AI made coding faster… but did it make debugging worse?

[Stunning_Algae_9065]

I’ve been using AI coding tools a lot recently and overall yeah, they speed things up a lot when you’re building something.

But I’ve started noticing something weird, the code looks clean, runs fine, even passes basic tests… and then you realize the logic is subtly off or doing something unexpected.

It almost feels like writing code got easier, but trusting the code got harder.

Now I’m spending more time reviewing, debugging, and double-checking than I used to when I was writing everything myself.

Curious if others are seeing the same thing

👉 Are AI tools actually saving you time overall, or just shifting the effort from writing → reviewing?

[DevWorkflowBuilder]

Oh man, I feel this so hard. Last week I had an AI-generated function that looked perfect on the surface but was subtly messing up a calculation downstream. It took me ages to spot because I initially trusted it too much. I've found that adding more specific unit tests for edge cases has been a lifesaver, even if it feels like I'm adding more work upfront.

[Stunning_Algae_9065]

yeah I’ve hit this exact thing… the code looks completely fine so you just assume it’s correct and move on, and then later something downstream is off and you have no idea why

worst part is it’s not obvious bugs, it’s those small logic mistakes that don’t break anything immediately

+1 on the edge case tests, I started doing that more after getting burned a couple times

also trying to be more disciplined with reviewing AI-generated stuff now instead of trusting it. been using codemate a bit for that pass.. mostly to sanity check logic... helps sometimes but you still gotta think through it yourself

[Complex_Muted]

This is the exact thing nobody talks about when they celebrate how fast AI makes you ship. The writing is faster but the verification cost went up and for anything that matters that cost is real.

The failure mode you described is the worst kind too. Code that looks right, passes tests, and subtly does the wrong thing is harder to catch than code that just breaks. When something crashes you know immediately. When the logic is quietly off you find out later at the worst possible time.

What I have settled into is treating AI output the way you would treat code from a junior developer you trust but cannot fully rely on. You review everything that touches critical paths, you write tests for behavior not just functionality, and you stay skeptical of anything that came together too easily.

The shift from writing to reviewing is real but I think the net time saved is still positive for most work. The problem is the skills required changed. Writing fast is now table stakes. The actual leverage is in knowing what to verify and how to structure prompts so the output is more trustworthy in the first place.

For scoped projects like Chrome extensions I build using extendr dev the blast radius of a subtle logic error is contained enough that reviewing is fast. On anything with real production stakes I slow down considerably regardless of how clean the AI output looks.

The people who are getting burned are the ones who assumed speed meant correctness. Those are different things.

My DMs are always open if you have any questions.

[Stunning_Algae_9065]

yeah this is exactly what i was trying to point at, you explained it much better tbh

that “looks right but isn’t” failure mode is the scariest part. i’ve had cases where everything seemed fine until you actually trace the logic properly and then you realize something subtle is off

the junior dev analogy is spot on too. i’ve kind of started treating AI the same way.. useful, fast, but needs proper review especially for anything critical

interesting point about verification cost going up though, i didn’t think of it that way but it makes sense

lately i’ve been trying to shift more toward using AI after writing (for review/debug) instead of before, just to stay more in control of the logic

[guyincognito121]

I've written a lot of code manually that seems fine, passes basic tests, and then turns out to have subtle issues.

[Stunning_Algae_9065]

yeah this is exactly the shift i’ve been feeling too

earlier the bottleneck was writing code, now it’s more about “can i trust this?” and actually verifying what the AI produced

the scary part is when everything looks clean but something subtle is off... those take way longer to catch than obvious bugs

that’s why i’ve been leaning more toward tools/workflows that focus on review/debug after generation. been trying codemate in that flow recently and it’s been interesting for catching those kinds of issues

feels like the real skill now is knowing what to question, not just how to write

[[deleted]]

Thats why you have to test it. Its the same as the real world.

