DEV Community: José D. Gutiérrez

[Boost]

José D. Gutiérrez — Thu, 13 Feb 2025 23:45:22 +0000

Setting Up a Powerful Windows Development Environment 💪

José D. Gutiérrez ・ Feb 13

#programming #tutorial #tooling #microsoft

Setting Up a Powerful Windows Development Environment 💪

José D. Gutiérrez — Thu, 13 Feb 2025 18:06:05 +0000

Gone are the days when developers needed to dual-boot Linux or settle for a subpar development experience on Windows. With modern tools and the right setup, Windows can be transformed into a robust development platform that rivals any Unix-based system.

This guide will walk you through creating a professional-grade development environment on Windows, combining the best of both worlds: Windows' user-friendly interface and Linux's powerful development tools.

What You'll Learn

Setting up WSL2 for a native Linux experience
Installing and configuring development tools
Managing multiple programming language versions
Creating an efficient coding environment
Running containerized applications seamlessly

Prerequisites

Windows 10/11 (Pro)
At least 8GB RAM (16GB recommended)
50GB free disk space
Administrator access

1. Windows Subsystem for Linux (WSL2)

WSL2 provides a full Linux kernel and seamless Windows-Linux integration. It's the foundation of our development environment.

Why WSL2?

Native Linux system calls
Full system call compatibility
Excellent filesystem performance
Seamless integration with Windows tools

Installation Steps

Just open a new PowerShell or Windows Command Prompt in administrator mode and enter the wsl install command.

# Install WSL 
wsl --install

The --install command performs the following actions:

Enables the optional WSL and Virtual Machine Platform components
Downloads and installs the latest Linux kernel
Sets WSL2 as the default
Downloads and installs the Ubuntu Linux distribution (default)

Note: You will need to restart your machine during this installation process.

Once the process of installing your Linux distribution with WSL is complete, open the distribution using the Start menu.

You will be asked to create a Username and Password for your Linux distribution. This username and password is specific to each separate Linux distribution that you install and has no bearing on your Windows username.

2. Terminal Setup

Now that we have Linux running inside Windows we can configure our distribution and terminal to our liking.

We can install Oh My Zsh, change the prompt, install plugins, etc.

For a detailed guide on terminal customization, check out my previous post: My Terminal Setup for 2025 🚀

3. Development Languages with ASDF

A development environment needs... development tools, right?

You'll probably want to install runtimes or compilers. For example, I want to install NodeJs.

ASDF provides a single tool for managing multiple language runtime versions.

Benefits of ASDF

Single tool for all languages
Project-specific runtime versions
Easy version switching
Plugin-based architecture

Setup Process

# Download asdf
git clone https://github.com/asdf-vm/asdf.git ~/.asdf --branch v0.15.0

# Add the following to ~/.zshrc
. "$HOME/.asdf/asdf.sh"

# Optional: Completions are configured by either a ZSH Framework asdf plugin
# or by adding the following to your .zshrc:
fpath=(${ASDF_DIR}/completions $fpath)
autoload -Uz compinit && compinit

Installing NodeJS becomes straightforward:

# Add NodeJS plugin
asdf plugin add nodejs https://github.com/asdf-vm/asdf-nodejs.git

# Install desired version
asdf install nodejs latest

# Or specify version
asdf install nodejs 22.14.0

My favorite part is that we can set the node version globally or locally. For example we could have latest globally but set specific projects to the LTS version.

# Set global version
asdf global nodejs latest

# Configure project-specific version
asdf local nodejs 22.14.0

Unlike nvm, asdf extends beyond Node.js, supporting Elixir, Erlang, Ruby through official plugins, and Python, Java through community contributions.

4. Visual Studio Code

_Photo by Mohammad Rahmani in unsplash_

VSCode delivers exceptional development experience through WSL integration.

Key Features to Configure

Remote WSL Development
Essential Extensions
Settings Sync
Integrated Terminal

Installation and Setup

Install the VSCode App (For Windows, not Linux) from the official site
Install WSL Extension Pack

That's it! Projects in your Linux distribution open directly in VSCode:

# From project directory
vscode .

Check out my previous post: My Favorite VSCode Extensions for Modern Development

5. Docker Desktop Integration

Docker Desktop with WSL2 backend offers native container performance.

Why Docker Desktop?

