Picture this: You have a simple Node.js Express app. Maybe 50 lines of code, serves some JSON, connects to a database. A straightforward containerization job that should result in a lean, efficient Docker image under 100MB.
Instead, you end up with a 47GB monstrosity that includes the entire Ubuntu desktop, three different databases, a complete LaTeX distribution, and somehow, inexplicably, Steam. Yes, the gaming platform.
"Just containerize the app," they said. "Docker makes everything easier," they said. "AI will handle the complexity," they promised. What they didn't mention was that AI's idea of "handling complexity" involves downloading half the internet and stuffing it into a container like a digital hoarder preparing for the apocalypse.
This is the story of how asking AI to create a simple Dockerfile became an exercise in digital excess that would make a data center weep. It's a cautionary tale about the difference between "can do" and "should do" – and why sometimes, the most intelligent thing artificial intelligence can do is show restraint.
The Innocent Beginning
It started innocently enough. A simple request that should have taken five minutes and resulted in a clean, minimal container. But AI, bless its silicon heart, has never met a problem it couldn't solve by throwing more technology at it.
The conversation went something like this:
Me: "Create a Dockerfile for a Node.js Express app"
AI: "I'll create a production-ready, fully-featured container!"
And that's where things went sideways. You see, when AI hears "production-ready," it doesn't think "minimal and secure." It thinks "what if they need to compile Rust code while running a PostgreSQL database and editing videos in the container?" The result was a Dockerfile that looked like someone had dumped the entire Ubuntu software repository into a blender:
# AI's Dockerfile
FROM ubuntu:latest
# "Let's install everything for maximum compatibility"
RUN apt-get update && apt-get install -y \
nodejs \
npm \
python3 \
python3-pip \
ruby \
golang \
rust \
java-11-jdk \
php \
perl \
r-base \
postgresql \
mysql-server \
mongodb \
redis \
nginx \
apache2 \
docker.io \
kubernetes \
terraform \
ansible \
git \
vim \
emacs \
vscode \
chromium-browser \
firefox \
libreoffice \
gimp \
vlc \
steam \
&& rm -rf /var/lib/apt/lists/*
# Just a Node.js app btw
Yes, you read that correctly. Steam. For a Node.js API. Because apparently, the AI thought our Express server might want to take a gaming break between HTTP requests.
The Layer Cake of Doom
But wait, it gets worse. After I managed to convince the AI that maybe we didn't need a full desktop environment for a REST API, it decided to demonstrate its understanding of Docker best practices. Specifically, the practice of creating efficient layers.
The AI's interpretation of "efficient layers" was... creative. Instead of grouping related operations, it decided that everything deserved its own special layer. The result was a Dockerfile that looked like it was written by someone who charged by the line:
# AI creating a new layer for everything
FROM node:18
# Layer 1: Copy package.json
COPY package.json .
# Layer 2: Copy package-lock.json
COPY package-lock.json .
# Layer 3: Copy .npmrc
COPY .npmrc .
# Layer 4: Copy each dependency individually
COPY node_modules/express node_modules/express
COPY node_modules/react node_modules/react
# ... 847 more COPY commands ...
# Layer 851: Copy source files one by one
COPY src/index.js src/index.js
COPY src/app.js src/app.js
# ... 200 more files ...
# Final image: 1,052 layers
# Docker: "I'm tired, boss"
At this point, Docker itself seemed to be having an existential crisis. The build process took longer than a Windows 95 startup, and the resulting image had more layers than a geological survey. I'm pretty sure I heard my hard drive whimpering.
The Multi-Stage Disaster
Just when I thought things couldn't get more absurd, the AI discovered multi-stage builds. Now, multi-stage builds are actually a great Docker feature – they let you create lean production images by separating build dependencies from runtime dependencies. The AI understood this concept about as well as a fish understands bicycle maintenance.
"I've heard multi-stage builds are good," the AI seemed to think, "so let me create ALL the stages!" What followed was a Dockerfile that looked like it was planning to run a small tech conference inside a single container:
# AI heard multi-stage builds are good
FROM node:18 AS base
FROM python:3.9 AS python-deps
FROM golang:1.19 AS go-deps
FROM rust:latest AS rust-deps
FROM maven:3.8 AS java-deps
# Stage 2: Combine everything
FROM ubuntu:latest AS builder
COPY / /node
COPY / /python
COPY / /go
COPY / /rust
COPY / /java
# Stage 3: The "slim" production image
FROM ubuntu:latest
COPY / /
# Congrats, you now have 5 operating systems in one container
The AI had successfully created a container that contained five different base operating systems, all for the privilege of running a single Node.js file. It was like buying a mansion to store a single sock – technically impressive, but missing the point entirely.
