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Internals Decoded

A technical publishing studio for AI. Free to read, and there is a lot of it.

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Written by the Internals Desk. admin@internalsdecoded.com

© 2026 Internals Decoded. Built for anyone curious about what really goes on inside AI.

Series · 10 of 10 parts · Beginner

AI, From Zero

The ground floor: what a language model actually is, why it works, and why it fails, explained for someone starting from nothing.

What you'll understand

By the end you can follow any AI conversation, read model announcements critically, and know exactly what is and isn't magic.

Episodes

01What an LLM Actually Is (and Why Predicting Words Works)It predicts the next word. That one trick, at scale, is doing everything you've seen.12 min→02Tokens: The Currency of AIWhy models see 'strawberry' as three pieces, why pricing is per token, and why that matters to you.12 min→03Attention, Gently: How Models Decide What MattersThe mechanism behind the magic, explained with zero math.14 min→04Embeddings: Meaning as NumbersHow machines measure similarity, and why it powers search, recommendations, and memory.12 min→05Training vs Inference: Why Building Costs Millions and Asking Costs CentsTwo completely different phases of a model's life, constantly confused.10 min→06Context Windows: The Model's Working MemoryWhat fits, what falls out, and why long conversations get weird.9 min→07Why Models Make Things UpHallucination isn't lying. It's the same mechanism that makes models work at all.10 min→08Open vs Closed Models: What 'Open Source AI' Really MeansWeights, data, licenses: the three things people conflate when they say open source.10 min→09Fine-Tuning, Explained: Teaching an Old Model New TricksWhen it helps, when RAG beats it, and why most teams never need it.14 min→10How to Actually Choose a ModelBenchmarks, price, latency, context: a working decision framework instead of hype.11 min→

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