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Meta's deal to buy Manus got blocked, so I gave Meta's Muse a Manus 2.0-style launch film instead. Left: the official Manus film. Right: Claude Opus 5.5 + Seedance 2.5 from one URL. (Unofficial concept.) Want one for your product? Reply with your URL. First 5 are free.

Researchers proved AI has deleted every reason universities exist. Harvard University ran a controlled experiment pitting a custom AI against their own top-tier classrooms. And the results are going to collapse the higher education bubble. They took 194 undergraduates and split them up. One group learned physics in one of Harvard’s best hands-on, active-learning physical classrooms. Group work. Instructor support. The premium university experience. The other group went home and learned the exact same material with an AI tutor. The AI didn't just win. It embarrassed the institution. Students using the AI learned more than twice as much as the students in the elite Harvard classroom. They scored 30% higher on the final assessment. And they did it in less time. Let that sink in. A piece of software sitting on a laptop outperformed a world-class faculty in one of the most elite learning environments on Earth. Universities have always justified their exorbitant tuition with two things: access to elite knowledge and the physical classroom experience. This study just proved both of those moats are gone. When software can teach you complex physics twice as well as a $60,000-a-year institution, the math of higher education breaks permanently. The AI didn't just give the students answers. It used strict pedagogical guardrails. It guided. It questioned. It forced the students to do the cognitive work. It offered perfect, one-to-one tutoring, personalized to the exact moment a student misunderstood a concept. That level of attention is mathematically impossible to scale in a physical lecture hall. For a thousand years, the university was the only place to get a premium education. Now, it’s the bottleneck. If AI can double your learning speed for a fraction of the cost, what exactly are students taking on decades of debt to pay for?

[开源学习资源] AI Engineering from Scratch 59.8K ⭐️ 作者 @ghumare64 课程共 20 个阶段，以 Python 为主要编程语言，主张在导入任何框架之前，先用纯数学把每个算法手写一遍。 课程地址： 开源地址： 它和常见教程的本质区别 ？ 大多数 AI 教程是“API 驱动”的：装个库、调个接口、跑个 demo。这个项目反其道而行，每节课遵循固定的六段结构： Motto → Problem → Concept → Build It → Use It → Ship It 以 Phase 10 的一节课「Tokenizers: BPE, WordPiece, SentencePiece」 为例，这个格式是真实落地的，而且写作质量相当高： · Problem 部分不讲废话，直接从代价切入：“你的 LLM 不读英语，它读整数。分词器决定这些整数是承载意义还是浪费意义”，然后解释为什么 tokenization 不是预处理，它是架构的一部分（影响上下文窗口利用率、API 计费、推理速度）。 · Concept 部分用“三种失败的方案和一种胜出的方案”来讲演化逻辑：词级切分（词表爆炸、[UNK] 问题）→ 字符级切分（序列过长）→ 子词切分（BPE 的折中），并配上真实语料上 BPE 逐步合并的手工演算。 每节约 3000+ 词，带 Mermaid 图、可运行代码和测试。 另外两个设计值得强调： · 每节课产出一个可复用的 artifact，一个 prompt、一个 skill、一个 agent 或一个 MCP server。学完整个课程，你手里有 523 个可展示的作品，不是 523 个跑完就扔的 notebook。 · 强调“证据留存”：保留命令、退出码、输出，作为学习发生的证明。这明显吸收了工程实践中“可验证性”的思路。 # 课程结构：从线性代数到自主智能体集群 20 个阶段构成一条完整的上升曲线，咱们它分成五个大块来看： 基础层（Phase 0–2）：环境与工具链、数学基础（线性代数/概率/微积分）、经典机器学习。这是给基础不牢的读者铺的路。 深度学习与感知层（Phase 3–6）：神经网络核心、计算机视觉（一路讲到 NeRF、高斯泼溅、世界模型，这已经超出一般教程的覆盖范围）、NLP、语音。 生成与决策层（Phase 7–9）：Transformer 深挖、生成式 AI（GAN、扩散模型、flow matching）、强化学习。 LLM 层（Phase 10–12）：这是全课程的重心之一。Phase 10 的目录我逐条看过，它不只是“从零实现 GPT”这种常规内容，还包含了相当前沿的论文级主题：DeepSeek-V3 架构走读、DualPipe 并行策略、Native Sparse Attention（NSA）、多 token 预测、Jamba 的 SSM-Transformer 混合架构、 speculative decoding 等。Phase 11 转向应用侧（RAG、LoRA、MCP、可观测性），Phase 12 覆盖多模态（从 CLIP 到 computer-use agent）。 智能体层（Phase 13–16）：工具与协议（MCP、A2A、Agent Skills）、54 节课的 agent 工程、自主系统与安全、多智能体集群。这一层的分量很能说明项目的判断：它认为 AI 工程的重心正在从“训模型”转向“构建可靠协作的智能体”。 收尾（Phase 17–19）：生产基础设施、伦理与对齐、毕业设计。 # 生态与周边：不只是一个课程仓库 网站：带浏览器本地存储的学习进度追踪、术语表、课程目录和路线图。 六卷本书籍：课程内容由 CI（pandoc）自动构建成 EPUB/PDF，附在 GitHub Releases 上，课程即书，且随仓库持续更新。 Agent 导师模式：运行 npx skills add rohitg00/ai-engineering-from-scratch，可以把整个课程装进 Claude Code、Codex 等编码智能体，变成一个带分级测验（placement quiz）和个性化路径的交互式导师，学习进度写在 LEARNING.md 里。这是“用你正在学的工具来学”的巧妙闭环。 认证备考：5 条备考路径、67 节课、505 道练习题，覆盖 Anthropic 的 Claude 认证和 Agentic AI Foundation 的 MCPA。项目明确声明与这些考试机构无关联，只是独立的备考材料。 12 种语言的翻译（含中文），以及四条核心学习路径（构建与部署 AI 应用、软件工程基础、Agent 辅助工程、产品判断与交付）供不同目标的人选路。

