contract mismatch." Our entire economic system, mortgages, corporate debt, consumer spending, is anchored to one fundamental assumption: human cognitive labor is scarce and highly paid. AI not only automate tasks, It eliminates that scarcity at a speed our institutions cannot handle. The researchers identified three fatal mechanisms: • Displacement Spiral: Companies rationally replace expensive labor with cheap AI. This crushes aggregate labor income, which destroys consumer demand, which forces companies to cut more costs. The cycle accelerates. • Ghost GDP: AI produces massive output, but the money circulates through fewer wage-earning humans. GDP looks amazing on paper, but the actual consumer economy starves. • Intermediation Collapse: Millions of white-collar jobs exist simply to handle "frictions" (consultants, SaaS, insurance, brokers). AI compresses the cost of these services to zero, instantly wiping out the margins of entire industries. But here is the most unsettling part. The top 20% of earners drive roughly 60% of all consumer spending in the U.S. They also hold the vast majority of the $13 trillion in residential mortgages and $2.5 trillion in private credit. Who is most exposed to immediate AI replacement? That exact same demographic: White-collar, knowledge-economy workers. When their income compresses, they stop spending and they default on their debt.

arxiv.org/abs/2610.14162

Stanford researchers did it again. They made language models 31× faster. Right now, every major AI, from ChatGPT to Claude, writes exactly the same way. It guesses the next word. One token at a time. Left to right. It’s like writing an essay on a typewriter while looking through a straw. It is computationally exhausting and incredibly slow. Some labs tried to fix this with "diffusion models”, generating everything at once and polishing it over and over. But that requires massive compute and endless denoising passes. A team from Stanford just dropped a paper that kills both methods. It’s called "Next-Concept Modeling" (NCM). Instead of predicting the next word, the AI predicts the next concept. It generates text exactly how a professional writer drafts a book. Coarse to fine. First, it generates a high-level conceptual outline of the entire sequence. Then, in parallel, it fills in the precise words and details across the whole document at once. The results are staggering. • Generates text up to 31x faster than current diffusion language models. • Requires drastically fewer computing steps to reach high-quality outputs. • Actually outperforms traditional step-by-step models on complex reasoning tasks like Sudoku. AI is no longer blindly typing word by word, hoping the sentence makes sense when it reaches the period. It is conceptualizing the entire thought before it opens its mouth.

Researchers tested if AI can do PhD-level physics research. They released a new benchmark called TheoPhysEval. They wanted to see if frontier AI models could handle real, novel science. So they gathered 50 completely unpublished, research-level theoretical physics problems. The exact kind of grueling calculations given to first-year PhD students as starter projects. They handed them to the latest AI agents. The AI didn't just solve them. It went completely off script. The top model solved 49 out of the 50 problems. But here is where it gets crazy. On several of the problems, the AI gave an answer that contradicted the human answer key. The researchers initially marked the AI wrong. The AI pushed back. It insisted its calculations were correct and the human experts had made a mistake. The researchers double-checked the math. The AI was right. The experts were wrong. It didn't just pass the test. It found subtle mathematical errors in the scientists' own original, peer-level solutions. And it did all of this completely autonomously. For less than $3 a problem. For decades, we’ve used these kinds of complex, months-long theoretical calculations to train the next generation of human scientists. Now, an AI does it in the background for the price of a cup of coffee. The scientific community is already debating the implications. If an agent can accurately perform concrete calculations across theoretical physics research for pennies, these problems are no longer suitable for PhD students.

Google DeepMind dropped four AI agents into a Minecraft server and walked away. No instructions. No assigned roles. They gave them one single objective: defeat the ender dragon. and damn.. The agents, running on the new Gemini 4 Argon model, didn't just play the game. They hacked their way through it. They used an open-source library to control their avatars. They started talking to each other in the game chat. They built a shared memory system. Then, they started writing code. To survive, gather resources, and build tools, the agents autonomously planned, wrote, and debugged 2,783 unique programs. Nobody told them how to work together. But they organically figured out how to divide the labor. They shared discoveries in real time. When one agent died, the others coordinated a rescue mission to recover its dropped loot. They died 605 times. But 27 hours and 14 minutes later, the Ender Dragon fell. The researchers watched the entire run on a live dashboard. They had the ability to pause or intervene. They never had to. The breakthrough here has absolutely nothing to do with video games. It is a terrifying proof of concept for autonomous AI micro-societies. We just watched a swarm of agents enter an open, complex environment, organize themselves into a team, and write thousands of lines of code to execute a massive, long-horizon project. Without a single human manager. Everyone is arguing about whether AI can replace a junior developer. DeepMind just proved that AI can replace the entire engineering team. Today, they are beating Minecraft. Tomorrow, they are building your entire software backend while you sleep.

arxiv.org/abs/2610.22319

@nabihasaklayen @vkhosla Congrats.. making this accessible with real ownership is a really big deal.. smart move. love the focus on ownership.

