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What 600k lines of leaked Claude Code source reveal: an unreleased autonomous agent (KAIROS) that runs 24/7 with its own memory consolidation, anti-distillation mechanisms to poison model copycats, DRM-level API attestation in Zig, a 3-layer memory architecture, and patterns any engineer building agents should adopt.

Technical breakdown of Claude Code's leaked source: anti-distillation via fake tool injection, native API attestation in Zig, frustration detection by regex, 250K wasted API calls/day from a 3-line bug, and KAIROS autonomous agent scaffolding.

Build a complete GPT from scratch in 200 lines of pure Python, understanding every component from autograd to attention to Adam, and see exactly how each piece scales up to production LLMs.

Apply 12 concrete architectural principles to build AI agents where you control the prompts, context window, control flow, and human-in-the-loop checkpoints instead of delegating them to framework abstractions.

Apply six context engineering principles to make AI agents faster, cheaper, and more reliable in production.

Build reliable LLM applications by mastering prompting techniques, RAG pipelines, deterministic workflows, and rigorous evaluation strategies.

Set up an autonomous AI research loop that modifies a GPT training script, evaluates results against a single metric, and runs ~100 experiments overnight on a single GPU with no human intervention.

Scale Karpathy's autoresearch from one GPU to 16 using SkyPilot, running ~910 experiments in 8 hours with factorial grid search that catches interaction effects greedy sequential search would miss.