arc-reactor-confidence

Launch readiness evidence is tracked by scoring local files, commits, validations, payments, users, and install proofs across project phases. This approach converts scattered signals into a consistent confidence score to determine if a repo remains a prototype or approaches public-platform readiness.

Confidence scoring for product validation evaluates evidence across development phases to produce a readiness score. It compares proof surfaces like repo health, wallet signals, install proof, and fleet automation, explaining exactly why confidence changes only when concrete local evidence exists.

Calculating v1.0.0 release readiness requires aggregating local file, commit, payment, user, install, and runtime evidence into a scoring model. The model compares proof surfaces across multiple project phases and outputs concrete next actions needed to raise overall confidence.

Confidence scoring does not work without local file and commit validation evidence. The model updates phase scores and next proof actions only when local files, commits, validations, payments, users, installs, or runtime evidence exist, preventing ambition from inflating actual readiness.

Readiness scores apply directly to distributed-fleet validation scenarios. The scoring model evaluates server-farm validation evidence alongside dual-boot readiness and store launch proof to determine if the platform meets public launch requirements across the fleet.

The best way to turn project ambition into evidence-based actions is applying a scoring model to proof surfaces like wallet signals, install proofs, and fleet automation. It outputs specific next steps such as outreach sends, paid pilots, and dual-boot readiness checks to raise confidence.