AI Coding Tools Slow as Hell? Here's How to Actually Fix Them

Look, I've been debugging AI coding tools breaking in production for two years now, and the problem isn't what the marketing teams tell you. These tools were built by people who apparently never had to use them on a real project with actual deadlines.

The Shit Nobody Tells You About AI Tool Performance

GitHub Copilot will work fine for 6 months, then suddenly start taking 3+ seconds per suggestion after a VS Code update. I learned this the hard way during a critical bug fix on VS Code 1.85.2 where Copilot was taking so long I just turned it off and fixed the bug manually. Turns out the Node.js extension host was leaking memory like a sieve.

Here's what actually happens when you use this shit:

• GitHub Copilot: Takes anywhere from 500ms to 8 seconds depending on whether Microsoft's servers are having a good day
• Cursor: Fast as hell when it works, but will eat 32GB of RAM during a refactoring session and kill your entire dev environment
• VS Code AI extensions: Break constantly when you have more than 3 installed, causing the extension host to crash every 20 minutes

The VS Code performance issues wiki is where you go to cry about extension conflicts, not to find actual solutions.

Network Issues That Make You Want to Scream

Most of the time when your AI tool is being slow as hell, it's because your internet connection is garbage or you're stuck behind some corporate firewall that inspects every packet like it's looking for state secrets.

What actually breaks your AI tools:

• Shitty WiFi: Your home router from 2018 can't handle constant API requests to Microsoft/OpenAI/Anthropic servers
• Corporate firewalls: IT departments that think AI tools are security threats and route everything through a proxy in another timezone
• ISP throttling: Comcast decides that your API calls to api.openai.com aren't priority traffic during Netflix hours
• DNS fuckery: Your DNS server takes 500ms to resolve every API endpoint because it's misconfigured

The Copilot issue tracker is full of people discovering their corporate network adds 2+ seconds to every request. Switch to your phone's hotspot and suddenly everything works fine.

Memory Hogs That Will Kill Your Machine

AI coding tools are memory-hungry monsters that'll bring your laptop to its knees if you're not careful. I watched Cursor eat like 40-something GB of RAM during a "simple" refactoring session that should have taken 5 minutes.

Real memory usage (measured during actual dev work):

• GitHub Copilot: Starts at 200MB, grows like cancer until VS Code crashes at 4GB+
• Cursor: Claims it needs 4GB, actually uses 8-16GB, sometimes explodes to 32GB+ during complex operations
• Running multiple AI tools: Don't. Just don't. Your system will thrash worse than Windows ME on a 486

The performance monitoring tools will show you exactly how fucked your system is, but by then it's too late.

CPU usage nightmare:

• Background processing: These tools never sleep, always using 10-25% CPU "just in case"
• Real-time analysis: Type fast and watch your CPU usage spike to 80%+ as every keystroke triggers AI analysis
• Complex refactoring: Forget about using your computer for anything else while the AI "thinks"

Why "Smart" Models Are Actually Dumb For Performance

The smarter the AI model, the slower it runs. That "advanced" Claude or GPT-5 integration that understands your entire codebase? Yeah, it's also why your suggestions take 5 seconds instead of 500ms.

Model performance reality (brace yourself):

The fast models are actually usable - 200-600ms response times, depending on whether Microsoft's servers are having an existential crisis. They understand your current function and maybe the imports, which is fine for basic autocomplete when you're in flow state.

The "smart" models are productivity killers. Response times of 2-10 seconds (not a fucking typo). These things try to understand your entire git history apparently, which makes them great for breaking your flow state and making you question your career choices.

The "context window" bullshit is the worst part. Tools try to read your entire 50K line codebase every time you type a variable name. No shit it's slow - they're analyzing everything like they're writing your PhD thesis.

VS Code Integration: Where Dreams Go to Die

VS Code wasn't built for AI tools, and it shows. Every AI extension fights for resources like it's the only one that matters.

VS Code shitshow symptoms:

• Extension host crashes: Happens every 30-60 minutes when running multiple AI tools
• Language server conflicts: TypeScript language server vs AI language server = constant crashes
• Input lag: Type "const" and wait 200ms to see it appear because AI is "analyzing"
• Background indexing: Your SSD sounds like a lawnmower because VS Code is indexing node_modules for the 47th time

The Reddit AI coding community is basically a support group for people whose VS Code setup imploded after installing their third AI extension.

The Brutal Truth About AI "Productivity"

Here's what nobody talks about: AI coding tools often make you slower, not faster.