Native WSL2 integration
Kubernetes support
GUI for container management
Resource optimization

Setup Instructions

Download and Install Docker Desktop for Windows from Official Site
Enable WSL Integration during installation
Restart your system
Verify installation:

docker --version

6. Extras

DeepSeek R1

DeepSeek's R1 model emerges as a powerful competitor to OpenAI's leading models. Let's integrate it using Ollama:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh
# Verify installation
ollama -v
# Run R1 model
ollama run deepseek-r1

Explore all R1 versions on Ollama Official Site

For a graphical interface, OpenWebUI integrates seamlessly through Docker:

docker run -d -p --network=host \
  -v open-webui:/app/backend/data \
  -e OLLAMA_BASE_URL=http://127.0.0.1:11434 \
  --name open-webui \
  --restart always \
  ghcr.io/open-webui/open-webui:main

If everything works well, we should be able to access the interface from http://localhost:8080

VSCode Integration

We just search and install the Continue extension from the marketplace

As we already have Ollama installed, we just need to select it as Provider, choose DeepSeek as model and that's it

Note: If the extension does not detect the selected DeepSeek model you can use Autodetect

You will now be able to interact with DeepSeek from the chat interface

Why Local AI Tools?

Development in 2025 isn't just about coding - it's about leveraging AI effectively. Running models locally offers:

No API costs
Full privacy
Offline capability
Lower latency

Curious about DeepSeek's potential? Read my full analysis: DeepSeek R1 Will Change the World: Advanced AI Accessible to Everyone

Conclusion

With this setup, you'll have a development environment that combines Windows' compatibility with Linux's development power. The key is choosing the right tools and configuring them properly.

Remember: A well-configured development environment is an investment in your productivity. Take the time to customize it to your needs.

What's Next?

Fine-tune your terminal configuration
Set up language-specific tools
Configure cloud development environments
Explore advanced WSL features

DeepSeek R1 Will Change the World: Advanced AI Accessible to Everyone

José D. Gutiérrez — Fri, 31 Jan 2025 15:10:49 +0000

No. This is not another "hype post" about R1.

Or maybe it is... 🤔

Controversial or not, there's an undeniable fact. DeepSeek has created a "before and after" in artificial intelligence, and we'll quickly see the evidence.

If you're late to the news, let me tell you what DeepSeek-R1 is all about 🐋

In July 2023, Liang Wenfeng founded DeepSeek. His vision was clear: Challenge everything we thought we knew about artificial intelligence.

The industry was obsessed with a simple formula:

💰 More money = better models

Tech giants investing billions and burning resources in datacenters. Everyone playing the same expensive game.

Obviously, not everyone could participate.

DeepSeek bet on an efficiency-based approach.

In fact, this isn't the first time DeepSeek has made headlines. Their previous models, especially DeepSeek V3, have drawn attention for their capabilities and ease of use. Sure, these models weren't as powerful as current leading competitors.

That's over now.

DeepSeek has released a new AI model, called R1. A model that rivals o1, one of OpenAI's most powerful models to date (only behind o3).

R1 offers very similar results in its reasoning level but at a fraction of the cost.

We're not talking about small optimizations. We're talking about massive resources no longer being required.

How much does it really cost to build a world-class AI model? 🤑

Until today, the answer always included absurd amounts of money... but R1 has changed that.

New approach, new rules

DeepSeek R1 represents a huge leap in language model development, standing out not only for its performance comparable to first-line models but also for its focus on efficiency and accessibility.

This approach allows the model to maintain the processing capability of larger models while significantly optimizing computational resource usage.

Performance and Benchmarks

Benchmark results demonstrate R1's impressive performance against popular models:

In mathematical reasoning tasks and complex problem-solving, R1 demonstrates exceptional performance, matching or surpassing strongly established models like OpenAI's.

However, it wasn't its response capability (although quite advanced) that impressed the world.

We had already seen surprising results with "human reasoning" from OpenAI.

R1's true revolution lies in its costs.

Model	Input cost	Output cost
DeepSeek-R1	$0.55	$2.19
OpenAI o1-1217	$15.00	$60.00

We're talking about approximately 96% cost savings. It's totally insane! 🤯

R1 matches (and sometimes surpasses) the most powerful models in the market... at a cost 27 times lower. 27 times!

If that weren't enough, they've published the code as Open Source. Transparent. Ready to be studied, modified, and improved by anyone (Including competitors).

Sounds weird.

This has important implications

It proves that high-level AI doesn't require billion-dollar investments
It establishes a new efficiency standard in training new models
Advanced AI models will be truly accessible to everyone
R1 is perfectly replicable

Before trying to analyze the impact this will bring to the industry and the world, let's first see how it works 🤓

Architecture

DeepSeek R1 combines computational efficiency with advanced reasoning capabilities.

R1's core implements an MoE (Mixture-of-Experts) architecture. The interesting thing about this approach is that, during each operation, the model selectively activates only the parameters it needs.

This selective activation works like a system of "specialists," where different model components activate according to the specific task. The model decides in real-time which components it needs for each type of processing. This means significant savings in operational costs.

R1's training was primarily developed using common techniques found in most large language models. But one of them, very interesting, is called Chain-of-Thought, which we can also find in OpenAI's o1 model.

