amitofo

Wir müssen wissen, wir werden wissen

[amitofo @amitofo_]

@Diverskyy raw😍

[amitofo @amitofo_]

@yugen_gg @opensea 正直、もう国産NFTには虚无感しかないわ。期待してたプロジェクトが全部電子ゴミになっちゃって… ( ´д｀ll

[amitofo @amitofo_]

@elsebaquemero 1994年rod steward在巴西的跨年免费演唱会，350万+人。

[amitofo @amitofo_]

@MOHAMMEDAL1982 @LunaClassWorld @LunaClassicHQ I'd go with 0.0000075-0.000009 (*σ´∀`)σ

[amitofo @amitofo_]

@CPlebeian18833 @clashreport hhhhhhhhhhhhh

[amitofo @amitofo_]

@wublockchain12 信号铃已经在疯狂鸣叫了

[amitofo @amitofo_]

@wublockchain12 lol

[amitofo @amitofo_]

@Atacan23210762 @ellio96057411 😂😂

[amitofo @amitofo_]

@Noas_Dad @binance @cz_binance 😂

[amitofo @amitofo_]

@playground777 @hidden_cosoo 😂

[amitofo @amitofo_]

@TerraHaberTr 这个燃烧速度想让它上1刀基本就是做梦，看后期政策了

[amitofo @amitofo_]

@terra_army 所以现在跌成这样了吗

[amitofo @amitofo_]

@kcharlespjr @DrWhaleReal yeah, exactly😂

[amitofo @amitofo_]

@Mary332211 @DrWhaleReal exactly

[amitofo @amitofo_]

@0xMilkRabbit hashkey和osl怎么样

[amitofo @amitofo_]

@AndrewBolis ai

[amitofo @amitofo_]

wow

[Crypto News Portal @TerraHaberTr]

TerraformLabs' legal proceedings will be completed. Claims for lost cryptocurrency have been received and compensation will be paid. Following this, US exchanges will list $LUNC and $USTC. BinanceUS and Coinbase will be among the first. The first to list wins...

#TerraClassic #LUNC @BinanceUS @coinbase

[amitofo @amitofo_]

@langlangdae777 @jacketchen13 @binance @binancezh hk账户可以吗？

[Haotian @tmel0211]

一觉睡醒，好多朋友让我看 #manus，号称一款全球真正意义上通用的AI Agent，能够实现独立思考并规划执行复杂任务，并交付完整结果。听起来非常Cool，但除了很多朋友圈焦虑的要失业的声音之外，它会给web3 DeFai场景大爆发带来什么？以下，谈谈我的思考：

1）约一个月前OpenAI 推出了同类别产品Operator，AI可以在浏览器中独立完成包括餐厅预订、购物、订票、外卖订餐等任务，用户可以可视化进行监督，并随时接管控制权。

这套Agent的出现并没有多少人讨论，原因在于它是单一模型驱动，还是工具调用的那套框架，用户一想到关键决策还需要干预进行就失去了依赖其执行任务的想法。

2）manus表面看似也差不太多，只是多了不少应用场景，包括筛选简历，研究股票、购买房产等等，但实际上是背后的框架和执行系统差异，Manus由多模态大模型驱动，并创新性的采用了多重签名系统。

简而言之，AI要模仿人执行（计划-执行-检查-行动）的PDCA循环行动，将由多个大模型共同协作完成，每个模型专注于特定环节，既能降低单个模型执行任务的决策风险，又能提高执行效率。所谓“多重签名系统”其实是多模型协作的决策验证机制，通过要求多个专业模型的共同确认来保证决策和执行的可靠性。

3）如此一比较，manus的优势显然就凸显出来了，加上视频Demo里展现的一系列操作体验，让人确实有一种非凡的体验感。但客观来说，Manus对Operator的迭代创新只是个开始，还达不到颠覆性革命意义。

关键点就在于其执行任务的复杂程度，以及非统一标准用户input Prompt进入之后大模型的容错率和交付结果成功率定义。要不然，顺着这套创新，web3的DeFai场景是不是立马就可以成熟应用了？显然，还做不到：

比如：DeFai场景下Agent要执行交易决策，需要有一个Oracle层的Agent负责链上数据收集和验证，并进行数据整合分析，还要实时监控链上价格捕捉交易机会，这个过程对实时分析有很大挑战，有可能一秒前还有用的交易机会，等Oracle大模型传输给交易执行Agent之后，交易机会就不存在了（套利窗口）；

