Six guides that take you from "I have no idea how this works" to the real engineering frontier of on-device AI. The first five need no prior AI or math; the last is the advanced deep end.
The absolute basics (models, tokens, weights), what "edge" means, the one golden rule of speed, then the tricks that make it possible: quantization, the KV cache, hardware backends, speculative decoding, memory-sharing, and serving.
Open guide 1 →Opens up the mechanism behind every LLM: how words become vectors, how attention lets each word look at the others (Query, Key, Value), multi-head attention, word order, the full Transformer block, and how the next word is chosen.
Open guide 2 →Turning text into numbers you can compare by meaning. The meaning-map, cosine similarity, the "king − man + woman ≈ queen" trick, semantic search, and a full walkthrough of RAG — letting an AI answer from your own documents.
Open guide 3 →How to specialize a general model without a data center. Prompting vs RAG vs fine-tuning, what full fine-tuning costs, the LoRA shortcut, QLoRA, a when-to-use-which guide, and why tiny swappable adapters are perfect for the edge.
Open guide 4 →The loop under everything else: random weights, the loss (how wrong it is), gradient descent (rolling downhill), backpropagation (assigning blame), epochs and batches, and overfitting vs generalizing — plus how it all connects back to the other four guides.
Open guide 5 →The engineering frontier: K-quants and mixed precision beyond Q4_0, squeezing the CPU with weight repacking and big-core threadpools, the reality of NPUs and QNN, and continuous batching built on paged-KV — mapped to EdgeLM's honest built-vs-in-progress status.
Open guide 6 →