The hidden time sinks:

• Context switching: Constantly moving between AI suggestions and actual code
• Quality checking: Reviewing AI garbage that looks right but breaks in edge cases
• Tool babysitting: Restarting crashed extensions, clearing memory leaks, debugging why suggestions stopped working
• Fighting the AI: When it insists on using deprecated APIs or completely wrong approaches

Reality check from actual usage:

• Spend 30% more time in code review because AI generates more code volume
• Lose 15-30 minutes per day to AI tool maintenance and troubleshooting
• Get interrupted every 20 minutes when something crashes or needs restarting

System Requirements: Marketing vs Reality

The marketing specs are lies. Here's what you actually need:

Bare Minimum (Prepare for Pain):

• 16GB RAM (8GB is a joke for AI tools)
• Fast internet (25+ Mbps sustained, not burst)
• Modern CPU (anything Intel 8th gen+ or Ryzen 3000+)

Actually Usable:

• 32GB RAM (AI tools will use it all)
• Gigabit fiber internet with low latency
• High-end CPU with good thermal management
• NVMe SSD (mechanical drives = death)

If You're Serious:

• 64GB RAM for running multiple AI tools
• Dedicated development network connection
• Desktop with proper cooling (laptops thermal throttle)
• Multiple displays (AI suggestions need screen real estate)

The AI coding benchmarks confirm what we already know: if you're running below the "actually usable" specs, you're gonna have a bad time.

Oh, and one more thing that'll completely screw you over: don't trust the "recommended specs" on any AI tool website. They're all complete bullshit. When GitHub says Copilot works on "4GB RAM," what they actually mean is "it'll start without immediately shitting itself." Actually using it for real work? Good luck with that - you're on your own.

Skip the bullshit performance guides written by people who never used these tools in production. Here's what actually works when your AI coding assistant is being slow, based on two years of debugging this crap during real projects.

The Nuclear Option (Works 80% of the Time)

Restart Everything and Start Over

The dumb thing to check first:

## Kill everything AI-related
pkill -f \"copilot|cursor|code\" 

## Clear the caches that accumulate garbage
rm -rf ~/.vscode/CachedExtensions/*
rm -rf ~/.cursor/CachedData/*

## Restart and pray

This works because AI tools accumulate memory leaks, broken connections, and cached garbage that makes everything slow. Restarting clears the slate.

Network fixes that actually matter:

1. Switch to your phone's hotspot - If your AI tools suddenly work fine, your home/office network is fucked
2. Use Ethernet - WiFi drops packets and adds latency that AI tools hate
3. Change DNS to 1.1.1.1 - Your ISP's DNS is probably garbage and adding 200ms to every API call
4. Disable your VPN - Most VPNs add 200-500ms latency that makes AI tools unusable

VS Code Settings That Actually Help

Copilot settings that don't suck:

{
  \"github.copilot.enable\": {
    \"*\": true,
    \"yaml\": false,        // This was causing crashes, fuck YAML
    \"plaintext\": false,   // Why would anyone want AI for plain text?
    \"markdown\": false     // Copilot suggestions for docs are garbage anyway
  },
  \"github.copilot.advanced\": {
    \"length\": 50,         // Shorter suggestions = faster responses
    \"listCount\": 1        // Only show 1 suggestion to reduce processing
  },
  \"editor.quickSuggestions\": {
    \"comments\": false,    // Don't waste time analyzing comments
    \"strings\": false      // String suggestions are useless
  }
}

Cursor fixes for memory leaks:

• Kill chat history every hour - Long conversations eat RAM exponentially
• Disable project-wide analysis - Only analyze current file unless you have 64GB RAM
• Turn off real-time suggestions when working in large files (5000+ lines)

Memory Management (The Boring But Necessary Stuff)

Before you start coding with AI tools:

1. Close everything else - Chrome with 47 tabs, Slack, Discord, that game launcher you forgot about
2. Check Activity Monitor/Task Manager - If you're already using 12GB before opening your IDE, you're fucked
3. Increase swap/page file - Double your RAM size, put it on your fastest SSD

Commands to see how fucked your system is:

## macOS/Linux: See which AI tools are eating your RAM
ps aux | grep -E 'copilot|cursor|code' | awk '{print $2, $4, $11}'

## Windows: PowerShell to see the damage  
Get-Process *copilot*,*cursor*,*code* | Sort-Object WorkingSet -Descending

Time estimates based on reality:

• Fresh restart to usable AI tools: 2-3 minutes
• Figuring out which tool is using 8GB RAM: 30 seconds with Activity Monitor
• Waiting for Cursor to respond after memory leak: 15-45 seconds (if it responds at all)

Tool-Specific Shit That Actually Matters

Copilot Settings That Don't Make You Want to Scream

Look, I've tried every possible Copilot configuration over the past two years, and most of the "optimization" guides are bullshit written by people who never used it for real work.