这其实暴露了这类多模态大模型做执行决策的一个最大软肋，如何联网、触链调取分析Real-Time级别的数据，并从中分析出交易机会，然后进行交易捕捉。联网环境其实还好，很多电商网站的订单价格并非实时变动，不容易给整个多模态协作造成巨大动态平衡困扰，要是在链上，这样的挑战几乎无时无刻存在。

4）所以，整体上manus的出现确实会在web2领域掀起一波朋友圈焦虑，毕竟很多重复性高的文职和信息处理工种可能会面临被AI取代的风险。但让他们焦虑他们的。

这事放在web3对DeFai应用场景的推动作用我们得客观认识：

必须得承认：意义肯定重大，毕竟它提出的LLM OS以及Less Structure more intelligence理念、尤其是多重签名系统会给web3拓展DeFi和AI的结合有很大的启迪思路。

这其实纠正了大部分DeFai项目的重大误区，不要上来就想依靠一个大模型实现AI Agent自主化思考+决策等复杂目标，这在金融场景下，根本不切合实际。

真正DeFai愿景的实现需要解决单体AI模型能力上限、多模态交互协作原子性保证、多模态系统统一资源调度和支配、系统容错和故障处理机制等等复杂问题。

比如：Oracle层 Agent，负责收集链上数据和分析，并监控价格，形成有效数据源；

决策层Agent，根据Oracle喂过来的数据进行分析和风险评估，并制定一套决策和行动方案；

执行层Agent，根据决策层给出的多种方案，并考虑实际情况进行执行，包括gas费用优化、跨链状态、交易排序冲突等等。

唯有这一系列的Agent都同步强大，并有一个庞大的系统框架落定，一个真正的DeFai革命才会掀起。

Note：以下附带 manus视频Demo，可配合以上的思考查看👀。觉得内容受用，麻烦帮忙一键三连支持下，谢谢！

[Gavin Baker @GavinSBaker]

1) DeepSeek r1 is real with important nuances. Most important is the fact that r1 is so much cheaper and more efficient to inference than o1, not from the $6m training figure. r1 costs 93% less to *use* than o1 per each API, can be run locally on a high end work station and does not seem to have hit any rate limits which is wild. Simple math is that every 1b active parameters requires 1 gb of RAM in FP8, so r1 requires 37 gb of RAM. Batching massively lowers costs and more compute increases tokens/second so still advantages to inference in the cloud. Would also note that there are true geopolitical dynamics at play here and I don’t think it is a coincidence that this came out right after “Stargate.” RIP, $500 billion - we hardly even knew you.

Real: 1) It is/was the #1 download in the relevant App Store category. Obviously ahead of ChatGPT; something neither Gemini nor Claude was able to accomplish. 2) It is comparable to o1 from a quality perspective although lags o3. 3) There were real algorithmic breakthroughs that led to it being dramatically more efficient both to train and inference. Training in FP8, MLA and multi-token prediction are significant. 4) It is easy to verify that the r1 training run only cost $6m. While this is literally true, it is also *deeply* misleading. 5) Even their hardware architecture is novel and I will note that they use PCI-Express for scale up.

Nuance: 1) The $6m does not include “costs associated with prior research and ablation experiments on architectures, algorithms and data” per the technical paper. “Other than that Mrs. Lincoln, how was the play?” This means that it is possible to train an r1 quality model with a $6m run *if* a lab has already spent hundreds of millions of dollars on prior research and has access to much larger clusters. Deepseek obviously has way more than 2048 H800s; one of their earlier papers referenced a cluster of 10k A100s. An equivalently smart team can’t just spin up a 2000 GPU cluster and train r1 from scratch with $6m. Roughly 20% of Nvidia’s revenue goes through Singapore. 20% of Nvidia’s GPUs are probably not in Singapore despite their best efforts. 2) There was a lot of distillation - i.e. it is unlikely they could have trained this without unhindered access to GPT-4o and o1. As @altcap pointed out to me yesterday, kinda funny to restrict access to leading edge GPUs and not do anything about China’s ability to distill leading edge American models - obviously defeats the purpose of the export restrictions. Why buy the cow when you can get the milk for free?