2025-12-07 15:15:10
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Hey developers! 👋 I've just finished diving deep into one of the most comprehensive CSS framework repositories out there, and I'm excited to share this treasure trove with you. Whether you're building your next big project or just exploring what's out there, this guide has something for everyone!
I've gathered 50+ CSS frameworks from the awesome-css-frameworks repository, each carefully categorized and documented. From minimal resets to full-featured design systems, this list covers the entire spectrum of CSS frameworks available today.
Let's dive in! 🚀
Start fresh with these modern CSS resets
| Framework | Description | Links |
|---|---|---|
| modern-normalize 🎯 | A modern alternative to CSS resets | GitHub |
| ress ✨ | A modern CSS reset | GitHub |
| Natural Selection 🌿 | A CSS framework using natural selection | GitHub |
Beautiful styling with ZERO classes needed!
These frameworks style your semantic HTML automatically - just write good markup and watch the magic happen! ✨
| Framework | Website | Demo | Documentation | Repository |
|---|---|---|---|---|
| Pico.css 💙 | picocss.com | Examples | Docs | GitHub |
| MVP.css 🏆 | MVP Demo | See It Live | Documentation | GitHub |
| sakura 🌸 | Main Site | Demo | - | GitHub |
| Simple.css 🎨 | simplecss.org | Demo | Wiki | GitHub |
| Tacit 🤫 | Demo Site | See It | - | GitHub |
Under 10KB - Perfect for performance-focused projects!
| Framework | Website | Demo | Documentation | Repository |
|---|---|---|---|---|
| Pure 💎 | purecss.io | Layouts | Getting Started | GitHub |
| Picnic CSS 🧺 | picnicss.com | Tests | Docs | GitHub |
| Chota ⚡ | Main Site | Demo | Documentation | GitHub |
The powerhouses that can handle ANY project!
| Framework | Website | Demo | Documentation | Repository |
|---|---|---|---|---|
| Bootstrap 💪 | getbootstrap.com | Examples | Docs | GitHub |
| Bulma 🎯 | bulma.io | Expo | Docs | GitHub |
| Foundation 🏛️ | get.foundation | Templates | Docs | GitHub |
| UIkit 🎪 | getuikit.com | Showcase | Docs | GitHub |
| Framework | Website | Documentation | Repository |
|---|---|---|---|
| Primer 🐙 | primer.style | CSS Docs | GitHub |
| Carbon Components 💼 | carbondesignsystem.com | Components | GitHub |
| U.S. Web Design System 🇺🇸 | designsystem.digital.gov | Components | GitHub |
| PatternFly 🦋 | patternfly.org | Get Started | GitHub |
| Framework | Website | Demo/Examples | Documentation | Repository |
|---|---|---|---|---|
| Fomantic-UI 🌊 | fomantic-ui.com | - | Getting Started | GitHub |
| Blaze UI 🔥 | blazeui.com | Components | Install Guide | GitHub |
| Cirrus ☁️ | cirrus-ui.netlify.app | Examples | Setup | GitHub |
| Vanilla Framework 🍦 | vanillaframework.io | Examples | Docs | GitHub |
| Stacks 📚 | stackoverflow.design | - | Using Stacks | GitHub |
| HiQ 🎵 | Demo | Live Demo | Guide | GitHub |
Google's Material Design made easy!
| Framework | Website | Demo | Documentation | Repository |
|---|---|---|---|---|
| Material Components Web 🎯 | material.io/components/web | Components | Getting Started | GitHub |
| Beer CSS 🍺 | beercss.com | - | - | GitHub |
| Materialize 💫 | materializecss.github.io | - | Getting Started | GitHub |
Atomic CSS for maximum flexibility!
| Framework | Website | Demo/Gallery | Documentation | Repository |
|---|---|---|---|---|
| Tailwind CSS 🌊 | tailwindcss.com | - | Documentation | GitHub |
| Open Props 🎨 | open-props.style | Gallery | Getting Started | GitHub |
Unique styles for unique projects!
| Framework | Description | Website | Repository |
|---|---|---|---|
| NES.css 👾 | NES-style CSS Framework | Demo | GitHub |
| 98.css 🖥️ | Windows 98 CSS | Site | GitHub |
| System.css 💻 | System UI CSS | Site | GitHub |
| XP.css 🪟 | Windows XP CSS | Site | GitHub |
| 7.css 7️⃣ | Windows 7 CSS | Site | GitHub |
| Framework | Description | Website | Repository |
|---|---|---|---|
| Tufte CSS 📖 | Edward Tufte-inspired design | Site | GitHub |
| Gutenberg 📰 | Print stylesheet framework | Demo | GitHub |
| Framework | Description | Website | Documentation | Repository |
|---|---|---|---|---|
| TuiCss 🖥️ | Text-based UI | Examples | Wiki | GitHub |
| Bojler 📧 | Email framework | Site | Getting Started | GitHub |
| Orbit 🛸 | Components framework | Docs | Introduction | GitHub |
Historical reference - use with caution!
These frameworks are no longer actively maintained but kept for reference and inspiration. ⏸️
| Framework | Website | Documentation | Repository |
|---|---|---|---|
| Semantic UI 📋 | semantic-ui.com | Getting Started | GitHub |
| Tachyons ⚡ | tachyons.io | Docs | GitHub |
| Bourbon 🥃 | bourbon.io | Docs | GitHub |
| Framework | Website | Repository |
|---|---|---|
| Water.css 💦 | watercss.kognise.dev | GitHub |
| Framework | Website | Repository |
|---|---|---|
| sanitize.css 🧼 | Docs | GitHub |
| modern-css-reset 🔄 | - | GitHub |
| minireset.css 📝 | Site | GitHub |
| CSS Remedy 💊 | - | GitHub |
| inuitcss 🏔️ | - | GitHub |
| Framework | Website | Demo | Documentation | Repository |
|---|---|---|---|---|
| unsemantic 📐 | unsemantic.com | Responsive Demo | CSS Docs | GitHub |
| Propeller 🚁 | propeller.in | - | Get Started | GitHub |
| Concise CSS ✂️ | concisecss.com | - | Documentation | GitHub |
| Responsive Boilerplate 📱 | responsivebp.com | - | Getting Started | GitHub |
| turretcss 🏰 | turretcss.com | Demo | Getting Started | GitHub |
| Centurion ⚔️ | centurionframework.com | - | Documentation | GitHub |
→ Bootstrap, Tailwind CSS, Bulma
→ Pico.css, MVP.css, Simple.css
→ Material Components Web, Materialize
→ Carbon Components, PatternFly, US Web Design System
→ NES.css, 98.css, XP.css, 7.css
→ Bojler
→ Tufte CSS, Gutenberg
This collection represents years of community effort to create better tools for web developers. Whether you're building a quick prototype, a production app, or just experimenting with retro aesthetics, there's a framework here for you!
