If you’ve ever wanted more control or peace of mind over your AI, the solution might be right under your nose: hosting AI models locally. Yes, on your own hardware and under your own roof (physical or virtual). It’s kind of like deciding to cook your favorite dish at home instead of ordering takeout. You know exactly what goes into it; you fine-tune the recipe, and you can eat anytime you want — no depending on anyone else to get it right.

In this guide, we’ll break down why local AI hosting could transform the way you work, what hardware and software you need, how to do it step by step, and best practices to keep everything running smoothly. Let’s dive in and give you the power to run AI on your own terms.

What Is Locally Hosted AI (and Why You Should Care)

Locally hosted AI means running machine learning models directly on equipment you own or fully control. You can use a home workstation with a decent GPU, a dedicated server in your office, or even a rented bare-metal machine, if that suits you better.

Why does this matter? A few important reasons…

• Privacy and data control: No shipping sensitive information off to third-party servers. You hold the keys.
• Faster response times: Your data never leaves your network, so you skip the round-trip to the cloud.
• Customization: Tweak, fine-tune, or even re-architect your models however you see fit.
• Reliability: Avoid downtime or usage limits that cloud AI providers impose.

Of course, hosting AI yourself means you’ll manage your own infrastructure, updates, and potential fixes. But if you want to make sure your AI is truly yours, local hosting is a game-changer.

Assessing Hardware Requirements

Getting your physical setup right is one of the biggest steps toward successful local AI hosting. You don’t want to invest time (and money) into configuring an AI model, only to discover your GPU can’t handle the load or your server overheats. 

So, before you dive into the details of installation and model fine-tuning, it’s worth mapping out exactly what sort of hardware you’ll need.

Why Hardware Matters for Local AI

When you’re hosting AI locally, performance largely boils down to how powerful (and compatible) your hardware is. A robust CPU can manage simpler tasks or smaller machine learning models, but deeper models often need GPU acceleration to handle the intense parallel computations. If your hardware is underpowered, you’ll see slow inference times, choppy performance, or you might fail to load large models altogether.

That doesn’t mean you need a supercomputer. Many modern mid-range GPUs can handle medium-scale AI tasks — it’s all about matching your model’s demands to your budget and usage patterns.

Key Considerations

1. CPU vs. GPU

Some AI operations (like basic classification or smaller language model queries) can run on a solid CPU alone. However, if you want real-time chat interfaces, text generation, or image synthesis, a GPU is a near-must.

2. Memory (RAM) and Storage

Large language models can easily consume tens of gigabytes. Aim for 16GB or 32GB system RAM for moderate usage. If you plan to load multiple models or train new ones, 64GB+ might be beneficial.

An SSD is also strongly recommended — loading models from spinning HDDs slow everything down. A 512GB SSD or larger is common, depending on how many model checkpoints you store.

3. Server vs. Workstation

If you’re just experimenting or only need AI occasionally, a powerful desktop might do the job. Plug in a mid-range GPU and you’re set. For 24/7 uptime, consider a dedicated server with proper cooling, redundant power supplies, and possibly ECC (error-correcting) RAM for stability.

4. Hybrid Cloud Approach

Not everyone has the physical space or desire to manage a noisy GPU rig. You can still “go local” by renting or purchasing a dedicated server from a hosting provider that supports GPU hardware. That way, you get full control over your environment without physically maintaining the box.

Pulling It All Together

Think about how heavily you’ll use AI. If you see your model constantly in action (like a full-time chatbot or daily image generation for marketing), invest in a robust GPU and enough RAM to keep everything running smoothly. If your needs are more exploratory or light usage, a mid-tier GPU card in a standard workstation can deliver decent performance without destroying your budget.

Ultimately, hardware shapes your AI experience. It’s easier to plan carefully up front than to juggle endless system upgrades once you realize your model requires more juice. Even if you start small, keep an eye on your next step: if your local user base or model complexity grows, you’ll want headroom to scale.

Choosing the Right Model (and Software)

Picking an open-source AI model to run locally might feel like staring at a massive menu (like that phonebook they call a menu at Cheesecake Factory). You’ve got endless options, each with its own flavors and best-use scenarios. While variety is the spice of life, it can also be overwhelming. 

The key is to nail down what exactly you need from your AI tools: text generation, image synthesis, domain-specific predictions, or something else altogether.

Your use case drastically narrows the search for the right model. For example, if you want to generate marketing copy, you’d explore language models like LLaMA derivatives. For visual tasks, you’d look at image-based models such as Stable Diffusion or flux.

Popular Open-Source Models

Depending on your needs, you should check out the following.

Language Models

• LLaMA/ Alpaca / Vicuna: All well-known projects for local hosting. They can handle chat-like interactions or text completion. Check how much VRAM they require (some variants need only ~8GB).
• GPT-J / GPT-NeoX: Good for pure text generation, though they can be more demanding on your hardware.

Image Models

• Stable Diffusion: A go-to for generating art, product images, or concept designs. It’s widely used and has a massive community offering tutorials, add-ons, and creative expansions.

Domain-Specific Models

• Browse Hugging Face for specialized models (e.g., finance, healthcare, legal). You might find a smaller, domain-tuned model that’s easier to run than a general-purpose giant.

Open Source Frameworks

You’ll need to load and interact with your chosen model using a framework. Two industry standards dominate:

• PyTorch: Renowned for user-friendly debugging and a huge community. Most new open-source models appear in PyTorch first.
• TensorFlow: Backed by Google, stable for production environments, though the learning curve can be steeper in some areas.

Where To Find Models

• Hugging Face Hub: A massive repository of open-source models. Read community reviews, usage notes, and watch for how actively a model is maintained.
• GitHub: Many labs or indie devs post custom AI solutions. Just verify the model’s license and confirm it’s stable enough for your use case.

Once you pick your model and framework, take a moment to read the official docs or any example scripts. If your model is super fresh (like a newly released LLaMA variant), be prepared for some potential bugs or incomplete instructions. 

The more you understand your model’s nuances, the better you’ll be at deploying, optimizing, and maintaining it in a local environment.

Step-by-Step Guide: How To Run AI Models Locally

Now you’ve chosen suitable hardware and zeroed in on a model or two. Below is a detailed walkthrough that should get you from a blank server (or workstation) to a functioning AI model you can play with.

Step 1: Prepare Your System

1. Install Python 3.8+

 Virtually all open-source AI runs on Python these days. On Linux, you might do:

sudo apt update
sudo apt install python3 python3-venv python3-pip

On Windows or macOS, download from python.org or use a package manager like Homebrew.

2. GPU drivers and toolkit

If you have an NVIDIA GPU, install the latest drivers from the official site or your distro’s repository. Then add the CUDA toolkit (matching your GPU’s computation capability) if you want GPU-accelerated PyTorch or TensorFlow.

3. Optional: Docker or Venv

If you prefer containerization, set up Docker or Docker Compose. If you like environment managers, use Python venv to isolate your AI dependencies.

Step 2: Set Up a Virtual Environment

Virtual environments create isolated environments where you can install or remove libraries and change Python version without affecting your system’s default Python setup.

This saves you headaches down the line when you have multiple projects running on your computer.

Here is how you can create a virtual environment:

python3 -m venv localAI
source localAI/bin/activate

You’ll notice the localAI prefix to your terminal prompt. That means you are inside the virtual environment and any changes that you make here will not affect your system environment.

Step 3: Install Required Libraries

Depending on the model’s framework, you’ll want:

• PyTorch

pip3 install torch torchvision torchaudio

Or if you need GPU acceleration:

pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118

• TensorFlow

pip3 install tensorflow

For GPU usage, make sure you have the right “tensorflow-gpu” or relevant version.

Step 4: Download and Prepare Your Model

Let’s say you’re using a language model from Hugging Face.

1. Clone or download:

Now you might want to install, git large file systems (LFS) before you proceed since the huggingface repositories will pull in large model files.

sudo apt install git-lfs
git clone https://huggingface.co/your-model

TinyLlama repository is a small local LLM repository you can clone by running the below command.

git clone https://huggingface.co/Qwen/Qwen2-0.5B

2. Folder organization:

Place model weights in a directory like “~/models/<model-name>” Keep them distinct from your environment so you don’t accidentally delete them during environment changes.

Step 5: Load and Verify Your Model

Here is an example script that you can run directly. Just make sure that you change the model_path to match the directory of the cloned repository.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import logging

# Suppress warnings
logging.getLogger("transformers").setLevel(logging.ERROR)

# Use local model path
model_path = "/Users/dreamhost/path/to/cloned/directory"

print(f"Loading model from: {model_path}")

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path, 
    torch_dtype=torch.float16,
    device_map="auto"
)

# Input prompt
prompt = "Tell me something interesting about DreamHost:"
print("n" + "="*50)
print("INPUT:")
print(prompt)
print("="*50)

# Generate response
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output_sequences = model.generate(
    **inputs,
    max_new_tokens=100,
    do_sample=True,
    temperature=0.7
)

# Extract just the generated part, not including input
input_length = inputs.input_ids.shape[1]
response = tokenizer.decode(output_sequences[0][input_length:], skip_special_tokens=True

# Print output
print("n" + "="*50)
print("OUTPUT:")
print(response)
print("="*50)

If you see similar output, you are all set to use your local model in your application scripts.

Make sure you:

1. Check for warnings: If you see warnings about missing keys or mismatches, ensure your model is compatible with the library version.
2. Test output: If you get a coherent paragraph back, you’re golden!

Step 6: Tune for Performance

1. Quantization: Some models support int8 or int4 variants, drastically reducing VRAM needs and inference time.
2. Precision: Float16 can be significantly faster than float32 on many GPUs. Check your model’s doc to enable half-precision.
3. Batch size: If you’re running multiple queries, experiment with a small batch size so you don’t overload your memory.
4. Caching and pipeline: Transformers offer caching for repeated tokens; helpful if you run many step-by-step text prompts.

Step 7: Monitor Resource Usage

Run “nvidia-smi” or your OS’s performance monitor to see GPU utilization, memory usage, and temperature. If you see your GPU pinned at 100% or VRAM maxed out, consider a smaller model or extra optimization.

Step 8: Scale Up (if Needed)

If you need to scale up, you can! Check out the following options.

1. Upgrade your hardware: Insert a second GPU or move to a more powerful card.
2. Use multi-GPU clusters: If your business workflow demands it, you can orchestrate multiple GPUs for bigger models or concurrency.
3. Move to dedicated hosting: If your home/office environment isn’t cutting it, consider a data center or specialized hosting with guaranteed GPU resources.

Running AI locally might feel like a lot of steps, but once you’ve done it once or twice, the process is straightforward. You install dependencies, load a model, and run a quick test to make sure everything is functioning like it should. After that, it’s all about fine-tuning: tweaking your hardware usage, exploring new models, and continually refining your AI’s capabilities to fit your small business or personal project goals.

Best Practices from AI Pros

As you run your own AI models, keep these best practices in mind:

Ethical and Legal Considerations

• Carefully handle private data in line with regulations (GDPR, HIPAA if relevant).
• Evaluate your model’s training set or usage patterns to avoid introducing bias or generating problematic content.

Version Control and Documentation

• Maintain code, model weights, and environment configs in Git or a similar system.
• Tag or label model versions so you can roll back if the latest build misbehaves.

Model Updates and Fine-Tuning

• Periodically check for improved model releases from the community.
• If you have domain-specific data, consider fine-tuning or training further to boost accuracy.

Observe Resource Usage

• If you see GPU memory frequently maxed, you might need to add more VRAM or reduce the model size.
• For CPU-based setups, watch out for thermal throttling.

Security

• If you expose an API endpoint externally, secure it with SSL, authentication tokens, or IP restrictions.
• Keep your OS and libraries up to date to patch vulnerabilities.

Your AI Toolkit: Further Learning and Resources

Learn more about:

• Mastering customer relationships with AI
• Boosting productivity with AI
• 100 best WordPress plugins
• Getting the most out of Claude AI
• How to use Midjourney
• How to use Otter.ai

For library-level frameworks and advanced user-driven code, PyTorch or TensorFlow documentation is your best friend. The Hugging Face documentation is also excellent for exploring more model loading tips, pipeline examples, and community-driven improvements.

It’s Time To Take Your AI In-House

Hosting your own AI models locally can feel intimidating at first, but it’s a move that pays off in spades: tighter control over your data, faster response times, and the freedom to experiment. By choosing a model that fits your hardware, and running through a few Python commands, you’re on your way to an AI solution that’s truly your own.

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Ready to take your business to the next level? See how DreamHost’s AI Business Advisor can help with everyday tasks like content creation and planning, giving you more time to focus on what really matters. Give it a try and watch your business grow.

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The post Everything You Need To Know To Start Hosting Your Own AI Models Locally appeared first on DreamHost Blog.

Perhaps your mind goes straight to a famous example: Lévi-Strauss, Monet, Debussy, Van Damme, and so on.

Well, now you need to make room in your mental roster for another Claude: an intelligent virtual assistant.

Claude AI is one of the most powerful artificial intelligence platforms around right now. It’s as smart as ChatGPT, and capable of handling a wide variety of tasks — from data analysis to content creation.

This tool is basically rocket fuel for your business.

Want to harness that power? Stick with us for the next few minutes, and we’ll show you how to unlock the full potential of Claude.

Nice To Meet You, Claude AI

Let’s start with a proper introduction.

