Skip to content

Playbook: AI-Assisted Dev Workflow

Objective

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

Pre-requisites

Workflow Architecture

flowchart TD
    A[Drafting: Cursor] -->|Outline & Python script| B[Implementation: Aider]
    B -->|Complex functions| C[Refactoring: Jules]
    C -->|Best practices & Unit tests| D[Verification: Anti-Gravity]
    D -->|Plan-Code-Test loop| E[Audit: Human Review]
    E -->|Approved| F[Merge to main]
    B -.->|Linter errors| B
    D -.->|Test failures| B

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.

Local-First Setup

A fully local-first development workflow ensures complete privacy and zero per-token costs.

  • Reasoning: Use Qwen3-Coder-Next via Ollama. This model is highly optimized for coding tasks and can be run on consumer hardware with 16GB+ VRAM.
  • Agent: Aider configured to use the local Ollama endpoint.
  • Context Management: Leverage Aider's repository map to provide the LLM with a concise overview of your codebase, ensuring high relevance while staying within local context limits.
  • Verification: Run local unit tests and linting autonomously after each AI-generated change.

Token-Efficiency & Value

  • Differential Context: Only send files that are directly related to the task. Use Aider's /add and /drop commands to manage context manually if the auto-selection is too broad.
  • Commit Summaries: Use the LLM to generate concise git commit messages, but review them to ensure they provide technical value without fluff.
  • Local Routing: Use LiteLLM to route simple tasks (like docstring generation) to smaller, faster local models while reserving larger models for complex logic.

Variants

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

Case Studies & References

Contribution Metadata

  • Confidence: high
  • Last reviewed: 2026-03-01

Sources / References

  • https://blog.cloudflare.com/vinext