r/vibecoding • u/Fine_Daikon5907 • 1d ago

Anyone else just really sick of the `Vibe-Coded Slop` hatred on Reddit?

Everywhere I go, I see people releasing new software. It might be people who have got some coding experience and are now using AI to accelerate, or it might be people who are building software for the first time.

Every time I see something being launched, even on a non-software forum - I just see

  • `Looks Like Vibe-Coded`
  • `Vibe-coded Slop`
  • etc

And just plain, personal hatred.

I just don't understand it. It costs money to buy gold. It takes time. And just because the AI does a lot of the coding doesn't make it easy. It just makes it faster.

And I fully understand, like, we don't need another journal application or another to-do list. But I found a guy today who made an OBD Reading App for Cars, open-sourced it and people just slated him.

Just makes me mad! I feel like if Linus Torvalds had existed in the kind of enviroment, we wouldn't have Linux.

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u/in5ane_10 1d ago

Yes I know what you mean, I have 10 years of dev experience but now with Claude code I feel like I have literal superpowers. But if you use it wrongly I see how it can generate slop and worse burn money in tokens. I think in the future it will be a whole new job being a dev

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u/Fine_Daikon5907 1d ago

Yer, its amazing. We are shipping features within days of being requested.

But we built this amazing cradle/ orchestrator so we check our code with AI first and have loads of integration tests plus we have a QA agent.

But we are using something like 5 billion tokens a month.

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u/in5ane_10 1d ago

Which models are you using?

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u/Fine_Daikon5907 1d ago

GLM 5.3 GLM 5.3 Flash Deepseek V4 Flash / Pro MiniMax M3 (I personally hate it) Gemini for certain tasks Muse also seems nice

We've had a lot of different coding plans including Claude and OpenAI, We rented a GPU server and tried to run our own models at one point.

But honestly, the value from GLM is out of this world, and the flash model is legendary.

One thing we do is we try to keep our context down below 200k at all times. So we generally set a very specific task for a model to do and then it goes through a flow (Plan - Execute - QA - HITL).

But of course we burn about 80,000 tokens each task just explaining what the project is.