Architecture diagramming tool with canonical graph model, multi-renderer output, and rule-based extraction from codebases.
7 lessons
When a system must produce multiple visual representations of the same architecture, build a single normalized graph model and derive all outputs from it. Renderers that read the same model cannot drift from each other; renderers that maintain their own state always will.
When building a knowledge graph that must support regeneration, deduplication, and cross-system references, enforce a structured ID format from day one. An ID like `entity_type.domain.name` is simultaneously human-readable, machine-parseable, and stable across re-extraction — properties that free-fo...
When building multiple rendering backends for the same data model, define the visual language (colors, shape semantics, edge style categories) once and map it to each renderer's syntax independently. Visual consistency across output formats is easy to lose when renderers are built in isolation; a sh...
When building a multi-phase system, track progress at the row level within each phase (Open → Started → Completed with timestamps), commit only when an entire phase is green, and never batch multiple phases into one commit. This granularity makes it possible to resume mid-phase, measure velocity, an...
When building a query or configuration system, provide a registry of named presets for the common cases and a full custom endpoint for everything else. Presets give users instant value without learning the schema; the custom path preserves full flexibility for power users.
In a split-stack project (separate frontend and backend processes on different ports), configure the frontend dev server to proxy API requests to the backend rather than hardcoding backend URLs or relying on CORS alone. The proxy eliminates cross-origin issues during development, keeps the frontend...
When building an entity extraction pipeline, implement rule-based heuristics first and defer LLM-assisted extraction until the deterministic baseline is tested and measured. The rule-based layer gives you a reproducible, cost-free, fast foundation that LLM extraction can extend — not replace.