Practical guide to adopting graph-based system models incrementally. Covers escape hatches, LLM workflows, and week-by-week adoption strategy.

Graph-based system models validate themselves through structural checks, semantic analysis, and automatic test generation from invariants.

Source code is a lossy format that discards intent. LLMs expose this weakness. Graph-based models preserve meaning as first-class structure.

How GPT-2's simple language prediction training led to unexpected capabilities like translation and reasoning that surprised its creators.

Learn how to build AI assistants that act like real employees using SOPs, LLMs, and automation frameworks like CrewAI and AutoGen for business automation.