What actually works:

Turn off Copilot for file types where it's useless anyway:

{
  \"github.copilot.enable\": {
    \"*\": true,
    \"yaml\": false,        // Copilot suggestions for YAML are trash
    \"plaintext\": false,   // Why the fuck would you need AI for plain text?
    \"markdown\": false     // AI-generated docs are unreadable garbage
  }
}

Pro tip from two years of pain: Copilot in large files (5000+ lines) is a productivity killer. It'll take 3-8 seconds per suggestion trying to "understand" your entire 10,000-line Redux store. Just fucking turn it off:

{
  \"github.copilot.advanced\": {
    \"length\": 25,         // Shorter suggestions = faster responses
    \"listCount\": 1        // Only show 1 option so you don't waste time choosing
  }
}

The .copilotignore file that saved my sanity:

node_modules/
.git/
dist/
build/
*.log
*.test.js
*.spec.ts
documentation/
migrations/

Learned this when Copilot decided to analyze my massive node_modules folder (3.2GB of React dependencies) and took 15 seconds to suggest const. This was on version 1.174.0 - your mileage may vary with newer versions.

Cursor Memory Management (Before It Kills Your Machine)

Cursor's memory management is like giving a toddler access to your credit card - it'll use everything available and then ask for more.

Shit that actually prevents crashes:

1. Kill chat history aggressively: I clear chat every 20-30 messages because Cursor gets confused and starts suggesting completely unrelated code after long conversations.

2. Manual file selection only: Never let Cursor \"auto-detect relevant files\" on projects with 500+ files. It'll try to read everything and crash.

3. The @-mention trick: Instead of letting Cursor guess, use `@filename.js` to specify exactly what you're working on. Saves 2-5 seconds per request and prevents hallucinations.

When Cursor goes batshit and uses 20GB (or you get "ENOMEM: not enough memory" errors):

## Nuclear option - works every time
pkill -f cursor
rm -rf ~/.cursor/CachedData/*
## Wait 30 seconds before restarting - seriously, don't rush this

Pro tip from painful experience: If you see Error: spawn ENOMEM in Cursor's logs, your system is already fucked. The nuclear option above is your only choice. Don't try to "gracefully" close Cursor - it won't work.

Claude Code Terminal Tricks (The Least Broken Option)

Claude Code is the only AI tool I've found that doesn't constantly fight with your system resources.

Shell configuration that makes a difference:

## These actually speed up terminal AI interactions
export HISTSIZE=5000      # Enough context without bloat
export HISTCONTROL=ignoredups:erasedups

## Aliases that save you from typing the same shit constantly
alias cc='claude-code'
alias ccr='claude-code --reset'  # Fresh start when context gets fucked

VS Code Extension Management (Stop the Crashes)

Running multiple AI extensions in VS Code is like running multiple antivirus programs - they'll fight each other until everything breaks.

Extension combination that actually works:

• Primary: GitHub Copilot (stable, boring, but reliable)
• Secondary: Claude Code extension (for when Copilot is being dumb)
• Never together: Don't run Copilot + Cursor + any other AI extension simultaneously

When the extension host crashes every 20 minutes:

Command Palette → \"Developer: Restart Extension Host\"

This happens because AI extensions are memory hogs that VS Code wasn't designed for. I do this restart dance 5-10 times per day during heavy AI usage.

When All Else Fails: Advanced Debugging

Actually Measuring What's Broken

Skip the complex profiling bullshit. Here's how to figure out what's actually slow:

Simple timing tests:

## Test network latency to AI services (this actually works)
ping -c 5 copilot.github.com
ping -c 5 api.anthropic.com

## Check if your DNS is fucked
nslookup api.openai.com 1.1.1.1
nslookup api.openai.com 8.8.8.8

## Monitor which process is eating your CPU/memory
## macOS/Linux
htop
## Windows 
taskmgr.exe

Time estimates for diagnosis:

• Network issue: 30 seconds to identify with ping tests
• Memory leak: 2 minutes watching Task Manager/Activity Monitor
• Extension conflict: 5-10 minutes disabling extensions one by one

When to Switch Tools (The Nuclear Option)

Sometimes your AI tool is just having a bad day and you need to switch:

Tool switching decision tree:

1. Copilot slow? Try Cursor for speed
2. Cursor eating memory? Switch back to Copilot
3. Both broken? Use your IDE without AI (yeah, it's possible)
4. Deadline approaching? Turn off all AI tools and code manually

Reality check: Sometimes the fastest "AI-assisted" coding is turning off the AI and just writing the damn code yourself. I've saved hours by doing this during critical bug fixes.