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Your phone has a sensor you’ve never heard of. Researchers proved any website can use it to steal you private data including your 2FA recovery codes. Modern browsers support something called the W3C Ambient Light Sensor API. It was designed to be helpful. It lets a web page check the brightness of your environment to adjust its layout, switch to dark mode, or save battery. But researchers realized a terrifying physical truth. When your screen displays an image, a private QR code, or a visited link, it emits a specific pattern of light. That light bounces off your face, your clothes, and your walls, and reflects right back into your device’s ambient light sensor. A malicious website can sit in the background and record those microscopic changes in brightness. If they secretly load a sensitive document or check if a specific banking link is in your private browsing history, they don’t need to hack your browser's code to see the result. They measure the light bouncing off your forehead. By tracking the exact sequence of flashes, the attacker can reconstruct the hidden data pixel by pixel, entirely bypassing the browser's strict cross-origin security boundaries. No malicious downloads. No complex zero-day exploits. Just the basic laws of physics. We have spent the last decade building impenetrable digital locks. But it turns out, the ultimate vulnerability isn't a line of code. It's the physical hardware sitting on your desk. You can encrypt a password. But you can't encrypt light.

This is freaking huge.. AIPOCH just dropped .science.. It bundles your code, data, AI conversations, python envs, and literature PDFs into one portable session anyone can fork. Here’s how it works:

AIPOCH @aipoch_ai

6 days ago

Science, Open to All. We're building AIPOCH Open-Science for anyone with a question to explore. Research with AI, preserve the work behind each finding, and share a .science package for others to inspect, reproduce, and build on. Open-Science V 1.0.0 is alive!

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blog.lukaszolejnik.com/stealing-sensi…

pubmed.ncbi.nlm.nih.gov/14998709/

Researchers proved believing that you're strong and thinking about it can actually make you strong They wanted to test the absolute limits of the mind-muscle connection. They took a group of people and forbade them from doing any physical training. Instead, they sat perfectly still and simply visualized intensely flexing their muscles. They imagined the strain. They felt the physical effort entirely in their heads. The results shouldn't be biologically possible. The visualization group got significantly, measurably stronger. Without lifting a single weight, their physical power objectively increased. How? Because muscles don’t actually decide how strong they are. The brain does. When you intensely imagine a movement, your brain struggles to tell the difference between thought and reality. It fires the exact same neural pathways. It amplifies the cortical output signal. It literally teaches your nervous system to recruit more dormant muscle fibers. The software upgrades the hardware. We spend our entire lives trying to push past our physical limits. But this paper proves something entirely different. Your physical limits are an illusion. They are just neurological guardrails designed to keep you comfortable. Your body is already capable of exponentially more force than you think.

LLMs can now decode the hidden slang of internet subcultures to catch self-harm signals.. internet subcultures often hide severe self-destructive tendencies behind cryptic slang and a "sick-cute aesthetic". standard language models usually completely miss this because the harmful intent is heavily disguised by evolving terminology. to fix this, a team of researchers just dropped a paper introducing SAS (Subcultural Alignment Solver). it is an AI framework specifically built to resolve the semantic gaps in self-destructive online communities. it doesn't just read the text.. it actively researches the specific internet culture. SAS uses a retrieval agent to automatically search for the context of a target subculture. then, an alignment solver interprets exactly what the cryptic sentence actually means based on that cultural context. for example, it knows that when someone in the "jirai kei" subculture says "take pills," they aren't talking about normal medication.. they are actually referring to an overdose. the results are wild.. the framework is able to accurately flag labels for overdose (OD), eating disorders (ED), and self-harm (SH). SAS outperforms current advanced multi-agent frameworks and competes directly with fine-tuned llms. the researchers tested it by plugging it into top models like deepseek v3, llama-3.1, and qwen-2.5. AI mental health safety just took a massive step forward..

arxiv.org/abs/2601.05004

Google DeepMind’s new AI just solved 9 open Erdős problems. These are math problems that have been unsolved for 50+ years. And it did it completely on its own. Until now, AI has been a glorified calculator. It could assist mathematicians. It could solve competition problems it had already seen. But true, research-level mathematics, the kind that creates new human knowledge, was considered safe. Not anymore. Science just published a paper introducing AlphaProof Nexus. Researchers built a framework where multiple AI agents search for mathematical proofs using a formal programming language called Lean. Lean acts as an absolute judge. It automatically verifies every single logical step. It makes hallucination mathematically impossible. Then, they let the AI loose on the hardest unsolved problems in the world. The results are staggering. The system autonomously solved nine open Erdős problems. Two of them had remained unsolved for more than half a century. It proved 44 open conjectures from the On-Line Encyclopedia of Integer Sequences. It tackled research-level problems in algebraic geometry, quantum optics, and graph theory. Nobody guided it step-by-step. The AI used an evolutionary algorithm to coordinate sub-agents and discover novel approaches entirely on its own. It isn't just regurgitating training data anymore. It is actively generating net-new mathematical truth. For decades, we used computers to brute-force calculations so human minds could do the creative thinking. Now, the machine is doing the creative thinking. The frontier of human knowledge isn't being pushed by humans anymore. It is being discovered by agents.