Fernando @Fernandonefro

🚨 谷歌前CEO揭露了一个秘密，它将在未来几年重塑世界，都因为AI。 他说现在创造财富和创办自己的生意变得容易得多，只有一个条件：你必须进入这个领域并真正理解游戏规则。 如果你只有30天时间开始，就按照这个建议行动，全力以赴。这是完整的路线图和你需要的所有工具，100%免费： 第一阶段：理解架构和基础知识 代理架构：学习如何构建一个智能独立的AI代理，它能做出自己的决策并执行。 🔗 code.claude.com/docs/en/how-cl… 🔗 langchain.com/blog Claude Code 101课程：学习使用Anthropic强大编码工具的基础知识。 🔗 anthropic.skilljar.com/claude-code-101 实战演练：工具构建真实项目的现场演示，让你亲眼看到它的能力。 🔗 anthropic.skilljar.com/claude-code-in… 第二阶段：掌握提示词和上下文 提示词工程官方指南：Anthropic官方教你如何写出专业级提示词。 🔗 platform.claude.com/docs/en/build-… 交互式提示词课程：GitHub上的实践培训，提升你的提示词技能。 🔗 github.com/anthropics/pro… CLAUDE.md和记忆：让Claude记住你项目的完整上下文和规则的秘密。 🔗 code.claude.com/docs/en/memory 第三阶段：自动化和连接真实世界 MCP协议：真正的革命。连接Claude到你每天使用的工具和数据库，如Slack、GitHub和谷歌。 🔗 code.claude.com/docs/en/mcp 技能：教Claude重复工作流和自定义函数，让它在你的项目中智能重用。 🔗 code.claude.com/docs/en/skills 第四阶段：社区指南和专业进阶 完整Claude Code指南：来自开发者社区的大型开源指南，充满秘诀和技巧。 🔗 github.com/FlorianBruniau… 超棒Claude Code集合：汇集最好的现成脚本、技能和插件。 🔗 github.com/hesreallyhim/a… Anthropic学院：公司所有13门官方课程，有证书，完全免费。 🔗 anthropic.skilljar.com 完整官方文档：Claude所有内容的最终参考指南。 🔗 code.claude.com/docs/en/overvi… 保存这个并打开通知，获取更多内容 🛎️ 这些资源完全免费，不花一分钱，但能让你掌握一项技能，独自完成整个团队的工作，质量相同。

ESTO ES UNA LOCURA Jack Dorsey (ex-CEO de Twitter) acaba de lanzar gratis un framework completo para crear un negocio gestionado al 100% por agentes de IA. ya supera las 29.000 estrellas en GitHub. cómo configurarlo (5min): 1. clona el repositorio. 2. despliega tu propio servidor: canales, búsqueda, Git y automatizaciones funcionan desde ahí. 3. añade tu agente a un canal como si fuera un compañero más, define sus permisos y deja que el equipo colabore con él en tiempo real. guárdate este post. os dejo el repo abajo.

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Anthropic Engineering: "At Anthropic, we don't write prompts anymore. we build loops" in 42 minutes, she shows how the Claude team creates loops that prompt themselves if this cost $400, people would call it the best agents course of the year it's free watch it, and Bookmark.

INSTEAD OF WATCHING AN HOUR OF NETFLIX TONIGHT. This 1 hour Stanford lecture by Joel Peterson will teach you more about negotiation and getting what you want than most people learn in years. Bookmark it and give it an hour, no matter what.

Complete Agentic AI Course - AI Agents, RAG, Embeddings, Architectures, ... youtu.be/Pn95eOlw5qk?is… via @YouTube

Demis Hassabis: "These days, one person who truly knows AI can outperform an entire startup team." I've watched hundreds of AI talks, this 60-minute Cambridge lecture is the one I wish I had seen a year ago. A Nobel Prize winner and the CEO of Google DeepMind just told you where this goes. The person who outperforms a whole team isn't smarter, they just know their tools deeper. Watch it, then read the full breakdown of the Claude features 99% never find below.

artificialanalysis.ai

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NVIDIA CEO, Jensen Huang: "Nobody writes prompts anymore. The new job is building Loops and Graphs." In 50 minutes he explains why the engineers who stopped prompting are already years ahead, and what they're building instead. The missing piece most people skip: loops handle the work, graphs handle the loops. Watch it, then read the full guide on loops and graphs below.

STEVE JOBS GOT FIRED FROM APPLE. Then he walked straight into MIT and dropped the most raw, unfiltered 60-minute business masterclass ever recorded. Zero PR bullshit. Zero image to protect. Just pure, brutal honesty from the man who built Apple once and was about to rebuild it even bigger. Stop scrolling. Watch this tonight instead of Netflix. Bookmark it. Come back to it.

Don't waste 2 years learning to build LLMs like Claude & ChatGPT. Stanford just dropped a 2 hour course on how to build LLMs from scratch. • 00:00 - LLM tokenization • 25:44 - how LLMs decode user prompts • 35:40 - training pipeline of LLMs • 1:16:47 - LLM architecture from scratch Anthropic pays $750,000/year to engineers who understand this exact knowledge of LLMs. Bookmark this & give 2 hours today, no matter what. Then read the article below.