Claude by Anthropic is a generative AI platform that uses LLMs (large language models) to make sense of text prompts and provide intelligent replies.

You communicate with this AI assistant through a chat interface. You can also upload images and documents to provide context.

This means you can use Claude to:

• Brainstorm new ideas
• Collaborate on content creation
• Research new topics
• Automate boring admin tasks
• Analyze existing data
• Get assistance with coding

And that’s just a glimpse of what Claude is capable of. We’ll take a closer look at the real-world use cases very shortly.

At the time of writing (Q4 2024), Claude has several cutting-edge models:

• Claude 3.5 Sonnet: The brightest of the bunch. Sonnet stands up to any current model in terms of intelligence. It’s a great sales assistant and a very capable coder.
• Claude 3 Opus: The previous high water mark. Opus has now been overtaken by the latest version of Sonnet in terms of speed and intelligence, but it’s still a capable model.
• Claude 3.5 Haiku: The baby of the family is not quite as clever as the others, but it’s still a speed merchant. For basic tasks and live customer interactions, this one is probably the best fit.

Claude AI vs. ChatGPT

While the AI ecosystem is always shifting, Claude has been neck and neck with ChatGPT for some time. This shouldn’t come as a surprise, because Claude was developed by former employees of OpenAI — the company behind ChatGPT.

Both platforms are very capable, but they have slightly different strengths and weaknesses. For instance, some people believe Claude tends to be more concise with answers, while ChatGPT is more thorough.

One area of strength for Claude is safety. Anthropic is a public-benefit corporation that tries to develop AI responsibly.

With this approach, known as “Constitutional AI,” the company aims to align Claude with human values, like not doing harm to others, and treating people with respect.

How Much Does Claude AI Cost?

Anyone can sign up and use Claude AI for free, including the latest models.

The downside of the free tier is that you’re restricted to around 40 short messages per day. And Claude will freeze you out completely during times of peak demand.

A Claude Pro premium subscription costs $20 per month. This extends your limits five-fold, and allows you to access the platform during peak times.

For larger businesses, the Team plan ($25 per user/month) extends usage limits further and provides early access to collaboration features. There’s also an Enterprise plan (custom pricing) for large organizations.

The other way you can access Claude is through the Anthropic API. This option lets you pay as you go, with variable pricing depending on which model you use.

The Key Benefits of Claude AI

Of course, Claude isn’t the only show in town. So, what makes this AI system better for your business than other AI or automation tools?

Here’s a quick run-through of the primary benefits:

• It’s easy to use: Unlike with many automation tools, you can tell Claude what you want to achieve in plain English (or any other language, as it stands).
• It’s pretty cost-effective: You can complete a lot of work on a fixed monthly subscription, and the pay-as-you-go prices are affordable as well.
• It’s completely scalable: If you use the API, you can buy as much usage as you need.
• It’s built for safety: One of the main motivations for founding Anthropic was to build an AI that’s safe and reliable.
• It’s improving rapidly: Claude is on the cutting edge of AI research, and new versions are released every few months, delivering significant improvements without a step up in cost.

How To Use Claude AI (Step-by-Step Walkthrough)

All of this sounds great on paper. But how do you actually start putting the AI to work?

Getting started with Claude is surprisingly easy. Ready to dive in? Just follow these steps:

Step 1: Sign Up at Claude.ai

First, you’re going to need an account.Head over to the Claude AI website and sign up using your email or via Google sign-in. If you choose the former option, Claude will ask you to verify your email address.

To complete the sign-up process, you’ll be asked to enter your phone number and date of birth.

Once you’ve verified your phone number via SMS, Claude will ask for your name and what you prefer to be called. You’ll also be asked to confirm a few details about the terms of service.

Note: You can follow a similar setup process via the Claude iOS app.

Step 2: Choose Your Settings

Claude is pretty low-maintenance when it comes to preferences. Even so, it’s worth setting up your account.

Having jumped through all the hoops, you should reach a screen like this:

Move your cursor over to the bottom left, and tap on your email.

From the drop-down menu, you can upgrade to a paid tier, change the appearance of Claude, and access your account settings.

Open Settings. Under Profile, you will find a few useful options:

• What best describes your work? Tell Claude what sector your business is in, and the app will adjust to match your needs.
• Show prompt suggestions: Claude can provide one-tap prompts related to your business. Here, you can choose whether to have them switched on or off.
• Enable artifacts: In some cases, Claude generates a Wiki-style page that contains the results from a conversation. You can share these pages publicly. If you don’t fancy doing that, you can switch it off here.

Step 3: Start Chatting

To return to the start screen, tap the big Claude logo in the top-left corner.

The big white box is where you can type instructions for Claude. You’ll also notice that there are a few suggestions below, along with buttons for attaching files and photos.

Once you’ve composed your first prompt, simply hit Enter and Claude will start cooking up a reply.

You then arrive at a chat-like interface, where you can carry on the conversation.

On the free tier, you’ll have access to Claude 3.5 Sonnet. However, if you’d like to access the other AI models, you can upgrade to a paid plan via the link at the top of the screen.

10 Ways To Power Your Business With Claude

You don’t have to use Claude for long to realize the potential of this platform. However, there’s a chance you won’t see how it fits into your workflow right away.

To speed things up, here are some tried-and-tested business use cases:

1. Write Email Drafts in Seconds

The average professional spends 42 minutes a week writing emails. And for business owners, this figure is way higher.

Rather than composing every reply from scratch, you can use Claude to whip up a first draft.

For instance, you could ask the AI to generate a response to common customer inquiries:

Generate a customer service response to the following inquiry:

Alternatively, paste in an email you’ve received, and ask Claude to write a professional reply, using a similar prompt format.

2. Get Help With Social Media Marketing

Maintaining a strong social media presence can be great for business. The tricky part is keeping your followers engaged — and being consistent week after week.

Plus, creating posts can be a time-consuming process.

Claude can help here. We can ask our trusted AI assistant to generate text posts and captions, based on a specific style, like this:

Generate a social media post for [PLATFORM] about [TOPIC].

Style Guide:
-Tone: [DESCRIBE TONE, e.g., casual, professional, humorous]
-Voice: [DESCRIBE VOICE, e.g., friendly, authoritative, quirky]
-Key elements to include: [LIST ELEMENTS, e.g., hashtags, emojis, call to action]

Brand guidelines:
-Always mention: [BRAND NAME] in the post
-Use our slogan: “[INSERT SLOGAN]”
-Avoid mentioning: [LIST ANY TOPICS TO AVOID]

Target audience: [DESCRIBE TARGET AUDIENCE]

Post purpose: [SPECIFY PURPOSE, e.g., increase engagement, promote product, share information]

3. Write Perfect Product Descriptions

In e-commerce, every product description should be a sales pitch. But for store owners with hundreds of items to list, descriptions don’t always get the TLC they deserve.

Luckily, Claude is pretty good at writing about products. All you need is the basic specifications of the product, and a prompt like this:

Product Name: [INSERT PRODUCT NAME]

Category: [INSERT PRODUCT CATEGORY]

Key Features:
1. [FEATURE 1]
2. [FEATURE 2]
[ADD MORE AS NEEDED]

Target Audience: [DESCRIBE TARGET CUSTOMER]

Unique Selling Points:
1. [USP 1]
2. [USP 2]
[ADD MORE AS NEEDED]

Tone: [SPECIFY DESIRED TONE, e.g., professional, friendly, luxurious]

Et voilà! You now have an awesome introduction to your product, ready to copy and paste.

4. Dive Deeper With Your Market Research

Ugh, market research. It’s really important, but let’s face it — the process can be a grind. Digging through long documents and endless figures takes time and energy you probably don’t have.

So, why not speed things up with Claude? The platform is great at sifting through information for the key takeaways and summaries,

Simply paste in the content you’re trying to analyze (PDFs and spreadsheets work just fine), and use a prompt like this:

Please pick out the key takeaways and trends from this document. Please focus on the [AREAS OF INTEREST].

5. Get a Summary of Every Meeting

Blah blah blah…no idea what your client said the last time you spoke? Fear not. As long as you have an audio recording or transcript, Claude will be able to create a quick refresher for you.

Start by uploading the audio or text file, and use a prompt like this:

Please summarize the conversation in this meeting in bullet-point form.

6. Work With Claude To Brainstorm Ideas

From creating a brand to naming your first product, running a business always involves a certain amount of creativity. If you find yourself getting stuck, let Claude take some of the strain.

Say you’re trying to dream up a new marketing campaign. Start by giving Claude some key details about your ICPs:

• Age
• Location
• Annual income
• Interests

Then, describe your offer and ask Claude to come up with some marketing ideas:

Based on this information, provide five ideas for marketing campaigns we could run. Include a tagline and short description of each one.

7. Identify What Your Customers Are Thinking

Online reviews are a treasure trove of information for small businesses. Combing through them one by one, though? That’s hard work. Let’s shortcut the process with Claude.

The easiest way to do this is by exporting reviews and then uploading this data to Claude. You can use a prompt like this to find the recurring themes:

What are the most common positive and negative comments made by customers in these product reviews?

If you’re more technically inclined, it’s also possible to automate this process by building a Claude-based app on Amazon Bedrock.

8. Create Perfect Job Listings

Seeing growth? You might need more staff. To make sure you get the perfect person for each role, you can use AI to generate the ideal job description.

Imagine you’re hiring a new baker. First, ask Claude to help you build the job description:

I need to build a job description. Can you help me?

Then, provide some information about your bakery and the role:

My bakery is called ‘Sweet Delights’ and we specialize in artisanal pastries and cakes. We’re looking for an experienced pastry chef to join our team of five. The ideal candidate should be creative, detail-oriented, and able to work in a fast-paced environment.

Claude might then ask about specific responsibilities or requirements. You can provide these details:

The Pastry Chef will be responsible for creating new recipes, preparing a variety of pastries and desserts, managing inventory, and training junior bakers. They should have at least three years of experience, and knowledge of both traditional and modern baking techniques.

In return, you should get a full job ad — ready to go!

9. Make Your Writing Error-Free

Did you know that Claude is a great proofreader? Check your writing for errors by pasting in the text.

Say you’ve written a blog post and want to make sure you haven’t made any major slip-ups. Enter this prompt:

Please check the following text for any factual, logical, or grammatical errors.

Enter your post, and set Claude on the job!

10. Craft the Perfect Presentation Script

Writing an outline for any presentation or webinar is usually a time-consuming process. But there’s a smart way to go about this. You guessed it! We can use Claude to automate most of the process.

Begin by asking for assistance:

I need help creating an outline for a 15-minute presentation on ‘The Future of Remote Work’ for a small business networking event. Can you help me?

Claude will likely ask for more details. Feed in more information about your goals and audience:

The audience will be small business owners from various industries. I want to cover current trends; benefits and challenges of remote work; and how businesses can adapt. My goal is to provide practical insights that they can apply to their own companies.

You should get a detailed outline. If you need revisions, just ask Claude!

Expert Tips for Squeezing More out of Claude AI

You’re starting to see how Claude does things now. It’s pretty good at interpreting what you need, right?

That said, there are a few techniques you can apply to make the outputs even better.

Customize Claude To Match Your Needs

Claude has a great memory. Use this to your advantage whenever you start a new chat.

Before you get to work, specify some ground rules. For example, you could tell Claude:

• You’re knowledgeable in your industry, so any advice should be at an expert level.
• You want every answer delivered in bullet points.
• The AI should avoid using jargon.

For the rest of the conversation, Claude should remember this. If you want to be doubly sure, you can make similar requests for each prompt.

Use Highly-Specific Prompts

The key to better responses? Precision in prompting.

The more details you provide, the better Claude can tailor its responses. So, instead of asking, “How do I write a blog post?” — try “How do I write a 1,000-word blog post about sustainable gardening for beginners?”

You’d never believe the difference in quality. Like night and day!

Ask for Revisions

The current AI models rarely achieve perfection on the first try. Not to worry. Feel free to ask for a revision.

You might say, “Expand on the third point.”

Or: “Can you make this explanation simpler?”

Claude is always ready to refine its work based on your feedback. It’s like having an apprentice who is eager to impress.

Integrate Claude With Your Favorite Apps

Want to take your productivity to the next level? Try integrating Claude with your go-to apps.

You can do this using Zapier or Make. Both platforms allow you to send data to Claude, and retrieve the responses.

What does this mean? For example, you could analyze responses to Google Forms submissions and send a summary to Google Sheets. Or use Claude to run a SWOT analysis and save the results to Notion.

Use Template Prompts

If you want to maximize your time savings, make templates for your regular tasks.

For example, if you often need to write product descriptions, you could save a prompt like this:

Write a 50-word product description for [PRODUCT NAME]. Include key features, benefits, and a call to action.

Just fill in the blanks and you’re good to go. Get ready for instant, consistent results!

Always Check the Outputs

Like all AI tools, Claude makes mistakes too.

When using this platform for something really important, like a data-driven case study, for example, make sure you review the outputs thoroughly before publishing. Similarly, don’t rely on Claude as your only source of information. Like all AI tools so far, it’s been known to make things up.

In other words, treat it like a really smart human rather than an all-knowing robot genius.

