A Skeptics Review of Windsurf for Other Skeptics

So you’ve been hearing a lot of hype around Claude Code, Windsurf, etc about how it’s magic that can do your entire job in mere minutes - yet when you try integrating AI into your workflows, it just feels like super-fancy auto-complete? This is a skeptics review for other skeptics, so I won’t try to fluff things up or talk down to your experience, since I was in your exact shoes a couple weeks ago. To set the stage - this review is for Windsurf using Claude 4 Sonnet in Nov 2025. I’ll walk you through the complexity of the problem I had it solving, call out any important trade-offs or unexpected things I wish I would have known to start, and then leave you with a few ideas for how you might test the waters.

My previous AI experiences 🔗︎

To give you context on where i land between “utter novice” and “AI whisperer”: I’ve been using some version of Copilot or Codeium for over a year, and copy-pasting code out of ChatGPT before that. I’m not a front-runner working with agents or MCP or whatever the next hot trend is, but I know enough to be productive.

That said, I’ve been skeptical of the “magic” of Claude Code/Windsurf, based on my experiences with existing tools. They’ve been handy as auto-complete, and for writing self-contained scripts/functions where the model doesn’t need a good grasp of the whole context, e.g. a function for parsing complicated regex or data structures with simple inputs & outputs.

From past attempts, I did not trust handing the tools a larger refactor/request. They would consistently create more work with their suggestions, as i tried to figure out if any of it was useful and wouldn’t bite me in the ass. With the more powerful models available now, though, I’m coming around.

Task Complexity 🔗︎

To make sure you can really understand what happened and not blindly trust me, let’s discuss the actual tasks I gave Windsurf. For my first foray, I had a green-field project for it to tackle. I needed it to write a medium-size Python script which interacts with a graph database, constructs a bunch of Cypher queries, and then does a bit of processing on the results. This ended up being about 2-4k lines of Python across a few different modules, including another script that loads a bunch of data into the graph database. So key elements which I think made this easier than some tasks you might set it on:

• No existing codebase to deal with
• Small total size (2-4k lines, ~10 files)
• Using Python, and only popular libraries that should have many examples in the training data
• I didn’t constrain it to particular patterns, I let it choose it’s own path
  • I wasn’t concerned about tech debt. It’s unlikely to be used in a year or two, so I really only care about minimum level of resiliency & maintainability. If it works, it works.

How Windsurf did 🔗︎

Without fangirling too hard - I was incredibly impressed. I was able to give it vague prompts, and it was able to make a lot of the same choices I would have done, but saving me all of the decision fatigue. I can be a perfectionist, so it’s really helpful that it lets me stay at the high level of “what am I trying to accomplish” rather than my usual experience of bike-shedding minor code quality concerns and losing track of what I needed to do.

I iterated with it probably 25-50 times as I had new ideas to refactor & add new features. I avoided fixing anything myself to see if it could get itself out of a pickle - and at least for this simpler codebase, it never got stuck to a point where i had to manually step in. I was incredibly impressed with how it was able to do sweeping refactors of large chunks of the script, and usually it would still be functioning correctly.

This new future, where devs avoid arthritis by making an AI do 90% of the typing, is looking pretty sweet.

What you need to know 🔗︎

There were a few takeaways I feel should be aware of:

• Judicious logging was surprisingly useful so the model could debug it’s own issues
  • As a “print-debugging developer” myself, It was a bit wild to read it’s train of thought and realize it mirrored how I would have approached it. I got this for free since the model started from a fresh repo and added a ton itself, but if you are working on an existing codebase, you would do yourself favors to add more logging.
• The diff view is only useful for accepting/rejecting CONCISE changes
  • Much like digging through a confusing git rebase diff, it can often be easier to just “accept all” and then ask the model to refactor again, rather than picking & choosing and risking breaking things. I think someday the industry will find a better UX for this, unless they like fooling themselves with fake stats on how often AI code gets accepted.
• The model likes to subtly create tech debt for you - be more vigilant than me
  • I was so busy mooning over the script continuing to work correctly, that I didn’t notice until much later that there was a variety of interesting tech debt I didn’t expect. The model is smart enough to refactor some relatively complex features, but then it creates a duplicate constants variable 5 lines below it’s twin?
  • It would also do silly things like disconnect core patterns I hadn’t asked to be changed, create brand new functions instead of refactoring, and sprinkle imports all throughout the file.

Dip your toe 🔗︎

Your work gave you a license to use this and you want to give it an honest try before shelving it - but on what? What tasks will showcase what it can do? If you’re like me, you default to giving it really scoped down tasks, and you have to fight that urge to experience what is possible. A few ideas for you, which should only take ~5 minutes:

• The Explainer (Read-only)
  • Think of a change you already know how you would approach (e.g. adding a new middleware to an API). Ask it to explain where in the codebase you would need to make changes, and see if it gets it right.
• The Wizened Architect (Read-only)
  • Send it off to dig through your codebase and document all the tech debt. Does it agree with your personal list? Does it point out anything new you like?
• The Excited Intern (Write - this is where it will start to change your mind)
  • Give it a piece of tech debt you’ve put off refactoring, add relevant context, and ask it to write a plan for how it would solve it. Skim the result, then send it on it’s way to try. Worst case even if it doesn’t solve it, it just got you thinking about it again - so why don’t you fix it right now you lazy bum?
• The Janitor (Write)
  • Have it refactor an ugly pattern within your codebase. Maybe hypothetically your team has a bunch of Python code masquerading as Golang (x, err = func()), and you’ve been putting off doing anything about it. Let it go to town and see how far it gets.

Whether or not you’re sold on the magic of Windsurf yet, it feels undeniable that the AI-era has reduced the toil & time it takes to bring something to life. Now more than ever, we need to be aggressive about asking “Should we?”, since “can we?” is gonna be yes way too often for our own good.

P.S. It sometimes gets stuck after running a terminal command within the Cascade window. “Kicking” it by re-running the same command always got it out of the rut.