This paper is f*cking insane. Prompt Engineering just got replaced by Graph Engineering. A new 20-page paper formalized what the best AI builders are already doing: Stop writing one giant prompt. Build a graph of agents instead. Planner → specialists → verifier → feedback loop. The authors test this definition against LangGraph, DSPy, AutoGen, CrewAI, Prompt Flow, and Claude Code subagents. The prompt is no longer the system. The graph around it is. Bookmark this, then read the full Graph Engineering guide below.

A dead MIT professor accidentally destroyed the $20 billion executive coaching industry with one hour of lecture, and ten million people have already watched him do it. He filmed it once in January 2018 and died eighteen months later. Executive coaches charge fifteen thousand dollars a session to teach a third of what he covered in that one hour for free. His name was Patrick Winston. He ran the MIT Artificial Intelligence Laboratory from 1972 to 1997 and wrote the AI textbook every computer science major in the world read for thirty years. Every January for four decades, he gave a lecture called "How to Speak." His entire framework fits on a napkin. Do not read. Be in the image. Keep images simple. Eliminate clutter. Start with an empathetic connection. End with a punch line the audience can repeat over dinner. Never open with a joke. Never end with "thank you." That last rule alone has probably cost the executive coaching industry a hundred million dollars. "Your success in life will be determined largely by your ability to speak, your ability to write, and the quality of your ideas. In that order." That is the actual opening line of the lecture. Winston believed it strongly enough to spend fifty years teaching computer scientists how to talk. Founders spend $80,000 on an MBA and then hire a communications coach to teach them the same material Winston filmed once for free. Engineers write brilliant code and lose promotions to teammates who watched this lecture on the train. The lecture is free on MIT OpenCourseWare. The textbook is free on his page. Winston died in 2019. Almost none of the ten million viewers have actually implemented the four rules on the napkin. The napkin is free. The willingness to actually use it in your next meeting is the entire edge.

🚨Este discurso de elon vale oro. En 2003, con 32 años, Elon Musk entró a una sala cerrada en Stanford y en 45 minutos destrozó todo lo que creías saber sobre startups. Vale más que 10 libros juntos. Desglosó, capa por capa, cómo llevó de cero a sobrevivir las 3 empresas que tenía en ese momento: Zip2 PayPal y SpaceX (que acababa de fundar un año antes) Cómo encontrar dirección cuando nadie cree en ti. Cómo controlar costos hasta el último centavo. Cómo mantenerte vivo cuando el dinero se está acabando. Cómo resolver cualquier problema con primeros principios. 20 años después… sigue siendo oro puro. Guárdalo. Vas a necesitarlo.

Dealing with difficult people may be hazardous to your health. Data: Each additional "hassler" in your network predicts 1.5% faster biological aging—and worse health a year later. Non-spouse family hasslers are the most problematic. Some ties aren't worth the toll they take.

别再靠短视频学怎么赚钱了 瑞·达利欧42分钟讲透财富创造 比大部分内容都扎实

Andrew Ng just dropped a 3-hour course on how to become an AI Engineer in 2026: • 00:00 - How to build agentic AI systems • 04:25 - Future of AI engineering • 23:38 - AI Prompting full course • 2:52:17 - Creating an app with AI in 30 minutes This 3-hour watch could replace 10 AI engineering courses on the internet. Watch it today, then read how to run a self-improving system in the article below.

Google Deepmind argues that LLMs can never make real scientific discoveries. They published a paper breaking down Albert Einstein’s private view of scientific discovery. In a famous letter to his friend Maurice Solovine, Einstein drew a diagram of how science actually happens. It is a cyclical loop. First, you experience raw sensory data. Then, through a mysterious, non-logical act of intuition, you make an intuitive "jump" to abstract axioms. Finally, you use strict logical deduction to derive consequences from those axioms. Generative AI has completely mastered two-thirds of this loop. • Induction: Statistical pattern matching across billions of tokens. • Deduction: Formal proof generation, like AlphaProof solving complex math Olympiads. AI can crunch data and it can prove theorems. But it cannot make the jump. The paper argues that AI completely lacks Abduction, the generation of novel explanatory hypotheses when observational data is scarce. The prevailing tech myth says that "creativity is just data compression." That if you feed an LLM enough text, scientific breakthroughs will naturally pop out. Einstein’s formulation of General Relativity proves that is a delusion. When Einstein formulated relativity, the observational data didn't demand a new physics framework; classical mechanics was still massively successful. The breakthrough required a conceptual rupture. An intuitive leap from physical reality to a brand-new set of foundational axioms. An LLM can execute the math once the axioms are given. But it is structurally incapable of formulating those premises on its own. It can interpolate inside existing human thought, but it cannot transcend it. The translation of physical reality into formal axioms remains the absolute, hard bottleneck of artificial scientific invention. We can build models with trillions of parameters. We can scale compute into the stratosphere. We can make the calculator infinitely fast. But until we solve grounding, the machine can process all the data in the universe. It still can't make the jump.