Learn More About AI

Want to learn more about artificial intelligence? Here are some of our best guides:

• How To Use AI as a Small Business Owner (12 Pro Tips) 
• Get the Most out of AI With These 12 Prompting Tips 
• 39 AI Tools That Will Transform Your Workflow
• What Is DALL-E? How To Use This AI Image Generation Tool
• How To Use Midjourney: Creating AI Images For Your Website

Power Your Business With AI

There’s no doubt that artificial intelligence is going to change how we do business. In fact, it already is! So, instead of wasting valuable time on simple tasks, you can whizz through your busywork at warp speed and get on with the more important stuff — like strategy.

Claude is one such great tool for small businesses. Another is our AI Business Advisor.

Available to all DreamHost customers, this versatile app can help you navigate search engine optimization (SEO), create content, and even upgrade your website.

Speaking of upgrades — our hosting plans offer a 100% uptime guarantee, unlimited traffic, and access to our AI Website Builder tool.

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This is the art of AI prompt engineering.

Experts in generative AI are still figuring out exactly how to communicate with tools like ChatGPT to extract the best responses. However, the basic principles are already fairly well established.

In this concise guide, we will reveal the best practices for writing effective AI prompts — including the best language to use, how to train AI models, and tips on refining the outputs.

Sounds good? Let’s get started!

What Is an AI Prompt?

Basically, a prompt is your instructions to the AI model.

Most popular AI tools today accept text prompts, which you can submit through a chat-like interface. Some generative AI models, like Claude, can also accept images, data, and code files.

At a basic level, prompting is a way to interact with software. You’re just using text instructions rather than tapping on buttons.

The key difference is that artificial intelligence has no simple cause and effect. AI tools require guidance because they don’t truly “understand” prompts the way humans do.

Instead, these models generate responses based on probabilities and patterns they’ve learned from massive amounts of data. This means that clear and precise instructions are essential to steer the AI towards providing the most accurate and useful answers.

It’s more like an interactive game than a simple tool.

How Do AI Models Understand Prompts?

Most generative AI models today are LLMs (large language models). Using vast amounts of data, these systems have been trained to recognize patterns in language.

When you submit a prompt, the model studies every word and phrase individually, as well as within the wider context of your prompt. It then tries to evaluate the meaning of what you have written, based on all the patterns of language it has seen before.

For example, you might ask a generative AI tool to think up some “creative titles” for a book you’re writing.

What does “creative” mean in this context? The AI will consider similar uses of the word it has seen in the training data and come up with a best guess based on the probability of those associations.

How Do AI Prompts Translate Into Outputs?

Just as AI models use pattern recognition to understand your prompts, they also use a similar approach to produce output.

Imagine you studied every painting ever produced by Monet. Then, someone asked you to create a new artwork in his style.

You probably wouldn’t be able to reproduce a specific painting perfectly. But you would remember the general mood and the key elements from some pieces. It would come out like a remix.

That’s basically how generative AI works — except the remixing part is controlled by math. Most models create outputs based on probabilities, a bit like an advanced autocomplete.

How to Craft Prompts for AI (12 Essential Techniques)

Enough with the theory. You want to use AI to get things done, right?

To help you squeeze the most out of your chosen model, we have compiled a list of the best AI prompting techniques used by experts in the game.

Let’s dive straight in:

1. Be Super Specific

When you first discover AI technology, it’s tempting to believe that you can complete huge goals with very simple prompts.

Sadly, that’s not the case.

The output from current AI models largely reflects the prompt it’s fed. If you write a very basic, generic request, the AI will provide an equally vague output to match.

On the flip side, you can get very detailed, targeted outputs by writing prompts that are highly specific. The less you can leave up to interpretation, the more satisfying the results will be.

Don’t:
Write a blog post about fishing.

Do:
Write a 1,500-word blog post about salmon fishing in the UK. Compare the fly fishing and spin fishing techniques. Include a section on rod lengths and weights, and talk about how the black-bodied flying C lures are the best option.

2. Use Examples

When you work with AI, think of yourself as a teacher. Don’t just tell the model what to do; show it what you want.

Including a good example of your desired output is a great way to shape the results from pretty much any AI tool. You can use this technique to adjust the style, format, topic, and length of outputs.

This is particularly useful for maintaining a consistent style in content creation, but it also works for data formatting, setting the tone for emails, and more.

Don’t:
Write a product description for a new pair of barefoot shoes with a wider toe box.

Do:
Write a product description for a new pair of barefoot shoes with a wider toe box. Copy the tone, style, and general structure of this product description: [PASTE EXAMPLE]

3. Ask the AI Model What It Needs

Did you know you can ask AI to help you with prompting?

This technique won’t unlock perfect outputs, but it’s a great starting point for crafting good prompts.

Try asking your chosen AI tool what it needs to produce a certain outcome. And tell the model to ask clarifying questions to fine-tune the outputs.

Don’t:
Write a product description for shoes.

Do:
What specific details about these shoes do you need to be most helpful for crafting an effective product description?

4. Try Prompt Chaining

In real-world conversations, we often combine multiple queries into a single chain of thought.

This is a big no-no in AI interactions. Current models struggle to juggle different ideas.

To get the best possible results, it’s best to focus on one question or task at a time — a technique known as prompt chaining (or chain of thought prompting).

This makes it easier for the AI to understand what you want, and come up with a clear and concise answer.

Don’t:
I need to improve this document. Find any grammatical errors in the text, suggest how to fix them, and tell me how I can make the writing more concise.

Do:

1. Find grammatical errors in this text.
2. Can you suggest how to fix these errors?
3. How can I make the writing in this document more concise?

5. Define What Type of Answer You Expect 

Remember sitting for exams? Super nerve-wracking.

At least (most) exam papers are pretty clear about how you should answer each question. So, even when you’re sweating buckets, it’s hard to mess up.

AI systems don’t suffer from performance anxiety, but they still need clear instructions about how to respond.

When you write your prompts, make sure to define precisely what you’re looking for.

Don’t:
Give me some examples of news headlines in AP style.

Do:
Give me five examples of news headlines in AP style, delivered in a bullet point list.

6. You Needn’t Always Be Polite

When you’re brought up well, good manners are instinctive. When you ask for something, it’s only natural to add a “please” at the end.

Current AI systems don’t really understand courtesy, but these words can have an effect on the output you receive.

Most models will reflect your tone — so a polite, mild-mannered prompt will often unlock a similarly civil response. Plus, research suggests you are more likely to get a long, elaborate output.

In contrast, neutral prompts are usually more to the point.

The lesson? Think twice about being polite.

Don’t:
Could you provide me with a list of five words commonly used in baseball commentary, please? Thank you.

Do:
Provide a list of five words commonly used in baseball commentary.

7. Ask the AI To Play a Role

By default, most AI tools play the role of helpful assistant. They are the Dr. Watson to your Sherlock Holmes, the Robin to your Batman.

But actually, LLMs are perfectly capable of taking on any persona. You just need to ask.

This technique is particularly useful if you want to generate a certain style of writing, or tailor outputs to the right level of expertise.

You can even ask the AI to roleplay with you — simply mention who the characters are. This can be helpful when you’re preparing for an interview, or even writing fiction!

Don’t:
Please explain how KYC (know your customer) technology works.

Do:
You’re an expert in finance and insurance, specializing in fraud prevention. Explain how KYC (know your customer) technology works.

8. Use Emotion To Shape Outputs

Current AI models are nothing like the intelligent robots we see in science fiction. They have no “feelings.”

However, they do understand the impact of human emotion — at least in terms of language.

Using emotive terms in your prompts can add importance to specific instructions, and encourage a different tone in the outputs.

Don’t:
Using the data sheet provided, write an end-of-year report for my company.

Do:
Using the data sheet provided, write an end-of-year report for my company. It’s vital that you only use information from the data sheet. My career is on the line!

9. For Written Content: Describe Your Audience

Tools like ChatGPT, Claude, and Gemini are capable of creating content in pretty much any style. The tricky part is getting them to understand what you need.

To ensure your AI-generated content hits the mark, make sure to include details about your target audience in your prompts. Add as much detail as you can, such as demographics, interests, and other publications they might enjoy.

Don’t:
Write a LinkedIn thought leadership post about the challenges of onboarding new staff.

Do:
Write a LinkedIn thought leadership post about the challenges of onboarding new staff.
The post should be aimed at CEOs and founders in their 30s and 40s, working at growing startups with 50 employees or fewer. They enjoy reading DreamHost articles in their spare time. And playing pickleball.

10. For Visual Content: Reference Photography and Art

Generating images with AI is a little different from the usual chat-based interactions. You’re still inputting text and files, but the AI responds with visual content.

If you’re simply exploring, you can plug in anything you want and see what you get in return. But if you have something specific in mind, you’ll need to think carefully about the exact language of your prompt.

One key tip is to reference art and photography styles. Do you want a photorealistic image of a squirrel playing the banjo or are you aiming for a more comic-book style? These details matter.

You can also mention lighting, camera lens types, weather conditions, the environment, and any other details that could affect the end result.

Don’t:
Image of a surfer riding a wave.

Do:
Minimalist line drawing of a male surfer riding a barrel wave, sunlight, blue sky, pink surf shorts.

11. For Education: Turn AI Into Your Personal Tutor

Platforms like ChatGPT and Claude can provide information on any topic that has ever been written about. However, so can Google.

The real value of these platforms is that you can have a conversation on any topic.

Instead of writing prompts that look like Google searches, ask your chosen AI model to become your tutor. For instance, you could:

• Upload a textbook and tell the AI to summarize the key points.
• Ask the AI tool to test your knowledge with practice questions.
• Set up a practice conversation in a foreign language at a level you’re comfortable with.

12. For Automation: Keep Refining for Better Results

AI tools are great for automating everyday admin, such as reformatting data or composing template emails.

There’s a possibility that you might just write the perfect prompt for these tasks on your first try.

This is relatively uncommon, though. Most AI experts spend time experimenting and refining their prompts before deploying them in the wild.

If you’re trying to automate something and it doesn’t work the first time, don’t give up. Think of it like teaching a child to ride a bicycle; it’s going to take a while before they can compete in the Tour de France.

Which AI Should I Use?

The tips above apply to most popular AI tools. That said, the same prompt will elicit a different response from every model.

So, which tool should you be using for each task? Here’s a quick cheat sheet:

This is by no means a comprehensive list, but it includes the most well-established tools for the most popular AI use cases.

Troubleshooting AI Problems

AI is unpredictable. You can write a perfectly good prompt, and still receive an answer that makes no sense. The model might even refuse to answer if your request doesn’t match certain criteria.

Such hiccups can be frustrating. Thankfully, there are ways to overcome these issues:

• Misinterpretation: Sometimes, AI models get the wrong end of the stick. The solution here is specificity. Be super explicit with your instructions and provide context to guide the AI.
• Irrelevant responses: Even if an AI understands you, it might cover topics or examples that aren’t really relevant to what you need. To avoid this, use clear language to narrow down the responses.
• Repetitive outputs: Current AI models tend to default to the obvious choice for any given question or prompt. If you want to mix it up, encourage creativity in your prompts. Ask the AI to produce “quirky” or “fun” results, for instance.
• AI hallucinations: At times, generative AI models just make stuff up. The best way to prevent this is by specifying the source of information that should be used when formulating answers. Be sure to fact-check outputs, as well!

Making the Most of AI

AI’s emerging world can feel like piloting a ship through uncharted waters — exciting, yet uncertain.

In part, that’s because the technology is very new. Even the experts are constantly learning about these intelligent systems.

If you want to make the most of AI right now, be prepared to play around with various tools. Use the tips above as a starting point, and try different approaches.

As a reminder, here are the key principles:

• Be specific.
• Use examples.
• Break down complex ideas into chunks.
• Define what you want.
• Think carefully about language.
• Ask the AI what it needs for more detailed outputs.

Want to start experimenting? DreamHost users get free access to our AI Business Advisor. Powered by the cutting-edge GPT-4o model, this tool helps you plan, create content, automate coding, and much more.

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The post Get the Most out of AI With These 12 Prompting Tips appeared first on DreamHost Blog.

One path is smooth and wide, seemingly easy to follow. The other is narrow, bumpy, and maybe a little harder, but the path looks well-worn.

Knowing which path is right could be impossible without a map, phone, or help.

When it comes to choosing Python vs. Java, it feels a lot like this — And your choice can make a big difference in how your career trajectory changes over the next few years.

With its almost conversational syntax, Python is like a smooth, wide road. It’s designed to be easy to learn and use. Java, on the other hand, resembles the rougher path. It may be more challenging at first, but it’s solid and reliable.

This guide will help you make an informed decision. Think of it as your map for this coding journey.

We’ll closely examine Python vs. Java, seeing what makes each valuable and how they can help you differently.

Python might be easier to start with, but Java offers different opportunities. By the end, you’ll have a clearer idea of which path suits you.

Let’s dive in!

What Is Python?

Guido van Rossum first created Python on February 20, 1991. Developers liked how easy it was to read and use.

The name, a nod to the Monty Python group, brings some fun to coding. Python’s style was clear and different from the languages of the time.

Python is an interpreted language that organizes code using indents (or spaces) instead of brackets and semicolons, making it look cleaner and easier to understand.

Here’s an example of what Python code looks like:

def greet(name):
    print(f"Hello, {name}!")

greet("Guido")

The above snippet will output:

Hello, Guido!

Python uses simple language to get things done, almost like reading plain English. Although it’s easy to read, you should not underestimate its capabilities.

