Check out what I made: A dashboard tracking Aider's suggestion acceptance rate.

I've been using Aider for a few months now, mainly for refactoring and feature additions in a Python/JS codebase. While I love the concept, I found myself wondering: how effective is it, really? I was asking for changes, but was I just generating a lot of suggestions I'd ultimately reject?

To answer that, I built a simple internal dashboard that tracks Aider's suggestion acceptance rate. The goal was to get a concrete metric on its efficiency for my workflow.

Here’s the basic flow I set up using a combination of tools:
* I use a wrapper script to launch Aider, which logs each session start to a small backend service.
* The service parses the conversation from `aider-chat.md` after each session (using a post-commit hook).
* It uses some simple regex to identify Aider's proposed code blocks and then checks the git diff to see which changes were actually applied.
* This data gets sent to a Tinybird workspace, which powers a simple dashboard showing:
* Acceptance rate over time (weekly/daily)
* Raw counts: suggestions offered vs. accepted
* Breakdown by project/file type (Python vs. JS had a noticeable difference for me)

The initial findings were pretty interesting. My acceptance rate hovers around 65-70%, but it dips significantly when I'm vague with my prompts. It's become a great feedback loop for me to write better instructions for Aider.

Has anyone else tried to measure Aider's performance in a quantitative way? I'm curious if others have built similar tracking or have thoughts on what other metrics would be valuable to log.