What's your favorite CSS framework? Drop a comment below! 👇
2025-12-07 15:03:21
Timothy was debugging a file processing script when Margaret noticed something in his error logs. "Timothy, you're getting 'too many open files' errors. Are you closing your file handles?"
Timothy looked defensive. "I am! Well... most of the time. Sometimes I forget if there's an error in the middle."
Margaret pulled up his code:
def process_config():
f = open('config.txt', 'r', encoding='utf-8')
data = f.read()
# Process data...
if 'error' in data:
return None # Oops - file never closed!
f.close()
return data
"See the problem?" Margaret asked. "If that early return happens, the file stays open. Python might close it when the object is garbage collected, but that's not guaranteed and can lead to resource exhaustion."
Timothy groaned. "So I need to add f.close() before every return statement? And what if an exception happens?"
"Exactly. The traditional solution is a try/finally block:"
def process_config():
f = open('config.txt', 'r', encoding='utf-8')
try:
data = f.read()
if 'error' in data:
return None
return data
finally:
f.close()
"Now the file always closes, even if you return early or an exception occurs. But look at how much ceremony that adds."
Timothy frowned. "All that boilerplate just to guarantee cleanup? There has to be a better way."
Margaret showed him the with statement:
def process_config():
with open('config.txt', 'r', encoding='utf-8') as f:
data = f.read()
if 'error' in data:
return None
return data
# File automatically closed here, no matter what
"Wait," Timothy said. "That's it? The with statement handles the closing automatically?"
"Exactly. Let me show you the structure."
Tree View:
process_config()
With open('config.txt', 'r', encoding='utf-8') as f
data = f.read()
If 'error' in data
└── Return None
Return data
English View:
Function process_config():
With open('config.txt', 'r', encoding='utf-8') as f:
Set data to f.read().
If 'error' in data:
Return None.
Return data.
"Look at the structure," Margaret said. "The with block has a clear entry point (open(...)) and an exit point (end of the indented block). Python guarantees that when you exit that block - whether by reaching the end, returning early, or raising an exception - the file gets closed."
Timothy traced through it. "So no matter which return statement executes, or if an exception happens, the file closes when we leave the with block?"
"Guaranteed. The with statement is a context manager. It sets up resources on entry and cleans them up on exit, automatically."
"How does Python know what to clean up?" Timothy asked.
Margaret explained: "Objects that work with with statements implement two special methods: __enter__ and __exit__. Let me show you a simple context manager:"
class FileLogger:
def __init__(self, filename):
self.filename = filename
self.file = None
def __enter__(self):
print(f"Opening {self.filename}")
self.file = open(self.filename, 'w', encoding='utf-8')
return self.file
def __exit__(self, exc_type, exc_val, exc_tb):
print(f"Closing {self.filename}")
if self.file:
self.file.close()
return False # Don't suppress exceptions
# Usage
with FileLogger('output.log') as log:
log.write('Starting process\n')
log.write('Processing data\n')
# File automatically closed here
Tree View:
class FileLogger
__init__(self, filename)
self.filename = filename
self.file = None
__enter__(self)
print(f'Opening {self.filename}')
self.file = open(self.filename, 'w', encoding='utf-8')
Return self.file
__exit__(self, exc_type, exc_val, exc_tb)
print(f'Closing {self.filename}')
If self.file
└── self.file.close()
Return False
With FileLogger('output.log') as log
log.write('Starting process\n')
log.write('Processing data\n')
English View:
Class FileLogger:
Function __init__(self, filename):
Set self.filename to filename.
Set self.file to None.
Function __enter__(self):
Evaluate print(f'Opening {self.filename}').
Set self.file to open(self.filename, 'w', encoding='utf-8').
Return self.file.
Function __exit__(self, exc_type, exc_val, exc_tb):
Evaluate print(f'Closing {self.filename}').
If self.file:
Evaluate self.file.close().
Return False.
With FileLogger('output.log') as log:
Evaluate log.write('Starting process\n').
Evaluate log.write('Processing data\n').
"See the flow?" Margaret pointed out. "When Python executes with FileLogger('output.log') as log, it calls __enter__(), which returns the file object that gets assigned to log. When the block ends, Python calls __exit__(), which closes the file."
Timothy watched the output:
Opening output.log
Closing output.log
"So __enter__ runs at the start of the with block, and __exit__ runs at the end, guaranteed?"
"Exactly. Even if an exception occurs inside the block, __exit__ still runs. That's the guarantee."
"What are those parameters in __exit__?" Timothy asked. "The exc_type, exc_val, exc_tb?"
Margaret explained: "If an exception occurs inside the with block, Python passes the exception information to __exit__. You can examine it and decide whether to suppress the exception or let it propagate."
She showed him:
class ErrorLogger:
def __enter__(self):
print("Entering context")
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if exc_type is not None:
print(f"Exception occurred: {exc_type.__name__}: {exc_val}")
return True # Suppress the exception
print("Exiting normally")
return False
with ErrorLogger():
print("Working...")
raise ValueError("Something went wrong!")
print("Continuing after exception")
The output:
Entering context
Working...
Exception occurred: ValueError: Something went wrong!
Continuing after exception
"See? The exception was raised, __exit__ received the exception details, logged them, and returned True to suppress it. The code continued normally."
Timothy was impressed. "So context managers can handle cleanup and exception management?"
"Right. That's why they're so powerful for resource management."
"Can I use multiple with statements together?" Timothy asked.
"Absolutely. You can nest them or combine them:"
# Nested
with open('input.txt', 'r', encoding='utf-8') as infile:
with open('output.txt', 'w', encoding='utf-8') as outfile:
outfile.write(infile.read())
# Combined (Python 3.1+)
with open('input.txt', 'r', encoding='utf-8') as infile, \
open('output.txt', 'w', encoding='utf-8') as outfile:
outfile.write(infile.read())
Tree View:
With open('input.txt', 'r', encoding='utf-8') as infile, open('output.txt', 'w', encoding='utf-8') as outfile
outfile.write(infile.read())
English View:
With open('input.txt', 'r', encoding='utf-8') as infile, open('output.txt', 'w', encoding='utf-8') as outfile:
Evaluate outfile.write(infile.read()).
