Capturing the perfect lines in my favorite shiny leather pants. 🌟 #Mesmerizing# #Fashion# #Style# #皮裤# #气质# #魅力# #レザー女子# #コーディネート# #가죽바지# #가죽레깅스#

What a showcase of elegance and magic on the ice at #MilanoCortina2026#! But don't forget: Wonderland has talents too! 🐰⛸️✨ Enchanted animals, Santa judging the show, pure fairytale vibes and mesmerizing animations in my #threejs# demo: #webgl# #shaders#

@GOT7Official Q: Who whisteled in Breath? & who came up with the idea of whistling because it sounds soo mesmerizing to ears!!! #AskGot7# #GOT7# #GOT7_LastPiece# @GOT7Official - @Shivanyaa__ A:

Pittsburgh thank you for making me feel sooooo at home in my home state. I mean… You broke the all time attendance record and we got to be the first tour to play your stadium twice. Thank you so much for everything this weekend. You were a mesmerizing crowd, like beyond 🥰😍 We’re coming for you next weekend Minneapolis! PS Happy Father’s Day to all the dads but especially mine who is one of my best friends, helped meticulously glue every teeny tiny crystal onto my guitar and still never misses a show 💕

At Montreal 1976... Romania's Nadia Comaneci stunned the world with her mesmerising 'perfect 10' gymnastics performances that earned the 14-year-old three gold medals and a legendary Olympic status. 🤩 #ThrowbackThursday#

Today I am announcing META-Bench, the first pure intelligence benchmark for AI. It leverages the hit auto-battler strategy game, TFT. I SWEAR I AM NOT TROLLING let me explain. The industry suffers from labs overfitting and giving us models that score high despite being fundamentally low IQ. Over the years there have been many attempts at benchmarking AI with competitive gaming. I am going to explain the failure points, and why META-Bench is truly the first of its kind. Chess. When picking a game to benchmark with, chess is the obvious first choice. It has clear rules, large player base, and a well defined elo system. The issue with static rule games though is that the best strategies can be figured out ahead of time and baked into the model during the training process. Too easily hacked. Memorizing more strategies is not a proof of intelligence. Dota2/ League. We’ve all heard of OpenAI Five. The issue with benchmarking on a MOBA is that reaction speed is a meaningless metric. We do not need our highly intelligent AI to be able to respond at the speed of top human pro players. And truth be told, we are years away from a LLM that is able to play MOBAs at the highest levels off of vision alone, even though the problem is seemingly solved years ago. What we need is a game that: - Has defined rules but cannot be results hacked during the training process - Large ecosystem of human players - Clear cut results and an elo system - Results that is not reaction time dependent There is only ONE game in the world that meet all the requirements needed for this benchmark. Teamfight Tactics. For those unfamiliar, TFT is a strategy based auto-battler created by Riot Games with ~100 million monthly active players worldwide. It is a highly competitive multiplayer turn based game. It’s as if Chess and League of Legends had a baby that’s born to be an AI benchmark: - There is a new set released every 3 months. - Time limitations in the 10-40 second range rather than the milliseconds required for MOBAs - Skill based enough for esports yet uncertain enough to require reasoning over hard scripts “Can’t labs just train models to be good at TFT?” Nope and the reason why it’s unhackable comes down to how the benchmark itself is set up. Due to the fact that the entire game is changed every 3 months and patched every 2 weeks, any data on a previous TFT set is effectively useless when it comes to raw pattern recognition. Strategy wise, there are core concepts that carries over from set to set. That’s why we have the same players hitting the highest elo every season even though each set is so different. Any efforts at overfitting here can be fully negated if the benchmark harness used for all models has every core strategy built in. You are never going to beat a carefully curated harness layer with strategy training at the model layer. By presenting the models in the harness with the same core strategic concepts, the only difference in outputs will be its ability to reason across the different scenarios of each game. The luck elements of TFT already ensures that no 2 games will be the same in the reasoning required. Run the models against each other enough times and you will have a clear winner. Aka, the world’s first true IQ test for AI. I really, really want to know which AI model would win this. So I am going to build this. Not too sure how I’m going to fund it yet so if you would like to invest HMU. I’m also looking to put together a small team of individuals who are both high elo in TFT and highly experienced with agentic AI. And if you are even remotely curious on the results, like and help share this post 🫡

In 2022, OpenAI researchers found something that broke every rule of machine learning. Their tiny model trained for 10,000 epochs. It learned absolutely nothing. Validation accuracy was dead stuck at 50%. Then at epoch 12,000, without warning, it jumped to 99%. This phenomenon is called "Grokking". And in 2026, it might be the most important discovery in AI nobody talks about. Neural networks can train for thousands of cycles without seeming to learn anything useful. Then, in a single epoch, they suddenly achieve near-perfect generalization. What started as a weird training glitch has become a foundational insight into how models truly learn. We’ve always been told: “If validation loss stops improving for a few hundred epochs, stop training.” Early stopping was the golden rule. Grokking says the exact opposite: Keep going. The model might look completely stuck, but real understanding is quietly forming under the hood. During that long, dead plateau, the machine isn't idle. It's doing deep internal work: - Circuits form, dissolve, and reform. - Spurious correlations get pruned away. - Weight patterns crystallize around true underlying rules. - The model shifts from brute-force memorization to genuine comprehension. It’s the machine version of a human “aha!” moment—a long, agonizing buildup followed by sudden clarity. Take modular addition as a real-world example. Researchers fed a small model just 30% of all possible examples. At epoch 500, it hit 100% training accuracy but stayed at 50% validation. It had memorized the test answers, but couldn't solve a new problem. At epoch 10,000, it still sat at 50% validation. It looked utterly hopeless. Then at epoch 12,000, it instantly shot to 99%. It didn't just guess right; it had grokked the actual mathematical rule. This explains the hidden mechanics behind the massive reasoning models we use today. When you see modern reinforcement learning or long-context reasoning models suddenly "click" after looking stuck, you are witnessing grokking at scale. Massive training runs aren’t wasteful, they are deliberately forcing the AI to stop memorizing and start thinking. And we are learning to induce this at inference time. Extended Chain-of-Thought prompts that force a model to think for thousands of tokens, self-consistency loops, and verification passes are all designed to do one thing: teach the model to grok your problem on the fly. The big philosophical takeaway is brutal for our short attention spans. Learning isn’t smooth. It isn’t gradual. It is discontinuous. Models, and humans, can stay “dumb” for ages, right up until they suddenly understand everything.