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How LLMs actually work — tokenization, embeddings, RAG, fine-tuning, agents — explained for engineers who ship production code, not papers.

31 posts below, newest first.

Securing AI Agents from Doing Bad Things

Show notes for AI Explained Part 31 — sandboxing, permission scoping, instruction hierarchy, and the metrics that tell you whether your agent is safe to ship.

Subjects that frequently appear alongside #ai. Click through to see every post on each one.

#llm 31 posts

Large language models — how they think, why they fail, what RAG fixes, and how to evaluate them. The fundamentals every engineer building on top of an LLM should internalise.

#ai-masterclass 20 posts

The AI Masterclass series: a numbered, beginner-friendly walkthrough of every concept you need to ship LLM-powered applications, from training to inference to RAG to alignment.

#beginners 20 posts

Posts written for people who are new to a topic — minimal jargon, real examples, and the context that more advanced material assumes you already have.

#machine-learning 20 posts

Machine learning from the perspective of someone shipping code, not writing papers. Algorithms, training, evaluation, and the practical trade-offs that decide which model you actually use.

#ai-explained 11 posts

The AI Explained series: short, focused episodes on individual AI building blocks — transformers, attention, tokenization, memory, tool use, multi-agent systems, and more.