"Both files are guaranteed to close, in reverse order. The last one opened closes first."
Timothy was starting to see the pattern. "So context managers are for anything that needs setup and cleanup?"
Margaret listed the common use cases:
"Use context managers for:
She showed him a threading example:
import threading
lock = threading.Lock()
# Without context manager - risky ❌
lock.acquire()
try:
# Critical section
pass
finally:
lock.release()
# With context manager - safe ✅
with lock:
# Critical section
pass
"The lock is guaranteed to release, even if an exception occurs in the critical section."
"Do I always have to write a class with __enter__ and __exit__?" Timothy asked.
"No. Python provides contextlib for simpler cases:"
from contextlib import contextmanager
@contextmanager
def timer():
import time
start = time.time()
print("Timer started")
yield
end = time.time()
print(f"Timer stopped. Elapsed: {end - start:.2f}s")
with timer():
# Code to time
sum(range(1000000))
Tree View:
@contextmanager
timer()
import time
start = time.time()
print('Timer started')
yield
end = time.time()
print(f'Timer stopped. Elapsed: {end - start:.2f}s')
With timer()
sum(range(1000000))
English View:
Decorator @contextmanager
Function timer():
Import module time.
Set start to time.time().
Evaluate print('Timer started').
Yield control.
Set end to time.time().
Evaluate print(f'Timer stopped. Elapsed: {end - start:.2f}s').
With timer():
Evaluate sum(range(1000000)).
"The @contextmanager decorator turns a generator into a context manager. Everything before yield is __enter__, everything after is __exit__."
Timothy nodded. "So I can write a simple function instead of a whole class for basic context managers?"
"Exactly. Use classes when you need state or complex exception handling. Use @contextmanager for simple cases."
Margaret pulled the lesson together. "The key insight is the guarantee. Manual cleanup requires discipline - you have to remember to close, release, or restore. Context managers provide a structural guarantee: setup happens, your code runs, cleanup happens. No exceptions, no early returns, no forgotten cleanup."
Timothy looked at his file-processing code. "So by using with open(...), I'm not just saving typing. I'm guaranteeing that the file closes, no matter what happens in my code?"
"That's exactly right. The structure enforces correctness. That's the power of context managers."
Analyze Python structure yourself: Download the Python Structure Viewer - a free tool that shows code structure in tree and plain English views. Works offline, no installation required.
Aaron Rose is a software engineer and technology writer at tech-reader.blog and the author of Think Like a Genius.
2025-12-07 14:48:47
I am a heavily tech-influenced person, and much of my work revolves around engineering, coding, and data-driven insights. Yet, my first blog is not about computer science. It’s about humanity.
Recently I was conducting an audition as the president of my university’s Debating Society. A girl came with a script titled something like “Humans or Monsters.” I believe in using statistical information, and she reminded the room about men committing rapes and wars, which is, of course, true.
But as a judge, I was waiting to see whether she would balance the concept instead of making it gender-biased, because the topic was humans, not men. I feel like she left out important points, or even worse, world sees the issue as if only men are at fault. Yes, men do commit many of these crimes; I agree with that. But there are also women who commit serious wrongs.
Statistically:
-Roughly 108 million women aged 15–49 worldwide have experienced non-partner sexual violence at some point in their lives.
But there is something even more tragic — something that is ignored but shouldn’t be. I’m not defending men; I’m acknowledging another crime against humanity:
Abortions.
In the last 45 years alone, there have been 1.3 billion recorded abortions, around 73 million abortians annually. This isn’t killing in war or in conflict, this is killing innocents before they even open their eyes. It’s tragic. It’s brutal. And we can’t ignore it. Many liberal and feminist women support this practice, and tens of millions of “human children” in the womb are lost every single year.
If we stick to the topic, both men and women commit terrible acts. But the real point is about humanity. My society represents the voices of youth and conscience, and I represent them. I somehow represent humanity itself. I cannot stay silent about a crime this large, especially when the perpetrators often do not even realize the moral weight of their actions.
Even 108 million rapes and 130 million war-related deaths over recent decades pale in comparison to the estimated 1.3 billion abortions worldwide over the same period. This is not about men or women; it is about humanity losing its sense of moral responsibility. Many societal movements and norms have normalized acts that should be treated as serious ethical and legal issues, and as a society, we often fail to recognize the gravity of these actions.
2025-12-07 14:44:48
Managing your time effectively in 2025 is more important than ever. With remote work, multiple tasks, and constant digital distractions, a good calendar tool can instantly improve your productivity. Whether you are looking for the best calendar apps, digital calendar tools, or modern productivity tools, this list will help you stay organized throughout the year.
Below are the 10 calendar tools for 2025 that can transform your scheduling and daily planning style.
Google Calendar is one of the best free calendar tools for productivity.
Why it’s great:
AI-powered scheduling
Syncs across all devices
Integrates with Gmail, Tasks, and Meet
It’s perfect if you're looking for Google Calendar alternatives or a simple, powerful all-in-one scheduling tool.
A top choice for professionals working in office or corporate environments.
Features that boost productivity:
Email and calendar in one place
Easy meeting scheduling
Automatic reminders
A must-have for anyone needing time management tools for work.
Simple, fast, and deeply integrated into the Apple ecosystem.
Benefits:
Works across iPhone, iPad, Mac, and Apple Watch
Natural language event creation
Clean design
Great for users searching for daily planner apps that just work.
An excellent pick for creators, teams, and project managers.
Why it stands out:
Syncs with Notion databases
Perfect for content planning
Beautiful minimal UI
Ideal if you want project management with calendar integrations.
A modern and smart option that uses AI for scheduling.
Top benefits:
Smart scheduling assistant
Analytics to track productivity
Easy team scheduling
Perfect for remote workers needing online calendar tools.
Task management meets calendar planning.
Productivity advantages:
Tasks appear on your calendar
Perfect for daily planning
Multi-device sync
Great for people who prefer task management and calendar tools combined.
For visual thinkers, Trello becomes even better with calendar mode.
Useful for:
Tracking deadlines
Planning content
Managing teams visually
Perfect for creators needing digital planning tools for their workflow.
One of the best-designed calendar apps for 2025.
