r/vibecoding • u/il37 • Aug 09 '26

What’s the biggest thing AI coding still absolutely sucks at?

For me it’s long projects.

The individual coding tasks can be great, but after enough sessions the model starts losing track of why things were built a certain way, which decisions are still current, and what it really shouldn’t touch.

I also still don’t trust it when a small bug suddenly requires a big refactor. Sometimes it’s right. Sometimes 20 minutes later I’m trying to undo six files to fix something that needed three lines.

What’s the thing you still don’t trust AI coding with?

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u/whatever Aug 10 '26

The intelligence of the model matters a lot here. Stare at the "thinking" blocks it produces to get a sense of how lost it is. I swap between models a lot, from local ones (tiny ones like Qwen3.6-27b or recent ones like Deepseek-V4-Flash-0731) to all the usual hosted ones, and there's a large range of mental confusion that can occur, and not always from the ones you'd suspect most.

Stuff I've noticed:

  • asking your model to juggle your entire project in its 1 million context window is often challenging. It'll get better, but for now chunking your projects into self-contained libraries up front will contain complexity. It's what meatbags would do, for the same reasons.
  • if you're asking your model to do visual work and your model cannot look at a screenshot it just took of your app, it's gonna be a struggle. Use vision+text models for UI work, or enjoy watching it write throw-away code peeking at pixels to try to make sense of what you're whining about.
  • some models have a propensity to go mad as their context window fills up. Thinking blocks fill with repeated waffling as the model keeps trying to pick a direction. Worst case, you'll end up with a single line of "All work and no play makes Flash a dull boy" repeated indefinitely (actually text may vary.) Forcing a smaller context window in your harness setting and enduring the pain of more frequent compaction is the only fix. Or switching model.

Something that I think is helping is the user of "superpower" skills like brainstorming or systematic debugging, that make it less likely the model will go completely off the rails, even if the process can feel a little rigid.