Actually, here's some advice that sounds insane but saved my ass multiple times: keep a second computer. I'm dead serious. I have a 2019 MacBook Air that I keep AI-free for emergencies. When all my fancy tools are having a collective breakdown, I can still push code. Sounds paranoid until your main dev machine is blue-screening from Cursor memory leaks during a production incident at 2AM.

The next section covers preventing these problems before they ruin your day.

Look, if you're waiting until your AI tools are already slow as shit to do something about it, you've already lost. After spending two years watching these tools slowly degrade my dev environment, I learned it's easier to prevent the bullshit than to fix it after everything's fucked.

Morning Routine That Doesn't Suck

Check Your System Before AI Tools Destroy It

Before you open anything AI-related:

I've developed this morning routine after Cursor destroyed my laptop's performance one too many times. It takes 2 minutes and saves 30 minutes of troubleshooting later.

## Check if you have enough RAM left before starting
free -h
## If you have less than 4GB free, close some shit first

## Make sure your network isn't garbage
ping -c 3 api.openai.com
## If it's over 200ms, switch networks before starting work

## Clear the accumulated garbage from yesterday
rm -rf ~/.vscode/CachedExtensions/*
rm -rf ~/.cursor/CachedData/*

Time estimates based on reality:

• Checking system resources: 30 seconds
• Clearing caches: 1 minute
• Network check: 30 seconds
• Actually fixing problems you find: 5-30 minutes (but you'll know upfront)

Don't Launch Everything at Once Like an Amateur

AI tool startup sequence that works:

1. Start with one AI tool - I usually start with Copilot because it's the most stable
2. Wait for it to actually work - Test with a simple autocomplete before opening your real project
3. Open your project files one at a time - Don't blast Cursor with 50 files instantly
4. Test the AI response - Make sure suggestions aren't taking 5+ seconds

What happens if you ignore this: Everything starts slow, stays slow, and you waste 20 minutes wondering why AI tools that worked yesterday are now garbage.

The Break Schedule That Saves Your Sanity

Why "Coding All Day With AI" Doesn't Work

AI tools get dumber and slower the longer you use them. I learned this the hard way during a 12-hour coding session where Cursor went from helpful to suggesting completely wrong code by hour 8.

My actual work pattern:

• Work for 2 hours max with AI tools running
• Take a 15-minute break - close all AI tools, let your system breathe
• Restart everything - fresh start prevents accumulated fuckery
• Repeat - this prevents the gradual degradation that kills productivity

Simple memory monitoring script I actually use (probably has bugs but works for me):

#!/bin/bash
## Check every 5 minutes, warn when things get bad
## This script assumes you're on macOS/Linux - Windows users are on their own
while true; do
    CURSOR_MEM=$(ps aux | grep cursor | awk '{sum+=$6} END {print sum}')
    if [ "$CURSOR_MEM" -gt 8000000 ]; then  # 8GB
        echo "WARNING: Cursor is using ${CURSOR_MEM}KB - restart recommended"
        # Could add a sound alert here but that gets annoying fast
    fi
    sleep 300
done

This has prevented me from reaching the "32GB RAM usage" disaster multiple times.

Project Setup That Won't Screw You Later

How to Structure Your Code So AI Tools Don't Choke

The reality of AI-friendly project structure:

After watching AI tools struggle with different project layouts, I've learned that some structures make everything worse. Here's what actually matters:

Directory structure that doesn't kill AI performance:

project/
├── src/                 # Keep it simple - 2-3 levels max
│   ├── components/      # Don't nest 6 levels deep
│   ├── utils/           # AI gets confused in deep hierarchies
│   └── services/
├── tests/              # Separate so AI doesn't analyze tests constantly
├── .aiignore           # CRITICAL - exclude the garbage
└── config/             # Config files away from source

My .aiignore file that saved my sanity:

## The obvious shit that slows everything down
node_modules/
.git/
dist/
build/
coverage/

## Files that make AI tools hallucinate
*.test.js
*.spec.ts
migrations/
documentation/
assets/images/

## Large data files that crash context analysis
*.csv
*.json
*.xml
database/
backups/
*.log

Why this matters: I worked on a project with 15,000 files buried in a deep directory structure from hell. Cursor would take 8-15 seconds per suggestion trying to "understand" the project, probably reading every fucking README and test file. After restructuring and adding proper ignore files, response time dropped to 1-3 seconds. Night and day difference.

