¿Próximo modelo de imagen de OpenAI? Si los resultados conservan este nivel de detalle incluso con imágenes menos típicas (e.g. mapas de zonas más locales, anatomías de otros seres vivos, interfaces menos conocidas) estaríamos ante otro salto más de calidad en generación de
LLMS
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AI Hallucinations in Medical Contexts: Reliability Concerns
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Hallucinations in medical contexts is a bit alarming TBH. Jagged intelligence at its finest, brilliant at some things and completely unreliable at others.
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Exploring New Open Source AI Models
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Excited to play around with this one! Always nice to see new open models.
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Introduction to Claude Cowork: collaborative AI for real projects
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→ Introduction to Claude Cowork Claude trabajando directamente sobre tus archivos y proyectos reales. No es un chatbot. Es un colaborador que entiende tu contexto. Aquí deja de ser un juguete y se convierte en herramienta de trabajo real. https://
anthropic.skilljar.com/introduction-t
o-claude-cowork
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Anthropic offers free certified AI academy with agent courses
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Anthropic tiene una academia gratuita con certificados oficiales. 16 cursos.
Desde cero hasta agentes de IA. Te dejo los más interesantes con link directo -
DeepSeek V4: Chinese AI Model on Huawei Silicon
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DeepSeek is about to release V4, and for the first time, a frontier Chinese AI model will run natively on Huawei silicon. A brief analysis and why its much bigger than most people think. Alibaba, ByteDance, and Tencent have placed bulk orders for hundreds of thousands of
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Optimizing AI agent performance through harness layer refinement
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You can now make your AI agent rewrite itself and get 6x better.
— AlphaSignal AI (@AlphaSignalAI) 4 avril 2026
Most AI optimization focuses on the model. Meta-Harness focuses on the harness instead.
That's the code wrapping the model. It controls memory, retrieval, and execution.
Changing just this layer creates a 6x… pic.twitter.com/O6B3KuWQ09You can now make your AI agent rewrite itself and get 6x better. Most AI optimization focuses on the model. Meta-Harness focuses on the harness instead. That's the code wrapping the model. It controls memory, retrieval, and execution. Changing just this layer creates a 6x
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OAI Models Compatibility and GPT Version Recommendations
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All OAI models work with it, just makes no sense to use older gpt models.
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Complete AI Learning Roadmap: Videos, Repos, Books, Papers, Courses
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Stop wasting hours trying to learn AI. 📘📚 I have already done it for you. With one list. Zero confusion. And no fluff 📹 Videos: 1. LLM Introduction: lnkd.in/dMqbaZdK 2. LLMs from Scratch: lnkd.in/dYYwEhYy 3. Agentic AI Overview (Stanford): lnkd.in/dArmMt2i 4. Building and Evaluating Agents: lnkd.in/dBWd2W8u 5. Building Effective Agents: lnkd.in/dHfdebqw 6. Building Agents with MCP: lnkd.in/dXuNHrRJ 7. Building an Agent from Scratch: lnkd.in/da3ANw3w 8. Philo Agents: lnkd.in/dq-BfZE5 🗂️ Repos 1. GenAI Agents: lnkd.in/d3UDtwwv 2. Microsoft's AI Agents for Beginners: lnkd.in/dHvTmJnv 3. Prompt Engineering Guide: lnkd.in/gJjGbxQr 4. Hands-On Large Language Models: lnkd.in/dxaVF86w 5. AI Agents for Beginners: lnkd.in/dHvTmJnv 6. GenAI Agentshttps://lnkd.in/dEt72MEy 7. Made with ML: lnkd.in/d2dMACMj 8. Hands-On AI Engineering:lnkd.in/dgQtRyk7 9. Awesome Generative AI Guide: lnkd.in/dJ8gxp3a 10. Designing Machine Learning Systems: lnkd.in/dEx8sQJK 11. Machine Learning for Beginners from Microsoft: lnkd.in/dBj3BAEY 12. LLM Course: lnkd.in/diZgGACG 🗺️ Guides 1. Google's Agent Whitepaper: lnkd.in/gFvCfbSN 2. Google's Agent Companion: lnkd.in/gfmCrgAH 3. Building Effective Agents by Anthropic: lnkd.in/gRWKANS4. 4. Claude Code Best Agentic Coding practices: lnkd.in/gs99zyCf 5. OpenAI's Practical Guide to Building Agents: lnkd.in/guRfXsFK 📚Books: 1. Understanding Deep Learning: lnkd.in/dgcB68Qt 2. Building an LLM from Scratch: lnkd.in/g2YGbnWS 3. The LLM Engineering Handbook: lnkd.in/gWUT2EXe 4. AI Agents: The Definitive Guide – Nicole Koenigstein: lnkd.in/dJ9wFNMD 5. Building Applications with AI Agents – Michael Albada: lnkd.in/dSs8srk5 6. AI Agents with MCP – Kyle Stratis: lnkd.in/dR22bEiZ 7. AI Engineering: lnkd.in/gi-mQcXa 📜 Papers 1. ReAct: lnkd.in/gRBH3ZRq 2. Generative Agents: lnkd.in/gsDCUsWm. 3. Toolformer: lnkd.in/gyzrege6 4. Chain-of-Thought Prompting: lnkd.in/gaK5CXzD. 🧑🏫 Courses: 1. HuggingFace's Agent Course: lnkd.in/gmTftTXV 2. MCP with Anthropic: lnkd.in/geffcwdq 3. Building Vector Databases with Pinecone: lnkd.in/gCS4sd7Y 4. Vector Databases from Embeddings to Apps: lnkd.in/gm9HR6_2 5. Agent Memory: lnkd.in/gNFpC542 Repost for your network ♻️