Indraneel Biswas @Neel73

Where is Saudi , Where is Saudi ? #fifa

@ADfanatic_ Thought the vocal was pretty ok. Has IT Cell started auto-tuning as well ? 🤔😆

@MFuturewala Sabka saath , sabka vikaas

@advsanjoy His dad is Ram. He missed being a Shyam, Jodhu , Modhu ... Being a chamcha , he ended up being a Ch***du

@RShivshankar This video is certainly not of Brigade Ground or Red Road.

@narendramodi Sir , aapke liye Special hoga. Unke liye sirf Strategic hai

@PunsterX TCS waalon... sunlo... bonus milne ka ninja technique 😆

@AtheistIndian_ @kolappan @MANJULtoons Looks like the Davy Jones character from Pirates of the Caribbean.

@samjawed65 @samar11

@realEstateTrent Yes kids do ask the most brilliant questions ! The most memorable moments of my parenthood have been the questions and opinions of my daughter. As a toddler, she would often ask the most deep or thought provoking questions at bedtime. Did ur little one also ask the q @ bedtime ?

Oh...the most adorable cooking video ive ever seen 🥺

Nico @NicoHQ1

2 months ago

Whatever he's cooking, I will eat it without hesitation...adore the accent ❤😋

413 2K 12K 488K 2K

@Tech_Marsha @ai_uncovered Claude

@ai_uncovered @AI_creator_Yara Gold

GOLD is flashing a 1979-style setup again. 1979: Iran crisis → oil shock → panic dump → explosive gold rally 2026: Iran tensions → oil spike → uncertainty → dump phase → possible repeat? The structure looks eerily similar. I built a full AI-driven model (OpenClaw + Claude Code) to track this pattern in real time. I’m sharing the exact breakdown FREE for 16 hours. To get it: • Comment “Gold” • Like + repost • Follow @ai_uncovered I’ll DM it directly. Don’t ignore historical echoes—markets rhyme in phases. Don't forget to bookmark and follow me @ai_uncovered

GOLD Is About to Repeat 1979. Last Time This Happened, It Pumped Hard. 1979: Iran war → oil price 2x → crisis → Dump → Pump 2026: Iran war → oil price 2x → crisis → Dump → (we are here) I've made the exact step-by-step guide to trade this pump before it happens using AI prediction models in OpenClaw & Claude Code. It's free for 24 hours, to get it: 1. Comment "Gold" 2. Like and Retweet 3. Follow me @codewithimanshu (so i can DM you) Same pattern. Same setup. History doesn't repeat but it rhymes. You Must Follow me @codewithimanshu, so i can send you DM. Turn on notifications before I call when the Pump starts.

@codewithimanshu GOLD

@GeniusGTX Models

MIT's Nobel Prize-winning economist just published a model with one of the most alarming conclusions in the AI literature so far. If AI becomes accurate enough, it can destroy human civilization's ability to generate new knowledge entirely. Not gradually degrade it. Collapse it. The paper is called AI, Human Cognition and Knowledge Collapse. Authors: Daron Acemoglu, Dingwen Kong, and Asuman Ozdaglar. MIT. Published February 20, 2026. Acemoglu won the Nobel Prize in Economics in 2024. He is not a doomer blogger. He is the most cited economist of his generation, and his models tend to be taken seriously by the people who set policy. Here is the argument in plain terms. Human knowledge is not just a collection of facts stored in individuals. It is a living system that requires continuous reproduction. People learn things. They apply them. They teach others. They build on prior work to generate new work. The entire engine of science, medicine, technology, and innovation runs on this cycle of active human cognition. What happens when AI provides personalized, accurate answers to every question people would otherwise have to learn themselves? Individually, each person is better off. They get correct answers faster. They make fewer errors. Their immediate outcomes improve. But they stop doing the cognitive work that sustains the collective knowledge base. Acemoglu's model shows this produces a non-monotone welfare curve. Modest AI accuracy: net positive. AI helps at the margin, humans still do enough learning to sustain collective knowledge, everyone gains. High AI accuracy: net catastrophic. AI is accurate enough that learning yourself feels unnecessary. Human learning effort collapses. The knowledge base that AI was trained on is no longer being refreshed or extended. Innovation stalls. Then stops. The model proves the existence of two stable steady states. A high-knowledge steady state where human learning and AI assistance coexist productively. A knowledge-collapse steady state where collective human knowledge has effectively vanished, individuals still receive good personalized AI recommendations, but the shared intellectual infrastructure that enables new discoveries is gone. And the transition between them is not gradual. It is a threshold effect. Below a certain level of AI accuracy, society stays in the high-knowledge equilibrium. Above that threshold, the system tips. And once it tips, the collapse is self-reinforcing. Because the people who would have learned the things that would have pushed the frontier forward never learned them. And the AI cannot push the frontier on its own. It can only recombine what humans already knew when it was trained. The dark irony at the center of the model: The AI does not fail. It keeps giving accurate, personalized, useful answers right through the collapse. From the individual's perspective, nothing looks wrong. You ask a question, you get a correct answer. But the collective capacity to ask questions nobody has asked before, to build the frameworks that generate new knowledge rather than retrieve existing knowledge, that capacity is quietly disappearing. Acemoglu has been the most prominent mainstream economist skeptical of transformative AI productivity claims. His prior work found that AI's actual measured productivity gains were much smaller than the technology industry projected. This paper is a different kind of warning. Not that AI will fail to deliver promised gains. But that if it succeeds too completely, it will undermine the human cognitive infrastructure that makes long-run progress possible at all. The welfare effect is non-monotone. That is the sentence worth sitting with. Helpful until it is not. Beneficial until it crosses a threshold. And past that threshold, the same accuracy that made it so useful is precisely what makes it devastating. Every student who uses AI instead of working through a problem is a data point. Every researcher who uses AI instead of developing intuition is a data point. Every generation that grows up with accurate AI answers and no incentive to develop deep domain knowledge is a data point. Individually rational. Collectively catastrophic. Acemoglu proved this is not just a cultural concern or a vague anxiety about screen time. It is a mathematically coherent equilibrium that a sufficiently accurate AI system will push society toward. And there is no visible warning sign before the threshold is crossed.

keyboard_arrow_left Previous keyboard_arrow_right Next

@SirKazamJeevi The secret ninja technique to get an additional Honors degree in Entire Cheerleading Science 🤷‍♂️

@vulturetrades Guide