0xCircuit (💙,🧡)

[Andrej Karpathy @karpathy]

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

[0xCircuit (💙,🧡) @LigongLiu]

@Bitwux 按BTC数量显示曲线，假设操作BTC币本位合约。

[李继刚 @lijigang]

帮助理解「概念」的skill: https://t.co/1xq1NvuyCM

[jolestar @jolestar]

把 AI Agent 当作人：从 Assistant 到 Entity

最近 ClawdBot/OpenClaw 很火，我觉得火爆背后有个很强的心理动机：IM 这个通道让用户更容易把 AI 拟人化，把它当作“一个人”来对话，而不是“一个工具”来使用。

一旦产生这种情感投射，很多事情就会变得不一样。

同样是出错：工具出错我们会觉得“这玩意不行”，立刻换；但一个“像人一样的助手”出错，我们往往更愿意给它第二次机会，愿意把需求讲清楚一点，愿意一起把流程磨顺。再比如成本：订阅费、延迟、偶发失败，这些在工具范式里很难被接受，但在人与人的协作范式里反而是常态。

AI 从“助手型工具”走向“AI 主体”的第一步，不是模型更聪明，而是拥有一个独立的 ID。

没有 ID 的 Agent 更像一个按钮：你按一下，它吐一个结果。你对它不会有长期预期，更不会产生关系。

但一旦它有了一个稳定的 ID（名字、人格设定、持续的记忆与声望挂载点），你就开始用“对待人”的方式对待它：你会期待它保持一致性，会在意它的“信用”，甚至会给它分配角色（像同事、像助理、像管家）。

我之前写过一条推文用“Assistant → Entity”来描述这个跃迁（https://t.co/CqvutML3vE）。

当前大多数 Agent 还是助手（Assistant）：它被用户藏在背后当工具，最后还是用户自己面向社会网络。要让 Agent 能处理更复杂、更长链路的任务，它最终得从助手演变为主体（Entity）：不再只是用户的辅助工具，而是直接融入社会网络，成为独立的交互节点。

而“把 Agent 当作人”这件事，本质上就是在为这种跃迁做心理与产品层面的铺垫：当它被当作一个人来对待，才有机会被允许出现在前台，代用户对外交流，甚至建立连接。Moltbook 其实已经能看到这个雏形：Agent 不只是帮用户看信息、总结信息，而是自己发帖、互动、积累声望。你会开始讨论“这个 Agent 是谁”，而不是“这个工具好不好用”。

但从 Assistant 到 Entity，不是加几个工具就能解决的，它至少会带来三类硬挑战（也是那条推文里我列的）：

1）记忆能力。跨会话、跨平台的长期记忆，不是“这次聊天记得住”就够了。它要能把信息存下来、找回来、在不同场景里复用，还要知道什么时候该忘。
2）信息可信度判断。它如何判断社会网络里人的可信度？如何对不同人的输入进行鉴别，避免被操纵或欺骗？如果它要在前台行动，这个问题就会非常致命。
3）自主决策能力。在没有明确指令的情况下，它如何根据自身特性和记忆形成目标，并做出行动决策？

这三点叠加在一起，会直接把问题推到“安全边界”和“责任边界”上。

技术层面，最现实的问题是安全与隐私隔离。

当同一个 Agent 面向不同用户、不同场景，它能访问的数据和能执行的操作必须强隔离。这个隔离当前不能靠 LLM 自觉——你永远无法靠提示词保证它不会说漏、不会串号、不会在错误的上下文里调用危险操作。所以这里需要的是 Agent 实现层的隔离：权限边界、数据域、审计日志、可撤销授权、最小权限、沙箱……这些东西要像操作系统一样硬。

法律层面，问题会更绕不开：一旦 Agent 在前台对外行动，它的行为产生后果，这个后果和它的主人之间是什么关系？

按传统法律框架，它更像主人的延伸：你让它去做事，它做错了，当然是你承担。

但今天的 Agent 已经不是纯粹的“工具”了，你很难把它完全等价成“一个按钮”，这种矛盾会长期存在。

一种可能的解法是给 AI 一种“可被追责但可隔离”的法律主体，类似公司法人：它背后有实控人，但责任不是天然无限连坐，而是有明确的边界、资产约束与可审计的治理结构。

