Our team started using Windsurf about three months ago. The promise of "fewer context switches" sounded great, but I needed to see the numbers. Specifically, I wanted to know if the AI suggestions were actually being *used*, or if they were just generating overhead.
I built a simple dashboard to track the acceptance rate of Windsurf's suggestions per developer. The goal: quantify the efficiency gain (or loss) and see if there's a correlation with experience level or language. Here's the gist of the setup:
* **Data Source:** Windsurf's local logs (they have decent structured output for events).
* **Extraction:** A small Python script that parses the logs, tracking `suggestion_offered` and `suggestion_accepted` events, keyed by a developer hash (for privacy).
* **Storage:** A tiny SQLite database.
* **Dashboard:** A simple Flask app with Chart.js. Shows acceptance rate (%) per dev over time, plus breakdowns by time of day and file type.
Early findings after two weeks of data:
* Team-wide acceptance rate is hovering around **28%**. Much lower than I expected.
* Huge variance between developers: one senior dev is at 45%, while two junior devs are below 15%.
* Acceptance rate plummets during afternoon "deep work" blocks. It's highest in the early morning during code review periods.
* `.tsx` and `.py` files have the highest acceptance; `.yml` and configuration files are near zero.
This feels like a classic FinOps problem: we're paying for a service (indirectly, via seat cost), and we need to measure its utilization and yield. A 28% acceptance rate begs the question: is the 72% noise slowing us down, or is the 28% valuable enough to justify the cost?
Has anyone else tried to measure Windsurf's ROI at this granular level? I'm particularly curious if others have seen similar acceptance patterns, or if your teams have found ways to improve the rate through better prompting or workflow integration. I can share the script if there's interest.