A visual deep-dive into how coding agents work: the loop, tools, context, orchestration, verification, and failure containment.
Sample chapters
- —The agentic loop
- —Tools and permissions architecture
- —Context engineering and memory
- —Subagents and orchestration patterns
- —Verification and self-correction
A guide to how inference works in production, from token generation and KV caches to batching, quantization, and serving economics.
Sample chapters
- —Tokens and the decode loop
- —Prefill vs decode
- —KV cache mechanics
- —PagedAttention and memory management
- —Quantization strategies
A story-driven treatment of the statistics behind production ML: drift, testing, monitoring, and continuous safeguards.
Sample chapters
- —Baseline distributions and drift detection
- —Covariate drift
- —Concept drift
- —A/B testing and SRM detection
- —Continuous monitoring
An interactive explainer for preference data, reward models, PPO, Constitutional AI, and alignment-oriented training loops.
Sample chapters
- —Why RLHF matters
- —Reward models from preference data
- —Policy optimization with PPO
- —Reward shaping playground
- —Constitutional AI and tool use