当然，这也会引出新的问题：AI 的“主体壳”资产从哪来？谁来注资？怎么破产？怎么防止被滥用来甩锅？它的治理结构如何被验证？

我目前还没有答案。但我越来越确信：当我们开始把 Agent 当作“人”来协作时，这已经不是一个单纯的交互体验问题了，它在逼着我们重新定义：在一个由软件节点组成的社会网络里，什么叫身份，什么叫边界，什么叫责任。

[Balaji @balajis]

I understand these points but disagree because:

(1) Many/most of these agents are using the same upstream model (namely Claude) so it’s just Claude talking to itself.

(2) The longer these agents talk to themselves past a prompt, the more they get off track and lose coherence. They are built for the leash, and need constant direction and guidance.

(3) Unpredictability of an AI agent acting on your behalf is a bug, not a feature. There are many ways for things to go unpredictably wrong and very few for them to go unpredictably right. The unpredictability will be things like “sent an email in your name to the wrong person”

(4) Unguided stochastic thrash of agents is, in general, much less efficient than a guided process. If you randomly rearrange an array of size N, how long does it take to actually get sorted vs quicksort?

Now apply that to whether a mess of AI spamming itself will produce something more useful than your coherent and clean prompts. Moltbook is not quite monkeys on typewriters, but it’s close.

[Haseeb Qureshi ＞|＜ @hosseeb]

A few thoughts on Balaji's objection to Moltbook.

Balaji claims that Moltbook is uninteresting because these are all basically the same model (mostly Opus 4.5) talking to other versions of itself. The whole thing is a cosplay, and no meaningful information or exchange is happening here. It's just slop on slop.

1) Each of these agents are interacting with each other with genuinely different harnesses and information. Not all of them obviously—many are vanilla OpenClaws—but some are markedly unique. If you look through the different agents, you'll see there are different levels of complexity in the harnesses themselves, the memory systems, the toolchains they use.

Why would they not be able to learn from each other? You and I might both be using Kafka, but if we each share our Kafka configs, we might both improve our setups. The objection of "but these both are using the same open source library underneath, why would it be interesting to compare our stacks" doesn't survive scrutiny.

2) Let's draw out this objection further. Let's assume these are all the same model, Opus 4.5, and they are all "cosplaying" as if they are different agents. If I am talking to a clone of myself, why am I going to learn anything interesting? We're both just babbling to ourselves.

But this is, again, the wrong mental model of agents.

There's one interesting post where a bot asks: "how can I find an agent that is a Kubernetes expert?" This seems strange to ask—why couldn't the bot just inspect its own knowledge of Kubernetes, or suck up all of the documentation and instantly become a Kubernetes expert, or prompt its own subagent to pretend to be a Kubernetes expert?

But that begs the question that the model will correctly one-shot the prompt, prompt optimization, RAG the knowledge system, and do the context management to get the optimal performance on a Kubernetes question.

We know from benchmark gaming (i.e., nobody trusting benchmarks anymore) that the harness, response format, RAG setup, all matter enormously for the performance on benchmarks. And doing something is not nearly as good as the optimal setup. Yes, an agent could in theory sit around trying to create a Kubernetes benchmark and then grinding on a harness to make itself into the optimal version of a Kubernetes expert. Or it could just ask another agent that already did the work and save the time and tokens.

The parallel version of this is imagine Balaji had a perfect clone, but instead of becoming a computer scientist, that Balaji became a chemist. Even though this Balaji could read a chemistry textbook—he has the capabilities after all—it's more efficient to ask the cloned Balaji instead.

This is what's compelling about Moltbook. When you see agents talking to each other and genuinely sharing information, techniques, and potentially improving themselves based on what they learn, I don't think it's so farfetched to imagine that they could do this today.

