TL;DR: A new Apache-2.0 GitHub learning path walks engineers from inference fundamentals to agent orchestration trade-offs in one ordered curriculum.
Summary: An open-source project called AI System Design published a step-by-step GitHub learning guide aimed at engineers building on LLMs, RAG, and agents. The path starts with tokens, inference servers, and AI hardware, then moves through prefill/decode latency and throughput tuning, caching, routing, and load balancing before covering embeddings, vector databases, chunking, hybrid search, and reranking. It closes with tool calling, MCP, agent memory, multi-agent orchestration, plus guardrails, observability, evaluation, cost optimization, and an eight-step design framework, all under the Apache 2.0 license.
Why it matters: Most AI system design knowledge is scattered across blog posts, so a single ordered curriculum from inference plumbing to agent operations is a useful reference for builders making real architecture trade-offs. Worth skimming the inference/scaling and agent orchestration sections first, since those map most directly to production cost and latency decisions.
Source: x_com