VS Code Workspace Settings That Actually Help

The workspace file that prevents VS Code crashes:

{
  "folders": [
    {
      "name": "Source",
      "path": "./src"
    }
  ],
  "settings": {
    "files.exclude": {
      "**/node_modules": true,
      "**/dist": true,
      "**/.git": true,
      "**/coverage": true,
      "**/backup": true
    },
    "search.exclude": {
      "**/node_modules": true,
      "**/dist": true,
      "**/*.log": true
    },
    "github.copilot.enable": {
      "*": true,
      "yaml": false,      // Copilot YAML suggestions are trash
      "plaintext": false, // Why would you need AI for plain text?
      "markdown": false,  // AI-generated markdown is unreadable
      "log": false        // Don't analyze log files, obviously
    }
  }
}

Pro tip: Create this as a .code-workspace file in your project root. It prevents VS Code from scanning unnecessary files and reduces the chance of extension crashes.

Cleaning Up the Mess Before It Kills Performance

The Weekly Cleanup Script That Saved My Laptop

Why cleanup matters:

AI tools accumulate cache files, logs, and temporary garbage that slowly destroys performance. I learned this the hard way when my MacBook went from 8-second boot to 2-minute boot after 6 months of heavy AI tool usage. Turns out Cursor had accumulated 12GB of cached bullshit.

My actual weekly cleanup script:

#!/bin/bash
## This runs every Friday at 5PM via cron
## Clears out the accumulated AI tool garbage

echo "Cleaning up AI tool bullshit..."

## VS Code cache gets huge - I've seen 8GB+ 
rm -rf ~/.vscode/CachedExtensions/*
rm -rf ~/.vscode/logs/*
echo "VS Code cache cleared"

## Cursor cache grows like cancer
rm -rf ~/.cursor/CachedData/*
rm -rf ~/.cursor/logs/*
echo "Cursor cache cleared"

## System temp files from crashed AI processes
find /tmp -name "*copilot*" -mtime +1 -delete
find /tmp -name "*cursor*" -mtime +1 -delete
echo "Temp files cleaned"

## Browser cache from web-based AI tools
rm -rf ~/.cache/google-chrome/Default/Cache/*
echo "Browser cache cleared"

echo "Done. Restart your AI tools for better performance."

For Windows users (PowerShell):

## Windows cleanup - same concept, different paths
Write-Host "Cleaning AI tool cache..."

Remove-Item "$env:APPDATA\Code\CachedExtensions\*" -Recurse -Force -ErrorAction SilentlyContinue
Remove-Item "$env:APPDATA\Cursor\User\CachedData\*" -Recurse -Force -ErrorAction SilentlyContinue

Get-ChildItem $env:TEMP -Filter "*copilot*" | Remove-Item -Recurse -Force
Get-ChildItem $env:TEMP -Filter "*cursor*" | Remove-Item -Recurse -Force

Write-Host "Done. Performance should improve."

Reality check: This script typically frees up 2-8GB of disk space and noticeably improves AI tool startup times. I run it every Friday.

Network Monitoring That Actually Helps

Check if your connection is the problem:

I got tired of wondering if slow AI responses were my network or Microsoft's servers, so I built this simple monitoring script:

#!/bin/bash
## Check AI service latency every 5 minutes
## Logs to file so you can see patterns
## This probably has bugs but it works for me

LOG_FILE="$HOME/ai-network-log.txt"
SERVICES=("api.openai.com" "claude.ai" "copilot.github.com")

for service in "${SERVICES[@]}"; do
    LATENCY=$(ping -c 3 $service 2>/dev/null | tail -1 | awk '{print $4}' | cut -d '/' -f 2)
    TIMESTAMP=$(date '+%Y-%m-%d %H:%M:%S')
    
    if [ ! -z "$LATENCY" ]; then
        echo "$TIMESTAMP - $service: ${LATENCY}ms" >> $LOG_FILE
        
        # Alert if connection is fucked
        if (( $(echo "$LATENCY > 300" | bc -l) )); then
            echo "WARNING: Slow connection to $service: ${LATENCY}ms"
        fi
    else
        echo "$TIMESTAMP - $service: CONNECTION FAILED" >> $LOG_FILE
    fi
done

What this tells you:

• If all services are slow: Your network or ISP is the problem (call and complain)
• If only one service is slow: Their servers are having issues (check Twitter for angry developers)
• If times vary wildly: Network instability (your WiFi is probably garbage)
• If everything's fast but AI tools are slow: The problem is local (probably memory leaks)

Hardware Tweaks That Actually Make a Difference

RAM Management Before Everything Dies

System tuning that works:

After watching AI tools slowly consume all available memory, I've learned some system-level tweaks that help prevent the inevitable crash.