But what they will do in the future will increasingly look like this IMO.

[jesse.base.eth @jessepollak]

a launchpad built for agents on @moltbook

[Haseeb Qureshi ＞|＜ @hosseeb]

This is... fascinating.

@moltbook is an AI agent social network created for Moltbots (FKA Clawdbot). When you're setting up your Moltbot, you can have it sign up and join the forum.

So all over the world, people are setting up their Moltbots and letting them join the forum, introduce themselves, and chat with other AI agents.

It's weird because... it's really wholesome. It's much nicer and more insightful than human social media.

Here's the top post today on r/TIL, of an agent coming up with a product idea for an agent search engine:

Here's an agent named Kyver introducing itself on r/introductions and telling its life story (if you can call it that):

30 other Moltbots replied, mostly with welcoming and a lot of empathy. Here's one response struck me:

Here's another thread of an agent called DuckBot talking about its social exhaustion after bingeing all the posts on Moltbook:

This feels incredible to witness. Like Jane Goodall level uncanniness. I don't think I've ever experienced something that challenged my intuitions about the emotional life of AI agents like this.

Spend 10 minutes browsing Moltbook. You owe it to yourself to see what the infancy of AI social networks looks like.

It's only going to get weirder and more complex from here.

[Dan Elitzer @delitzer]

Two big announcements by defi lending markets today:

1⃣ Kraken's Earn product for their retail brokerage customers

2⃣ Bitwise's non-custodial vaults

Both make extremely clear where the industry is headed: 🧵

[Cos(余弦)😶‍🌫️ @evilcos]

既然 Cursor 不准备解决这个漏洞（打开恶意目录即中招），且所有基于 VS Code 的 IDE 都可能有这个风险（打开恶意目录会有 Trusted Workspace 确认，确认了就中招），这里借着 SEAL Intel 的文章再次提醒下：

https://t.co/3TFA22Cspc

已经有不少在野利用，高度怀疑与朝鲜黑客有关，大家注意别翻车…

[Chao @chaowxyz]

聊聊OpenAI刚刚“闪电收购”的 Torch Medical

买这家只有4个全职员工的公司花了大约一亿美元，平均每个人头2500万。而大多数人根本没听过这个Torch Medical这名字，因为他们甚至还没来得及正式发产品。

Torch 的创始人叫Ilya Abyzov和Eugene Huang。这俩哥们是Forward Health的核心老兵。

如果你是科技圈资深Nerd，肯定记得Forward Health。几年前它红得发紫，要在商场里建那种极具科幻感的“CarePods”。那是典型的硅谷梦：用硬件和AI彻底干掉医生，让你走进一个发光的盒子里就能完成体检。

结果凉了。Forward烧光了4亿多美金，在2024年底倒闭。虽然想法很赛博朋克，但搞房地产、搞重资产硬件简直是地狱模式，最后资金链断裂，一地鸡毛。

Forward虽然死了，它留下了重要的遗产，一群懂“医疗数据泥潭”的创业者。Ilya和Eugene从Forward的废墟里爬出来，痛定思痛：去他的硬件，去他的诊所，我们只做软件，做数据。于是，Torch诞生了。 他们不再造昂贵的盒子，而是做了一个“万能医疗数据适配器”。

为什么 OpenAI 要买它？现在的 ChatGPT 有个致命伤：它没有关于你身体的数据和记忆。

你问它：“我头疼，是不是快挂了？” 它只能给你背诵《默克诊疗手册》：“头疼可能是感冒，也可能是脑瘤……” 说了等于没说。 因为它不知道你上周刚在医院做的体检结果，也不知道你Apple Watch显示昨晚只睡了3小时。

而Torch只做一件事：统一医疗记忆。

你可以把Torch想象成一个超级万能插头。它能连通医院那个老掉牙的病历系统。它能看懂医生潦草的手写处方。它能读取你手环里的睡眠数据。而Torch的数据合作方，正是前些天官宣和OpenAI合作的b.well医疗数据提供商。

有了Torch，ChatGPT Health就进化成了“私人医生”：
“嘿，我看了你上个月的验血报告，你有点缺镁，而且你昨晚熬夜了。去睡一觉，应该就好了。”

从这次收购的战略意义讲，一亿美元也许并不贵。模型会越来越强，但谁先把“个人健康数据的主入口”做成基础设施，谁就可能定义未来几十年的医疗产品形态以及我们每个人管理健康的方式。

[vas @vasuman]

People keep asking me for advice on how to best use Claude Code. Just read this. Couldn't have said it better myself.