Ethereum is both an abstract philosophical concept (the ontological Turing machine) and a practical research and development effort that aims to answer this question that pertains to the nature of Ethereum. How the nature of "Ethereum" as a protocol comes to be determined through the ACD governance mechanism, how cryptographic truth comes to be defined, and how convergent consensus is reached using majority representation of Ethereum's observers is what reduces the ontology of the world computer down to what we today call the Ethereum network. Here's how the world computer comes to be defined:

Opus 5.5 is incredible at instructional video generation. I made this launch video for a inference startup in 1min for ~$2. Videos like these used to take weeks if not months and a lot of coordination with agencies and 1000x the costs. Humans broadly prefer video to text. This changes the substrate of communication. These videos actually help communicate technical ideas in seconds (photorealistic video gen like Seedance is not very useful here). - changes how often marketing should be talking about products and launches - change how sales people can talk about technical products to their customers - allow technical people to easily explain concepts internally without long docs And thats just scratching the surface within startups. Prompt: “make a modern slick and punchy video for a modern startup that works on inference”

A note on recursive STARK mempools (EIP-8288) This is an EIP that I am hoping we can get included in I-star (the fork after Hegota) that you can think of as the next step after Frames, that would unlock extreme amounts of power. Particularly: * Ultra-cheap quantum-safe signatures (SPHINCS-). Much of the cost savings comes from the fact that the signature data (~3 kB) does not have to go onchain * Ultra-cheap quantum-safe privacy protocols. Status quo minimum cost for private txs is ~300k if you engineer very well (no one does), status quo quantum-safe is ~10M gas, this could reduce it to low tens of thousands. * Universal support for your favorite new signature or proof scheme without needing EVM changes. Whatever you use (Falcon, ML-DSA, some other lattice-based thing, something code-based or isogeny-based or even more esoteric), you can just wrap it client-side in a STARK, onchain gas cost low tens of thousands just like privacy protocols. Hopefully, Ethereum will never need "please support my favorite cryptographic algo" politics again. * Private account abstraction: keep your account logic private, and in a private location onchain. Then you can make one transaction to change the ownership of all your onchain state - accounts, defi positions, privacy protocol notes, everything - without revealing which objects' ownership you're changing. Here's how it works. Your transaction can include a type of frame that we call a "dependency frame". The frame is a list of statements, asserting claims like "message hash M was signed by SPHINCS- public key P" and "data hash D was proven to satisfy a statement defined by verification key V". When you send your transaction, you send it in an envelope, which includes a signature or a STARK for each statement in a dependency frame. Once the transaction reaches the mempool, nodes aggregate them. Each node runs a loop: wait one tick (eg. 500ms), aggregate all new envelopes (either single-tx or multi-tx) that you've seen, remove any transactions that are expired, generate a STARK recursively proving all dependencies, and send a new multi-tx envelope containing that STARK. Hence, the bandwidth load is bounded: each node's outbound is one STARK (~100-300 kB) per tick, plus each transaction getting broadcasted through the network once (as happens already). The block builder acts as "yet another mempool node", receiving envelopes from the mempool (plus any side channels), generates its own STARK covering the subset of transactions it intends to include in the block, and adds that STARK to the block. Total onchain overhead: one STARK (100-300 kB), plus 96 bytes for each statement being proven. This is what I've called before ( ) "The Proof Singularity". Today, we have all the ingredients to actually implement it. As a developer, this requires a somewhat different workflow than you are used to, but it is conceptually simple. Any signatures or STARKs, you put into a separate frame. Then the main logic that today is verifying a signature or STARK, you replace with checking for the existence of a frame that includes the correct statement as a dependency. Examples of useful statements: * [tx sighash] verifies against [the pubkey at sload(0)] * there exists a secret and a merkle branch such that hashing secret+0 and applying the merkle branch outputs (public) root R, and hashing secret+1 outputs (public) nullifier N * there exists a secret address A, salt S and signature Z such that sload(0) = hash(A, S) and a merkle proof of address A inside a recent ethereum state contains some pubkey D where [tx sighash] was signed by D [this is private account abstraction; all variables except [tx sighash] and sload(0) are private; you can also make D a STARK verification key] * there exists an ML-DSA signature signing [tx sighash], that verifies against an ML-DSA pubkey whose hash is sload(0) At the core, this is moving any compute and data other than bookkeeping "business logic" outside the core path of Ethereum execution, sharding and parallelizing it via the mempool. Notice also that this requires agreeing on a _language_ (aka. an ISA) for the recursive STARKs to define statements in. The current leading candidate is RISC-V. So this would also de-facto be Ethereum adding RISC-V (or something else we decide on) as a canonical ISA - a big decision that should be done carefully, but that I think will be necessary to drive Ethereum forward.

Jordan McDonald was a curious case. That one photo of her went viral countless times for about two years, yet nobody knew who she was. I can't quite wrap my head around the fact that the entire internet didn't know about the concept of reverse image search

AI is opening up new ways for creators to explore ideas and bring visual concepts to life. Powered by Dreamina Seedance, SEEN Partners is developing an AI-supported creative workflow that offers greater flexibility and creative control. Watch how SEEN Partners is exploring new creative possibilities with AI.

AI is opening up new ways for creators to explore ideas and bring visual concepts to life. Powered by Dreamina Seedance, SEEN is developing an AI-supported creative workflow that offers greater flexibility and creative control. Watch how SEEN is exploring new creative possibilities with AI.