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OKX预测活动&欢乐开黑夜 第一枪DOTA2正式打响 🎮 上车选手1000xp直接免费送～ 游戏内观战（等位）300xp拿走不谢 来X、星球看直播的观众朋友各100xp*20人 边打游戏边预测 OKX给你的枪里装满子弹🔫 你还想玩什么游戏？留言投票！ 怎么玩？跟Misa一起看过来～ 8月22日 23日8点 连续两晚 每晚 3 场 每天第一局先到先得 前 10 人到齐就开 第二 三局公开 ROLL 点 选马局！ 点数最高的两人做队长，再各选 4 名队友 热爱预测、游戏的朋友们都可以进群 群内通知我们的相关信息哦 以后会有更多比赛～ lol？王者？cs？瓦？ 没有 OKX 的先下载注册就能参与 积分可以参与我们的足球、电竞等等赛事的竞猜 可以换成💰哦 快带着你爱打游戏的好朋友来OKX玩吧 #dota2# #lol# #游戏# #电竞#

最近世界杯⚽️我有在尝试各种不同的预测市场，发现了一个宝藏链上预测市场@bagel_win 今天用体验金试水玩了两场球全赢了，这是直接做了一个app，界面非常清爽，玩球玩法也很全！最牛逼的是这个可以像玩meme一样关注聪明钱地址，后期还会出内置copy trading功能一键跟单👀这就有点牛逼了说实话🫡 很符合我胃口的是app里内置聊天社群，比如世界杯、LOL、CS2、Dota2等，相当于把预测市场➕社群➕地址追踪聚合到了一个app，简直完美！ 最近还有好多活动，收集卡牌瓜分奖池，交易量奖池，还有每日活动，比如交易今晚葡萄牙-克罗地亚这场球，直接送你3u~ 💡大家可以走我链接玩：

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 🫡

最近参与了 Firefly 第二期 Polymarket Trading League，polymarket交易大赛。做了几天冠军以后，ufc一场亏了2000多u。 我给所有我社区跟我单的用户赔偿的亏损，没别的原因，主要是要面子。赔偿额远大于我自身亏损 目前盈利还在水下。相信最后两周我开启认真模式，还能盈利翻回来。欢迎围观 先介绍一下 @fireflyappcn 近期进展 Firefly 作为移动端预测市场交易的先锋，近期完成了重大产品升级： Web 端多链入金正式上线（4月22日更新） 跨链多资产直接充值到 Polymarket 账户 取款支持直接转换资产 跨链提款功能 这意味着用户无需手动处理繁琐的跨链转账，参赛体验更加丝滑。从移动端到 Web 端，Firefly 正在全方位重塑 Polymarket 交易体验。 项目地址： 二、第二期 Trading League 活动内容，非常简单好撸的小毛 活动时间：2026年4月10日 - 5月10日（为期一个月） 总奖金池：3000 USDC 活动定位："绝对不卷" —— 低门槛参与，高回报机会 奖金分配 总榜奖池（1500 USDC）按赛季结束 PNL 排名： 第1名：500 USDC 第2名：300 USDC 第3名：200 USDC 第4名：120 USDC 第5名：80 USDC 第6~15名：每人30 USDC 每日奖金池：1500 USDC（按日分配，可撸，每天交易量10u，就能领1～3u的补贴，生命担保上非常简单好撸的简单毛） 三、我在比赛选手中，发掘的细分领域高手 天气交易领域 @yummyuyu_（yuyu）：每天用 Firefly 交易天气，已完全适应移动端下单 @Mask4che："中英文区顶流天气玩家" 0xfe1d87d5067db83fbbd0ef97fdbc725b0371a99a：专注 Polymarket 天气预测，天气数据+预测模型=稳定收益 AI 电竞预测 0x3eabcb19db80c2cd8e2fd163b00835b047bc92da："AI 玩电竞"用 AI 模型分析电竞赛事数据 预测 DOTA2、LOL、CS:GO 等热门赛事 算法驱动，情绪零干扰 社交事件预测 0x9e5ecaba3d2030c68bd4aec6ec3892fbb7d5a393 0x0ded0c85b41f53cc788d90cfa9b9ed1b70b298c3 ："马斯克发推高手"专注预测马斯克 Twitter 行为 从发推频率到内容主题，都有市场定价 社交事件预测的新兴领域 其中0x0ded0c85b41f53cc788d90cfa9b9ed1b70b298c3 是我的帐号。继续带单。而且跟单用户没有一个走的我的反佣链接，我不推poly的反佣，谢谢。就是喜欢带。 四、firefly平台的亮点 移动端体验最佳：随时随地交易预测市场，出门时快速下单超方便 社交化功能丰富：分享、学习，跟随高手交易 多链支持，入金便捷：Web 端跨链多资产入金，无需手动处理跨链转账 社区活跃，高手云集：天气、电竞、社交、BTC... 万物皆可预测 低门槛高回报的简单撸毛：3000 USDC 总奖金池，每日奖励+总榜奖励双重激励 五、加入firefly的新手教程 第一步：注册账户 打开 点击 Join Now 注册 Firefly 账户 链接 X（Twitter）社交媒体账号 第二步：确认链接 回到 确认社交媒体已成功链接 第三步：充值资金 向你的 Firefly Wallet 中的 POL 收款地址转入至少 10 USDC 同时转入一些 POL 代币作为 Gas 费 第四步：开始交易 通过 Firefly Wallet 中的 Predictions 栏目 点击入金并交易 Polymarket 选择你感兴趣的预测市场，开始你的第一笔交易！ 六、为什么选 Firefly？ 特性Firefly其他平台移动端体验原生优化多为网页适配入金便捷多链支持单链或跨链繁琐社交功能跟单+分享多为独立交易社区氛围"不卷，好玩"竞争激烈奖金激励3000U+每日奖励通常较低