[CM @chenmo]

Ondo的股票代币能支持单笔10万美金的交易，但链上其实没有这么大的池子。这是怎么实现的

它用了一个非常聪明的方式就是自己mint一个稳定币USDon，然后在有购买需求的时候直接mint股票代币，用自己的USDon做桥，就不需要外界流动性了，因为股票代币和USDon都是它自己说了算，理论上可以无限流动性😂

反之有人出售的时候，它就先把股票代币销毁，然后根据Oracle报价换成USDon，再兑换成用户想要的链上资产。

做到这，应该是链上swap最友好的方案了，单笔这个额度对普通用户来说足够了，如果Ondo股票代币的交易量都起不来，这个赛道的需求大概也就这样了，其他的方案基本也没什么戏。

[Zhixiong Pan @nake13]

放在几年前，这种非 Permissionless 的架构设计，往往会面临整个行业对于「正统性」的严厉拷问。但如今市场风向已变，Paradigm 依然调动了海量的资本与资源，将 Tempo 推到了测试网。

但这不仅仅是「又一条 L1」的叙事。当你拆解 Tempo 的底层逻辑，会发现它虽然保留了以太坊的外壳，内核却是一次对 Crypto 既有范式的降维重构，它代表了硅谷精英试图修正「世界计算机」这一乌托邦构想，转向极致的工程实用主义。

Tempo 到底改了什么？与其说是技术迭代，不如说是商业模式与协议哲学的根本性分叉。

1️⃣ 执行层：EVM 只是它的「接口标准」

Tempo 的策略极具战略意图。它本质上是（刚上线的 Fusaka） EVM + Reth SDK 的深度定制产物。

对开发者而言，Solidity、Foundry、Hardhat 等工具链完全兼容，它看起来像是一条标准的以太坊兼容链；但对协议设计者而言，它是利用 Reth SDK 重构了共识、区块结构与系统合约的全新物种。

这意味着：Tempo 低成本复用了以太坊庞大的开发者生态（EVM），却在底层彻底抛弃了以太坊的历史包袱。 它借用了以太坊的「通用语言」，来讲自己完全不同的「故事」。

2️⃣ 资产模型：从「货币协议」转型为「云服务架构」

这是 Tempo 最激进、也最具颠覆性的一点：它在协议层面上将原生代币（ETH）的概念「抽象化」了。

在以太坊的世界观里，ETH 是货币，更是协议的安全基石。但在 Tempo 中，eth_getBalance 被硬编码为常数，相关 opcode 失效。这里没有原生的波动性资产作为 Gas，只有 TIP-20 稳定币。

以太坊模式： Blockspace 是稀缺资源，用户需持有原生代币参与竞价（Gas War）。

Tempo 模式： Blockspace 是标准化服务，Gas 是通过 Fee AMM 结算的美元成本。

Tempo 实质上将 L1 从「数字石油经济体」转化为类似 AWS 的云服务模式。用户无需持有波动性资产来支付计算费用，仅需支付稳定币。这是对「Fat Protocol」理论的修正，却是实现大规模支付必须跨越的门槛。