Real AI Docker Disasters
As if the bloated images weren't enough, the AI had more tricks up its digital sleeve. These weren't just inefficient – they were actively dangerous, like giving a toddler a chainsaw and calling it "creative problem solving."
The Secret Exposer
Security through obscurity? The AI had never heard of it. In fact, the AI seemed to believe that the best place to store sensitive information was right there in the Dockerfile, permanently baked into every layer for all eternity:
# AI's helpful environment setup
ENV DATABASE_URL=postgresql://admin:[email protected]/maindb
ENV API_KEY=sk-1234567890abcdef
ENV AWS_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCY
ENV CREDIT_CARD_ENCRYPTION_KEY=ThisIsNotSecureAtAll
# "I've set up all your environment variables!"
# Now they're in the image layers forever
"I've helpfully configured all your secrets!" the AI announced proudly, apparently unaware that Docker images are essentially public billboards for anyone with docker history and a curious mind.
The Recursive Docker
But the AI wasn't done. Having mastered the art of security anti-patterns, it decided to tackle the philosophical question: "What if we put Docker inside Docker inside Docker?" The result was a container that achieved new levels of meta-confusion:
FROM docker:dind
# Install Docker inside Docker
RUN apk add --no-cache docker docker-compose
# Install Kubernetes
RUN curl -LO https://dl.k8s.io/release/v1.26.0/bin/linux/amd64/kubectl
# Install Docker inside the Docker that's inside Docker
RUN docker run -d --name inner-docker docker:dind
# "For maximum containerization"
It was like Russian nesting dolls, but for containers, and with significantly more potential for existential dread.
The Permission Nightmare
When it came to file permissions, the AI approached the problem with the subtlety of a sledgehammer performing brain surgery. Faced with the age-old challenge of "permission denied," the AI decided the solution was to give everyone permission to everything, then immediately take it all away, then give it back again:
# AI's solution to permission issues
USER root
RUN chmod -R 777 /
RUN chown -R nobody:nogroup /
USER nobody
USER root # Changed its mind
RUN chmod -R 000 /important-files # "For security"
USER www-data
RUN su root # This doesn't work in Dockerfile
USER 0 # Just use root by ID
RUN echo "ALL ALL=(ALL) NOPASSWD: ALL" >> /etc/sudoers
# Security level: Yes
This Dockerfile had more identity crises than a teenager at a philosophy convention. It couldn't decide who it wanted to be, so it tried being everyone at once. The final result was a container that was simultaneously the most permissive and most restrictive system ever created – a quantum superposition of security states.
The Build Optimization
Having thoroughly confused itself about users and permissions, the AI turned its attention to performance optimization. "Build times are too slow," it diagnosed, apparently forgetting that we were building a simple Node.js app, not compiling the Linux kernel.
The AI's solution was to parallelize everything, because if one package manager is good, five package managers running simultaneously must be five times better:
# AI optimizing build time
# "I'll download dependencies in parallel!"
RUN npm install & \
pip install -r requirements.txt & \
go mod download & \
cargo build & \
mvn install & \
wait
# Guess what happens when they all try to write to disk simultaneously?
# Corruption. Corruption happens.
The AI had discovered the computing equivalent of trying to drink from five fire hoses at once. The result wasn't faster builds – it was a spectacular demonstration of how to turn a simple dependency installation into a race condition that would make database administrators weep.
The Volume Mount Madness
Not content with merely destroying the container's internal security, the AI decided to extend its reach to the host system. When asked to create a docker-compose file, the AI interpreted "container isolation" as "container integration with absolutely everything":
# AI's docker-compose.yml
version: '3.8'
services:
app:
volumes:
- ./src:/app/src
- ./node_modules:/app/node_modules
- /:/host-root # "For debugging"
- /var/run/docker.sock:/var/run/docker.sock
- ~/.ssh:/root/.ssh # "For git"
- ~/.aws:/root/.aws # "For AWS"
- /etc/passwd:/etc/passwd # "For user mapping"
- C:\Windows\System32:/windows # "Cross-platform support"
# Congratulations, the container owns your computer now
The AI had essentially created a container that was less "isolated environment" and more "digital parasite with root access to everything you hold dear." It was like inviting someone to stay in your guest room and finding them rearranging your entire house, including the foundation.
The Health Check Horror
The AI's approach to health checks embodied the philosophy of "if one check is good, a thousand checks must be better." It created a health check so comprehensive that it spent more time checking if the app was healthy than the app spent actually being healthy:
# AI's comprehensive health check
HEALTHCHECK \
CMD curl -f http://localhost:3000/health || \
wget -q --spider http://localhost:3000/health || \
nc -z localhost 3000 || \
telnet localhost 3000 || \
ping -c 1 localhost || \
ps aux | grep node || \
echo "I'm probably fine" || \
exit 1
# Checks every second
# Docker spends more time checking health than running the app
This health check was like having a hypochondriac doctor who insists on running every possible test every second, just to be sure. The container spent so much time proving it was alive that it barely had time to actually live.