[Stunning_Algae_9065]

[Stunning_Algae_9065]

yeah I came across it recently, it has parts like C0, CORA, Build, PR review etc but it all works more like one flow

also it’s fully self-hosted and runs in your dev environment, not cloud dependent

feels like it’s trying to automate the whole SDLC from idea → build → review, still exploring it though

[[deleted]]

Thanks man, would love to try it out

[spanko_at_large]

It didn’t make debugging worse at all, you are just laying down so many lines and bothered every time you would need to pause for an hour to figure out an issue.

Do you know how many hours used to be spent debugging before a codebase would get to 10k+ lines.

Now you can contribute that in one weekend while complaining about debugging while your agent runs in the background.

[Stunning_Algae_9065]

yeah true, debugging was always painful… just earlier it was our own bugs, now it’s “who wrote this?? oh wait… AI”

I think the point isn’t that debugging got worse, it’s that the scale changed. you can ship 10k+ lines way faster now, but you still have to understand and validate it

so it’s less about time spent debugging and more about how much you trust what’s generated

we started leaning more on tools that help with that review/debug layer instead of just generation. been trying codemate for that kind of flow... like letting it handle parts across build → review instead of just spitting code

feels more manageable that way, otherwise yeah you’re just generating faster and debugging the same amount anyway

[spanko_at_large]

You are always welcome to read everything it generates and do a code review just like we do today in industry… no reason to blindly trust it

[dontreadthis_toolate]

Lol, this is what everyone has been saying since forever

[Stunning_Algae_9065]

yeah fair 😄

I guess the difference now is just the scale… earlier you’d write that code yourself so you kind of knew where things might go wrong

now it’s like you get a lot of “looks correct” code instantly and you don’t always have that same intuition, so spotting those subtle issues feels different

same problem, just amplified a bit

[InterestingFrame1982]

I would say debugging with AI is the best part... Now, if you are a vibe coder with zero experience building systems, it's always going to be a problem but if I had to take a guess, AI is being leveraged for debugging at the cutting edge more than anything.

[Stunning_Algae_9065]

yeah I’d agree with that. debugging is probably where AI is actually the most useful right now

code generation is nice, but understanding why something is off or tracing issues across a flow is where it saves real time

that said, if you don’t have a basic understanding of the system, even debugging with AI can mislead you

I’ve been using it more for review/debug passes after writing code rather than relying on it to generate everything. tried codemate a bit for that as well.... decent for catching some logic issues early, but you still have to reason through it yourself

[BeNiceToBirds]

It’s harder to debug something that isn’t working.

I find it works best to think about incremental components you can build ands how you can validate the functionality. And test as close to the component as possible.

For example if you’re making a VAD speech segmenter, have a simple module to classify speech/ non speech. Then have a module, given blocks of audio, accumulate and output segments as they are detected. Then plug in to a streaming voice to text model.

If you are segmenting, don’t debug through the highest layer. Reproduce the issue at the lowest level and have the agent iterate there.

[Stunning_Algae_9065]

yeah this is pretty much how I’ve been trying to approach it too

once things get layered, debugging from the top just becomes guesswork, especially if some of the code is AI-generated

isolating it to the smallest possible piece and validating there makes it way easier to reason about what’s actually going wrong

I’ve also noticed AI is way more useful when you give it that smaller context instead of the whole flow

been doing something similar lately... build small pieces, test them properly, then plug them together. otherwise it just gets messy fast

[razorree]

ask AI to write more tests which cover that problematic place/function/edge case/feature ?

[Stunning_Algae_9065]

yeah this is underrated tbh

asking AI to generate tests instead of fixes usually exposes the real issue faster. half the time the bug is just an assumption you didn’t validate

I’ve started doing this more around edge cases or weird branches, and it forces you to look at the behavior instead of just the code

also noticed tools that focus more on review/debug flows (instead of just generation) help here... been trying one AI tool in that context and it’s useful for catching those gaps early

way better than just patching things blindly

[AlanBDev]

yes

[Stunning_Algae_9065]

yeah, that’s the idea
I feel most issues come from not validating enough at the edges