七喜 | 7UP

@0xVeryBigOrange 所以到底谁会赢 x.com/bitcoin136/sta…

最后抄底的机会！

大橙子：最后抄底的机会！ 杀破狼：最后的逃命机会！ 逃命派 vs 抄底派，谁会赢？ 同一根K线，会不会是两种人生？

@Crypto_Yang8888 @TurboFlow_xyz 我好想搞了点交易量，好久没看了 看来要继续搞起来了

兄弟们 不容易呀 憋到今天终于 @TurboFlow_xyz 正式宣布与Susquehanna Crypto达成战略合作！ 未来，Susquehanna Crypto 将作为 @TurboFlow_xyz 全产品线的核心流动性提供者与做市商，为平台带来真正的「 华尔街级流动性 」！ 本次合作最核心的地方，不只是「品牌背书」，而是整个交易体验与市场结构的升级～ @TurboFlow_xyz 正在逐步从早期固定赔率模式，转向真正的「动态赔率」机制，未来赔率将根据： - 即时流动性深度 - 市场方向性需求 - 波动率变化 进行动态调整！ 也就是说，市场不再是单纯的静态开盘，迎来的是更接近真实金融市场中的博弈与定价逻辑 👀 搭配 Susquehanna Crypto 的流动性与定价能力后， 不管是千倍永续合约，还是三十秒事件合约，都会更接近真正的机构级成交体验： - 更低滑点 - 更快成交 - 更稳定的深度 - 更健康的 Market structure 当然 如果你需要代理 可以随时联系我～ 永续合约和事件合约都会最大的额度给到老师们～ 如果你想体验产品 这条推特下面留下你的uid 随机空投三十位用户 我直接给你们发U 直接玩 走我的邀请链接就行！！ 前提是得先充值点体验一下 turboflow.xyz/join?r=ZYJ888 最后 未来TGE更多细节 以及更多合作方案可以直接私信我TG + TF0814S 欢迎各位老师们来这里赚钱！！

@0xAstraSpark 千问月活有三亿，我咋不太信呢😂😂😂

@TC8880 又欲又仙 不管换哪张 我已经做好舔的准备了

想换个头像 让我的AI助理给我做了4版 你们觉得哪个好看👀 🔮

@moon_bgb 应该能吃几顿猪脚饭

@bitcoin136 哈哈，tge了你就麻了吧

今天，TermMax从YZiLabs正式毕业了 但社区里最热的话题始终只有一个：什么时候TGE？现在大家都在埋头苦干地刷积分，可热度这东西，凉起来也很快。再不抓紧TGE，错过这波叙事的窗口期，就真的太可惜了 TermMax TVL前几天也突破1亿美元！并且背靠顶级机构(Cumberland、HashKey等)，基本面还是挺扎实的 趁现在，快点TGE吧 ！ @TermMaxFi

@Baisircrypto 确实能解决一部分传统行业跨境支付的问题

@Za_Enal58 @AIxC_Official @aixcfoundation 平衡被打破，AI主导一切。

@AIxC_Official AI will destroy the market and create a new balance. @AIxC_Official @aixcfoundation

the market every 24 hours: AI will save humanity 🤝 AI will destroy humanity🔁

@Za_Enal58 @AIxC_Official @aixcfoundation AI毁市场，生新秩序。

@Za_Enal58 @AIxC_Official @aixcfoundation 摧毁旧平衡，创造新未来。

@Za_Enal58 @AIxC_Official @aixcfoundation 市场被AI重构，机会来了

@Za_Enal58 @AIxC_Official @aixcfoundation AI颠覆市场，重塑新平衡！

@Za_Enal58 @AIxC_Official @aixcfoundation #Tenki

@heyibinance 全球一半用户么？ 真假啊

We're now on Binance Square covering the road from 300 Million to 3 Billion users. Join us binance.com/en/square/audi…

@wenxue600 @TermMaxFi @ZF_lab tge太磨叽了

撸了 @TermMaxFi 的要麻？

@0xMyoung 我觉得阿里最大优势就是它的电商生态和用户基数 现在确实声音小了点 但基本盘最起码还在

这篇写的挺好的 我在1月份梳理「AI投资核心」公司时，阿里是唯一一家我选的国内公司。 原因很简单：如果说国内有一家能把AI真正打通并融入到日常生活的公司，那大概率是阿里。 但中间千问团队更迭，豆包依赖抖音流量又顺势崛起，让这件事看起来似乎遥远了一些。 春节期间，千问搞点奶茶活动，这其实比单纯的抢红包要好，因为带来的是实际生活里的真实的影响，这个体验要远胜于元宝的红包拉新。 但似乎后续仍然没有了声音。 但目前我个人仍然保留对阿里的观察，依赖庞大的电商体系、用户量级，仍然看好阿里后续的动作。

@WaterX_app @GalxeQuest @Galxe galxe很久没打开了 因为一直被反撸

WaterX Tide Campaign powered by @GalxeQuest Complete quests and claim your WaterX Tide #GalxeOAT: app.galxe.com/quest/WaterX/G… 🔹 May 12th 14:00 UTC - May 16th 14:00 UTC 🔹 WaterX Tide #GalxeOAT + @Galxe Space points 🔹 Any form of cheating or malicious behavior will result in disqualification

@wang_jl80536 零撸的风刮到了灯塔

果然，林子大什么鸟都有了，灯塔也要防被撸了！！

@ivyruiz427 @AIxC_Official @aixcfoundation 每一次失败都是宝贵训练数据。

@AIxC_Official LFG @AIxC_Official @AIxCFoundation

The next trillion-dollar AI dataset probably won’t come from the internet. It’ll come from machines interacting with the real world. Trillions of pages. Twenty years of human text. Language models inherited one of the largest freely available datasets in history. But there is no equivalent dataset for robots. There is no giant archive of physical experience for machines to learn from. A robot cannot read its way into understanding how to: • grasp a glass without crushing it • move through a crowded kitchen • react when a chair suddenly shifts • recover from imbalance • navigate unpredictable environments Those capabilities only emerge through interaction with the physical world. Which means the next major bottleneck in AI may come down to collecting experience itself. Data from actual real-world machine interaction: • movement trajectories • sensor feedback • force response • environmental adaptation • task execution • failure correction The raw ingredients for physical intelligence. And unlike internet data, this is extremely expensive to gather. Most robotics collection today still happens in constrained environments: • small fleets • controlled rooms • human operators • limited variability The result is often narrow data that struggles to generalize outside the environment it was collected in. That’s why embodied AI companies are increasingly converging on the same realization: the real moat may become scalable real-world data collection. Not just building robots, but building systems that continuously generate useful physical experience at scale. Because every deployed robot creates something valuable beyond labor output: training data. Every delivery route. Every warehouse task. Every navigation failure. Every real-world correction loop. The machine is simultaneously working and learning. The companies that solve scalable physical data collection may end up doing for embodied intelligence what the open internet did for language models. And that may define the next phase of AI.