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In December 2015, during my first year at Stanford GSB, I chose to intern at a top hedge fund. Researching advanced driver assistance systems (ADAS) and autonomous driving, I saw a new growth opportunity for GPUs beyond gaming. The only stock I pitched during my internship was NVDA—then worth roughly $18 billion, trading around $0.80 a share, split-adjusted. The fund took a large position (and still hold today), and I bought some too. I didn’t hold it all the way to its peak, but I’m grateful of the returns from the investment. @JensenHuang has created tremendous value for shareholders. Pushing him out is a mistake. After 33 years building NVIDIA, his leadership is still central to its future. NVIDIA and the broader AI developer community still need his leadership and spirit of innovation. (Back then no AI to help on stock research, have to learn those terminologies in this old fashion…😂) Look at the impact Jensen has created to the world, unbelievable. Still can’t believe what just happened. We need a prediction market on this: when will Jensen be back?

Stanford tiene una clase de 2h34 que explica cómo se construyen ChatGPT y Claude, y es mejor plan que Netflix esta noche 🍿 Va de la tokenización BPE al Transformer, la atención, el entrenamiento y la generación token a token. Yo la guardaría hoy mismo.

🔊 Big Update! All materials are now being released publicly for our NEW Stanford course CS329Z: Engineering AI Agents! Starting with the lectures, which are going live daily on the Stanford Online YouTube channel! Lecture 1: 🧵 (1/5)

Instead of spending 2 hours watching Netflix tonight, invest that time in this Stanford lecture. It's one of the clearest end-to-end breakdowns I've seen of how ChatGPT and Claude are built. It covers everything from tokenization and BPE to transformer architecture, the training pipeline, and next-token prediction. Whether you're completely new to AI or already building AI agents every day, this 2-hour-and-34-minute lecture will help you understand what's happening behind the scenes. Bookmark this, set aside some time this weekend, and give it your full attention. Because understanding how AI actually works could be one of the most valuable skills you develop this year.

Sam Altman, CEO of OpenAI: “You don’t need to write prompts anymore.” In just 27 minutes, he explains how to use ChatGPT in ways most people haven’t even explored yet. This was a talk he gave to Stanford students. A friend sent me the recording last night. After watching it, I realized I was barely using 15% of what ChatGPT can actually do. Watch the full talk, then check out the guide below on how to build a system that can prompt itself.

My top 5 robotics profiles that are 6 months ahead of the timeline: - @DrJimFan NVIDIA's Director of Robotics. co-leads the lab behind GR00T, NVIDIA's open humanoid brain - @chelseabfinn Stanford professor + co-founder of Physical Intelligence. teaches the free RL course, builds the robot brain - @svlevine Berkeley professor + also a co-founder of Physical Intelligence. one of the most cited names in robot learning - @tonyzzhao built ALOHA, then dropped out of a Stanford PhD to start Sunday Robotics - @RemiCadene started LeRobot at Hugging Face, now building robots on top of it at UMA follow all 5 and you'll stop being surprised by robot videos

Stanford acaba de filtrar esta clase gratuita que explica cómo funcionan Claude y ChatGPT desde el interior La mayoría desperdicia el 90% de su potencial Stanford te lo enseña en 1 hora y 50 minutos Guarda esto en favoritos para que no lo pierdas.

Sam Altman, CEO de OpenAI: "Ya no hace falta escribir prompts." En 38 minutos explica cómo usar ChatGPT a un nivel que la mayoría ni se imagina. Es una charla que dio a estudiantes de Stanford. Un amigo me pasó la grabación anoche. Cuando terminé de verla me di cuenta de que solo le estaba sacando como un 15% de lo que puede hacer.