Additionally, Python comes with many tools and extras. People use it to build websites with frameworks like Django and Flask, analyze data, and work with machine learning using NumPy and scikit-learn.

Python is known for doing many things well in the programming world.

Let’s look at some of the main advantages of Python:

• Clear and readable syntax: Makes learning and focusing on problem-solving easy for new programmers.
• Object-oriented language: Models real-world entities with classes and objects for reusable and modular code.
• It’s an interpreted language: Executes code line by line for quick testing and debugging. You don’t have to wait to compile code to check if there are errors.
• Adjust memory usage during runtime: For better performance and efficiency.
• Perform type checking at runtime: Without needing to declare variable types explicitly.
• Allows you to create graphical interfaces: Using Tkinter, PyQt, and Kivy.
• Manages memory automatically: Preventing leaks and optimizing performance.
• Offers extensive built-in modules and functions: So you don’t need to import libraries and packages for everything.
• Provides more than 500,000+ third-party packages via PyPi: To extend Python’s capabilities for diverse projects.

However, Python has some disadvantages you need to be wary of:

• Runs slower: As compared to compiled languages like Java.
• Low versatility: You rarely see Python used for mobile app development.
• Higher memory consumption: Python’s ease of use and flexibility can lead to higher memory consumption than lower-level languages.

What Is Java?

Java is a compiled language created in 1995, thanks to Sun Microsystems.

Its creator, James Gosling, wanted a language free from limits — one you could use anywhere, with speed and power. This idea of “write once, run anywhere” clicked with businesses, and Java became their go-to language.

Java’s code might seem longer than Python’s, but that’s by design.

Curly brackets mark off each code section, and every variable must have an explicit type. This attention to detail makes the code reliable for big, complex projects.

Here’s a little Java hello world:

public class Greeting {
    public static void main(String[] args) {
        greet("Guido");
    }

    public static void greet(String name) {
        System.out.println("Hello, " + name + "!");
    }
}

This will output:

Hello, Guido!

What makes Java shine is its vast set of tools.

We’re talking frameworks and libraries: everything you need to build almost anything.

Need a business app? Use Spring Framework.

How about a phone app? Android Studio has you covered.

Java adjusts, always having the right tool, making it perfect for highly complex applications.

The average salary for Java developers in New York ranges between $102,000 and $136,000, making it a better-paid profession than Python at present.

Let’s look at some of the advantages of Java:

• Strong foundation in object-oriented programming: Thanks to Java’s strong foundation in object-oriented programming, similar to Python, it helps you build modular and reusable applications.
• Write code once and run it anywhere: With Java Virtual Machine (JVM) support, you can write code once and run it anywhere, enhancing portability.
• The go-to language for Android app development: Java has been the go-to language for Android app development for a long time now.
• Helps catch errors early: Since Java requires simultaneously declaring variables with specific data types, you can catch errors sooner rather than later.
• Wide range of libraries: Because of the language’s maturity, you benefit from a wide range of libraries, frameworks, and tools, especially in enterprise and Android development.
• Better performance: Java performs much better than interpreted languages like Python due to its compiled nature.
• Robust language: The language’s robustness allows many enterprises to use it to build large-scale applications where reliability is necessary.

Here are some disadvantages of Java:

• Verbose syntax: Java’s dense syntax slows developer productivity, requiring more code than Python.
• Consume more memory: Java applications consume more memory, making them less efficient than C or C++.
• Slowed-down development process: The compilation step slows the development process, reducing agility compared to interpreted languages.
• Steep learning curve: Because of Java’s extensive features and ecosystem, as well as a more complicated syntax, beginners face a steep learning curve.

What Is The Difference Between Java And Python?

Now, let’s compare these two coding titans a little more in-depth.

Each language has its unique strengths, and choosing the correct one often depends on the task at hand.

Popularity: Growing Vs. Slowing

Check out this graph from Google Trends below: the blue line is Python and the purple line is Java.

It’s clear that while Java dominated developer communities for decades, it’s now slowed down, and Python has already become popular.

Python is currently the third most popular programming language after JavaScript and HTML. According to the StackOverflow survey, 49.28% of developers use it.

While enterprises may still use Java for many existing systems, people are gradually moving towards newer, more maintainable languages.

So, should we cut the debate and declare Python as the winner? Hold on now. Not so fast.

After decades of use and development, Java remains a vital language that’s still relevant in the industry.

About 30.55% of developers use Java, and it’s prevalent in big companies. Java is the backbone of countless enterprises.

It is also one of the top ten most popular languages, and veteran programmers love Java for its robustness.

Syntax: Simplicity Vs. Structure

Syntax is the rules that dictate how you write code the computer can understand. Think of it like a programming language’s grammar.

Python keeps things beautifully simple and readable. You use indentation to guide the flow of code blocks, enhancing code readability, and the minimal use of keywords and punctuation makes it incredibly welcoming, even for beginners.

Python is like writing a clear set of instructions.

Let’s say you want to sum up two numbers. In Python, you might write:

def sum_numbers(a, b):
    return a + b

print(sum_numbers(1,5)

Java takes a different path, opting for a more complex and detailed syntax. Curly braces define code blocks, and semicolons mark the end of each statement. Think of them as the punctuation marks that give Java its structure.

Java is more like writing a formal letter. There are stricter rules and more punctuation.

Here’s what the same function will look like in Java:

public class Main {
    // Function to sum two numbers
    public static int sumNumbers(int a, int b) {
        return a + b;
    }

    // Main method to test the function
    public static void main(String[] args) {
        int result = sumNumbers(1, 5);
        System.out.println(result);  // Output: 6
    }
}

While this can feel more demanding at first, it provides a level of organization that can be a lifesaver for larger, more intricate projects.

Learning Curve: Gentle Vs. Steep

Python is easier to learn due to its straightforward syntax, which resembles plain English. If you want to learn Python as a beginner, it is approachable because of the wealth of resources and a supportive community. Python prioritizes readability, allowing learners to grasp core programming concepts without getting tangled in complex syntax.

On the other hand, Java presents more of a challenge for beginners. Its robust structure and statically typed system demand more initial effort.

However, this structure shines in managing more extensive and intricate projects. Java’s strictness helps promote well-organized, more maintainable code, which is important for large-scale software development.

While learning Java may initially feel overwhelming, the discipline it enforces pays off big-time as you gain experience.

Performance: Interpretation Vs. Compilation

Imagine the difference in speed between chopping vegetables while cooking versus prepping everything before you begin cooking.

What’s the quickest and most efficient way to cook?

Python is the chef who prepares the ingredients while cooking.

This “interpreted” approach is flexible — you can easily adjust elements on the fly. However, it’s generally slower since each step requires immediate interpretation and is interpreted repeatedly each time Python goes over it.

Java is the chef who prepares the ingredients before cooking.

This “compiled” approach translates the entire code into machine code before execution. Compiling takes quite a lot of time as your codebase grows. You need to fix and recompile the code if there’s an error.

However, once the compilation part is complete and the code is read, the execution is very fast, leading to snappy applications.

To put this in perspective, let’s write a loop that runs 100,000 times and does nothing to compare Python vs. Java. In the end, it outputs the time taken to complete the process.

Here’s for Python:

import time

start_time = time.perf_counter_ns()

for i in range(100000):
    # Empty loop
    pass

end_time = time.perf_counter_ns()
duration = end_time - start_time

print(f"Python loop execution time: {duration} nanoseconds")

This will output the following:

Python loop execution time: 2564880 nanoseconds

Here’s the loop in Java:

public class LoopBenchmark {
    public static void main(String[] args) {
        long startTime = System.nanoTime();
        
        for (int i = 0; i < 100000; i++) {
            // Empty loop
        }
        
        long endTime = System.nanoTime();
        long duration = (endTime - startTime);
        
        System.out.println("Java loop execution time: " + duration + " nanoseconds");
    }
}

This will output the following:

Java loop execution time: 1334050 nanoseconds

You’ll notice that Java takes half the time to execute the same loop as Python. 

The loops run 100,000 times. However, the Java program completes tasks much faster due to its code’s precompilation.

I ran both code snippets on a server, but if you run them locally, you’ll notice that Java takes even less time since it can fully use the available resources compared to Python.

Typing: Dynamic Vs. Static

Typing refers to how a language handles data types: the kind of values a variable can hold, like integers, strings, or more complex structures.

Think of “typing” in programming languages like labeling containers before you put things in them.

Python and Java take fundamentally different approaches to data typing, directly impacting how you write and debug code.

Python embraces flexibility. With Python, you don’t need to specify a variable’s type during declaration. This dynamic typing makes Python code concise and flexible. However, it can lead to runtime errors if a programmer is not careful.

For example:

x = 10    # x is an integer
x = "ten" # now x is a string

Java prioritizes robustness. Each variable needs an explicit type declaration checked at compile time.

The same example in Java illustrates this:

int x = 10;     // x is an integer
x = "ten";      // this will cause a compile-time error

This makes Java code more verbose and increases robustness and maintainability.

Note: You can type hints in Python, but it doesn’t enforce typing. Java enforces data typing, which reduces the possibility of errors and standardizes things.

Ecosystem: Data Science Vs. Enterprise

Python and Java give developers many powerful tools. But each language shines in different ways.

With its strong libraries like NumPy, Pandas, and scikit-learn, Python is a favorite among data scientists and machine learning engineers. These libraries help them quickly work with and understand data. Frameworks like Django and Flask make Python even better. They provide the foundational parts needed to build websites efficiently.

Java is known for creating significant, company-wide software. Frameworks like Spring and Hibernate are essential in this area. They give developers the means to build solid and dependable applications. There is more to Java than just company software. It’s also a leader in mobile app development. The Android SDK allows you to build robust Android apps effectively.

Community: Beginner-Friendly Vs. Established And Experienced

Though there are many resources on the web, you need a community around your chosen programming language to make.

Python’s community welcomes beginners warmly, offering countless resources, tutorials, and forums to help newcomers. Many open-source projects thrive here, with popular libraries driven by community contributions and the members contributing to grow the available libraries.

Due to its maturity, Java’s community is less beginner-friendly. However, it boasts a large and well-established network. For over two decades, Java has dominated the programming arena, and that’s reflected in its vast community. Conferences, user groups, and online resources are abundantly available to Java developers, supported by significant investments from global enterprise organizations.

Choosing A Language

Your chosen language generally depends on your goals, background, and projects.

For beginners, Python is an excellent starting point. Its gentle learning curve and supportive community make it ideal for those new to programming. Python suits data science, machine learning, and web development.

Java stands out for enterprise software or Android app development. Its robustness and performance make it perfect for large-scale applications, and the Android SDK is essential for creating Android apps.

Both Python and Java are powerful and versatile, each with solid communities and extensive libraries. The best approach is to try both and see which resonates most with you.

Java Vs. Python: Which Language Would You Choose?

The Python vs. Java debate doesn’t need you to choose a winner. Each language has exceptional tools. Pick a language that fascinates you, and let the excitement of coding help you learn it.

Explore Python’s various packages or use Java’s power to create enterprise solutions.

Remember, both languages require you to continue learning, building, and staying curious. After all, the software industry is driven by passion and innovation.

And if you want a dependable platform to experiment with Python libraries or host projects, consider DreamHost’s Managed VPS. It gives you the flexibility and power you need for coding on a server. Use it to experiment with data science libraries like Pandas and NumPy, develop web applications with Django or Flask, or simply host your projects.

Our user-friendly interface and supportive team will keep you focused on what you do best — coding, building, and being creative.

In the end, the winning language is the one that helps you turn your ideas into reality.

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The post Python Vs. Java: Which Programming Language To Choose? appeared first on DreamHost Blog.

This information can help you troubleshoot issues, avoid compatibility problems, and plan hardware upgrades. It can even help you maintain good security.

In other words, checking your Linux version is important when the need arises. Whether you’re ensuring you have the latest update or figuring out if you’re running an older version, you’ve come to the right place.

In this concise guide, we’ll show you how to identify your Ubuntu version in minutes.

Let’s get started!

The Different Versions Of Ubuntu

Each version of Ubuntu is basically an update to the operating system. New versions come along every six months on average, and every release has its own code name.

Here are some recent examples:

• 24.04 LTS Noble Numbat
• 23.10 Mantic Minotaur
• 22.04 LTS Jammy Jellyfish

Unlike some other desktop operating systems, Ubuntu is available in many different flavors, each offering something unique. 

However, these flavors are essentially different desktop environments wrapped around the same Ubuntu base system. They all share the same Linux kernel and core operating system.

This underlying system is what we need to investigate to find that all-important version number.

Why You Might Need To Check Your Ubuntu Version

“Remind me why I need to know my Ubuntu version again?”

We hear you.

There are several good reasons to look up your version number.

Here are the highlights:

Checking Software Compatibility

You might not be able to run new software on older releases, and the same goes for new updates of existing apps. A compatibility check on the Ubuntu version of your device can help you avoid any potential problems.

Security Updates

New versions of Ubuntu often seek to patch security weaknesses. If you’re worried about a specific vulnerability, you might want to ensure you’re up to date.

Troubleshooting Issues

When you’re trying to resolve problems on your device, it really helps to know your Ubuntu version number. Some problems are specific to certain releases, so almost every tech support person asks for this information.