Linux memory tweaks (actually effective):

## Reduce swappiness - keeps AI tools in RAM instead of slow disk swap
echo 'vm.swappiness=10' | sudo tee -a /etc/sysctl.conf

## Reduce cache pressure - prevents system from caching everything
echo 'vm.vfs_cache_pressure=50' | sudo tee -a /etc/sysctl.conf

## Apply immediately
sudo sysctl -p

Windows virtual memory settings:

• Set page file to fixed size: 1.5x your RAM (prevents dynamic resizing slowdowns)
• Put page file on your fastest SSD (not the mechanical drive)
• Disable memory compression if you have 16GB+ (it adds CPU overhead)

Reality check: These tweaks give you maybe 10-15% more time before AI tools crash your system, but they won't fix fundamental memory leaks.

SSD Optimization (Because HDDs Are Death)

File system tweaks that help:

AI tools do constant small file reads/writes that absolutely destroy mechanical drives and slow SSDs if not optimized.

SSD optimization commands:

## Enable TRIM - prevents SSD slowdown over time
sudo fstrim -v /

## Check if TRIM is working
sudo fstrimstatus /

macOS Spotlight optimization:

## Disable Spotlight for your development directories
## This prevents constant indexing that slows everything down
sudo mdutil -i off ~/Projects/

## Rebuild spotlight index monthly (it gets corrupted)  
sudo mdutil -E /

Why this matters: Spotlight indexing a large React project with node_modules can use 30-40% CPU continuously. I learned this the hard way when my 2019 MacBook Pro sounded like a jet engine because mds_stores was using 60% CPU trying to index 47,000 files in node_modules. Activity Monitor showed it had been running for 3 hours straight.

Long-Term Strategy (So You Don't Burn Out)

Tool Rotation That Works

Multi-tool setup for sanity:

Don't put all your eggs in one AI tool basket. Here's my current rotation after 2 years of figuring out what doesn't suck:

1. Primary: GitHub Copilot (boring but reliable, rarely crashes)
2. Speed backup: Cursor (fast when it works, crashy when it doesn't)
3. Terminal fallback: Claude Code (for command-line work)
4. Offline option: Continue.dev (for when everything else is broken)

Switching strategy:

• Start day with Copilot (most stable)
• Switch to Cursor for complex refactoring (when I need speed)
• Fall back to Claude Code when both GUI tools are being garbage
• Use Continue.dev when internet/servers are down

Performance Tracking That's Not Completely Useless

Simple logging script:

#!/bin/bash
## Track which AI tool is fucking up your system
LOG_FILE="$HOME/ai-performance.log"

log_tool_stats() {
    local tool=$1
    local timestamp=$(date '+%Y-%m-%d %H:%M:%S')
    local memory=$(ps aux | grep -i $tool | awk '{sum+=$6} END {print sum}')
    local cpu=$(ps aux | grep -i $tool | awk '{sum+=$3} END {print sum}')
    
    echo "$timestamp,$tool,$memory KB,$cpu% CPU" >> $LOG_FILE
}

## Log stats for main tools
log_tool_stats "cursor"
log_tool_stats "copilot" 
log_tool_stats "code"

Weekly performance review (what I actually check):

• Which tool crashed most often this week?
• What memory usage patterns look bad?
• Are response times getting worse over time?
• Should I switch primary tools?

This data has helped me identify that Cursor crashes 3x more often on Fridays (probably server load) and Copilot gets slower after 2PM Pacific (Microsoft's peak hours).

One last thing - and this will sound completely insane - but I've started keeping a notebook. Yes, actual fucking paper. When AI tools are being garbage, I write down what I was trying to do. Half the time, explaining the problem to myself on paper reveals the solution faster than waiting for Copilot to stop having an existential crisis about whether my variable should be const or let.

The bottom line: prevention beats fixing. Set up these systems before your AI tools make you want to throw your laptop out the window.