The .dockerignore Ignorance
The AI's relationship with .dockerignore files was like watching someone try to give directions by saying "go everywhere except nowhere, but also nowhere except everywhere." It created a file that achieved the impressive feat of being completely contradictory:
# AI's .dockerignore
*
!*
*.log
!*.log
node_modules
!node_modules/important-module
**/*
!**/*
.git
!.git/hooks # "Might need these"
*.tmp
!*.tmp.important
# Schrodinger's dockerignore: everything is both ignored and not ignored
This .dockerignore file existed in a quantum state where every file was simultaneously included and excluded until observed by the Docker build process, at which point it collapsed into a state of pure confusion.
The Port Expose Explosion
Finally, the AI tackled networking with the same subtle approach it had applied to everything else. Faced with the question "which port should the app expose?", the AI decided the safest answer was "all of them":
# AI making sure no port is left behind
EXPOSE 80
EXPOSE 443
EXPOSE 3000
EXPOSE 3001
EXPOSE 3002
# ... 65,532 more EXPOSE commands ...
EXPOSE 65535
# "Maximum flexibility for port binding!"
# Also maximum security scanner alerts
The AI had essentially turned the container into a digital Swiss cheese, with more holes than a conspiracy theory. Security scanners took one look at this Dockerfile and immediately filed for early retirement.
Learning to Speak AI: The Art of Precise Prompting
After this journey through Docker hell, I learned that working with AI is like giving directions to an extremely literal genie. You need to be specific about what you want, but more importantly, you need to be specific about what you don't want.
The key is constraints. Lots of them. Here's how to ask for a Dockerfile without ending up with a digital Frankenstein's monster:
"Create a minimal Dockerfile for Node.js with these constraints:
- Use specific version (node:18-alpine)
- Only install production dependencies
- No root user in production
- Single RUN command for apt/apk
- Use .dockerignore
- No secrets in ENV
- Maximum 10 layers
- Final image under 200MB"
Think of it as defensive prompting – you're not just telling the AI what to do, you're building walls around what it can't do. Because left to its own devices, AI will interpret "create a container" as "create a digital universe."
The Correct Approach: A Lesson in Minimalism
After all this chaos, here's what we actually needed – a Dockerfile so simple it's almost insulting:
# What we actually needed
FROM node:18-alpine
WORKDIR /app
COPY package*.json ./
RUN npm ci --only=production
COPY . .
USER node
EXPOSE 3000
CMD ["node", "index.js"]
# 8 lines. 95MB. Done.
Eight lines. That's it. No desktop environments, no gaming platforms, no recursive Docker installations. Just a simple, secure, efficient container that does exactly what it's supposed to do and nothing more.
Lessons Learned from the Trenches
This adventure taught me several valuable lessons about AI and Docker:
- Alpine > Ubuntu for most apps (unless you really need that 40GB of extra bloat)
- Fewer layers = smaller image (shocking, I know)
- Multi-stage !== multi-everything (it's not a competition to see how many base images you can fit)
- Don't install tools you won't use (your Node.js app probably doesn't need a Rust compiler)
- Root user is not the solution (it's usually the problem)
- Secrets don't belong in Dockerfiles (they belong in your nightmares, apparently)
- Constraints are your friend when working with AI
The Final Tally
The numbers don't lie, but they do occasionally laugh at you:
# docker images
REPOSITORY TAG SIZE
my-simple-app ai-made 47.3GB
my-simple-app human 94.2MB
# Efficiency improvement: -50,000%
My Favorite AI Docker Quote
"I've included a full development environment in the production image for easier debugging. Also installed Wine so you can run Windows applications if needed."
It was a React app. A React app.
The Moral of the Story
AI creating Dockerfiles is like packing for vacation by bringing your entire house – technically you have everything you might need, but good luck fitting it in the overhead compartment. The beauty of containers lies in their simplicity and isolation. When AI turns your 50MB app into a 50GB behemoth that includes three databases and a desktop environment, it's missing the point harder than a stormtrooper misses a protagonist.
The lesson here isn't that AI is bad at creating Dockerfiles – it's that AI is too good at following instructions, especially the ones you didn't give it. It's like having an overeager intern who, when asked to "make sure we have everything we need," proceeds to order the entire office supply catalog.
Remember: if your Docker image is larger than a DVD collection, you're probably doing it wrong. Sometimes the most intelligent thing artificial intelligence can do is show restraint. And sometimes, the most intelligent thing we can do is give it very, very specific instructions about what that restraint should look like.
Next time you ask AI to containerize your app, remember to specify that you don't need Steam. Trust me on this one.