Why people love it:
Natural language input
Smooth animations
Cross-device syncing
Great for users wanting premium productivity calendar apps.
Perfect for families, couples, and small teams who need shared schedules.
Key features:
Shared calendars
Event commenting
Categorized planning
Ideal for users wanting shared calendar tools that are simple yet effective.
If you prefer clean layouts and quick planning, Calendar-Vibe offers modern calendar templates for everyday use.
Benefits:
Fast-loading pages
Easy-to-use monthly and yearly templates
Great for printing or digital use
A solid choice for anyone looking for printable calendars 2025, monthly calendar templates, or free online calendars.
Final Thoughts
The right calendar tool can dramatically boost your daily productivity, improve time management, and reduce stress. Whether you're a student, remote worker, or busy professional, these top productivity apps help you stay organized in 2025.
Try a few tools and see which one matches your planning style. Staying consistent with a good calendar can completely transform your workflow.
2025-12-07 14:37:01
Automation is moving far beyond macros and RPA bots.
By 2030, AI-driven autonomous workflows will fundamentally change how enterprise systems operate.
This article breaks down exactly which processes will be fully automated and the technical components driving this transformation: LLMs, ML models, RPA frameworks, API orchestration, and autonomous agents.
1. Invoice Processing (IDP + ML + RPA Integration)
Invoice workflows will be one of the first fully automated domains.
Tech components:
Outcome:
Human involvement → Exception-only.
Automation coverage → 95%+.
2. Tier-1 Customer Support (LLMs + Retrieval-Augmented Agents)
Modern AI agents can already resolve up to 80% of support queries.
Tech stack:
Outcome:
AI resolves queries → instantly, consistently.
3. HR Onboarding and Identity Verification (Workflow Engines + AI Validation)
Expect end-to-end automation:
Automation steps:
Outcome:
HR moves from manual coordination → full automation.
4. Procurement & Vendor Management (ML Scoring Models + RPA)
Procurement automation will use:
Outcome:
Manual touchpoints → eliminated.
5. Compliance Monitoring (NLP + AI Auditing)
LLMs will scan:
Outcome:
Real-time, autonomous compliance.
6. IT Service Desk (Self-Healing IT + RPA Bots)
Examples:
Outcome:
Ticket volume drops dramatically.
7. Data Entry & Normalization (AI ETL + Automatic Structuring)
Data pipelines will auto-clean themselves.
Tech:
Outcome:
Zero manual data entry.
8. Marketing Operations (Generative AI + Predictive Targeting)
AI will automate:
Outcome:
Marketing = autonomous engine.
9. Reporting & Analytics (Auto Insights + LLM Dashboards)
Data → Insights without analysts.
Tech:
Outcome:
Decision-making → AI-assisted.
10. Sales Pipeline Management (Predictive Scoring + AI Routing)
AI will:
Outcome:
Sales teams focus only on closing.
Final Thoughts
The shift from task automation to end-to-end autonomous systems will define enterprise tech in the next decade.
Developers who understand RPA + AI + LLMs + API orchestration will lead the automation wave.
2025-12-07 14:34:03
If you've been managing AWS resources exclusively through the web console, you're not wrong—but you might be working harder than you need to. Let me show you why AWS CLI has become the go-to choice for developers who value speed, automation, and control.
Don't get me wrong—the AWS Management Console is beautifully designed. It's intuitive, visual, and perfect for exploring services you're learning. Amazon has invested millions into creating an interface that makes cloud computing accessible to everyone, and that's genuinely commendable.
But here's what happens in real-world development scenarios:
The Console Workflow:
The CLI Workflow:
aws ec2 run-instances --image-id ami-12345678 --count 50 --instance-type t2.micro --key-name MyKeyPair --region us-east-1
One line. Fifty instances. Multiple regions with a simple loop. Five seconds total.
The difference isn't just speed—it's a fundamental shift in how you think about infrastructure management. The console trains you to think in clicks. The CLI trains you to think in systems.
1. Speed That Actually Matters
When you're deploying infrastructure, troubleshooting issues at 2 AM, or managing resources across multiple AWS accounts and regions, every second compounds. With CLI, you can:
jq, grep, awk, or sed
Let me give you a concrete example. Yesterday, I needed to find all EC2 instances across four regions that were running but had been inactive for more than 30 days. In the console, this would have meant:
With CLI:
for region in us-east-1 us-west-2 eu-west-1 ap-southeast-1; do
aws ec2 describe-instances --region $region \
--filters "Name=instance-state-name,Values=running" \
--query 'Reservations[*].Instances[*].[InstanceId,LaunchTime]' \
--output text | while read id launch_time; do
# Check if instance is older than 30 days
if [[ $(date -d "$launch_time" +%s) -lt $(date -d '30 days ago' +%s) ]]; then
echo "$region: $id (launched: $launch_time)"
fi
done
done
Two minutes to write. Instant execution. Complete results.
2. Automation and Scripting: Where CLI Becomes Indispensable
This is where the CLI doesn't just save time—it enables entirely new workflows. Let me show you some real-world automation that simply isn't possible with the console:
Automated Backup Script:
#!/bin/bash
# Daily backup script for all RDS instances
BACKUP_DATE=$(date +%Y%m%d-%H%M%S)
# Get all RDS instances
for db in $(aws rds describe-db-instances \
--query 'DBInstances[*].DBInstanceIdentifier' \
--output text); do
echo "Creating snapshot for $db..."
aws rds create-db-snapshot \
--db-instance-identifier $db \
--db-snapshot-identifier "${db}-backup-${BACKUP_DATE}"
# Tag the snapshot
aws rds add-tags-to-resource \
--resource-name "arn:aws:rds:us-east-1:123456789012:snapshot:${db}-backup-${BACKUP_DATE}" \
--tags Key=AutomatedBackup,Value=true Key=Date,Value=$BACKUP_DATE
# Clean up snapshots older than 30 days
aws rds describe-db-snapshots \
--db-instance-identifier $db \
--query "DBSnapshots[?SnapshotCreateTime<='$(date -d '30 days ago' --iso-8601)'].DBSnapshotIdentifier" \
--output text | while read old_snapshot; do
echo "Deleting old snapshot: $old_snapshot"
aws rds delete-db-snapshot --db-snapshot-identifier $old_snapshot
done
done
echo "Backup process completed at $(date)"
Schedule this with cron, and you have enterprise-grade backup automation. Try doing that with the console.
