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Scenario: solo developer + AI tools

Who this is for

A single developer shipping a side project or small product, leaning heavily on Claude Code / Cursor / Copilot. You move fast, there is no second reviewer, and you do not want a SaaS subscription just to keep yourself honest.

The risk

When you are the only reviewer, you are also the bottleneck and the blind spot. AI tools cheerfully produce a hardcoded key, an exec on user input, or a dropped auth check — and at 1 a.m. you will merge it. You need a second opinion that never gets tired and costs nothing.

The setup (15 minutes)

  1. Run Meridian locally, Ollama-only so it costs $0 and needs no API keys:

git clone https://github.com/weilmaschinchen/meridian.git
cd meridian
echo 'OLLAMA_BASE_URL=http://host.docker.internal:11434' > .env
docker compose up -d --build
(See Docker Compose and LLM cost control → Setup A.)

  1. Add a pre-commit hook that runs the check script from AI-generated code. Now every commit is gated before it exists.

  2. Feed blocks back to your AI tool. When the hook prints findings, paste them to the agent and let it fix them.

A day in this workflow

flowchart LR
    A[Ask Claude for a feature] --> B[git add]
    B --> C[pre-commit -> Meridian]
    C -->|BLOCKED| D[Paste findings to Claude -> fix]
    D --> B
    C -->|APPROVED| E[commit + push]

What you get

  • A tireless second reviewer for $0.
  • No code leaves your machine (air-gap friendly) if you stay Ollama-only.
  • A growing set of RFCs — useful even solo, e.g. to remember why you accepted a flagged change.

What you should still do

  • Keep your tests. Meridian gates risk/shape, not correctness.
  • If you ever push to a shared remote, add a server-side gate — local hooks can be --no-verify'd (by you, at 1 a.m.).
  • Tune custom rules for your stack's specific footguns.

Honest caveat

Ollama-only review quality depends on your local model. It will catch obvious issues reliably (Gates 1+2 are deterministic); the LLM layer's depth scales with the model you can run. If you have spare budget, add a small LLM_DAILY_CAP_USD cloud fallback for the hard cases — see LLM cost control.

Next: Small team scenario