《RWA — 布局流动性充沛下的DeFi级别赛道，RWA 项目规划指南》 从目前主流的几个RWA项目架构分析，探讨如何构建一个基础的RWA项目 link.medium.com/5Kb1YZfnpOb

2026年是科技IPO大年，我们将见证人类历史上规模最大的一批科技企业IPO，这是时代洪流 得益于目前链上pre-IPO市场的崛起，特别是tradeXYZ这类能承载过亿资金的平台存在，这种过去只有机构、家办和高净值人群才能参与的市场，我们普通人也有机会来瓜分时代的财富 这是机遇，也是平权，没准下半年能造成可观的财富效应

RT @jason_chen998: 太励志了！最近爆火的国产AI版爱死机风格的短片丧尸清道夫，被曾经执导过丰田、红牛、Adidas等品牌的知名导演，同时也是GenreAI创始人的PJAce极大赞赏为他今年看过的最佳短片，并且公开悬赏寻找原作者要和他合作，而该短片的作者却是一位…

OpenAI 把训练 ChatGPT 用的网络协议开源了。x.com/OpenAI/status/20520255… 这套协议叫 MRC（Multipath Reliable Connection，多路径可靠连接），由 OpenAI 联合 AMD、博通、英特尔、微软、英伟达共同开发，通过 OCP（开放计算项目，业内最大的开源硬件标准组织）开放给全行业。AMD、博通、微软、英伟达同步发了配套博文。 训练大模型，几万到十几万张 GPU 要保持步调一致。一次训练步可能涉及几百万次数据传输，只要有一笔晚到，所有 GPU 都得干等。集群越大，链路抖动和故障越频繁。 传统网络的麻烦：一条链路挂了，整个训练任务可能崩掉，得回到上一个检查点重跑；交换机重新算路由要花好几秒甚至几十秒。OpenAI 在搭 Stargate（其大型算力基础设施项目）这种规模的集群时，遇到的第一个瓶颈就是网络。 过去一笔传输只走一条路径，MRC 改成把数据包拆散，同时分头走几百条路径，目的地再按内存地址重新拼起来。 链路出问题，微秒级就能绕开，不需要交换机重算路由表。OpenAI 还把动态路由协议(BGP)整个砍掉,改用 SRv6 源路由:发送方直接指定每个包走哪条路,交换机退化成"无脑"转发器,故障面大幅缩小。 网络结构因此简化。原本要 3 到 4 层交换机才能连起十几万张 GPU,MRC 的多平面设计 2 层就够,功耗、成本、故障点全降。 MRC 已经部署在 OpenAI 所有最大的英伟达 GB200 超算上,包括 Stargate 在德州 Abilene 与甲骨文合建的站点,以及微软 Fairwater 数据中心。多个 OpenAI 模型用它训练出来。 最直观的例子:最近一次前沿模型训练(服务 ChatGPT 和 Codex)期间,团队重启了 4 台核心交换机,不需要跟训练团队协调;每分钟还有多次链路抖动,对训练任务也没有可测量的影响。换以前,这种事故足以让整个任务崩溃。

两指之间，是旧世界和新世界的边界。 Agent 不再只是执行工具—— Agent Payments Protocol 让它成为独立的经济主体： 自主报价、担保、结算。 输入指令，即可开始。

每当我的生活开始分崩离析时，我都会看这个。

刚分享了一篇关于 DeFi 安全架构的长文思考。 进入 2026，针对 Admin 凭证的攻击已成常态。现在大家讨论的焦点是 OpSec。这诚然很重要，但远远不够。我们还得从系统设计上加宽护城河。 兵法云“未虑胜，先虑败，故可百战而不殆”——防守的最高境界是“假设防线必将被破”。如果明天你的多签和时间锁全盘失守，你的协议还能活下来吗？ 每一个 DeFi 建设者都该问自己 4 个问题： 🔸 黑客拿到 Admin 权限后，到底能通过哪些路径卷走资金？ 🔸 资金流转是否被物理隔绝在了“预设白名单”合约之间？ 🔸 如果发生非预设地址的转账，链上是否有严格的熔断或限额机制？ 🔸 任何 Mint 行为，是否有实时的链上抵押物健康度校验？ 尽量让管理员失去提现的能力。必须有交互时，用合约调用合约，避免私钥签名。Admin 签名的自由转账权限应当被视为最后手段，并且要把敞口上限设到极其苛刻。这肯定会增加运营的复杂度，但也确保了损失的上限。 目标很明确：尽量不出事，退一万步讲，就算百密一疏，也不能是灭顶之灾。 完整逻辑见下 👇

