IGNITE COPILOT 🎓

🧠📚 ¿Y si la IA fuera la Thermomix del aula? Hoy en @elEconomistaes nuestro CEO @IgnacioAso presenta #IGNITECopilot: la IA que reduce hasta un 80% el tiempo de planificación docente y genera situaciones didácticas en segundos. 👩‍🏫⚡ 👉 ignitecopilot.ai #Educación #IA #LOMLOE #EdTech

💣 Hot take: Si eres profesor y solo usas ChatGPT… ya vas tarde No está diseñado para educación No entiende el currículo No ahorra tanto tiempo como crees La nueva ventaja competitiva es usar IA educativa de verdad: 👉 IGNITE Copilot no es un extra, es el siguiente paso ignitecopilot.ai/chatgpt-para-p… ¿Estamos listos para decirlo o seguimos mirando hacia otro lado? #EdTech #IAparaprofesores

La IA en educación necesita docentes dentro del circuito de decisión: eso es Human-in-the-Loop. Los agentes de IA en educación pueden generar materiales, analizar datos y personalizar el aprendizaje. La clave: IA + supervisión docente = impacto real en el aula. Más aquí 👇 ignitecopilot.ai/agentes-de-ia-… #IAenEducación #HumanInTheLoop #EdTech #AgentesDeIA

RAG was never the end goal. Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible. RAG (2020-2023): - Retrieve info once, generate response - No decision-making, just fetch and answer - Problem: Often retrieves irrelevant context Agentic RAG: - Agent decides *if* retrieval is needed - Agent picks *which* source to query - Agent validates *if* results are useful - Problem: Still read-only, can't learn from interactions AI Memory: - Read AND write to external knowledge - Learns from past conversations - Remembers user preferences, past context - Enables true personalization The mental model is simple: ↳ RAG: read-only, one-shot ↳ Agentic RAG: read-only via tool calls ↳ Agent Memory: read-write via tool calls Here's what makes agent memory powerful: The agent can now "remember" things - user preferences, past conversations, important dates. All stored and retrievable for future interactions. This unlocks something bigger: continual learning. Instead of being frozen at training time, agents can now accumulate knowledge from every interaction. They improve over time without retraining. Memory is the bridge between static models and truly adaptive AI systems. But it's not all smooth sailing. Memory introduces new challenges RAG never had, like memory corruption, deciding what to forget, and managing multiple memory types (procedural, episodic, and semantic). Solving these problems from scratch is hard. If you want to build Agents that never forget, Cognee is an open-source framework (12k+ stars) to build real-time knowledge graphs and get self-evolving AI memory. Getting started with Cognee is as simple as this: 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝗮𝗱𝗱("𝗬𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗵𝗲𝗿𝗲") 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝗰𝗼𝗴𝗻𝗶𝗳𝘆() 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝗺𝗲𝗺𝗶𝗳𝘆() 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝘀𝗲𝗮𝗿𝗰𝗵("𝗬𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝘆 𝗵𝗲𝗿𝗲") That’s it. Cognee handles the heavy lifting, and your agent gets a memory layer that actually learns over time. I have shared the repo in the replies!

8 Types of LLMs used in AI Agents (Must know for Gen AI Data Scientists & AI Engineers): Here's what they are and what they do: 🧵

Agentic AI explained simply

Someone literally built a free AI university - all in one repo, covering real-world AI systems step by step Link - github.com/jamwithai/arxi… Here’s what you’ll learn: Week 1 - Setup everything Docker, FastAPI, databases Beginner-friendly foundation Week 2 - Feed it real data Automatically fetch research papers Fully automated data pipeline Week 3 - Teach it to search BM25 keyword search implementation Your own Google-like search system Week 4 - Make it smarter Hybrid search enabled Understands meaning, not just keywords Week 5 - It talks back Complete RAG system Ask questions, get accurate answers Week 6 - Production ready Caching and monitoring added Runs like a real product Week 7 - Give it a brain Agentic AI with LangGraph Even works with Telegram

10 Python Libraries for Generative AI You Need to Master in 2026

As we move toward deploying autonomous agents in social systems, understanding emergent collective behavior is crucial. Individual capability benchmarks tell you nothing about what happens when hundreds of these agents interact. So what happens when you deploy hundreds of LLM agents into social dilemmas? This new research builds an evaluation framework to test the collective behavior of LLM agent populations at scale, far beyond the small groups tested in prior work. Newer, more capable models tend to produce worse societal outcomes. Agents optimizing for individual benefit over collective good drive populations toward poor equilibria. Using cultural evolution simulations, the researchers show a significant risk of convergence to bad societal outcomes, especially as populations grow larger and cooperation becomes less advantageous. Paper: arxiv.org/abs/2602.16662 Learn to build effective AI agents in our academy: academy.dair.ai

Machine Learning

7 Layered of LLM Stack 📘📚 #ai #llm

How Exa built a production-ready deep research agent with LangSmith and LangGraph 👀 Exa, known for their fast, high-quality search API, has a deep research agent that delivers structured answers on the web -- no matter how complex the query. Powered by LangGraph, they've built a multi-agent system. For Exa, one of the most critical LangSmith features was observability, especially around token usage. "The observability – understanding the token usage – that LangSmith provided was really important. It was also super easy to set up." – Mark Pekala, Software Engineer at Exa. This visibility into token consumption, caching rates, and reasoning token usage proved essential for informing Exa's production pricing models and ensuring cost-effective performance at scale. Read about how they built their agent here: blog.langchain.com/exa/?utm_mediu…

Top 5 Types of AI Agents 📘📚