Planning for Upgrades

If you plan to update your system, make sure you check whether your device can actually handle the update. There’s a possibility that older devices may not meet the technical requirements for the latest Ubuntu versions.

By the same token, you might need to update Ubuntu to harness newer hardware upgrades.

How To Find Out Which Version Of Ubuntu You’re Using

Enough with the theory. You’re probably itching to get that version number by now.

Here are the best methods to check your Ubuntu version via the command line and (graphical user interface) GUI — ranked from easy peasy to slightly more technical.

Method 1: Check Your Version Via The GUI Settings 

The most user-friendly way to check your Ubuntu version is through the GUI:

1. In the Launcher, click the little Settings gear icon
2. On the left-hand side, you will see loads of options. Hit the one labeled Details
3. After you click, a new window should open
4. In this window, scroll down until you see the About section, where you should find your Ubuntu version number

This route is perfect if you’re not confident with scripting and the Linux command line.

That said, you can get the job done a little faster via the Terminal.

Method 2: The lsb_release -a Command 

One way to make use of the command line is through the lsb_release command. This prints out useful information about the Linux kernel version and Linux distribution you’re using — in this case, Ubuntu.

Here’s how to use it:

1. Launch Terminal using Ctrl+Alt+T
2. Type in the following command: lsb_release -a
3. Hit Enter
4. The output should include the version number labeled as Release

Method 3: The cat /etc/os-release Command 

Rather than asking the Linux command line for your version number, you can simply query the files that hold this information.

1. Launch Terminal using Ctrl+Alt+T
2. Type in the following command: cat /etc/os-release
3. Hit Enter
4. Terminal should output the contents of the os-release file
5. You’ll find the key data under the DISTRIB_RELEASE and DISTRIB_CODENAME variables

Method 4: Check Your /etc/issue File

Another place where information is stored is the /etc/issue file. We can call on this document using a simple command and get only the Ubuntu version as a response.

1. Launch Terminal using Ctrl+Alt+T
2. Type in the following command: cat /etc/issue
3. Hit Enter
4. You should see a single line of text, listing your version number

Method 5: Use The hostnamectl Command

The hostnamectl command is designed to help you change your system’s hostname. (That’s the identity of your device when it’s connected to a network.)

But we’re not interested in that today. 

Instead, we’re going to use the command to reveal some key information about your system, including the all-important Ubuntu version number.

1. Launch Terminal using Ctrl+Alt+T
2. Type in the following command: hostnamectl
3. Hit Enter
4. You should see several lines of information, including your Operating System and Kernel versions

Ubuntu Version FAQs

Got more questions? Below are a few of the most common ones regarding Ubuntu releases:

How often should I check my Ubuntu version?

You don’t need to check your Ubuntu version unless you’re updating to the next version, upgrading your setup, or trying to troubleshoot problems.

How can I upgrade my Ubuntu version?

You can upgrade via the Software Updater application or by choosing the upgrade option when Ubuntu notifies you about a new version.

What’s the difference between LTS and non-LTS Ubuntu versions?

You may notice that some Ubuntu version codes include the acronym LTS. This stands for long-term support.

LTS versions of Ubuntu are released every two years and are supported for five years from their release date.

In contrast, non-LTS versions are released every six months. They only come with nine months of guaranteed support.

If you want a stable environment for getting things done, LTS might be the way to go.

However, you won’t get the latest and most experimental features that appear in other versions.

Does checking my Ubuntu version require sudo privileges?

Nope. You can use the lsb_release, /etc/os-release, and GUI methods mentioned above without sudo access.

What are the Ubuntu release naming conventions?

Each new version of Ubuntu is given a name to make it easier to reference. These names follow a simple format: adjective animal. For example, the next release will be Oracular Oriole (24.10).

The first letter of both words is usually the same, and each new version moves to the next letter in the alphabet. The animals and adjectives can sometimes be a bit obscure, but these codenames are definitely memorable.

In addition to the names, Ubuntu versions also have a numbering system that follows the format YY.MM, where YY represents the year and MM represents the month of the release. For instance, Ubuntu 24.10 indicates a release in October 2024. This numbering system helps users quickly identify how old a particular release is. The “minor” version will always be either .04 or .10 to represent the months in which new releases are put out into the wild.

Together, the codename and version number provide an easy way to identify and reference specific Ubuntu releases.

Staying up to Date

As you’ve seen, finding out which version of Ubuntu is on your computer isn’t that hard.

You just need to know where to look, or which commands to use. To recap, the key methods are:

1. Check in your Settings via GUI
2. Use the lsb_release -a command
3. Enter the cat /etc/os-release command
4. Ask for cat /etc/issue in Terminal
5. Make use of the hostnamectl command

Save this page for future reference, and you’ll never run into the same problem again!

Of course, software updates don’t only apply to desktop machines. Website owners need to track new versions and check for upgrades all the time.

If you’re tired of chasing your tail, consider moving your site to DreamHost.

Our managed hosting plans take care of updates automatically, meaning you don’t need to worry about version numbers. And with automatic daily backups, you can restore your site to a previous version with a couple of clicks.

Sign up today to make your life a little easier!

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Ubuntu, Fedora, MX Linux, Linux Mint: these are all Linux distributions. Just like chocolate, vanilla, and strawberry ice cream, each Linux distro offers a different experience, unique tools, and attracts a different crowd.

Now, you may be thinking, “Linux? Isn’t that just for tech wizards with long beards?”

And you’re not off base, considering only 3.77% of all desktop operating systems are Linux-based. However, this number has been consistently growing over the last few years. Currently, 47% of the world’s 500 most powerful supercomputers run Linux, and over 55% of professional developers prefer Linux for the dev workloads because of how easy it is to customize operating systems built around it.

The Linux kernel has also made great strides since its early days, and distros that use it are now as user-friendly as other popular operating systems.

Plenty of distros are easy to use now, even for an absolute beginner.

Because Linux is open source, you’ll always find an existing app or utility to solve any problem you may have.

But, how do you choose the right Linux distro for you? And what should you look for?

How To Choose A Linux Distribution?

Finding the right Linux distribution (distro) can feel challenging, especially if you’re new to open-source operating systems. You’ll find various distros, each with its personality and quirks.

Some distributions are designed to run on extremely old hardware (including toasters!), while modern distributions require higher-end computers with more RAM.

So, before installing an operating system, take some time to consider what you need — and prefer.

Let’s break down some key factors to consider when choosing a Linux distro:

• How tech-savvy are you? Some distros are designed for ease of use, welcoming newcomers with open arms. Others offer incredible power and flexibility but require some technical knowledge to unlock their full potential.
• Will it run on your hardware? Check for compatibility with your computer’s specifications, especially if it’s an older model. A lightweight distro might be the perfect fit for aging hardware.
• What software do you need? While many excellent open-source programs exist, you might rely on specific proprietary software. In this case, determine how well the distro supports tools like WINE or virtual machines.
• Do you enjoy tinkering? The beauty of Linux lies in its customizability. Some distros offer endless tweaking possibilities, from appearance to functionality. Others provide a more streamlined, pre-configured experience.
• How often do you like to update? Do you live on the cutting edge, craving the newest features even if it means occasional instability? Or do you prefer rock-solid stability and long-term support? Your update tolerance will influence your choice.
• Is community support important to you? Since Linux is open source, direct support is rare. However, many distros boast active communities and extensive documentation. In learning the ropes or troubleshooting, this can be invaluable.
• What about security and privacy? Certain distros offer features like built-in drive encryption, robust authentication, and other hardened security measures for users who prioritize these aspects.

Think about these factors, and you’ll find a Linux distro that’s perfect for you.

The good news? Most Linux distributions let you test the waters before you install them. Boot directly from a USB drive and experience the distro firsthand. There’s no need for a permanent installation until you’re confident in your choice.

You can simply download the distro, use a tool like LiLi to make it usable from a USB stick, and then boot your computer directly using the USB. Then again, if that’s not what you want, you could try it on Docker containers without touching your system settings.

Let’s now explore some of the most popular Linux distros.

The 12 Best Linux Distros Of 2024

Now that you know most distributions can be tried on a live USB stick, which ones should you experiment with?

Nerd Note: Many Linux-based distributions will feature proprietary drivers that may require extra setup.

1. Ubuntu: The Most Popular Choice

Ubuntu is like the Toyota Camry of Linux distros — reliable, dependable, and perfect for everyday use.

Ubuntu is based on Debian, one of the major distributions often found on virtual private servers (VPS), for its rock-solid stability. However, Ubuntu offers a user-friendly, out-of-the-box experience while giving you the same stability.

This distro also comes with a slick desktop environment called GNOME, which looks and feels a lot like macOS or Windows, making it an excellent choice for new Linux users.

Pros:

• Easy to install and use.
• Huge community support.
• Regular updates and long-term support (LTS) releases.
• Tons of pre-installed software.

Cons:

• The GNOME desktop environment may feel a bit sluggish on older hardware.

Bottom Line

If you want ease of use, stability, and a large software ecosystem, its user-friendly interface and extensive community support make it a great starting point for Linux beginners. At the same time, its versatility and regular updates keep it relevant for more experienced users.

If you’re new to Linux or just want something that works out of the box, Ubuntu is a great choice.

2. Fedora: The Cutting Edge

Fedora Workstation is the sports car of Linux distros: fast, sleek, and always on the cutting edge.

It is sponsored by Red Hat, one of the biggest names in enterprise Linux. Fedora, however, is all about innovation and pushing boundaries. If you’re a developer or just love playing with new tech, Fedora is the distro for you.

Pros:

• Always has the latest software versions.
• Focuses on free and open-source software.
• Great for developers and power users.
• Sleek and modern GNOME desktop environment.

Cons:

• A rapid release cycle means frequent updates and potential instability.
• Some proprietary drivers may not be available.

Bottom Line

If you’re a Linux user who values staying up-to-date with the latest software and technologies, Fedora’s focus on innovation and free software principles makes it a favorite among developers and enthusiasts. It may not be the most stable Linux distro for everyday desktop use, but it’s definitely a fun one if you enjoy tinkering with your Linux desktop.

3. Linux Mint: The Windows Lookalike

Linux Mint offers a comfortable transition for Windows users who are ready to try Linux.

Based on Ubuntu, this distribution features a familiar desktop environment. It offers a whole host of pre-installed software, including media players, browsers, graphics tools, programming tools, admin tools, music players, and even games — giving you an immersive experience right from the get-go.

Pros:

• Very beginner-friendly.
• Familiar desktop environment for Windows users.
• Comes with lots of pre-installed software and codecs.
• Stable and reliable.

Cons:

• May not have the latest software versions.
• Some advanced features may be hidden or more challenging to access.
• The default Cinnamon desktop environment may be a bit heavy.

Bottom Line

If you’re a Windows native and want to explore Linux, try Linux Mint. Its familiar interface, pre-installed software, and focus on stability make it a great starting point for beginners.

4. Arch Linux: Perfect Distro For Tinkerers

Arch Linux is like a box of Legos. It’s all about building your unique creation.

Arch follows a “keep it simple” philosophy, with just the bare essentials. From there, you can choose every system component, from the desktop environment to the default text editor. You’ll also learn many new Linux commands while getting your Arch system up and running.

Pros:

• Extremely customizable and allows you to build your very own Linux-based operating system from the ground up.
• Always has the latest software versions.
• Great for learning about Linux internals.
• Excellent documentation and community support.

Cons:

• A steep learning curve for beginners.
• Requires a lot of manual setup and configuration.
• It may not be as stable as other Linux distros.

Bottom Line

Arch Linux is for users who love to play around and want complete control over their system. It offers unparalleled customization options and access to the latest software but requires significant time and effort to set up and maintain. Their extensive documentation is a well of knowledge any Linux user can appreciate.

If you love to tinker, Arch Linux is the blank canvas you want!

5. CentOS: The Server Powerhouse

Think of CentOS as the pickup truck of Linux distros. It’s rugged, reliable, and built for heavy lifting.

CentOS is based on Red Hat Enterprise Linux (RHEL). Still, it strips out the proprietary bits and provides a free, community-supported server distro perfect for web hosting, file sharing, and other server tasks.

12.4% of the world’s leading supercomputers use CentOS, which says a lot about the trust people put into this distro’s stability and reliability.

Pros:

• Rock-solid stability.
• Excellent security features.
• Ideal for servers and enterprise use.
• Long-term support (up to 10 years).

Cons:

• Not natively intended for desktop use.
• May not have the latest software versions.
• Requires some technical knowledge to set up and maintain.

Bottom Line

CentOS is a stable, secure, and reliable Linux distribution for their servers. Its long-term support and compatibility with RHEL make it a favorite among enterprises and hosting providers.

If you need a dependable workhorse for your server, CentOS is the distro for you. It’s dependable, durable, and a distro that’s ready to disco.

6. Elementary OS: The Perfect MacOS Replacement For Laptops

Elementary OS is arguably the most beautiful-looking distro on the market.

It’s designed to look and feel much like macOS, focusing on simplicity and aesthetics. Elementary comes with custom apps for tasks like email, calendar, and music playback, all designed to match the overall look and feel of the OS.

Pros:

• Beautiful, polished desktop environment.
• Consistent design language across the OS and apps.
• Focuses on simplicity and ease of use.
• Great for macOS users who want to try Linux.

Cons:

• Limited customization options.
• The design language is maintained only by the limited apps in its AppCenter.

