Managing Local Storage in the AI Development Era

The Problem

You’re building with Claude Code, Kiro, and other AI-assisted development tools. Your projects are thriving. Then one day: “No space left on device.”

Where did 50GB go? AI-assisted development introduces hidden storage consumers that traditional developers never worried about:

• Package manager caches grow faster with AI suggesting more dependencies
• MCP servers download Python packages, models, and tools
• IDE and tool caches multiply across multiple AI assistants
• Logs and conversation history accumulate silently

This guide shows you exactly where your storage is going and how to manage it.

Storage Breakdown: Where Does It Go?

A typical AI-assisted development environment consumes storage across these categories:

Category	Typical Size	Growth Rate
Python (uv/pip)	5-20 GB	Fast
Node.js (npm/pnpm)	2-10 GB	Medium
Docker	10-50 GB	Fast
Homebrew	5-15 GB	Slow
IDE Caches	2-5 GB	Medium
AI Tool Data	1-5 GB	Medium
Logs	1-10 GB	Slow

AI development accelerates this growth because:

1. AI assistants suggest more packages to try
2. MCP servers install their own dependencies
3. Multiple Python environments for different agents
4. Large ML/AI packages (numpy, torch, transformers)

Finding the Storage Consumers

Quick Audit Commands

# Overall disk usage
df -h

# Find large directories in home
du -sh ~/* 2>/dev/null | sort -hr | head -20

# Find hidden directories (caches live here)
du -sh ~/.* 2>/dev/null | sort -hr | head -20

Package Manager Caches

Python (uv):

# Check uv cache size
du -sh $(uv cache dir)

# See what's inside
ls -la $(uv cache dir)/wheels/ | head -20

Python (pip):

# Check pip cache
du -sh ~/.cache/pip
pip cache info

Node.js (npm):

# Check npm cache
du -sh ~/.npm
npm cache ls 2>/dev/null | wc -l

Node.js (pnpm):

# Check pnpm store
du -sh ~/.local/share/pnpm

Homebrew:

# Check Homebrew cache
du -sh $(brew --cache)

Docker Storage

# Docker disk usage summary
docker system df

# Detailed breakdown
docker system df -v

AI Tool Storage

Claude Code:

# Claude Code stores data in ~/.claude
du -sh ~/.claude

Kiro:

# Kiro configuration and cache
du -sh ~/.kiro 2>/dev/null

MCP Servers:

# MCP servers may create their own caches
# Check common locations
du -sh ~/.cache/mcp* 2>/dev/null
du -sh ~/Library/Caches/*mcp* 2>/dev/null

Cleaning Up

Python Cleanup

# uv - clean everything
uv cache clean

# uv - prune old entries only (safer)
uv cache prune

# pip - clean cache
pip cache purge

# Remove orphaned virtual environments
# First, list them:
find ~ -type d -name ".venv" -o -name "venv" 2>/dev/null

# Remove unused ones manually after review

Node.js Cleanup

# npm - clean cache
npm cache clean --force

# pnpm - clean store
pnpm store prune

# Remove orphaned node_modules (be careful!)
# First, find them:
find ~ -type d -name "node_modules" -prune 2>/dev/null | head -20

Docker Cleanup

# Remove unused containers, networks, images
docker system prune

# More aggressive - remove all unused images
docker system prune -a

# Remove build cache
docker builder prune

# Nuclear option - remove everything
docker system prune -a --volumes

Homebrew Cleanup

# Remove old versions
brew cleanup

# Remove all cached downloads
brew cleanup -s

# See what would be removed
brew cleanup -n

IDE and Tool Caches

VS Code:

# Clear VS Code cache (macOS)
du -sh ~/Library/Application\ Support/Code/Cache
rm -rf ~/Library/Application\ Support/Code/Cache/*

JetBrains IDEs:

# Check JetBrains caches (macOS)
du -sh ~/Library/Caches/JetBrains

Automation: The Cleanup Script

Create a script to run periodically:

#!/bin/bash
# cleanup-dev-storage.sh

echo "=== AI Dev Storage Cleanup ==="
echo ""

# Python
echo "Cleaning Python caches..."
uv cache prune 2>/dev/null || echo "uv not installed"
pip cache purge 2>/dev/null || echo "pip cache empty"

# Node.js
echo "Cleaning Node.js caches..."
npm cache clean --force 2>/dev/null
pnpm store prune 2>/dev/null || echo "pnpm not installed"

# Docker (conservative)
echo "Cleaning Docker..."
docker system prune -f 2>/dev/null || echo "Docker not running"

# Homebrew
echo "Cleaning Homebrew..."
brew cleanup -s 2>/dev/null || echo "Homebrew not installed"

echo ""
echo "=== Cleanup Complete ==="

Make it executable and run weekly:

chmod +x ~/scripts/cleanup-dev-storage.sh

# Add to crontab (weekly on Sunday at 2am)
# crontab -e
# 0 2 * * 0 ~/scripts/cleanup-dev-storage.sh >> ~/logs/cleanup.log 2>&1

Monitoring: Stay Ahead of the Problem

Quick Status Alias

Add to your ~/.zshrc or ~/.bashrc:

# Check dev storage status
alias dev-storage='echo "=== Dev Storage ===" && \
  echo "uv: $(du -sh $(uv cache dir) 2>/dev/null | cut -f1)" && \
  echo "npm: $(du -sh ~/.npm 2>/dev/null | cut -f1)" && \
  echo "Docker: $(docker system df 2>/dev/null | grep "Images" | awk "{print \$4}")" && \
  echo "Homebrew: $(du -sh $(brew --cache) 2>/dev/null | cut -f1)"'

Set Disk Space Alerts

Create a simple monitor script:

#!/bin/bash
# check-disk-space.sh

THRESHOLD=85
USAGE=$(df -h / | awk 'NR==2 {print $5}' | sed 's/%//')

if [ "$USAGE" -gt "$THRESHOLD" ]; then
  echo "WARNING: Disk usage at ${USAGE}%"
  echo "Run: cleanup-dev-storage.sh"
  # Optional: send notification
  # osascript -e 'display notification "Disk space low!" with title "Storage Alert"'
fi

Key Takeaways

1. Know your storage consumers: AI development tools add layers of caching you might not expect. Python packages for MCP servers, conversation logs, and model caches add up fast.

2. Schedule regular cleanups: Don’t wait for “disk full” errors. Run cleanup commands weekly or set up automation.

3. AI tools require storage discipline: The convenience of AI-assisted development comes with a hidden cost. New tools need new habits - add storage monitoring to your workflow.

Quick Reference

What	Check Size	Clean
uv cache	du -sh $(uv cache dir)	uv cache prune
pip cache	pip cache info	pip cache purge
npm cache	du -sh ~/.npm	npm cache clean --force
Docker	docker system df	docker system prune
Homebrew	du -sh $(brew --cache)	brew cleanup -s

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