4 posts
LLMs write flawless-looking math and still get the answer wrong, because next-token prediction is probabilistic, not provable. How knowledge graphs fix that: the neurosymbolic pipeline, Paths-over-Graph and KG-RAR, and why traceable reasoning beats raw accuracy.
A model on its own can't perceive, act, or get feedback from the physical world. The Life Harness is the four-layer interface that makes it reliable — environment contract, trajectory regulation, action fixer, and procedural memory.
Most agents forget everything between sessions. This is the opposite: a self-evolving memory built with Claude Code and a Karpathy-style architecture — why standard RAG fails agents, how a compile-time knowledge graph beats query-time retrieval, and the loop that makes knowledge compound.
Shrinking a 1,500-line multi-agent workflow to 45 lines and letting an AI compiler wire the graph for you. The cost of hand-wiring agents, intent-to-executable-graph, the shift from coding to reviewing, and the economics of a graph-centric future.