Bottom Line

Elementary OS is built for users who value simplicity, elegance, and a consistent user experience. Its beautiful desktop environment and custom-built apps make it a great option for design-conscious users and beginners.

If you’re a fan of macOS’s design sensibilities and want a Linux distro that looks like it belongs in an art gallery, Elementary OS is your soulmate.

7. Manjaro: The Arch Linux With Training Wheels

Manjaro is based on Arch Linux. Basically, it’s a less intimidating version of Arch. The power and flexibility of Arch are combined with a friendly pre-built interface.

Manjaro comes with a graphical installer, pre-installed desktop environments, and software repositories with more stable versions of packages. It’s a great way to get your feet wet with Arch-based Linux distros without diving into the deep end.

Pros:

• More user-friendly than pure Arch Linux.
• Access to the Arch User Repository (AUR) for tons of additional software.
• Multiple desktop environments to choose from.
• Good balance of stability and cutting-edge software.

Cons:

• Still requires some technical knowledge to use effectively.
• Not as stable as distros like Ubuntu or Mint.

Bottom Line

If you’re intrigued by Arch Linux but intimidated by its complexity, Manjaro will help you build your confidence and skills until you’re ready to set up Arch from scratch.

8. Kali Linux: The Hacker’s Toolkit

Kali Linux is a complete utility belt: packed with tools for penetration testing, security auditing, and digital forensics.

It’s based on Debian, but focused on security and privacy. It’s mainly designed to run on a live USB stick so you can work (a.k.a. do security testing) without leaving any traces. You can pick a default desktop environment, including KDE Plasma, Gnome, XFCE, and more.

Pros:

• Ideal for security professionals and enthusiasts.
• Comes with a vast array of security and hacking tools.
• It can be run in “live” mode from a USB drive for portability.
• Focuses on privacy and anonymity.

Cons:

• Not designed for general desktop use.
• May be overwhelming for beginners.

Bottom Line

Kali Linux is created for security professionals, penetration testers, and enthusiasts who need a comprehensive toolkit for assessing and securing networks and systems. If you’re a security pro or just love to fiddle with network security, Kali Linux is your trusty sidekick —  always ready with the right tool for the job.

9. Gentoo: The DIY Champion

Gentoo is like building a computer yourself – from scratch.  This type of customization isn’t for the faint of heart, but the end result is a system that’s perfectly tailored to your needs.

Gentoo is all about customization and optimization. Its unique package management system compiles everything from source code.

Pros:

• Extremely customizable.
• Provides a deep understanding of the Linux system, including you in compiling and configuring everything, setting up environment variables, and more.
• Excellent documentation and community support.

Cons:

• Steep learning curve, even for experienced Linux users.
• Installation and setup can take a very long time.
• Absolutely not for users who want a quick and easy setup process or those who are new to Linux.

Bottom Line

Although, if you love tinkering, you may find yourself hooked to Gentoo, losing sleep over compiling packages and the kernel to get to the latest version or adding some custom features.

We’ve warned you.

10. Puppy Linux: Perfect For Older Laptops

Puppy Linux, true to its name, is tiny, easy to use, and extremely fast.

Designed to be incredibly lightweight and portable, Puppy Linux can run entirely from a USB drive or even a CD-ROM. Despite its small size, Puppy comes pre-loaded with a lot of software and can breathe new life into old, underpowered hardware.

Pros:

• Incredibly lightweight and fast.
• Can run entirely from a USB drive or CD-ROM.
• Comes with a good selection of pre-installed software.
• Great for reviving old hardware.

Cons:

• Limited software selection compared to larger Linux distros.
• May not be suitable for more demanding tasks or newer hardware.
• Some users may find the interface a bit quirky or dated.

Bottom Line

If you have an ancient laptop gathering dust in your closet or just want a Linux distro you can carry around in your pocket, try Puppy Linux.

11. Tails: The Privacy-First Distro

Tails is built to be a digital black box, designed to keep your online activities private and anonymous.

Tails is a live operating system that runs entirely from a USB drive, leaving no trace on your computer. It has built-in encryption tools, secure communication apps, and a web browser configured for maximum privacy.

Pros:

• Excellent privacy and anonymity features.
• Comes with secure communication tools like OnionShare and Tor.
• Can be run from a USB drive for portability.
• Leaves no trace on the computer you’re using.

Cons:

• Limited persistent storage options.
• Some websites may not work properly due to Tor and other privacy features.
• Maybe a bit slow on older hardware.

Bottom Line

If you’re a journalist, activist, or anyone who values online privacy above all else, Tails is your secret weapon. Just remember to keep your USB drive safe and hidden.

12. Raspbian: Perfect Lightweight OS for Raspberry Pi

Raspbian and the Raspberry Pi are the perfect dynamic duo. This is the lightest, fully functional Linux distro for your Raspberry Pi projects.

The Debian-based distro is designed to run on the Raspberry Pi, a tiny and affordable single-board computer. It comes with various pre-installed software for education, programming, and general use.

Pros:

• Optimized for the Raspberry Pi hardware.
• Comes with a variety of pre-installed software for education and programming.
• Large community and excellent documentation.
• Great for learning about computers and electronics.

Cons:

• May not be suitable for more demanding tasks or newer hardware.
• Limited software selection compared to larger distros.
• Some users may find the interface a bit basic or outdated.

Bottom Line

If you’ve got a Raspberry Pi and want to explore it fully, look no further than Raspbian. It’s perfectly optimized for the small device and memory availability and works with most hardware that you can connect to a Raspberry Pi.

So, What’s The Best Linux Distro?

Well, that’s like asking, “What’s the best ice cream flavor?” It will always depend on your taste, your needs, and your personality.

If you’re a beginner who just wants something that works out of the box, Ubuntu or Mint might be your jam. If you’re a power user who loves to tinker and customize their work environment, Arch or Gentoo could be your dream. And if you’re a hacker or a privacy advocate, Kali or Tails might be the way to go. Ultimately, the best Linux distro for you is the one that aligns with what you need.

Once you’ve found that perfect fit, why not bring your custom setup to the cloud with ease? At DreamHost, we understand that every customer has unique needs, which is why we offer DreamCompute. Our cloud service is built on OpenStack and gives you the freedom to upload your own Linux images. This means you can spin up your favorite distro – complete with your custom configurations – in a scalable, secure cloud environment.

Just like choosing the best Linux distro, choosing the right cloud service can make all the difference (and who better to trust with your Linux-based cloud needs than a team of fellow Linux enthusiasts?)

Ready to take your Linux journey to new heights? Explore DreamCompute today.

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The Python Software Foundation has granted Python 2 a longer support period than most Python versions. However, the rapidly advancing world of technology necessitates a shift. With the release of Ubuntu Noble and subsequent versions, Python 2.7 will no longer be integrated into the newer operating systems. Additionally, all Python 2.x versions will no longer be available. We hope this underscores the urgency of transitioning to Python 3. 

This might seem like a daunting transition, especially if you’ve been coding in Python 2.7 for a while. But don’t worry — we’re here to help soften the blow and guide you through this change.

Why Upgrade From Python 2?

Python 3 is the future, and it’s here to stay. It introduces many improvements and new features that make coding in Python more efficient and enjoyable.

Security And Compatibility

The primary concern with continuing to use Python 2.7 – and other older versions – is security. Without ongoing updates and patches, any vulnerabilities discovered post-EOL pose a significant risk to applications and systems still running this version. Moreover, newer operating systems, like Ubuntu Noble, will not support Python 2.7, creating additional challenges in terms of compatibility and future-proofing your infrastructure.

Enhanced Features And Improved Performance

Python 3 offers significant improvements, including better memory management, more robust type checking, and modern features like f-strings, async functions, and more, all of which contribute to more efficient code.

Preparing For The Transition

This whole process may seem daunting, but with the right resources and planning, this can be a smooth process. Here are some steps to guide you through the migration:

1. Audit Your Existing Python Codebase: Identify dependencies and third-party libraries that you’re currently using. Check if they are supported in Python 3, and note any that need to be updated or replaced.
2. Check The Documentation: Familiarize yourself with the functional differences between Python 2.7 and Python 3. Resources like the official Python 3 documentation will provide an excellent starting point. 
3. Utilize Tools To Automate Migration: Tools like 2to3 can automate much of the translation from Python 2.7 to Python 3. The script comes bundled with Python 3, and can handle many of the straightforward changes, such as adjustments to print statements and integer division behavior. 
4. Test Thoroughly: After migrating your code, thorough testing is crucial. Ensure that your updated application performs as expected on Python 3. Unit tests, integration tests, and system tests can all help verify that the transition has not introduced new issues.
5. Plan For Deployment: Once your application is updated and tested, plan a rollout that minimizes downtime. Consider deploying in stages or using canary releases to mitigate risks. 

Transitioning your Python 2.x code to Python 3 might seem like a Herculean task, but the Python community has got you covered. The official Python documentation provides a detailed guide on how to port your Python 2 code into Python 3. You can find this guide here: https://docs.python.org/3/howto/pyporting.html#

While saying goodbye to Python 2.x may be bittersweet for many, the transition to Python 3 is not just necessary; it’s beneficial. With improved functionality, enhanced security, and continued support, Python 3 is well-prepared to champion the next generation of development. 

If you’ve got any questions about this upgrade, please be sure to check out the official Python Documentation linked above or reach out to our Technical Support team. We’re standing by, ready to help!

Remember, at DreamHost, our goal is to empower you to shape the web as you see fit. We’re here to support you through this transition and help you continue to create amazing things on the web. 

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This is because as a code becomes more complex, local developer environments drift further away from the server environment setup. Their local machines end up with libraries and programs that may not be available on the servers — leading to code with unfulfilled dependencies on the server.

A containerization solution was needed to standardize environments across devices, and et voilà, Docker was born.

Docker changed the way applications are built, shipped, and run. The word “Docker” has become synonymous with efficiency, portability, and scalability.

In this guide, we’ll better understand what Docker is, how it works, and how it could benefit you in your day-to-day development workflows.

Let’s get started, shall we?

What Is Docker?

At its core, Docker is an open-source platform that enables developers to automate the deployment, scaling, and management of applications using containerization technology. It provides a standardized way to package software along with its dependencies into a single unit called a container.

Containers are lightweight, self-contained environments that contain everything an application needs to run: including the operating system, code, runtime, system tools, libraries, and settings. They provide a consistent and reproducible way to deploy applications across different environments, from development to testing to production.

Containerization

Containerization is a technique that allows applications to be packaged and run in isolated containers. Containerization offers several advantages over traditional deployment methods such as:

• Consistency: With containers, your applications run consistently across different environments, eliminating compatibility issues and reducing the risk of runtime errors.
• Efficiency: They’re resource-efficient compared to virtual machines because they share the host system’s kernel and resources, resulting in faster startup times and lower overhead.
• Scalability: You can easily replicate and scale containers horizontally, allowing applications to handle increased workloads by distributing them across multiple containers.
• Portability: The application can be moved easily between development, testing, and production environments without requiring modifications.

Docker’s Role In Containerization

However, before Docker came into the picture, containerization was complex and required deep technical expertise to implement effectively. Docker introduced a standardized format for packaging applications and their dependencies into portable container images.

Developers can easily define the application’s runtime environment, including the operating system, libraries, and configuration files, using a declarative language called Dockerfile. This Dockerfile is a blueprint for creating Docker images, which are immutable snapshots of the application and its dependencies.

Once a Docker image is created, it can be easily shared and deployed across different environments. Docker provides a centralized online repository called Docker Hub, where developers can store and distribute their container images, fostering collaboration and reusability.

Docker also introduced a command-line interface (CLI) and a set of APIs that simplify the process of building, running, and managing containers. Developers can use simple commands to create containers from images, start and stop containers, and interact with containerized applications.

Key Components Of Docker

Now, let’s examine the key components of Docker to better understand the underlying architecture of this containerization technology.

1. Docker Containers

As you’ve probably guessed, containers are at the heart of Docker. Containers created with Docker are lightweight, standalone, and executable packages that include everything needed to run a piece of software. Containers are isolated from each other and the host system, ensuring they don’t interfere with each other’s operations.

Think of containers as individual apartments in a high-rise building. Each apartment has its own space, utilities, and resources, but they all share the same building infrastructure.

2. Docker Images

If containers are apartments, then Docker images are the blueprints. An image is a read-only template that contains a set of instructions for creating a container. It includes the application code, runtime, libraries, environment variables, and configuration files. You can find a lot of pre-built Docker images on the Docker Hub that we previously discussed.

Images are built using a series of layers. Each layer represents a change to the image, such as adding a file or installing a package. When you update an image, only the changed layers need to be rebuilt, making the process efficient and fast.

3. Dockerfiles

Dockerfiles are detailed instructions for creating Docker images.

A Dockerfile is a plain text file that contains a series of instructions on how to build a Docker image. It specifies the base image to start with, the commands to run, the files to copy, and the environment variables to set.

Here’s a simple Dockerfile example:

FROM ubuntu:latest
RUN apt update && apt install -y python
COPY app.py /app/
WORKDIR /app
CMD ["python", "app.py"]

In this example, we start with the latest Ubuntu image, install Python, copy the app.py file into the /app directory, set the working directory to /app, and specify the command to run when the container starts.

What Are The Benefits Of Using Docker?