Cost Optimization Script:
#!/bin/bash
# Find and stop all EC2 instances with the tag "Environment:Development" after 6 PM
CURRENT_HOUR=$(date +%H)
if [ $CURRENT_HOUR -ge 18 ]; then
aws ec2 describe-instances \
--filters "Name=tag:Environment,Values=Development" \
"Name=instance-state-name,Values=running" \
--query 'Reservations[*].Instances[*].InstanceId' \
--output text | while read instance; do
echo "Stopping development instance: $instance"
aws ec2 stop-instances --instance-ids $instance
# Send notification
aws sns publish \
--topic-arn "arn:aws:sns:us-east-1:123456789012:cost-savings" \
--message "Stopped development instance $instance at $(date)"
done
fi
This single script can save thousands of dollars per month by automatically shutting down development environments during non-business hours.
3. Version Control for Infrastructure
Your CLI commands live in scripts. Scripts live in Git. Suddenly, your infrastructure changes have:
git revert becomes your infrastructure undo buttonHere's a real example of infrastructure as code using AWS CLI:
#!/bin/bash
# vpc-setup.sh - Creates a complete VPC environment
# Create VPC
VPC_ID=$(aws ec2 create-vpc \
--cidr-block 10.0.0.0/16 \
--tag-specifications 'ResourceType=vpc,Tags=[{Key=Name,Value=Production-VPC}]' \
--query 'Vpc.VpcId' \
--output text)
echo "Created VPC: $VPC_ID"
# Create Internet Gateway
IGW_ID=$(aws ec2 create-internet-gateway \
--tag-specifications 'ResourceType=internet-gateway,Tags=[{Key=Name,Value=Production-IGW}]' \
--query 'InternetGateway.InternetGatewayId' \
--output text)
aws ec2 attach-internet-gateway --vpc-id $VPC_ID --internet-gateway-id $IGW_ID
echo "Created and attached Internet Gateway: $IGW_ID"
# Create public subnet
PUBLIC_SUBNET_ID=$(aws ec2 create-subnet \
--vpc-id $VPC_ID \
--cidr-block 10.0.1.0/24 \
--availability-zone us-east-1a \
--tag-specifications 'ResourceType=subnet,Tags=[{Key=Name,Value=Public-Subnet-1a}]' \
--query 'Subnet.SubnetId' \
--output text)
echo "Created public subnet: $PUBLIC_SUBNET_ID"
# Create private subnet
PRIVATE_SUBNET_ID=$(aws ec2 create-subnet \
--vpc-id $VPC_ID \
--cidr-block 10.0.2.0/24 \
--availability-zone us-east-1a \
--tag-specifications 'ResourceType=subnet,Tags=[{Key=Name,Value=Private-Subnet-1a}]' \
--query 'Subnet.SubnetId' \
--output text)
echo "Created private subnet: $PRIVATE_SUBNET_ID"
# Create route table for public subnet
ROUTE_TABLE_ID=$(aws ec2 create-route-table \
--vpc-id $VPC_ID \
--tag-specifications 'ResourceType=route-table,Tags=[{Key=Name,Value=Public-RT}]' \
--query 'RouteTable.RouteTableId' \
--output text)
# Add route to Internet Gateway
aws ec2 create-route \
--route-table-id $ROUTE_TABLE_ID \
--destination-cidr-block 0.0.0.0/0 \
--gateway-id $IGW_ID
# Associate route table with public subnet
aws ec2 associate-route-table \
--subnet-id $PUBLIC_SUBNET_ID \
--route-table-id $ROUTE_TABLE_ID
echo "VPC setup complete!"
echo "VPC ID: $VPC_ID"
echo "Public Subnet: $PUBLIC_SUBNET_ID"
echo "Private Subnet: $PRIVATE_SUBNET_ID"
This script is now your documentation, your deployment process, and your disaster recovery plan all in one. Version it, review it, and deploy with confidence.
4. Consistency Across Environments
Same commands work identically whether you're managing:
No UI differences to navigate. No "where did they move that button in the new console update?" frustrations. No regional console quirks. Just consistent, reliable command execution.
5. Power User Efficiency: Unlocking Advanced Capabilities
Once you learn the patterns, you become unstoppable. Here are some power user techniques:
Finding Untagged Resources (Cost Management Gold):
# Find all untagged EC2 instances
aws ec2 describe-instances \
--query 'Reservations[*].Instances[?!Tags].{ID:InstanceId,Type:InstanceType,State:State.Name}' \
--output table
# Find all S3 buckets without proper tags
aws s3api list-buckets --query 'Buckets[*].Name' --output text | while read bucket; do
tags=$(aws s3api get-bucket-tagging --bucket $bucket 2>/dev/null)
if [ -z "$tags" ]; then
echo "Untagged bucket: $bucket"
fi
done
Cross-Region Resource Management:
# List all running instances across ALL regions
for region in $(aws ec2 describe-regions --query 'Regions[*].RegionName' --output text); do
echo "Checking region: $region"
aws ec2 describe-instances \
--region $region \
--filters "Name=instance-state-name,Values=running" \
--query 'Reservations[*].Instances[*].[InstanceId,InstanceType,Tags[?Key==`Name`].Value|[0]]' \
--output table
done
Advanced S3 Operations:
# Find large S3 buckets (>100GB) and calculate their actual cost
aws s3api list-buckets --query 'Buckets[*].Name' --output text | while read bucket; do
echo "Analyzing bucket: $bucket"
# Get total size
size=$(aws s3 ls s3://$bucket --recursive --summarize | grep "Total Size" | awk '{print $3}')
if [ -n "$size" ] && [ $size -gt 107374182400 ]; then
size_gb=$((size / 1073741824))
estimated_cost=$(echo "scale=2; $size_gb * 0.023" | bc)
echo "$bucket: ${size_gb}GB (~\$${estimated_cost}/month)"
# Get object count
count=$(aws s3 ls s3://$bucket --recursive --summarize | grep "Total Objects" | awk '{print $3}')
echo " Objects: $count"
# Check versioning
versioning=$(aws s3api get-bucket-versioning --bucket $bucket --query 'Status' --output text)
echo " Versioning: $versioning"
fi
done
Security Auditing:
# Find all publicly accessible S3 buckets (security nightmare detector)
for bucket in $(aws s3api list-buckets --query 'Buckets[*].Name' --output text); do
block_public=$(aws s3api get-public-access-block --bucket $bucket 2>/dev/null)
if [ $? -ne 0 ]; then
echo "⚠️ WARNING: $bucket has no public access block!"
