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DARE Method

Design. Architect. Review. Execute. Methodology + CLI for AI-assisted software development, with mandatory human checkpoints.

DARE separates strategy (human) from tactics (AI) with explicit checkpoints: the human defines what and why and approves the plan; the AI implements how, iterating until tests/lint/types pass (the Ralph Loop).

flowchart LR
    D[1. Design<br/>human defines] --> A[2. Architect<br/>AI proposes]
    A --> R[3. Review<br/>human approves]
    R --> E[4. Execute<br/>AI + Ralph Loop]
    D -.-> dd[DESIGN.md]
    A -.-> bb[BLUEPRINT.md]
    R -.-> ap[✓ approval]
    E -.-> cc[Code + green tests]
Phase What Who Output
Design the problem and the success criteria human (AI assists) DARE/DESIGN.md
Architect architecture, contracts and tasks AI proposes, human validates DARE/BLUEPRINT.md
Review explicit approval before spending tokens human ✓ approval
Execute task-by-task implementation with the Ralph Loop AI code + green tests

Start here

Quick install

npm install -g @dewtech/dare-cli
dare init meu-projeto
cd meu-projeto
dare design "Quero uma API de autenticação JWT"

What's new

  • v3.8.0 — Formal Verification Gate: opt-in gate that PROVES (not just tests) marked critical modules against a Dafny spec (anti-bypass, exit 5).
  • v3.7.0 — Brownfield Discovery: deterministic auto-discovery of patterns (dare patterns) + lightweight planners.
  • v3.6.0 — Agent Hooks + Steering: event-driven automations + pattern injection via MCP.
  • v3.5.0 — Dual Graph: Requirement↔Code graph + dare graph owners/impact/trace/locate.
  • v3.4.0 — Security Hardening: hardened MCP server + publish with provenance.
  • v3.3.0 — Reliable Verification Core: mutation testing, fail-to-pass, decay policy, best-of-N and dare bench.

Details for each release in the CHANGELOG.