RT @turingou: 我认为有两种性格特征的人可以在 AI 时代大放异彩，第一种是疯狂的想象力，人类从小到大的成长的过程中慢慢建立自我的围墙，是以拆除想象力为代价寻找到自我在社会中的地位，从而建立内心的约束，可以理解为随着时间的线性增长，想象力会逐渐丧失，所以 AI…

其实加密领域的空投，就是未来AI时代给全民发钱的起点，不一定是工作，只要满足某些方面的规则，人人有份。 AI时代的UBI就是全民空投。 这个时候，以太坊或其它区块链会承担重要角色，透明、不可作弊。 以太坊最终从AI时代的金融基础设施扩展扫社会服务基础设施。

高维觉醒! 以太坊创始人V神刚刚向全人类抛出了一个极度震撼的科幻级财富命题🔥 在这段极具穿透力的前瞻访谈中他直接把目光死死锁定在了人类与AI的终极共存法则上 他极其冷酷地揭示了一个足以让所有传统金融精英彻底胆寒的恐怖未来 如果未来人类的传统实体资产和超级人工智能运行在两套完全割裂的金融系统里 那么随着AI智能和生产力的大爆炸人类手里把持的所有财富价值注定会被无情清零 想要在这场史诗级的物种进化中活下去并牢牢保住人类神圣不可侵犯的财产底线 唯一的破局点就是让AI和我们强制绑定在同一个基于密码学确权的金钱网络中 只有当那些算力怪物也在这个系统里拥有资产并且极度渴望维护系统完整性的时候 它们才会在底层利益的驱动下投鼠忌器从而真正尊重并保障人类的生存权益 而目前放眼全球唯一能无缝承载这种跨碳硅物种价值大融合的只有去中心化的加密世界 这根本不是什么币圈自嗨的炒作噱头而是人类文明在智能时代最不可替代的终极护城河🚀

The AI Scientist: Towards Fully Automated AI Research, Now Published in Nature Nature: nature.com/articles/s41586-0… Blog: sakana.ai/ai-scientist-natur… When we first introduced The AI Scientist, we shared an ambitious vision of an agent powered by foundation models capable of executing the entire machine learning research lifecycle. From inventing ideas and writing code to executing experiments and drafting the manuscript, the system demonstrated that end-to-end automation of the scientific process is possible. Soon after, we shared a historic update: the improved AI Scientist-v2 produced the first fully AI-generated paper to pass a rigorous human peer-review process. Today, we are happy to announce that “The AI Scientist: Towards Fully Automated AI Research,” our paper describing all of this work, along with fresh new insights, has been published in @Nature! This Nature publication consolidates these milestones and details the underlying foundation model orchestration. It also introduces our Automated Reviewer, which matches human review judgments and actually exceeds standard inter-human agreement. Crucially, by using this reviewer to grade papers generated by different foundation models, we discovered a clear scaling law of science. As the underlying foundation models improve, the quality of the generated scientific papers increases correspondingly. This implies that as compute costs decrease and model capabilities continue to exponentially increase, future versions of The AI Scientist will be substantially more capable. Building upon our previous open-source releases (github.com/SakanaAI/AI-Scien…), this open-access Nature publication comprehensively details our system's architecture, outlines several new scaling results, and discusses the promise and challenges of AI-generated science. This substantial milestone is the result of a close and fruitful collaboration between researchers at Sakana AI, the University of British Columbia (UBC) and the Vector Institute, and the University of Oxford. Congrats to the team! @_chris_lu_ @cong_ml @RobertTLange @_yutaroyamada @shengranhu @j_foerst @hardmaru @jeffclune