斯坦福 @stanfordnlp 新课 CME 295，双胞胎兄弟 @afshinea @shervinea 主讲，9 讲走完 LLM 全部版图：从 Transformer 架构、注意力变体、RoPE，到 LoRA 微调、RLHF/GRPO 训练，再到 Flash Attention 推理优化、MCP 与 AI Agent、扩散语言模型。 课程和知名速查表全部公开。

CS 329Z: Engineering AI Agents Stanford / Fall 2026 @stanfordnlp 课程定位：从"模型"到"系统"的工程学 覆盖：简单 LLM 流水线 → 复合 AI 系统 → 自主 Agent。三位讲师的背景也高度互补： @Diyi_Yang（斯坦福 NLP 教授，人机交互与社会计算方向） @michaelryan207（DSPy 核心贡献者，自动评估 AutoMetrics 作者） @jyangballin（SWE-agent / SWE-bench / SWE-smith 作者，软件工程 Agent 领域最重要的研究者之一） # 课程主线：三大工程挑战 贯穿全课的三个核心问题——分解（decomposition）、数据（data）、评估（evaluation）。11 周的内容基本围绕这三条线展开，可以分为五个模块： 模块 1：构建基元（Week 2–3） · LLM 作为构建材料：API/SDK（litellm）、结构化输出、约束生成、解码策略、test-time compute、上下文工程、模型选型与成本/延迟权衡 · RAG：embedding、向量库、分块策略、混合检索、cross-encoder 与 ColBERT 后期交互 · 工具调用：函数调用 API、MCP（Model Context Protocol）、工具设计、代码沙箱、错误处理与重试 模块 2：框架与设计模式（Week 3–5） · 框架层：DSPy（signature / module / optimizer）、LangChain/LangGraph、LlamaIndex，重点是"框架抽象了什么 vs. 你手写了什么" · 设计模式：workflow vs. agent 的分类学，五种可组合 workflow 模式，ReAct / plan-and-execute / reflection，"scaffold（脚手架）本身就是设计决策" · 记忆架构：短期/长期记忆、记忆作为工具动作、文件系统作为外化记忆、跨 Agent 记忆（MemGPT、Mem0、Generative Agents） · 多 Agent 系统：编排模式、handoff 与状态传递，以及一个很有态度的对照阅读——既读 AutoGen，也读《Why Do Multi-Agent LLM Systems Fail?》和 Neubig 的《Don't Sleep on Single-agent Systems》 模块 3：优化（Week 5） · 从提示词到微调的全景：GEPA、MIPROv2、OPRO、TextGrad（提示优化）；LoRA/QLoRA、蒸馏、RLHF/DPO（权重优化）；test-time scaling（推理算力） · 核心问题是决策框架：什么时候优化 prompt、什么时候优化 weights、什么时候堆推理算力 模块 4：数据与评估（Week 6–8）——最有分量的部分 · 数据：trace、demonstration、feedback 三类数据；训练数据 vs. 评估数据；数据飞轮；合成数据；从 Agent 轨迹构建数据集（SWE-smith） · 评估基础：为什么 eval 难；4 元组框架（request / environment / stopping criteria / scorer）；好 benchmark 的性质；tinyBenchmarks · 评估基础设施：三类 grader、LLM-as-judge 的 prompt 设计与已知偏差、pairwise vs. pointwise、非确定性指标 pass@k vs. pass^k、harness 设计 模块 5：安全与前沿（Week 8–11） · 安全：工具访问的隐私风险、prompt injection（含间接注入）、红队、沙箱与权限模型、输出护栏、human-in-the-loop · Coding Agent：SWE-agent、Claude Code、OpenHands 的端到端架构对比 · 主动式 Agent：从 reactive 到 proactive，General User Models（GUM）、Next Action Prediction，以及"Agent 何时应主动、何时应等待"的 mixed-initiative 问题 · 开放问题：多模态/web/计算机使用 Agent、科学 Agent、长时运行架构、生产可观测性（tracing、monitoring、成本管理） # 作业设计：一手建、一手评 HW1：从零构建 Agent 系统（10%） 给定论文库，构建能检索并推理回答科学问题的 Agent。 · Part A：只用 litellm 手写 RAG + 工具调用 + ReAct 式 Agent 循环 · Part B：用 DSPy 重建关键组件，并反思框架抽象了什么 HW2：评估一个 Agent（10%） 给定一个预构建 Agent，设计完整评估套件：代码型 grader、至少一个 LLM-as-judge、用 4 元组框架构建 benchmark 任务、错误分析。