A deep technical guide to how LLMs actually work — from the transformer architecture and attention mechanism to tokenization, training at scale, KV caching, inference acceleration, fine-tuning, and the modern innovations powering GPT-4o, Claude, Llama 3, and beyond. Backed by 30+ research papers.
A comprehensive guide to maximizing LLM inference performance on Apple Silicon — MLX vs llama.cpp benchmarks, quantization formats, RAM requirements, MoE models, speculative decoding, KV cache optimization, and the best models for every Mac configuration.
A complete guide to building Andrej Karpathy's LLM Wiki — the AI-maintained knowledge base pattern that replaces RAG with structured markdown. Covers the three-layer architecture, setup with Claude Code + Obsidian, ingest/query/lint workflows, and community implementations.
A practical guide to decoding LLM model names — what B, IT, GGUF, Q4_K_M, MoE, and every other suffix means. Includes comparison tables, links to the best research resources, and real examples from Gemma 4, Qwen 3.5, and Llama 4.
A comprehensive comparison of 30+ tools for tracking AI token usage across Claude Code, Codex CLI, Cursor, Gemini CLI, and more — from lightweight CLI utilities to enterprise observability platforms.
