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#llm

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.

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 #llm. Click through to see every post on each one.

#ai 31 posts

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

#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.