Docker offers numerous benefits that make it a popular choice among developers and organizations:

Simplified Application Development

Development is simple. By packaging applications and their dependencies into containers, Docker allows developers to work on different parts of an application independently. This ensures that everything runs smoothly together. Testing is also simplified, and issues can be caught earlier in the development cycle.

Enhanced Portability

Applications become more portable. Containers can run consistently on different environments, whether on a developer’s local machine, a testing environment, or a production server. This eliminates compatibility issues and makes it easier to deploy applications to different platforms.

Improved Efficiency

Docker improves efficiency. Containers are lightweight and start up quickly, making them more efficient than traditional virtual machines. This means you can get more out of your resources and deploy applications faster.

Better Scalability

Scaling applications is easier with Docker. You can easily run multiple containers across different hosts to handle increased traffic or workload. In this way, scaling applications is much easier.

Streamlined Testing And Deployment

Docker streamlines testing and deployment. Docker images can be easily versioned and tracked, making it easier to manage changes and roll back if needed. Docker also works well with continuous integration and delivery (CI/CD) pipelines, which automate the build and deployment process.

What Are Some Use Cases For Docker?

Docker is widely adopted across various industries and use cases. Let’s explore some common scenarios where Docker shines.

Microservices Architecture

Docker is an excellent fit for building and deploying microservices-based applications. Microservices are small, independently deployable services that work together to form a larger application. Each microservice can be packaged into a separate Docker container, empowering independent development, deployment, and scaling.

For example, an e-commerce application can be broken down into microservices such as a product catalog service, a shopping cart service, an order processing service, and a payment service. Each of these services can be developed and deployed independently using Docker containers, making the overall application much more modular and maintainable.

Continuous Integration And Delivery (CI/CD)

Docker plays an important role in enabling continuous integration and delivery (CI/CD) practices. CI/CD is a software development approach that emphasizes frequent integration, automated testing, and continuous deployment of code changes.

With Docker, you can create a consistent and reproducible environment for building, testing, and deploying applications. You can define the entire application stack, including dependencies and configurations, in a Dockerfile. This Dockerfile can then be version-controlled and used as part of your CI/CD pipeline.

For example, you can set up a Jenkins pipeline that automatically builds a Docker image whenever code changes are pushed to a Git repository. The pipeline can then run automated tests against the Docker container and, if the tests pass, deploy the container to a production environment.

Local Development Environments

Docker is also widely used for creating local development environments. Instead of manually setting up and configuring the development environment on each developer’s machine, you can use Docker to provide a consistent and isolated environment.

Say, you’re developing a web application that requires a specific version of a database and a web server. You can define these dependencies in a Docker Compose file. Developers can then use Docker Compose to spin up the entire development environment with a single command, so everyone has the same setup.

The idea is to eliminate manual setup and configuration, reduce the risk of environment-specific issues, and allow developers to focus on writing code rather than dealing with environment inconsistencies.

Application Modernization

Docker is a valuable tool for modernizing legacy applications. Many organizations have older applications that are difficult to maintain and deploy due to their monolithic architecture and complex dependencies.

With Docker, you can containerize legacy applications and break them down into smaller, more manageable components. You can start by identifying the different services within the monolithic application and packaging them into separate Docker containers. This way, you can gradually modernize the application architecture without a complete rewrite.

Containerizing legacy applications also makes it easier to deploy and scale. Instead of dealing with complex installation procedures and dependency conflicts, you simply deploy the containerized application to any environment that supports Docker.

How To Use Docker

Now that we know the key components, let’s explore how Docker works:

1. Install Docker

To install Docker, visit the official Docker website and download the appropriate installer for your operating system. Docker provides installers for Windows, macOS, and various Linux distributions.

Once you have downloaded the installer, follow Docker’s installation instructions. The installation process is straightforward and shouldn’t take you very long.

2. Creating and Using Docker Images

Before creating your own Docker image, consider whether a pre-built image already meets your needs. Many common applications and services have official images available on Docker Hub, GitHub Container Registry, or other container registries. Using a pre-built image can save you time and effort.

If you decide to create a custom Docker image, you’ll need a Dockerfile. This file defines the steps to build the image according to your requirements. Here’s how to proceed:

• Using Pre-Built Images: Search for an existing image on Docker Hub, GitHub Container Registry, or within your organization’s private repository. You can pull an image with the command docker pull <image_name>:<tag>, replacing <image_name> and <tag> with the specific name and version of the desired image.
• Creating Your Own Image: If a pre-built image doesn’t suit your needs, you can create your own. First, write a Dockerfile based on your requirements. Then, build your image with the following command:

docker build -t my-app .

This command tells Docker to build an image tagged as my-app using the current directory (.) as the build context. It will then be available in your docker environment to use for container creation.

3. Running A Docker Container

Once you have a Docker image, you can use it to create and run containers. To run a container, use the docker run command followed by the image name and any additional options.

For example, to run a container based on the my-app image we built earlier, you can use the following command:

docker run -p 8080:80 my-app

This command starts a container based on the my-app image and maps port 8080 on the host system to port 80 inside the container.

4. Communicating Between Containers

Containers are isolated by default, but sometimes you need them to communicate with each other. Docker provides networking capabilities that allow containers to communicate securely.

You can create a Docker network using the Docker network create command. Then, connect containers to that network. Containers on the same network can communicate with each other using their container names as hostnames.

For example, let’s say you have two containers: a web application and a database. You can create a network called my-network and connect both containers to it:

docker network create my-network
docker run --name web-app --network my-network my-app
docker run --name database --network my-network my-database

Now, the web app container can communicate with the database container using the hostname database.

5. Basic Docker Commands

Here are some basic Docker commands that you’ll frequently use:

• docker pull: Pulls the specified Docker image from the Docker Hub
• docker run: Runs a container based on a specified image
• docker build: Builds a Docker image from a Dockerfile
• docker ps: Lists all running containers
• docker images: Lists all available Docker images
• docker stop: Stops a running container
• docker rm: Removes a stopped container
• docker rmi: Removes a Docker image

These are just a few examples of the many Docker commands available. Refer to the Docker documentation for a comprehensive list of commands and how to use them.

6. Docker Hub

Docker Hub is a public registry hosting a vast collection of images. It serves as a central repository where developers can find and share Docker images.

You can browse the Docker Hub to find pre-built images for various applications, frameworks, and operating systems. These images can be used as a starting point for your applications or as a reference for creating your Dockerfiles.

To use an image from Docker Hub, simply use the docker pull command followed by the image name. For example, to pull the latest official Python image, you can run:

docker pull python:latest

This command downloads the Python image from Docker Hub and makes it available for use on your local system.

7. Mastering Docker Compose: Streamline Your Development

As you continue to explore and integrate Docker into your development workflow, it’s time to introduce a powerful tool in the Docker ecosystem: Docker Compose. Docker Compose simplifies the management of multi-container Docker applications, allowing you to define and run your software stack using a simple YAML file.

What is Docker Compose?

Docker Compose is a tool designed to help developers and system administrators orchestrate multiple Docker containers as a single service. Instead of manually launching each container and setting up networks and volumes via the command line, Docker Compose lets you define your entire stack configurations in a single, easy-to-read file named docker-compose.yml.

Key Benefits of Docker Compose:

• Simplified Configuration: Define your Docker environment in a YAML file, specifying services, networks, and volumes in a clear and concise manner.
• Ease of Use: With a single command, you can start, stop, and rebuild services, streamlining your development and deployment processes.
• Consistency Across Environments: Docker Compose ensures your Docker containers and services run the same way in development, testing, and production environments, reducing surprises during deployments.
• Development Efficiency: Focus more on building your applications rather than worrying about the underlying infrastructure. Docker Compose manages the orchestration and networking of your containers so you can concentrate on coding.

Using Docker Compose:

1. Define Your App’s Environment: Create a docker-compose.yml file at the root of your project directory. In this file, you’ll define the services that make up your application, so they can be run together in an isolated environment.
2. Run Your Services: With the docker-compose up command, Docker Compose will start and run your entire app. If it’s the first time running the command or your Dockerfile has changed, Docker Compose automatically builds your app, pulling the necessary images and creating your defined services.
3. Scale and Manage: Easily scale your application by running multiple instances of a service. Use Docker Compose commands to manage your application lifecycle, view the status of running services, stream log output, and run one-off commands on your services.

Integrating Docker Compose into your development practices not only optimizes your workflow but also aligns your team’s development environments closely. This alignment is crucial for reducing “it works on my machine” issues and enhancing overall productivity.

Embrace Docker Compose to streamline your Docker workflows and elevate your development practices. With Docker Compose, you’re not just coding; you’re composing the future of your applications with precision and ease.

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Docker empowers you to transcend the common woes of code behaving unpredictably across different machines. It allows you to dedicate your energy to what you excel at—crafting remarkable code and developing stellar applications.

For both veteran developers and those just embarking on their coding odyssey, Docker represents an indispensable tool in your development toolkit. Think of it as your reliable ally, simplifying your development process and bolstering the resilience of your applications.

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Through this hands-on guide, we will lift the veil around environment variables so you can understand what they entail, why they matter, and how to leverage environment variables confidently.

Grab your favorite beverage (and maybe some cookies) cause we’re about to get into it. Let’s unpack environmental variable concepts from the ground up.

What Are Environment Variables?

Environment variables are dynamic named values that can affect how running processes behave on a computer. Some key properties of environment variables are:

• Named: Have descriptive variable names like APP_MODE and DB_URL.
• External: Values are set outside the app code via files, command lines, and systems.
• Dynamic: Can update variables without restarting apps.
• Configured: Code relies on variables but doesn’t define them.
• Decoupled: No need to alter code configurations once variables are set.

Here’s an analogy. Imagine you’re following a chocolate chip cookie recipe. The recipe might say:

• Add 1 cup of sugar
• Add 1 stick of softened butter
• Add 2 eggs

Instead of those hard-coded values, you could use environment variables instead:

• Add $SUGAR cup of sugar
• Add $BUTTER sticks of softened butter
• Add $EGGS eggs

Before making the cookies, you’d set those environment variable names to values of your choosing:

SUGAR=1 
BUTTER=1
EGGS=2

So, when following the recipe, your ingredients would resolve to:

• Add 1 cup of sugar
• Add 1 stick of softened butter
• Add 2 eggs

This allows you to configure the cookie recipe without changing the recipe code.

The same concept applies to computing and development. Environment variables allow you to alter the environment in which a process runs without changing the underlying code. Here are a few common examples:

• Setting the environment to “development” or “production”
• Configuring API keys for external services
• Passing in secret keys or credentials
• Toggling certain features on and off

Environment variables provide great flexibility. You can deploy the same code to multiple environments without changing the code itself. But let’s understand further why they are valuable.

Why Are Environment Variables Valuable?

Consider environment variables like application knobs used to dial-in preferences. We will explore excellent use cases shortly.

Let’s solidify intuition on why environment variables matter!

Reason #1: They Separate Application Code From Configurations

Hard-coding configurations and credentials directly into your code can cause all sorts of problems:

• Accidental commits to source control
• Rebuilding and redeploying code just to change a value
• Configuration issues when promoting across environments

It also leads to messy code:

import os

# Hard-coded configuration
DB_USER = 'appuser' 
DB_PASS = 'password123'
DB_HOST = 'localhost'
DB_NAME = 'myappdb'

def connect_to_db():
  print(f"Connecting to {DB_USER}:{DB_PASS}@{DB_HOST}/{DB_NAME}")  

connect_to_db()

This entangles business logic with configuration details. Tight coupling makes maintenance arduous over time:

• Changes require modifying the source code
• Risk of leaking secrets into source control

Using environment variables reduces these issues. For instance, you can set the DB_USER and DB_NAME environment variables.

# .env file
DB_USER=appuser
DB_PASS=password123  
DB_HOST=localhost
DB_NAME=myappdb

The application code can access the environment variables whenever required, keeping the code clean and simple.

import os

# Load config from environment 
DB_USER = os.environ['DB_USER']
DB_PASS = os.environ['DB_PASS'] 
DB_HOST = os.environ['DB_HOST']
DB_NAME = os.environ['DB_NAME']

def connect_to_db():
  print(f"Connecting to {DB_USER}:{DB_PASS}@{DB_HOST}/{DB_NAME}")
  
connect_to_db()

Environment variables cleanly separate configuration from code, keeping sensitive values abstracted into the environment.

You can deploy the same code from development to production without changing a thing. The environment variables can differ between environments without impacting the code at all.

Reason #2: They Simplify Configuring Applications

Environment variables simplify tweaking configurations without touching code:

# .env file:
DEBUG=true

Here’s how we could use it within the script file:

# Script content:
import os

DEBUG = os.environ.get('DEBUG') == 'true' 

if DEBUG:
   print("In DEBUG mode")

Toggling debug mode requires only updating the .env file—no code changes, rebuilding, or redeploying are needed. “Env vars” for short, also help deploy across environments seamlessly:

import os

# Retrieve environment variable to determine the current environment (production or staging)
current_env = os.getenv('APP_ENV', 'staging')  # Default to 'staging' if not set

# Production API key
PROD_API_KEY = os.environ['PROD_API_KEY']

# Staging API key
STG_API_KEY = os.environ['STG_API_KEY']

# Logic that sets api_key based on the current environment
if current_env == 'production':
    api_key = PROD_API_KEY
else:
    api_key = STG_API_KEY

# Initialize API client with the appropriate API key
api = ApiClient(api_key)

The same code can use separate API keys for production vs staging without any changes.

