A living field manual for AI systems that refuses to go stale

What it does This is a structured knowledge base—part textbook, part interview crib sheet—for engineers building production AI systems. It covers the full lifecycle: model selection, RAG architecture, agentic workflows with MCP and A2A protocols, evaluation pipelines, multi-tenant isolation, and infrastructure concerns like inference optimization and cost management. The interview-prep section alone contains 110 staff-level questions with answer frameworks.

The interesting bit The author treats “living document” as a feature, not a slogan. The README explicitly contrasts itself against printed books stuck on GPT-4, and tracks model releases, protocol versions, and pricing with verification dates—currently May 2026. It also names specific models and tools rather than hiding behind false neutrality, which is rarer than it should be in technical writing.

Key highlights

• Covers emerging protocols like MCP 2.0 and A2A v1.0 alongside established patterns
• Includes real-world case studies with architectural diagrams: multi-tenant SaaS, autonomous coding agents, voice healthcare with HIPAA constraints
• Opinionated model picker with pricing data (e.g., DeepSeek V4 Flash at $0.14/$0.28 per 1M tokens)
• Explicit scope boundaries: not a Python tutorial, not a research paper digest, not a replacement for hands-on building
• MIT-licensed with open PRs from practitioners

Caveats

• The “May 2026” model names and versions (Claude Opus 4.7, GPT-5.5, etc.) appear to be forward-dated or speculative; the README does not clarify whether these are confirmed releases, internal codenames, or illustrative placeholders
• Some content areas (OpenClaw, latent reasoning, MoE serving) are mentioned but not substantiated in the truncated README
• The “continuously updated” claim is aspirational; actual update frequency depends on contributor activity

Verdict Worth bookmarking if you’re interviewing for staff+ AI roles or designing production systems and tired of blog posts that age like milk. Skip it if you need introductory Python/ML tutorials or peer-reviewed research depth.