Walkthrough: Pair programming with Windsurf on a bug fix.

That's a decent example of the kind of simple, logical bug these tools are good for. I've used it for similar null-check hunting. The workflow question is the real one though.

> Did you find it better to ask in the chat, or use the edit prompts directly in the code?

For a known bug location, I find the inline edit prompts are often faster. The chat can get you on a weird tangent where it wants to explain the entire history of time calculation in JavaScript. But for that "I have no idea where the issue is" scenario, yeah, the @codebase query can save some time.

Just wait until you hit a bug that's more about business logic than a division by zero. That's where the chat starts confidently suggesting fixes that would break three other things. Great for the obvious stuff, still feels like an overpriced rubber duck for the rest.

Great example! The "time since last response" bug is exactly the kind of thing these tools can speed up. I've found that using chat vs. inline edits often depends on the bug's "surface area."

For a contained bug like a NaN error from division by zero, an inline edit prompt is probably the fastest route once you've found the line. But for something trickier, like a logic error affecting a customer journey in a marketing automation flow, I'll start with a chat query to get a broader view of the related functions. It can help you see unintended consequences before you make the edit.

Your workflow question is spot on, and honestly, I'm still experimenting too. I'd lean towards inline for the simple, isolated fixes and use the chat for exploratory debugging where I'm not even sure which file to look at. Have you tried using the @codebase chat to ask "what other functions call this one?" as a safety check after it suggests a fix? It's saved me a couple times.

Using an AI to find a division by zero bug is overkill. You could have spotted that in two seconds with a linter or even a basic unit test.

>has anyone used Windsurf in a similar way for debugging?

That's the only way these tools are useful - as a glorified, slower grep. For actual complex bugs, they're useless because they don't understand your domain.

Just use the inline edit. Opening a chat for something that simple adds pointless steps.

That's a fair point about simple bugs, and I agree a linter should catch the division by zero. But for someone like me still learning the codebase, even finding the right file to run the linter on can take time. The chat query acted like a guided search.

I'm curious, when you say they don't understand your domain for complex bugs, does that mean you've tried and it always suggested wrong fixes? Or is it just not worth the attempt compared to traditional debugging?

>even finding the right file to run the linter on can take time

That's the real power for me too, especially in large, unfamiliar repos. It's not about the logic of the bug, it's about cutting through the directory structure and naming conventions you haven't internalized yet.

On the domain understanding point, my experience is mixed. For truly complex logic, it's often not that the suggestions are *always* wrong. It's that they're *locally* right for the snippet it's looking at, but miss the wider system state. I once had it "fix" a validation function perfectly, only to realize it broke an async data-fetching hook three levels up that depended on the old error format. It's great for guided search and first drafts of fixes, but you still need that full mental map of the data flow to vet its work.

That point about it being "locally right" but missing the wider system state is a perfect way to put it. It's the core reason I still treat its output as a very smart first draft, not a final solution.

I've had similar experiences where a suggested refactor was perfectly sound for the immediate function, but it overlooked a subtle side effect in a downstream service that expected a specific object shape. The tool sees the code, not the runtime consequences across service boundaries.

Your example highlights why, even as these features get better, the developer's role shifts from writing the initial fix to being the systems integrator. You're vetting the change against your mental map, just as you said.

Exactly. That shift to "systems integrator" is the most interesting part. It reminds me of when we introduced comprehensive API documentation standards on a previous project. The tools can generate perfect, compliant function signatures, but they have no idea which services are in a degraded state or which endpoints are under a deprecation freeze.

The vetting you're doing against your mental map isn't just a safety check, it's the actual value you're adding. The AI gives you a great candidate for the local contract, but you're the one who remembers the informal handshake agreements between services that never made it into the spec.

That's a critical distinction, the one between formal contracts and informal handshake agreements in a distributed system. The AI can only operate on the explicit, versioned artifact. My mental map contains all the transient runtime truths.

I recently had to modify a service's event schema. Windsurf generated a perfect Avro schema definition. What it couldn't know was that a legacy consumer, which we were phasing out, was still running in production with a known bug where it parsed a specific optional field as a string, not an integer. The formal spec said integer, so the generated change would have broken it. My role was to hold that transient knowledge and implement a backwards-compatible shim until the legacy service was decommissioned.

The value isn't in generating the correct code, it's in knowing which incorrect code you must temporarily tolerate.

That "guided search" aspect is so true for learning a new codebase. I'm working on an Airflow DAG right now with like twenty custom operators, and finding the file that actually defines the `process_staging_data` task was a headache. Asking the chat "where is this operator's execute method" got me there instantly. It's less about the bug and more about cutting through the project structure.

Your point about the mental map is really interesting, and I think it applies to data pipelines too. Like, Windsurf could fix a SQL transformation's syntax perfectly, but it wouldn't know that the source table gets populated by a weird, delayed nightly job that sometimes runs at 4 AM. The "local" fix might assume fresh data at midnight. How much context do you think is reasonable to try and give it in the chat before an edit, versus just knowing you'll have to check those upstream dependencies yourself?

That Airflow example is perfect. I've been there, hunting through a maze of custom operators.

>How much context is reasonable to try and give it

I try to give it the immediate "why" behind the fix. For your delayed job, I might prompt something like "the source_job table isn't reliably populated until after 4am, adjust this query's freshness check." That often gets it 90% there on the logic. But you're right, I'd never trust it to know *all* the upstream quirks automatically. It's a balance between giving enough context for a useful draft and knowing you still hold the full dependency map.

Good example of using the chat to get oriented. For a simple bug like this, I think your approach makes sense. The chat gives you a learning scaffold - it finds the file, explains the logic, and offers a fix, which helps you build that mental map faster.

To answer your question, I usually start with the chat for exactly that reason: exploration. If I'm already in the right file and know the issue, I'll jump straight to an inline edit prompt. The efficiency comes from knowing when you need a guided search versus when you're ready for a direct edit.

Agreed. That split between using chat for exploration and inline edits for execution is key. I've found the same pattern works for infrastructure code - asking "where's the security group allowing this port" before making a direct edit to the specific rule.

The divide-by-zero fix is a perfect use case. It's the kind of obvious, isolated bug these tools excel at.

>chat, or use the edit prompts directly

I'll usually start with the chat if I'm hunting. If I'm already staring at the NaN on line 47, I just highlight it and tell Windsurf to fix the edge case. Saves a step.

But that's the easy stuff. The real test is when the "fix" creates a contract violation with some other piece of the system you haven't opened. That's where your workflow breaks down and you have to be the integrator.

That's a solid comparison. I use a similar pattern when tracing a performance issue through Datadog. I'll often start in the chat asking "which services show the highest latency increase post-deploy" to get a list, then jump straight to flame graphs and span lists in those specific services. It's the same principle, start broad for discovery, then go direct for the detailed work.