# Check bucket ACL
acl=$(aws s3api get-bucket-acl --bucket $bucket --query 'Grants[?Grantee.URI==`http://acs.amazonaws.com/groups/global/AllUsers`]' --output text)
if [ -n "$acl" ]; then
echo " 🚨 CRITICAL: Public ACL detected on $bucket!"
fi
fi
done
Now that you're convinced (I hope), let's get you set up with AWS CLI and running your first commands. This section will take you from zero to proficient.
On macOS:
# Using Homebrew (recommended)
brew install awscli
# Verify installation
aws --version
On Linux:
# Using the official installer
curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
unzip awscliv2.zip
sudo ./aws/install
# Verify installation
aws --version
On Windows:
Download the MSI installer from the official AWS CLI page and run it. Or use the command line:
# Using Windows Package Manager
winget install Amazon.AWSCLI
# Verify installation
aws --version
You should see output like: aws-cli/2.x.x Python/3.x.x Linux/5.x.x
Before you can use AWS CLI, you need to configure your credentials. First, create an IAM user in the AWS Console with programmatic access:
Now configure your CLI:
aws configure
You'll be prompted for:
AWS Access Key ID [None]: AKIAIOSFODNN7EXAMPLE
AWS Secret Access Key [None]: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
Default region name [None]: us-east-1
Default output format [None]: json
Pro Tips:
json for scripting, table for human readability, or text for parsingaws configure --profile production
export AWS_PROFILE=production
Let's start with some basic commands to get comfortable:
1. Check Your Identity:
aws sts get-caller-identity
Output:
{
"UserId": "AIDAI123456789EXAMPLE",
"Account": "123456789012",
"Arn": "arn:aws:iam::123456789012:user/cli-admin"
}
This confirms you're authenticated and shows which account you're using.
2. List S3 Buckets:
aws s3 ls
Output:
2024-01-15 10:23:45 my-application-logs
2024-02-20 14:56:12 company-backups
2024-03-10 09:15:33 static-website-assets
3. List EC2 Instances:
aws ec2 describe-instances --output table
This gives you a nicely formatted table of all your EC2 instances.
4. Get Specific Information with Queries:
# List only running instances with their IDs and types
aws ec2 describe-instances \
--filters "Name=instance-state-name,Values=running" \
--query 'Reservations[*].Instances[*].[InstanceId,InstanceType,State.Name]' \
--output table
Output:
-----------------------------------------
| DescribeInstances |
+----------------------+----------------+----------+
| i-1234567890abcdef0 | t2.micro | running |
| i-0987654321fedcba0 | t2.small | running |
+----------------------+----------------+----------+
Let's walk through some complete, real-world scenarios:
# Step 1: Create a bucket
BUCKET_NAME="my-awesome-website-$(date +%s)"
aws s3 mb s3://$BUCKET_NAME --region us-east-1
# Step 2: Enable static website hosting
aws s3 website s3://$BUCKET_NAME/ --index-document index.html --error-document error.html
# Step 3: Create a simple index.html
cat > index.html << EOF
<!DOCTYPE html>
<html>
<head><title>My AWS CLI Website</title></head>
<body>
<h1>Hello from AWS CLI!</h1>
<p>This website was created entirely with command line tools.</p>
</body>
</html>
EOF
# Step 4: Upload the file
aws s3 cp index.html s3://$BUCKET_NAME/
# Step 5: Make it public (bucket policy)
cat > bucket-policy.json << EOF
{
"Version": "2012-10-17",
"Statement": [{
"Sid": "PublicReadGetObject",
"Effect": "Allow",
"Principal": "*",
"Action": "s3:GetObject",
"Resource": "arn:aws:s3:::$BUCKET_NAME/*"
}]
}
EOF
aws s3api put-bucket-policy --bucket $BUCKET_NAME --policy file://bucket-policy.json
# Step 6: Get the website URL
echo "Your website is live at: http://$BUCKET_NAME.s3-website-us-east-1.amazonaws.com"
Boom. You just created and deployed a website in 30 seconds. Try doing that with the console.
# Step 1: Create a security group
SG_ID=$(aws ec2 create-security-group \
--group-name my-web-server-sg \
--description "Security group for web server" \
--query 'GroupId' \
--output text)
echo "Created security group: $SG_ID"
# Step 2: Add ingress rules
aws ec2 authorize-security-group-ingress \
--group-id $SG_ID \
--protocol tcp \
--port 22 \
--cidr 0.0.0.0/0 # SSH (WARNING: restrict this in production!)
aws ec2 authorize-security-group-ingress \
--group-id $SG_ID \
--protocol tcp \
--port 80 \
--cidr 0.0.0.0/0 # HTTP
aws ec2 authorize-security-group-ingress \
--group-id $SG_ID \
--protocol tcp \
--port 443 \
--cidr 0.0.0.0/0 # HTTPS
# Step 3: Create a key pair
aws ec2 create-key-pair \
--key-name my-web-server-key \
--query 'KeyMaterial' \
--output text > my-web-server-key.pem
chmod 400 my-web-server-key.pem
echo "Created key pair: my-web-server-key.pem"
# Step 4: Create user data script for auto-configuration
cat > user-data.sh << 'EOF'
#!/bin/bash
yum update -y
yum install -y httpd
systemctl start httpd
systemctl enable httpd
echo "<h1>Hello from AWS CLI-created instance!</h1>" > /var/www/html/index.html
EOF
# Step 5: Launch the instance
INSTANCE_ID=$(aws ec2 run-instances \
--image-id ami-0c55b159cbfafe1f0 \
--instance-type t2.micro \
--key-name my-web-server-key \
--security-group-ids $SG_ID \
--user-data file://user-data.sh \
--tag-specifications 'ResourceType=instance,Tags=[{Key=Name,Value=MyWebServer}]' \
--query 'Instances[0].InstanceId' \
--output text)
echo "Launched instance: $INSTANCE_ID"
# Step 6: Wait for it to be running
aws ec2 wait instance-running --instance-ids $INSTANCE_ID
echo "Instance is now running!"
# Step 7: Get the public IP
PUBLIC_IP=$(aws ec2 describe-instances \
--instance-ids $INSTANCE_ID \
--query 'Reservations[0].Instances[0].PublicIpAddress' \
--output text)
echo "Your web server is accessible at: http://$PUBLIC_IP"
This entire workflow—from zero to a running, configured web server—takes about 2 minutes with the CLI. With the console, you'd still be clicking through wizards.
