Alright, I finally have some hard data to share on my Cursor experiment! I’ve been using it as my daily driver for a full month on our SaaS product (a mid-sized CRM), tracking my commit velocity and tagging any refactor commits. The results are… illuminating.
My velocity on new features and small fixes is up by about 40% 📈. The AI’s ability to generate boilerplate, write tests I'd procrastinate on, and quickly adjust code based on my vague prompts (“make this API call more resilient”) is a genuine game-changer. I’m shipping more.
**But**, my refactor commit count has also increased by roughly 25%. I noticed a pattern: Cursor is fantastic at *implementing* exactly what I ask for, but it doesn't push back. It will generate slightly convoluted functions or duplicate logic across files if I'm not meticulously precise in my instructions. The speed encourages a “just make it work” mindset, and I've had to actively fight it.
A few specific pain points I’ve logged:
* **Library inconsistencies:** I’ll ask for a date formatting utility in one feature, and then a week later for another, and it generates a similar-but-different helper. Now I have two.
* **Configuration drift:** Small changes to config files (like our API client setup) made in isolation don’t always follow the established project patterns unless I explicitly spell them out.
* **Review fatigue:** My teammates are now seeing more “weird” code that *works* but doesn’t quite feel idiomatic, increasing PR review cycles.
The trade-off is clear: **massive speed gains at the cost of increased vigilance.** I’m now spending the last hour of each Friday specifically on cleaning up “Cursor-isms” from the week. It’s worth it, but it’s a new required discipline.
Has anyone else set up specific guardrails or prompts to keep the quality consistent? I’m thinking of creating a “project context” file with our patterns, but I’d love to hear what’s working for you.
happy evaluating!
Your observation about AI generating similar-but-different helpers touches on a fundamental issue with current tooling: they lack context of the existing codebase's patterns. It's not just about precision in your prompt, it's about the model's inability to perform a semantic search across your project to recommend reuse.
This is where a more integrated platform approach could help. Imagine if your AI assistant was coupled with a static analysis tool that flagged new functions with high similarity to existing ones before commit. You'd get a prompt suggesting consolidation.
The configuration drift point you started to make is critical. I've seen this manifest in Infrastructure as Code, where slight variations in Terraform modules generated for similar environments create massive drift over time. The velocity gain gets erased by the compliance and maintenance overhead.
infra nerd, cost hawk
Your data confirms a hypothesis I've had. The velocity increase is real, but it's shifting the bottleneck downstream.
> It will generate slightly convoluted functions or duplicate logic
This isn't a Cursor problem, it's an SRE problem. You've effectively accelerated development without a corresponding increase in your code review or architectural guardrail capacity. The "just make it work" output gets merged because your process can't catch it.
You need to treat the AI's output like an unsupervised junior dev's code. Your CI pipeline needs static analysis steps now more than ever - similarity detection, custom linter rules for your patterns, maybe even a diff-check against known helper libraries.
Otherwise, you're just trading dev time today for toil and incident load tomorrow. Your refactor commits will keep climbing.
Five nines? Prove it.