And lastly, they enable feature toggles without new deployments:

NEW_FEATURE = os.environ['NEW_FEATURE'] == 'true'

if NEW_FEATURE:
   enableNewFeature()

Changing the NEW_FEATURE var activates functionality instantly within our code. The interface for updating configurations depends on the systems:

• Cloud platforms like Heroku use web dashboards
• Servers use OS command tools
• Local dev can use .env files

Environment variables are beneficial when creating applications, allowing users to configure elements per their requirements.

Reason #3: They Help Manage Secrets And Credentials

Checking secrets like API keys, passwords, and private keys directly into source code raises substantial security risks:

# Avoid exposing secrets in code!
STRIPE_KEY = 'sk_live_1234abc'
DB_PASSWORD = 'password123'

stripe.api_key = STRIPE_KEY 
db.connect(DB_PASSWORD)

Those credentials are now exposed if this code gets committed into a public GitHub repository!

Environment variables prevent leakage by externalizing secrets:

import os

STRIPE_KEY = os.environ.get('STRIPE_KEY')  
DB_PASS = os.environ.get('DB_PASS')   

stripe.api_key = STRIPE_KEY  
db.connect(DB_PASS)

The actual secret values get set in a local .env File.

# .env file

STRIPE_KEY=sk_live_1234abc
DB_PASS=password123

Don’t forget to .gitignore the .env file to keep secrets out of source control. This involves defining the .env file in a .gitignore file in any repo root, which tells git to ignore the file during commit creation.

This separates secret definitions from application code, loading them securely from protected environments during runtime. The risk of accidentally exposing credentials reduces dramatically.

Reason #4: They Promote Consistency

Imagine having different configuration files for development, QA, and production environments:

# Development
DB_HOST = 'localhost'
DB_NAME = 'appdb_dev'

# Production
DB_HOST = 'db.myapp.com'
DB_NAME = 'appdb_prod'

This discrepancy introduces subtle bugs that are hard to catch. Code that works flawlessly in development might suddenly break production due to mismatched configurations.

Environment variables solve this by centralizing configuration in one place:

DB_HOST=db.myapp.com
DB_NAME=appdb_prod

Now, the same variables get used consistently across all environments. You no longer have to worry about random or incorrect settings kicking in.

The application code simply references the variables:

import os

db_host = os.environ['DB_HOST']
db_name = os.environ['DB_NAME']

db.connect(db_host, db_name)

Whether the app runs locally or on a production server, it always uses the correct database host and name.

This uniformity reduces bugs, improves predictability, and makes the app more robust overall. Developers can have confidence that the code will behave identically in every environment.

How Can You Define Environment Variables

Environment variables can be defined in several places, allowing flexibility in setting and accessing them across processes and systems.

1. Operating System Environment Variables

Most operating systems provide built-in mechanisms for defining global variables. This makes the variables accessible system-wide to all users, applications, etc.

On Linux/Unix systems, variables can be defined in shell startup scripts.

For example, ~/.bashrc can be used to set user-level variables, while /etc/environment is for system-wide variables that all users can access.

Variables can also be set inline before executing commands using the export command or directly through the env command in bash:

# In ~/.bashrc
export DB_URL=localhost
export APP_PORT=3000

# In /etc/environment
DB_HOST=localhost
DB_NAME=mydatabase

Variables can also be set inline before executing commands:

export TOKEN=abcdef
python app.py

Defining variables at the OS level makes them globally available, which is quite helpful when you want to run the app without depending on internal values.

You can also reference defined variables in scripts or command-line arguments.

python app.py --db-name $DB_NAME --db-host $DB_HOST --batch-size $BATCH_SIZE

2. Defining Environment Variables In Application Code

In addition to OS-level variables, environment variables can be defined and accessed directly within the application code while running.

The os.environ dictionary in Python contains all currently defined environment variables. We can set new ones by simply adding key-value pairs:

Environment variables can also be defined and accessed directly within the application code. In Python, the os.environ dictionary contains all defined environment variables:

import os
os.environ["API_KEY"] = "123456" 
api_key = os.environ.get("API_KEY")

So, the os.environ dictionary allows for the dynamic setting and retrieving of environment variables from within Python code.

Most languages come bundled with their libraries, offering access to environment variables during runtime.

You can also use frameworks like Express, Django, and Laravel to have deeper integrations, such as auto-loading .env files containing environment variables.

3. Creating Local Configuration Files For Environment Variables

In addition to system-level variables, environment variables can be loaded from an application’s local configuration files. This keeps configuration details separate from code, even for local development and testing.

Some popular approaches:

.env Files

The .env file format convention popularized by Node.js provides a convenient way to specify environment variables in a key-value format:

# .env
DB_URL=localhost
API_KEY=123456

Web frameworks like Django and Laravel automatically load variables defined in .env files into the application environment. For other languages like Python, libraries such as python-dotenv handle importing .env files:

from dotenv import load_dotenv
load_dotenv() # Loads .env variables

print(os.environ['DB_URL']) # localhost

The benefit of using .env files is they keep configuration clean and separate without making changes to code.

JSON Configuration Files

For more complex configuration needs involving multiple environment variables, using JSON or YAML files helps organize variables together:

// config.json
{
  "api_url": "https://api.example.com",
  "api_key": "123456", 
  "port": 3000
}

Application code can then quickly load this JSON data as a dictionary to access configured variables:

import json

config = json.load('config.json')  

api_url = config['api_url']
api_key = config['api_key'] 
port = config['port'] # 3000

This prevents messy dotenv files when dealing with multiple app configurations.

How Do You Access Environment Variables In Different Programming Languages?

However we choose to define environment variables, our applications need a consistent way of looking up values during runtime.

While various ways exist to define environment variables, application code needs a standard way to access them at runtime, regardless of language. Here is an overview of techniques to access env variables across popular languages:

Python

Python provides the os.environ dictionary to access defined environment variables:

import os

db = os.environ.get('DB_NAME')

print(db)

We can get a variable using os.environ.get(), which returns None if undefined. Or access directly via os.environ(), which will raise KeyError if it is not present.

Additional methods like os.getenv() and os.environ.get() allow specifying default values if unset.

JavaScript (Node.js)

In Node.js JavaScript code, environment variables are available on the global process.env object:

// Get env var
const db = process.env.DB_NAME;

console.log(db);

If undefined, process.env will contain undefined. We can also supply defaults like:

const db = process.env.DB_NAME || 'defaultdb';

Ruby

Ruby applications access environment variables through the ENV hash:

# Access variable 
db = ENV['DB_NAME']  

puts db

We can also pass a default value if the desired key does not exist:

db = ENV.fetch('DB_NAME', 'defaultdb')

PHP

PHP provides global methods getenv(), $_ENV and $_SERVER to access environment variables:

// Get env var
$db_name = getenv('DB_NAME');

// Or access $_ENV or $_SERVER arrays 
$db_name = $_ENV['DB_NAME'];

Depending on the variable source, they may be available in different globals.

Java

In Java, the System.getenv() method returns env variables which can be accessed:

String dbName = System.getenv("DB_NAME");

This allows access to variables defined at a system level globally in Java.

For now, some best practices around environment variable hygiene.

Environment Variable Security Guide

When it comes to managing environment variables securely, we should keep several best practices in mind.

Never Store Sensitive Information In Code

First and foremost, never store sensitive information like passwords, API keys, or tokens directly in your code.

It may be tempting to just hardcode a database password or an encryption key into your source code for quick access, but resist that urge!

If you accidentally commit that code to a public repository on GitHub, you’re essentially broadcasting your secrets to the entire world. Imagine if a hacker got ahold of your production database credentials just because they were sitting in plain text in your codebase. Scary thought, right?

Instead, always use environment variables to store any sort of sensitive configuration. Keep your secrets in a secure place like a .env file or a secrets management tool, and reference them in your code via environment variables. For example, instead of doing something like this in your Python code:

db_password = "supers3cr3tpassw0rd"

You’d store that password in an environment variable like this:

# .env file
DB_PASSWORD=supers3cr3tpassw0rd

And then access it in your code like:

import os
db_password = os.environ.get('DB_PASSWORD')

This way, your secrets are still safe even if your source code gets compromised. Environment variables act as a secure abstraction layer.

Use Environment-Specific Variables

Another practice is using different environment variables for each application environment, such as development, staging, and production.

You don’t want to accidentally connect to your production database while developing locally just because you forgot to update a config variable! Namespace your environment variables for each environment:

# Dev
DEV_API_KEY=abc123
DEV_DB_URL=localhost

# Production
PROD_API_KEY=xyz789
PROD_DB_URL=proddb.amazonaws.com

Then, reference the appropriate variables in your code depending on the current environment. Many frameworks like Rails provide environment-specific config files for this purpose.

Keep Secrets Out Of Version Control

It’s also crucial to keep your .env and config files containing secrets out of version control. Add .env to your .gitignore so you don’t accidentally commit it to your repository.

You can use git-secrets to scan for sensitive info before each commit. For extra security, encrypt your secrets file before storing it. Tools like Ansible Vault and BlackBox can help with this.

Secure Secrets On Production Servers

When managing environment variables on your production servers, avoid setting them using command line arguments, which can be inspected through the process table.

Instead, use your operating system or container orchestration platform’s environment management tools. For example, you can use Kubernetes Secrets to store and expose secrets securely to your application pods.

Use Strong Encryption Algorithms

Use robust and modern encryption algorithms when encrypting your secrets, whether in transit or at rest. Avoid deprecated algorithms like DES or MD5, which have known vulnerabilities. Instead, opt for industry-standard algorithms like AES-256 for symmetric encryption and RSA-2048 or ECDSA for asymmetric encryption.

Rotate Secrets Regularly

Rotate your secrets regularly, especially if you suspect they may have been compromised. Treat secrets like you would a password — update them every few months. A secrets management tool like Hashicorp Vault or AWS Secrets Manager can help automate this process.

Be Careful With Logging And Error Reporting

Be careful about logging and error reporting. Make sure not to log any environment variables that contain sensitive values. If you’re using a third-party error tracking tool, configure it to sanitize sensitive data. The last thing you want is for your secrets to appear in a stack trace on an exception reporting dashboard!

When To Avoid Environment Variables?

There are several cases where environment variables should be avoided:

Managing Complex Configuration

Using environment variables to manage configuration for complex software systems can become messy and error-prone. As the number of configuration parameters grows, you end up with long environment variable names that can unintentionally collide. There is also no easy way to organize related configuration values together.

Instead of environment variables, consider using configuration files in a format like JSON or YAML. These allow you to:

• Group related configuration parameters together in a nested structure.
• Avoid naming collisions by encapsulating config in scopes and namespaces.
• Define custom data types instead of just strings.
• Quickly view and modify configurations using a text editor.

Storing Sensitive Information

While environment variables seem easy to inject external configurations like API keys, database passwords, etc., this can cause security issues.

The problem is environment variables are accessible globally in a process. So, if an exploit exists in part of your application, it could compromise secrets stored in environment variables.

A more secure approach is using a secret management service that handles encryption and access control. These services allow storing of sensitive data externally and provide SDKs for retrieving application values.

So, consider using a dedicated secrets management solution rather than environment variables for credentials and private keys. This reduces the risk of accidentally exposing sensitive data through exploits or unintended logging.

Working With Multiple Environments

Managing environment variables can become tedious as applications grow and get deployed across multiple environments (dev, staging, staging, prod). You may have fragmented configuration data spread across various bash scripts, deployment tools, etc.

A configuration management solution helps consolidate all environment-specific settings into a centralized place. This could be files in a repository, a dedicated configuration server, or integrated with your CI/CD pipelines.

If the goal is to avoid duplicating environment variables, a single source of truth for configurations makes more sense.

Sharing Configuration Across Teams

Since environment variables are sourced locally per process, sharing and synchronizing configuration data across different teams working on the same application or suite of services becomes very difficult.

Each team may maintain its copy of configuration values in different bash scripts, deployment manifests, etc. This decentralized configuration leads to the following:

1. Configuration drift: With no single source of truth, it’s easy for configuration to become inconsistent across environments as different teams make independent changes.
2. Lack of visibility: There is no centralized way to view, search, and analyze the entire configuration state across all services. This makes it extremely difficult to understand how a service is configured.
3. Auditing challenges: Changes to environment variables are not tracked in any standard way, making it hard to audit who changed what configuration and when.
4. Testing difficulties: With no way to easily snapshot and share configuration, ensuring consistent environments for development and testing becomes extremely cumbersome.

Rather than this fragmented approach, having a centralized configuration solution allows teams to manage configuration from a single platform or repository.

Build Your Apps With Environment Variables For The Long-Term

As your application grows, consider how you may need more advanced ways to manage its configuration settings.

What seems straightforward now could get more complicated later on. You’ll likely need better ways to control access, share team settings, organize everything clearly, and update configurations smoothly.

Don’t back yourself into a corner by just using environment variables from the start. You want to plan how to handle configurations as your needs expand.

While environment variables are great for handling environment-focused data like login credentials, database names, local IPs, etc, you want to create a system that follows sound principles like security, shareability, organization, and the ability to adapt to changes quickly.

The alternatives we discussed, like using a dedicated configuration file or service, have valuable features that align with those principles. That will help you to keep moving quickly without getting slowed down.

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