this post was submitted on 11 Apr 2026
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Programming
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The difference between you and people that say it's amazing is that you are capable of discerning this reality.
What I don't get, though, is how the vibe code bros can't discern this reality.
How can they sit there and not see that their vibe-coded app just doesn't do what they wanted it to do? Eventually, you've got to try actually running the app, right? And how do you keep drinking the AI kool-aid when you find out that the app doesn't work?
You do try running the app, and then you see what is broken and then you have Claude fix it. The process is still iterative just like regular coding. I haven't met a software engineer that wrote a perfect app the first try, its always broken, even in subtle ways. Why does everyone think vibecoding needs to be perfect on the first shot?
Vibe code bros aren't real programmers. They're business people, not computer people. Even if they have a CS degree, they only got that because they think it'll get them more money. They lack passion and they don't care about understanding anything. They probably don't even care about what they're generating beyond its potential to be used in a grift.
I graduated college not that long ago and my CS classes had quite a few former business majors. They switched because they think it'll be more lucrative for them but since they only care about money they didn't bother to actually learn the material especially since they could just vibe code through everything.
So much this.
After working in tech companies for the last 10 years I've noticed the difference between people that "generate code" and those that engineer code.
My worry about the industry is that vibe coding gives the code generators the ability to generate even more code. The engineers (even those that use vibe tools) are not engineering as much code by volume compared to "the generators".
My hope is that this is one of those "short term gain, long term pain" things that might self correct in a couple of years 🤞.
It's insane that companies are going back to metrics like LOC (or tokens generated), when the industry figured out decades ago that these are horrible, counterproductive metrics.
"The hard thing about building software is deciding what one wants to say, not saying it. No facilitation of expression can give more than marginal gains." - No Silver Bullet (1986)
They're the same people that copied code from stack overflow that you had to tell them how to actually fix every PR. The difference is the C suite types are backing them this time
At least at my company, no, they just start selling it.
Yes. Exactly. In my experience, there's more code shops that ship shit than that catch their mistakes.
I do apps that work, i do patches that are production quality. Half the cs world does… I do full stack ai debugging of esp32 projects.
It’s a powerful tool, you just need to learn it’s strong and weak points, just like any other tool you use.
What's the basis for this claim? I'm doubtful, but don't have wide data for this.
Rough estimate from my personal connections only. Some work places where ai is not possible, but all that have made an effort report good code. You need to work with what it is - a word generator that sometimes gives correct results. Make it research and not trust training. Never let it do things on its own, require a plan and reason. Make it evaluate its own work/plan.
Most issues i have stem from models beeing too eager. Restrain them and remove the “i can do this next…”behaviour.
Context is king - so proper mcp and documentation that is agent facing. I use serena as i can get lsp for yaml, markup and keep these docs like that
Any luck with integrating platform.io? Have a esp32 project but VSCode can't provide type hinting with it's main c++ extension that is used by platform.io.
No sry. I only use idf, and use their create vscode files for lsp to work
And tmux + skills for idf.py work including debug. Also repl on console/uart - agents love cli - including this.
Imo mcp > pure skills for tmux
Of course they do, it is hyperbole to think they are completely useless
I wonder if it was even able to compile. I am a shitty hobby coder who just does it to make my embedded hardware projects function.
I have yet to get compilable code out of any of the AI bots I have tried. Gemini, mistral, and chatGPT. I am not making an account lol.
I have gotten some compilable python and VBA code for data analysis stuff at work, so I wonder if it is because embedded stuff uses specific SDKs that it can't handle.
Either way I have given up on it for anything besides bouncing ideas off of or debugging where electromagnetics issues could lie (though it has been completely wrong about that also even though it is using the wrong concepts, it just reminds me of concepts that I might have overlooked)