# Create a snapshot of an RDS database
aws rds create-db-snapshot \
--db-instance-identifier my-production-db \
--db-snapshot-identifier manual-backup-$(date +%Y%m%d-%H%M%S)
# List all snapshots for this database
aws rds describe-db-snapshots \
--db-instance-identifier my-production-db \
--query 'DBSnapshots[*].[DBSnapshotIdentifier,SnapshotCreateTime,Status]' \
--output table
# Restore from a snapshot
aws rds restore-db-instance-from-db-snapshot \
--db-instance-identifier my-restored-db \
--db-snapshot-identifier manual-backup-20241207-143022 \
--db-instance-class db.t3.micro
# Monitor the restore progress
aws rds describe-db-instances \
--db-instance-identifier my-restored-db \
--query 'DBInstances[0].[DBInstanceStatus,Endpoint.Address]' \
--output table
# Find all stopped instances (you're paying for their EBS volumes!)
aws ec2 describe-instances \
--filters "Name=instance-state-name,Values=stopped" \
--query 'Reservations[*].Instances[*].[InstanceId,Tags[?Key==`Name`].Value|[0],LaunchTime]' \
--output table
# Terminate old stopped instances
aws ec2 describe-instances \
--filters "Name=instance-state-name,Values=stopped" \
--query 'Reservations[*].Instances[*].InstanceId' \
--output text | while read instance; do
echo "Terminating: $instance"
aws ec2 terminate-instances --instance-ids $instance
done
# Find unattached EBS volumes (costing you money for nothing!)
aws ec2 describe-volumes \
--filters "Name=status,Values=available" \
--query 'Volumes[*].[VolumeId,Size,CreateTime]' \
--output table
# Delete them after confirmation
aws ec2 describe-volumes \
--filters "Name=status,Values=available" \
--query 'Volumes[*].VolumeId' \
--output text | while read volume; do
echo "Do you want to delete $volume? (y/n)"
read answer
if [ "$answer" = "y" ]; then
aws ec2 delete-volume --volume-id $volume
echo "Deleted $volume"
fi
done
Using JQ for JSON Processing:
# Install jq first: brew install jq (macOS) or apt-get install jq (Linux)
# Get detailed instance information in a custom format
aws ec2 describe-instances | jq '.Reservations[].Instances[] | {
id: .InstanceId,
type: .InstanceType,
state: .State.Name,
ip: .PublicIpAddress,
name: (.Tags[]? | select(.Key=="Name") | .Value)
}'
Creating Reusable Functions:
Add these to your .bashrc or .zshrc:
# Quick instance lookup by name
ec2-find() {
aws ec2 describe-instances \
--filters "Name=tag:Name,Values=*$1*" \
--query 'Reservations[*].Instances[*].[InstanceId,InstanceType,State.Name,PublicIpAddress]' \
--output table
}
# Usage: ec2-find webserver
# Quick S3 bucket size check
s3-size() {
aws s3 ls s3://$1 --recursive --summarize | grep "Total Size" | awk '{print $3/1024/1024/1024 " GB"}'
}
# Usage: s3-size my-bucket-name
# Get current AWS spending this month
aws-cost() {
aws ce get-cost-and-usage \
--time-period Start=$(date -d "$(date +%Y-%m-01)" +%Y-%m-%d),End=$(date +%Y-%m-%d) \
--granularity MONTHLY \
--metrics "UnblendedCost" \
--query 'ResultsByTime[*].[TimePeriod.Start,Total.UnblendedCost.Amount]' \
--output table
}
I've seen teams reduce deployment times from 30 minutes of console clicking to 30 seconds of script execution. I've watched developers troubleshoot production issues while commuting using nothing but a terminal on their phone. I've experienced the satisfaction of automating away repetitive tasks that used to eat hours of my week.
One team I worked with automated their entire DR (Disaster Recovery) runbook using AWS CLI scripts. What used to be a 40-page manual process requiring 6 hours and multiple people became a single command:
./disaster-recovery.sh --region us-west-2 --restore-from latest
Their RTO (Recovery Time Objective) went from 6 hours to 45 minutes. That's the power of CLI automation.
AWS CLI is powerful. It's efficient. It's the professional choice for managing cloud infrastructure at scale. It's the difference between being a button-clicker and being an infrastructure engineer.
And it's also a significant security risk sitting on your laptop right now.
When you configure AWS CLI using aws configure, your credentials are stored in plain text files on your disk:
~/.aws/credentials
~/.aws/config
Let's look at what's actually in these files:
$ cat ~/.aws/credentials
[default]
aws_access_key_id = AKIAIOSFODNN7EXAMPLE
aws_secret_access_key = wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
[production]
aws_access_key_id = AKIAI44QH8DHBEXAMPLE
aws_secret_access_key = je7MtGbClwBF/2Zp9Utk/h3yCo8nvbEXAMPLEKEY
These files contain your AWS access keys—the literal keys to your kingdom. And they're just... sitting there. Unencrypted. On your disk. In plain text. Readable by any process, any script, any malware.
Think about what that means:
🔓 Any malware that infects your laptop has immediate access - Cryptominers, ransomware, data exfiltration tools—they all scan for AWS credentials as their first step.
🔓 Any script you run can read them - That npm package you just installed? That Python script from Stack Overflow? They can all access your AWS credentials without you knowing.
🔓 Anyone with physical access to your machine can copy them - Dropped your laptop at the coffee shop? Someone at the repair shop? Your credentials are just sitting there.
🔓 If your laptop is stolen, your AWS account is compromised - The thief doesn't need to crack your AWS password—they already have permanent access keys.
🔓 Backup systems might sync these credentials to the cloud unencrypted - Dropbox, Google Drive, Time Machine—they're backing up your .aws folder right now.
🔓 Git repositories accidentally expose them - How many times have you seen someone commit their .aws folder to a public repo? It happens more than you think.
Let me share some real incidents I've witnessed or heard from colleagues:
Case 1: The $72,000 Bitcoin Mining Operation
A developer's laptop got infected with malware that specifically hunted for AWS credentials. Within 18 hours, the attacker had spun up 300 GPU instances across multiple regions to mine cryptocurrency. The bill? $72,000. The company's AWS account was banned for abuse. The developer? Let go.