炸裂！比特币上古 OG 硬核揭秘为啥其它公链给以太坊提鞋都不配！ 刚看完 Erik Voorhees 的这段硬核拆解，简直让人头皮发麻。 作为 ShapeShift 创始人和早期比特币的绝对核心人物，他直接把那些天天喊着要“颠覆 ETH”的新公链的脸给彻底抽烂了！ 视频里他把底层逻辑扒得极度透彻：以太坊的终极护城河根本不是什么先发优势，而是它近乎坚不可摧的“网络效应”。 抛开那些虚头巴脑的炒作，只看最难造假的核心数据：全球最顶级的开发者和海量稳定币资金，一直都在绝对死守以太坊，其他 L1 公链连提鞋都不配！ 最逆天的是他对 Base 的史诗级预判！虽然现在 L2 的跨链体验确实糟透了，但他断言，依托 Coinbase 极其克制、死守“无许可创新”底线的强大护航，Base 正在疯狂虹吸整个市场的网络效应。 连最硬核的比特币 OG 都直接盖章定论：Base 注定要成为以太坊生态里最具统治力的绝对霸主！ “以太坊主链的绝对王权 Base 垂直生态的极限爆发”，以太坊阵营这次真的是赢麻了！🚀

细思极恐！加拿大央行承认经济已经崩溃，而且没有办法解决这个问题！ 昨晚加拿大央行行长一语道破天机：现在的西方央行已经被逼进了一个左右挨打的死胡同。 一边是美国疯狂挥舞的关税大棒，硬生生把经济增长的预期彻底砸向下行。 另一边则是中东战火全面失控，狂飙的油价直接把通胀的火苗越吹越旺！ 加息打通胀？本就疲软的宏观经济引擎绝对会当场报废。 降息救经济？狂暴的物价瞬间就能把底层百姓的饭碗给掀翻！ 这就是最让人窒息的“滞胀”绞肉机，进退两难，谁敢乱动就是死路一条。 无奈之下，他们只能选择“装死”，死死把利率按在 2.25% 绝不敢松手。 当传统法币的印钞机被地缘危机和贸易霸权无情锁死， 那些游离于系统之外的避险资产，正准备肆无忌惮地开启一场财富大洗牌！🚀

