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Playbook: AI-Assisted Dev Workflow

Objective

Accelerate homelab infrastructure development using a hierarchy of AI coding agents.

Pre-requisites

Step-by-Step Flow

  1. Drafting: Use Cursor to outline a new automation script in Python.
  2. Implementation: Use Aider to perform targeted code generation for complex functions.
  3. Refactoring: Assign Jules to refactor the repository asynchronously, focusing on best practices and unit test coverage.
  4. Verification: Anti-Gravity runs a plan-code-test loop to ensure the new script doesn't break existing Home Assistant configurations.
  5. Audit: Review AI-generated commits before merging into the main branch.

Data Contract

  • Input: Natural language prompt + Codebase context.
  • Output: Git diff + Commit message.

Failure Modes & Recovery

  • Hallucination: AI generates non-existent API calls.
    • Detection: Linter or compiler errors.
    • Recovery: Feed error logs back to Aider for automated fixing.
  • Context Limit: Large repositories exceed LLM context window.
    • Recovery: Use Aider's repository map feature.

Variants

  • Cloud-Based: Use GPT-4o via LiteLLM for better reasoning.
  • Privacy-First: Use local Llama-3-Coder models in Ollama.

Case Studies & References