Field notes on Context-Driven Development, the knowledge-graph engine, and what it actually takes to make AI write production code.
Loading every MCP tool schema upfront costs 50–140K tokens and breaks selection accuracy past 30–50 visible tools. Four agents, same architectural pattern, four incompatible implementations — and one outlier still paying the full bill on every turn. A 2026 field comparison.
Read postA real impact-mode run on a hotspot before a single line gets written. 47 downstream files, 12 tests, 3 config templates — and a refactor plan reshaped before /draft:implement starts. Three loops, one shared graph: pre-change risk, post-completion memory, cross-track overlap.
/draft:decompose WalkthroughA real run on a 14-file track: eight modules, a circular dependency caught and broken before any code, and an LLD with contracts for the two high-complexity modules. The decomposition skill isn't a planner — it's an architectural guardrail.
Read postMost "AI for code" tools improve context by sending more of your code to a third party. Draft's graph engine does the opposite: a local, deterministic, version-controlled knowledge graph that ships with the plugin. No MCP server, no SaaS, no embeddings, no API key, no leaked code.
Read postAmazon's Kiro and Draft both solve the same problem: AI coding without structure is chaos. The similarities end there. Bug hunting, ACID enforcement, three-stage review, architecture discovery, IDE portability, and a $0 price tag — the honest comparison.
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