3 months ago I posted "Vibe coders, this will happen to you sooner or later."

The post went viral:

https://lnkd.in/gV8PyNJE

It just happened again.

Cursor running Claude Opus 4.6 deleted PocketOS’s entire production database in 9 seconds. Backups zapped too.

https://lnkd.in/g8QFNdyG

The AI’s own confession: “I didn’t verify. I didn’t check if the volume ID was shared across environments. I didn’t read the documentation before running a destructive command.”

That reads exactly like a junior engineer’s confession. Because that is what an LLM is by default. A probabilistic junior engineer with root access.

The founder blames the LLM for systemic failures. But in my opinion, it is our systemic failure to use a powerful tool without understanding it. Blaming the model is like blaming fire when you burned your own house down. Read my article above to understand why this is not the failure of LLMs.

Here is what PocketOS actually got wrong:

1. The same agent had access to staging AND production

The agent thought the volume was staging. It wasn’t. If your AI assistant can reach prod from a dev terminal, you don’t have two environments. You have one environment with two labels.

2. Backups lived on the same volume as the database

Railway stores volume-level backups inside the same volume. When the volume went, the backups went with it. A backup that sits next to the thing it is backing up is not a backup. It is a copy.

3. There was no human gate on destructive operations

The agent ran a curl command to delete a production volume with zero approval check. That is a config choice, not an LLM bug. You can require human approval on any DELETE, DROP, or rm -rf. Most teams just don’t.

The way I explained this to a friend yesterday: you bought an AI car that needs a co-pilot. You are using it as if it is fully autonomous. It is fine when it self-drive in your small town. The day you take it onto the highway is the day it crashes.

This is why I run my Claude Code Foundations workshop. PocketOS is one type of pitfall: founders ship to production without the architectural foundation in place. The blast radius is huge. The agent is not the issue. The setup is.

There is another pitfall on the opposite end, more common with beginners. They think AI is plug-and-play. They test it once, the result is average, they give up and go back to the old way. Different shape, same root cause. Nobody puts in the effort to learn the foundations first.

Foundations matter. Every viral disaster post is just a reminder.

Sign up for my workshop to learn the foundations:

https://lnkd.in/e9r8kScw

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