与「Legends」相关的搜索结果

THIS GUY HAD OPUS 4.8 BUILD A FULL LEAGUE OF LEGENDS CLONE IN UNDER A DAY AND CALLED IT "LMAO" temu league of legends, fully playable, multiplayer, and all built by claude ALL the art is claude too he had claude generate every character, animation, and background as SVG code then converted them to procedural canvas for smooth animation. used dedicated sub-agents for each champion the tech stack: typescript, react, canvas, and partykit for multiplayer the workflow: > used /goal to kick off the entire project > ultracode workflows for big tasks like optimization > /goal again to queue up lists of bug fixes > treated claude like a dev team the original champion names claude came up with: the original champion names claude came up with: Teehee" instead of Teemo. "CtrlAltDefeat" as a champion name. he had to run through and clean them all up so nothing infringed on riot's IP claude even built the bots on its own with "chill," "normal," and "sweaty" difficulty levels the token bill was 2.7 billion total tokens consumed. mostly cache reads. 15.5 million actual output tokens. would have cost roughly $6,600 at list price but he just used his pro max subscriptions people are already benchmarking claude by how good of a game clone it can build game developers are watching someone build a multiplayer MOBA in a day with zero art skills and zero game dev experience 2026 is wild (and its going to get even wilder)

Legends live on. Shooting in this heat, the sand burned my feet 🥺 Original Art Design by WLOP (Wang Ling) GhostBlade Cosplay 傳奇永留。 在炎熱的天氣拍攝，沙子燙傷了腳嗚嗚嗚😵‍💫 Wlop 王凌 鬼刀 Cosplay

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 🫡

Tomorrow we introduce the world to @Rewkang, one of the best investors of the last decade. He is relatively unknown, but I believe he is on track to become one of the legends of finance. Episode out tomorrow.

Windows on ARM Gaming Gets Major Anti-Cheat Support Nvidia’s new RTX Spark platform is helping solve one of the biggest problems of Windows on ARM gaming: anti-cheat support. Anti-cheat software runs at a very low level of the operating system and is required by many online games, If the anti-cheat doesn’t support ARM, the game usually won’t launch, regardless of how powerful the hardware is. Nvidia is working with major anti-cheat providers like Easy Anti-Cheat, BattlEye, and Denuvo to add support for games including Fortnite, VALORANT, League of Legends, and PUBG.