system-resource-footprint-is-minimal-at-all-phases
OUT derived (depth 2)
The system achieves minimal resource footprint across all lifecycle phases: zero external dependencies at both packaging and implementation levels eliminate installation overhead and version conflicts, while lazy module imports in both API and CLI layers defer heavy computation until actually needed — minimizing deployment complexity, startup time, and memory consumption simultaneously.
Summary
The system is lightweight from every angle — it installs nothing beyond the standard library, so there are no dependency headaches or supply-chain concerns, and it delays loading expensive modules until they are actually called, so both startup and memory stay lean. This combination means deploying and running the tool costs almost nothing in terms of resources, whether you are packaging it, booting it up, or using it in production.
Justifications
SL — Deployment and startup minimality established; gated on runtime/shutdown audit (issue #123)
Antecedents (all must be IN):
- startup-performance-uses-lazy-loading — Both the API and CLI layers defer importing heavy modules (derive, compact, ask, asyncio, Storage) to function bodies rather than module top-level, minimizing import-time overhead for CLI responsiveness.
- project-has-zero-external-coupling — The project enforces zero external coupling at both the packaging level (empty `dependencies` list in pyproject.toml) and the implementation level (core Network class uses only stdlib imports), eliminating supply-chain risk and simplifying deployment.
Unless (any of these IN defeats this justification):
- issue-123-resource-footprint-audit — Issue #123: Audit resource footprint across all lifecycle phases — only deployment and startup phases are currently evidenced
Dependents
These beliefs depend on this one:
- resource-efficiency-spans-footprint-through-budgets — The system achieves resource efficiency from the broadest to the narrowest scope: zero external dependencies and lazy module loading minimize the static footprint at packaging and startup, while efficient O(1) budget tracking with approximate token estimation constrains resource consumption during both compact distillation and derive belief allocation at runtime.
- self-sustainability-is-reinforced-by-resource-efficiency — The system's self-sustaining minimality loop — where minimality generates the closed maintenance loop and self-correction mechanisms that actively maintain minimality itself — is reinforced by pervasive resource efficiency: zero external dependencies eliminate supply-chain risk to the loop's operation, lazy loading reduces maintenance overhead, and O(1) budget tracking ensures the loop operates within bounded computational cost.
- system-efficiency-spans-packaging-and-runtime — Resource efficiency is enforced at every system phase: zero external dependencies with lazy loading minimize the static footprint at packaging and startup, while O(1) per-line budget tracking with chars/4 token estimation minimize computational overhead during runtime belief distillation.