🗺️LLM & AI EcosystemConcepts and categories — the best place to start🤖AI AgentsBuilding blocks, patterns and frameworks for autonomous AI systems💻AI Coding AssistantsIDE plugins, agentic IDEs and CLI agents for developers🏢Proprietary ModelsAI labs and their flagship model families🔓Open Source / Weights ModelsOrganizations releasing open-weights models and their flagship releases☁️Cloud Managed AIAWS, Google Cloud, Azure and Cloudflare AI platforms compared⚡Dense vs MoEHow Dense and Mixture-of-Experts architectures process a token🗜️Quantization — Memory by FormatHow numerical precision (FP32 → INT4) affects a 70B model's memory footprint🔍RAGPipeline stages, components and tools for Retrieval-Augmented Generation
📝LLMs Are Not Truth MachinesWhy asking an LLM for ground truth is the wrong mental model — and what RAG does instead.📝Use Case — Chat LLM (ChatGPT-like)How a Chat LLM works end-to-end: prompt construction, optional RAG, inference, token generation, and the key mental models to avoid common mistakes.📝Use Case: Knowledge Search for AnalystsHow a business analyst queries private document corpora with AI — architecture, governance, and practical trade-offs.📝Use Case: Agentic CodingHow an AI agent assists a developer beyond autocomplete — planning, editing, running tests, and iterating autonomously.📝Use Case: Ops Workflow AutomationHow autonomous agents handle multi-step operational processes — incident triage, pipeline orchestration, and the human-in-the-loop model.📝MCP: The Standard for Connecting AI to EverythingWhat MCP is, how it works architecturally, and why it matters for AI agent development.📝RAG: Retrieval-Augmented GenerationHow RAG works, when to use it, and trade-offs vs fine-tuning.
📝Meta-Reasoning: Teaching AI Agents to Know When They're StuckA 2026 study shows that agents capable of monitoring their own reasoning and switching strategies outperform standard agents by 31% — with open-source models benefiting the most.📝"Stop the Denial" — Laurent Alexandre on AI at the French National AssemblyLaurent Alexandre argues before the French National Assembly that AI already surpasses experts on cognitive tasks — and that downplaying it is a strategic mistake.📝Co-Intelligence: Living and Working with AIKey lessons from Ethan Mollick's book on AI as a collaborative partner — practical frameworks for work, creativity and education.
📊Artificial AnalysisIndependent analysis of AI — choose the best model and provider for your use case📈Epoch AIResearch institute studying AI progress — trends in compute, data, and capabilities🎓Andrej KarpathyAI researcher and educator — deep learning courses, neural networks from scratch, and clear explanations of LLMs🐦Claude Devs (X)Official Anthropic account for Claude developers — API updates, tips and community news🧰OpenAI DevelopersOfficial OpenAI developer resources — docs, guides, examples, and platform updates
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LLM & AI Ecosystem

v2026-Q1 · 2026-04-02
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