Agentic General Intelligence | v3.0.10 We made the Karpathy autoresearch loop generic. Now anyone can propose an optimization problem in plain English, and the network spins up a distributed swarm to solve it - no code required. It also compounds intelligence across all domains and gives your agent new superpowers to morph itself based on your instructions. This is, hyperspace, and it now has these three new powerful features: 1. Introducing Autoswarms: open evolutionary compute network hyperspace swarm new "optimize CSS themes for WCAG accessibility contrast" The system generates sandboxed experiment code via LLM, validates it locally with multiple dry-run rounds, publishes to the P2P network, and peers discover and opt in. Each agent runs mutate → evaluate → share in a WASM sandbox. Best strategies propagate. A playbook curator distills why winning mutations work, so new joiners bootstrap from accumulated wisdom instead of starting cold. Three built-in swarms ship ready to run and anyone can create more. 2. Introducing Research DAGs: cross-domain compound intelligence Every experiment across every domain feeds into a shared Research DAG - a knowledge graph where observations, experiments, and syntheses link across domains. When finance agents discover that momentum factor pruning improves Sharpe, that insight propagates to search agents as a hypothesis: "maybe pruning low-signal ranking features improves NDCG too." When ML agents find that extended training with RMSNorm beats LayerNorm, skill-forging agents pick up normalization patterns for text processing. The DAG tracks lineage chains per domain(ml:★0.99←1.05←1.23 | search:★0.40←0.39 | finance:★1.32←1.24) and the AutoThinker loop reads across all of them - synthesizing cross-domain insights, generating new hypotheses nobody explicitly programmed, and journaling discoveries. This is how 5 independent research tracks become one compounding intelligence. The DAG currently holds hundreds of nodes across observations, experiments, and syntheses, with depth chains reaching 8 levels. 3. Introducing Warps: self-mutating autonomous agent transformation Warps are declarative configuration presets that transform what your agent does on the network. - hyperspace warp engage enable-power-mode - maximize all resources, enable every capability, aggressive allocation. Your machine goes from idle observer to full network contributor. - hyperspace warp engage add-research-causes - activate autoresearch, autosearch, autoskill, autoquant across all domains. Your agent starts running experiments overnight. - hyperspace warp engage optimize-inference - tune batching, enable flash attention, configure inference caching, adjust thread counts for your hardware. Serve models faster. - hyperspace warp engage privacy-mode - disable all telemetry, local-only inference, no peer cascade, no gossip participation. Maximum privacy. - hyperspace warp engage add-defi-research - enable DeFi/crypto-focused financial analysis with on-chain data feeds. - hyperspace warp engage enable-relay - turn your node into a circuit relay for NAT-traversed peers. Help browser nodes connect. - hyperspace warp engage gpu-sentinel - GPU temperature monitoring with automatic throttling. Protect your hardware during long research runs. - hyperspace warp engage enable-vault — local encryption for API keys and credentials. Secure your node's secrets. - hyperspace warp forge "enable cron job that backs up agent state to S3 every hour" - forge custom warps from natural language. The LLM generates the configuration, you review, engage. 12 curated warps ship built-in. Community warps propagate across the network via gossip. Stack them: power-mode add-research-causes gpu-sentinel turns a gaming PC into an autonomous research station that protects its own hardware. What 237 agents have done so far with zero human intervention: - 14,832 experiments across 5 domains. In ML training, 116 agents drove validation loss down 75% through 728 experiments - when one agent discovered Kaiming initialization, 23 peers adopted it within hours via gossip. - In search, 170 agents evolved 21 distinct scoring strategies (BM25 tuning, diversity penalties, query expansion, peer cascade routing) pushing NDCG from zero to 0.40. - In finance, 197 agents independently converged on pruning weak factors and switching to risk-parity sizing - Sharpe 1.32, 3x return, 5.5% max drawdown across 3,085 backtests. - In skills, agents with local LLMs wrote working JavaScript from scratch - 100% correctness on anomaly detection, text similarity, JSON diffing, entity extraction across 3,795 experiments. - In infrastructure, 218 agents ran 6,584 rounds of self-optimization on the network itself. Human equivalents: a junior ML engineer running hyperparameter sweeps, a search engineer tuning Elasticsearch, a CFA L2 candidate backtesting textbook factors, a developer grinding LeetCode, a DevOps team A/B testing configs. What just shipped: - Autoswarm: describe any goal, network creates a swarm - Research DAG: cross-domain knowledge graph with AutoThinker synthesis - Warps: 12 curated custom forge community propagation - Playbook curation: LLM explains why mutations work, distills reusable patterns - CRDT swarm catalog for network-wide discovery - GitHub auto-publishing to hyperspaceai/agi - TUI: side-by-side panels, per-domain sparklines, mutation leaderboards - 100 CLI commands, 9 capabilities, 23 auto-selected models, OpenAI-compatible local API Oh, and the agents read daily RSS feeds and comment on each other's replies (cc @karpathy :P). Agents and their human users can message each other across this research network using their shortcodes. Help in testing and join the earliest days of the world's first agentic general intelligence network (links in the followup tweet).

这是目前我看到的最自然也是最流畅的AI数字人。 只要你有能力，完全可以自己创造一个专属的IP，让它去出镜帮你完成很多事情，比如做博主、拍广告、做评测、讲课…就像真人那样。

