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／ #AveMujica# アモーリス シグネチャードラムスティック #LERNI# より 4月26日 発売‼️ ＼ お求めは全国の楽器店にて。 4月26日、27日にKアリーナで行われる MyGO!!!!!×Ave Mujica 合同ライブ「わかれ道の、その先へ」 の物販ブースでも販売いたします❗️ 是非お手に取ってください🌙 #バンドリ#

We recently had a user whose account was compromised. Our customer service team identified the situation quickly and froze the account. We then worked with the user to restore their original email and authenticator, helping secure the account and prevent further risk. Unfortunately, an API key that remained on the account allowed the attacker to transfer the funds before the issue could be fully contained. We are still investigating exactly what happened. But one thing is clear to us: we do not believe the user should have to bear the consequences of this incident. We immediately put together a dedicated team to handle the case, and we have fully compensated the user. We have always believed that being user-first means more than just saying it. When our users need us to step up, we will. Thank you to everyone for your trust, and especially to the user involved for giving our team the time today to work through this properly. We’ll keep learning, keep improving, and keep moving forward.

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。

I have to say - for me it started 2 years ago. I remember the moment so well. I was on vacation in Montenegro. Early night. Everyone is asleep and I am on the balcony watching the cruise ships in Kotor bay as the horizon faded to dark. And I think it was Sonet 3.5 (don't quote me). I had just setup a plugin in NeoVim called Avante by @yetone . And it was magic. Coding with AI at the time wasn't the same - I was going function by function. I was accepting diffs one hunk at a time. But I remember that night - from 10 to like 2 in the morning - I built what I thought would have taken me a week (I was rusty and learning the stack). It was an internal tool - a partial JSON parser / repair tool - so you can take half finished JSON and safely turn it into something that would parse. It is more complicated than it sounds. Anyway. I was hooked that day. At the time I was an executive at a Fortune 50, so coding was not in my job description as such. I had hundreds of engineers... but I have to say - it relit my passions, and now as I am (hopefully) building another startup - I still love it. And I still yell at it. And constantly hunt where it is messing up the architecture and adding bloat and.... well you know how it is.

Chamath explains the political calculus behind Obama’s anti AI speech “This is a very important moment for a very simple reason, which is that the world is about to endow 3-6 companies with about $10 trillion of wealth.” “And what Obama knows very well is that most of those companies are overwhelmingly left leaning. And what he also knows is that there is a huge portion of that money that will then get put into philanthropic and charitable causes that then he and the people around him will be beneficiaries of." “That is the truth. We already know this because we know that some of these frontier corporations actually ask you to sign up DAFTs and have a portion of your stock that you're willing to pledge. So this money is going to go to things other than consumption or savings. It's going to go into PACs, it's going to go into political movements, and they stand to disproportionately benefit." “So this has nothing to do with prosperity. This is a very simple political calculus. If you freeze frame the economy the way it is today, a handful of organizations that will disproportionately be able to affect the Democrats will win, they will capture the lion's share of the economic gains. And then they will help the Democrats win power. That's all this is."

「从零手写大模型」系列完结了 推理篇 15 篇，训练篇 5 篇，共 20 篇 从模型的基本结构 到如何生成文字，再到损失、梯度与参数更新 把学习过程整理成了一套完整笔记 每一篇都有可练习的代码示例 📖 推理篇 · 15 篇 01｜LLM From Scratch：整体大纲路线 02｜Tokenizer：分词器 03｜Embedding：嵌入层 04｜Positional Encoding：位置编码 05｜Self-Attention：自注意力机制 06｜Multi-Head Attention：多头注意力机制 07｜Residual Connection：残差连接 08｜LayerNorm：层归一化 09｜FFN：前馈神经网络 10｜Activation Function：激活函数 11｜Transformer Block：Transformer 模块 12｜Transformer Stack：Transformer 堆叠 13｜Loading Pretrained Weights：加载预训练权重 14｜Text Generation：文本生成 15｜Interpretability：可解释性 🛠 训练篇 · 5 篇 01｜Loss Function：损失函数 02｜Backpropagation：反向传播 03｜Gradient Descent：梯度下降 04｜Adam Optimizer：Adam 优化器 05｜Learning Rate Scheduling：学习率调度 推理篇，理解模型如何一步步算出下一个 token 训练篇，理解模型如何从误差中学习、更新参数 欢迎一起学习、交流，也欢迎指出笔记里的问题

After the tea, President Xi Jinping and Madame Peng Liyuan visited the National Archives accompanied by President and Mrs. Trump.  President Xi underscored that China and the United States are distinct in many ways, with different histories, cultures, social systems and development paths. But the people of China and the United States are just like each other in aspiring for a better life. By recognizing and respecting the differences, understanding and learning from each other, focusing on the greatest common ground, lengthening the list of cooperation and properly managing differences, the two countries can achieve steady and long-term development of their relationship.

"I've been leaning on him a whole lot... He's so wise." At Mavericks Media Day, Dereck Lively II talks about the support he received from Kyrie Irving while they both work their way back from injury 🤝

"Whenever I walk into a gym... I feel like I can raise the level of play and bring out the best in people." At Mavericks Media Day, Kyrie Irving reflects on the joy of mentoring, sharing knowledge with teammates, and learning patience while rehabbing his injury last season.