Observed Signal · Jul 3, 2026 · Technical Release · Source: DEV Community · Impact: 1/5 · Sentiment: Neutral
Free 84‑page AI handbook for LLMs and agents
Vinicius Pereira published a free 84-page handbook titled "Artificial Intelligence in Practice" that guides beginners from basic LLM concepts (tokens, context windows, temperature) through running models locally and building AI agents. The book, available on GitHub under a CC BY-NC-SA license, covers foundations, running models on personal hardware (mentions Ollama and Apple Silicon), Retrieval-Augmented Generation (embeddings, vector databases, chunking), multi-agent orchestration (CrewAI, LangGraph, tool integrations, browsing, MCP), and deeper topics like fine-tuning, cost, security, privacy, and testing. It includes three capstone projects and was hand-tested; prices and version numbers were verified in mid-2026. The author plans to serialize some chapters on dev.to.
A practical, free educational resource that helps practitioners learn LLMs and agents; useful but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Vinicius Pereira published a free 84-page handbook called "Artificial Intelligence in Practice."
- The handbook is hosted on GitHub and released under a CC BY-NC-SA license.
- Content covers foundations (tokens, context windows), running local models (mentions Ollama and Apple Silicon), RAG (embeddings, vector databases, chunking), and AI agents (CrewAI, LangGraph, tools, browsing, MCP).
- The material was hand-tested and prices/version numbers were verified in mid-2026.
- The author will serialize deeper chapters on dev.to.
Connected Companies & Entities
5 Entities mapped“It is free, and it just went up on GitHub:”
“Running models on your own machine. Ollama, picking a model family for your task, and honestly sizing what your hardware can run, including ...”
“Agents. The loop that turns a chatbot into something that does work: CrewAI, LangGraph, tools, browsing, MCP, and agents from different fram...”
“Running models on your own machine. Ollama, picking a model family for your task, and honestly sizing what your hardware can run, including ...”
Ontology Mapping & Concepts
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