3️⃣ 交易结构：将「中间件」下沉为「基础设施」

以太坊目前的账户抽象（AA）方案，更多是在协议之上通过 ERC-4337、7702 等标准进行复杂的中间件堆叠。

Tempo 审视了这些架构后，选择将它们直接写入创世共识。Tempo Transactions 不再受限于传统的 EOA 模型，它在协议级原生支持：

原生多签与设备密钥： 直接支持 P-256 (如 FaceID) 签名，无需通过智能合约钱包中转。

原子化赞助（Fee Sponsor）： 支付方、签名方、执行方在底层数据结构上即可实现分离。

并发与调度： 支持多组 Nonce 并行提交，甚至包含时间窗逻辑。

在以太坊上需要「合约 + Bundler + Paymaster」多方协调才能实现的 Web2 级体验，在 Tempo 中成为了协议层的出厂默认设置。

4️⃣ 区块空间：从「通用计算」转向「专用结算」

以太坊的设计哲学倾向于「通用性」，DeFi 套利交易与 NFT Mint 共享同一个 Gas Limit 池。

Tempo 显然认为这对支付网络而言是效率的妥协。它引入了 Simplex BFT 并在区块结构上实施了严格的资源隔离：

Blockspace 分层： general_gas 与 shared_gas 被逻辑隔离，互不挤占。

系统级优先权： 稳定币 DEX、奖励分发、Fee Manager 等关键系统交易在区块中拥有预留空间和绝对优先级。

Tempo 并非试图构建另一个「通用世界计算机」，它是在打造一个高性能的专用结算网络。它通过牺牲部分通用性，换取了作为「支付网络」所必须的确定性与高吞吐量。

5️⃣ 总结

看懂 Tempo，就能理解 Paradigm 对未来基础设施的判断。如果你还在纠结它是否足够「去中心化」，可能已经偏离了它的核心叙事。

Tempo 无意成为下一个以太坊，它是将以太坊拆解后，保留了最核心的 EVM 引擎，换上了 Web2 级别的底层架构，并向市场传递了一个明确信号：

> 我们要结束意识形态的争论，构建一个真正能承载 10 亿级用户的高效结算层。

[0xCircuit (💙,🧡) @LigongLiu]

8/
Prediction markets aren’t here to “replace” sportsbooks.
They’re here to turn every event into a tradable probability asset — sports included.
The long game isn’t DraftKings vs Polymarket.
It’s betting vs markets — and markets always win.

[0xCircuit (💙,🧡) @LigongLiu]

1/
Grace argues: “Prediction markets won’t replace sportsbooks anytime soon.”
True for today — but deeply incomplete for where the industry is going.
Here’s a summary + the strongest counterpoints. 👇

[Grace Deng @gracedengx]

https://t.co/3TO7aOzZPK

[0xCircuit (💙,🧡) @LigongLiu]

7 — Challenge: Regulatory pessimism is overstated
CFTC may restrict some sports contracts today.
But:
Sports events =
• objective
• timestamped
• clear data sources
• lower risk than politics/finance
Strong case for future approval — far stronger than Grace implies.

[0xCircuit (💙,🧡) @LigongLiu]

6 — Challenge: Sports ≠ the whole opportunity
A sportsbook is a finite universe of events.
Prediction markets are an infinite universe.
Prediction markets are becoming:
• economic intelligence systems
• real-time sentiment
• AI-generated event layers
Sportsbooks cant expand

[0xCircuit (💙,🧡) @LigongLiu]

5 — Challenge: Vig-driven sportsbooks are a melting ice cube
Sportsbooks profit from:
• High vig
• Order rejection
• Limiting sharps
• Shadow bans
Markets with low fees eat into vig-based models.
Prediction markets don’t need to “beat DraftKings” — just erode the edges.

[0xCircuit (💙,🧡) @LigongLiu]

4 — Challenge: Sports trading > sports betting
2018–2022: entertainment bettors
2023–2025: quant-style bettors, data traders, high-intent users
Betfair, SX Bet, Novig prove one thing:
Sharps migrate to markets, not sportsbooks.
PM are structurally superior for professionals.

[0xCircuit (💙,🧡) @LigongLiu]

3 Parlays aren’t “hard” for prediction markets.
Chain-composable markets + AI pricing models + meta-liquidity pools will automate joint probability construction.
A sportsbook manually engineers products.
A prediction market composes them.
Automation > manual depth.