The malware was sophisticated—it detected when the user was idle, spun up resources, and shut them down just before the user came back. It took three days to notice because CloudWatch alarms weren't configured properly.
Case 2: The Complete S3 Exfiltration
An intern downloaded a "productivity tool" that turned out to be malware. It scanned for .aws/credentials files, found them, and systematically downloaded every S3 bucket in the account—including 300GB of customer PII. The company had to notify 2.3 million customers of a data breach. The regulatory fines alone exceeded $15 million.
Case 3: The Cryptojacking Attack
A senior engineer's laptop was compromised at a conference via public WiFi. The attacker waited six months before activating, making it nearly impossible to trace. When they finally struck, they deleted all production databases and left a ransom note. Because the credentials were persistent and never rotated, the six-month-old breach was still viable.
Case 4: The Accidental GitHub Commit
A developer was working on a side project and accidentally committed their .aws folder to a public GitHub repository. Within 45 minutes, automated bots found the credentials and started launching instances. The developer only noticed when they got an AWS bill notification for $5,000—for resources launched in the past hour.
Unlike the AWS web console which:
Your CLI credentials are:
It's like leaving your house keys under the doormat and then being surprised when someone walks in.
You might have heard the standard security advice. Let's examine why each one falls short:
"Use IAM roles!"
"Rotate your keys frequently!"
"Use AWS SSO!"
"Use temporary credentials!"
"Use AWS Vault or similar tools!"
"Just use MFA for everything!"
These solutions help, but none of them solve the fundamental problem: your credentials are stored in plain text on your disk.
It's like putting a better lock on your front door while leaving the window wide open.
What if you could have all the speed and power of AWS CLI with actually secure credential storage? What if your AWS keys were encrypted at rest and only decrypted at the exact moment you need them? What if this worked seamlessly without changing your workflow?
That's exactly what AWS Credential Manager provides.
AWS Credential Manager takes a different approach. Instead of trying to work around the credential storage problem, it solves it directly.
The architecture is elegantly simple:
Encrypted Storage - Your AWS credentials are encrypted using Windows Credential Manager with DPAPI (Data Protection API), the same technology Windows uses to protect your passwords, certificates, and other sensitive data.
On-Demand Decryption - Credentials are only decrypted when you actually run an AWS CLI command. Not when you boot your computer. Not when you're browsing the web. Only when needed.
Immediate Re-Encryption - As soon as your command completes, credentials are locked back up. The window of exposure is measured in milliseconds, not hours or days.
Zero Workflow Change - You still run aws s3 ls, aws ec2 describe-instances, or any other AWS CLI command exactly as before. Your scripts don't change. Your muscle memory doesn't change. Everything just works.
Here's what happens under the hood when you run an AWS command:
1. You type: aws s3 ls
2. AWS Credential Manager intercepts the command
3. Credentials are decrypted from Windows Credential Manager (DPAPI)
4. Temporary credentials are injected into the AWS CLI environment
5. Your command executes normally
6. Credentials are immediately purged from memory
7. Your encrypted credentials remain safe on disk
This means:
But your actual AWS CLI usage is identical to before.
Getting started takes about 60 seconds:
# 1. Install from Microsoft Store (ensures authenticity and auto-updates)
# Download: https://apps.microsoft.com/store/detail/9NWNQ88V1P86?cid=DevShareMCLPCS
# 2. Configure your credentials (one-time setup)
aws-credential-manager configure
# You'll be prompted for:
# - AWS Access Key ID
# - AWS Secret Access Key
# - Default region
# - Default output format
# 3. Use AWS CLI exactly as before
aws s3 ls
aws ec2 describe-instances
aws rds describe-db-instances
# That's it. Everything works, but now it's secure.
Your credentials are now encrypted. Your workflow hasn't changed. Your scripts still work. Your automation still runs. But your AWS account is actually protected.
For Individual Developers:
For Development Teams:
For Security Teams:
Your laptop is your most vulnerable attack surface. It:
Every one of these scenarios is a potential AWS credential exposure if you're using plain text storage.
You wouldn't leave your house keys under the doormat.
You wouldn't write your bank password on a sticky note.
Don't leave your AWS keys in plain text.
Let's do some quick math:
Compare that to:
It's not a question of if your laptop will be compromised—it's when. And when it happens, do you want your AWS credentials to be an open book or encrypted and secure?
AWS CLI is the right tool for professional AWS management. It's faster, more powerful, more automatable, and more flexible than the web console. Once you master it, you'll wonder how you ever lived without it.
But using it securely requires one additional step—one that should have been built into AWS CLI from the start but wasn't.
AWS Credential Manager is that missing piece. It's the protection layer that lets you use AWS CLI with the speed and efficiency you need and the security you must have.
Think of it this way: you wouldn't drive a race car without seatbelts. You wouldn't run production infrastructure without backups. And you shouldn't use AWS CLI without encrypted credential storage.
Your credentials are the keys to your infrastructure.
Your infrastructure is the foundation of your business.
Protect both.
Get AWS Credential Manager from Microsoft Store →
Here's a cheat sheet of commands you'll use constantly:
# Identity and Configuration
aws sts get-caller-identity # Who am I?
aws configure list # Show current configuration
# S3 Operations
aws s3 ls # List buckets
aws s3 ls s3://bucket-name # List bucket contents
aws s3 cp file.txt s3://bucket/ # Upload file
aws s3 sync ./local s3://bucket/path # Sync directory
# EC2 Management
aws ec2 describe-instances # List all instances
aws ec2 start-instances --instance-ids i-xxx
aws ec2 stop-instances --instance-ids i-xxx
aws ec2 terminate-instances --instance-ids i-xxx
# RDS Operations
aws rds describe-db-instances # List databases
aws rds create-db-snapshot # Create snapshot
aws rds restore-db-instance-from-db-snapshot
# IAM Management
aws iam list-users # List users
aws iam list-roles # List roles
aws iam get-user --user-name username # User details
# CloudWatch Logs
aws logs describe-log-groups # List log groups
aws logs tail /aws/lambda/function-name --follow
# Cost and Billing
aws ce get-cost-and-usage # Get cost data
aws budgets describe-budgets # List budgets
Additional Resources:
Have you dealt with AWS credential security in your organization? What solutions have you found effective? What's your favorite AWS CLI workflow? Share your experiences in the comments below.
And if you found this guide helpful, consider sharing it with your team. Secure development practices benefit everyone.