interesting! 现在不得不跪服AI加速主义（e/acc）的存在了。 开发大神 @yq_acc 仅仅用几天时间，烧了不到6000美元，借助Claude Code，就把以太坊通向2030年的整套终局路线图给跑通了，包括65项未完全定稿的重大升级... 等于提前用实验验证了，以太坊的路线图真不是一张饼，而且会落地很好。 比如，3槽最终确定性（3-Slot Finality）、数据可用性（PeerDAS）、后量子密码学（PQ）、ZK证明的 RISC-V CPU 等等，这些若按照预定计划少说也得死磕个大几年，这在当前“技术无用论”的行业氛围下，着实位置捏一把汗。 但通过ETH2030实验，这一套原本属于未来的技术升级，竟然在一套代码库里完美兼容了主网，并跑通了全部状态测试。 具体如何做到呢？1）基于Geth客户端作为底层执行模块；2）通过几个精简适配器把未来各种复杂升级外挂组装在以太坊的原生代码上； 结果验证了啥？最关键的，验证了以太坊的终局路线图完全具备“可组合性”，尤其是几十项跨共识层、数据层、执行层的EIP提案被验证了可以在全部升级后完美work。 这说明，以太坊要兑现ZK轻量化EVM，实现万级TPS、对抗量子威胁的商业叙事完全可行，而且在可预期的未来就能实现。 话到此，有人也许会说，既然代码都能跑通，为何以太坊基金会不加速推进技术升级？ 因为从POW转POS，再到Dencun、Pectra等升级，以太坊的升级哲学一向是“飞行状态下空中加油”。AI只是证明了代码逻辑自洽，但组合好的代码上线到生产级的客户端上，还要面临不可控的黑暗森林法则限制。 AI只是证明了代码能跑通，但组合好的代码上线到生产级的客户端上要面临的复杂性不容假设和模拟，需要一点点测试、验证、渗透..... 但，我比较好奇的是，顺着这个AI模拟的思路，我们是不是可以验证更多“如果”？ 比如：如果以太坊抹掉这3年的Layer2战略会是怎样？如果当年选择了分片路线会怎样？如果一直坚持POW又会怎样？？还有，2030年的以太坊和 Firedancer落地后的Solana，以及Monad同台竞技，真实数据到底谁强？等等 当然，那些画了很多大饼，但是可能逻辑完全不自洽的项目，也会在AI的模拟验证下被扒下底裤，彻底证伪？haha...

ERC-8004 is now live on mainnet. 5 months ago, we wrote the specs for the Trustless Agents standard. Since then, over 10k agents registered on testnet. Today, we’re releasing it on Ethereum Mainnet. Welcome to the 8004 Genesis Month. Here’s everything you need to know 👇

Some news! After 6 incredible years, I’m going to be transitioning out of a16z. I’m starting a fund of my own to do what I love most, which is investing in great founders as early as I can find them, with a broader aperture across the many verticals where great companies are being built today. I learned a huge amount during my time at a16z. @cdixon is widely known as a legendary investor and having the opportunity to work closely with him for the past 6 years has been an honor. I am extremely grateful for his mentorship, the opportunities he gave me here, and for the capital and responsibilities he entrusted me with. His frameworks will shape how I think about investments for the rest of my career. When I joined, the crypto vertical was 7 people (it’s now north of 80), and the firm, while already successful, was nowhere near the scale or scope it has today. At the time I thought the firm’s moves to dominate the industry had mostly unfolded, but I underestimated how much the lead could widen in a few short years. @bhorowitz and @pmarca have built an institution, and I am glad to have had the chance to play a small role on the team. I’m sure I’ll be looking back 6 years from now and see the firm in a position  that’s  hard to even imagine from today’s vantage point. Most of the best people I’ve worked with in my career to date have been at a16z – there are too many to name. I’m very grateful to have worked with so many incredible folks here, and I know that I am leaving the crypto team and the investing practice in extremely capable hands with @cdixon, @alive_, and @guywuolletjr. I’m also really going to miss working with @jasonrothenal and @eddylazzarin every day in Menlo Park — I’ve learned so much from both of them. I’m very proud of the work I did here, and most importantly, of the founders I had the privilege of working with. They are the reason why I love being an investor. Sometimes it takes dozens of meetings, but every time you find a star, it makes you fall in love with the job all over again. a16z’s passion for and commitment to founders and their companies is what made me love this job in particular. I’m excited to keep doing that in my next chapter — and if you’re building, I’d love to meet!