This technique divides the query into multiple stages instead of generating a direct response. At each stage, it executes the reasoning or processing tasks it was trained on, and its conclusion is taken by the next stage's process. It automatically feeds back.

It's like "thinking step by step" 🧠

In simple terms, what happens when interacting with these models is:

Analyzes the problem using chain of thought
Proposes multiple solutions considering temporal and spatial complexity
Implements the most efficient solution
Verifies the solution

This allows it to rival OpenAI's no longer so novel model.

Ok, we know it's a powerful model. How did they optimize it so much?

No, they didn't create impossible methods from a sci-fi series 🎬

No, they're not aliens or engineers from the year 3000 👽

No, AI won't take our jobs 🙄

They used good old software engineering fundamentals. Mathematics, linear algebra, probability, and statistics.

They optimized memory bits. Integrated a specialized cache system. Used Assembler instead of CUDA, a language created by NVIDIA to program chips. (NVIDIA didn't take it well)

They knew how to maximize the potential of the chips used. Very cheap chips, by the way. (H800)

Techniques that aren't out of this world. It's not a "magical" invention. That "simplicity" is what makes it so interesting.

R1's architecture proves it's possible to match, and even exceed, popular models' performance through intelligent optimizations and careful design.

It's a game-changer. It's simply a new way of thinking about artificial intelligence.

Its architecture, training, and other technical aspects are explained in detail in their Official Paper.

Practical capabilities

DeepSeek R1 particularly excels in mathematical tasks, code generation and debugging, as well as reasoning tasks.

For example, when trying to solve a mathematical problem:

R1 begins to solve the problem systematically:

Starts by squaring both sides
Performs algebraic manipulations
Reaches a point of "reflection"

The curious thing here is that in the middle of the process, R1 has what it calls an "aha moment" (a reflection or questioning). It interrupts its own process and decides to reevaluate its approach from the beginning.

This behavior is particularly interesting because it shows:

Self-correction capability
Transparency in the thought process
Similarity to human behavior when we realize we might be taking an overly complicated path to solve a problem

The model "realized" it was unnecessarily complicating the solution and decided to step back to look for a more elegant or efficient approach.

This is thanks to its "simulation of human reasoning" through the chain of thought. The problem is broken down step by step, and it can identify if the solution isn't going in the right direction to return to a previous step in the process.

Of course, natural expressions like an "aha" come from the thousands of examples it was trained on.

Let's now try with a classic coding problem. The Fibonacci sequence.

I used the following prompt: "Write a JavaScript function that calculates the nth number in the Fibonacci sequence. The sequence starts with 0 and 1, and each subsequent number is the sum of the previous two."

I first asked ChatGPT, specifically its GPT4-o model, and this was its response:

function fibonacci(n, memo = {}) {
    if (n in memo) return memo[n];
    if (n <= 1) return n;

    memo[n] = fibonacci(n - 1, memo) + fibonacci(n - 2, memo);
    return memo[n];
}

// Example usage
console.log(fibonacci(10)); // 55

ChatGPT successfully completed the task. However, it used a recursive approach, which tends to have serious performance issues. This makes the provided solution inefficient ❌

Now, if we ask exactly the same question to DeepSeek R1, we can see something totally different.

The first thing we notice is the chain of thought, where it breaks down the task we've assigned ☁️

Shortly after, we can observe that it has concluded that using recursion is not a viable option ✅

After a few seconds, it finally gives us the result.

function fibonacci(n) {
    if (n === 0) return 0;
    if (n === 1) return 1;

    let a = 0, b = 1;
    for (let i = 2; i <= n; i++) {
        const next = a + b;
        a = b;
        b = next;
    }
    return b;
}

While the code might not be perfect, the interesting thing about all this is that we can transparently observe the steps that led to this solution.

This can offer us a lot of context when trying to carry out tasks we might not have a good understanding of, or simply want to understand what aspects it considered to reach that result.

I haven't been able to perform the same test with OpenAI's o1 model. Most likely, it would have reached the same conclusion.

We can experiment directly with R1 from its official site, interact with its API (which also uses the same library as OpenAI, so if you already had an application developed, migration is very simple).

Or better yet. Install R1 on your own computer. Without depending on external servers.

DeepSeek R1 is available for local installation from Ollama and compatible with Open WebUI.

(Technical knowledge required)

The disruptive impact of R1

DeepSeek R1's launch has caused a significant impact on the industry, questioning established practices about the development and deployment of advanced AI models. This impact has manifested particularly dramatically in the stock market.

On January 27, 2025, NVIDIA experienced one of the biggest drops in tech market history. Their shares fell more than 18% in a single day, resulting in a loss of nearly 600 billion dollars 🔻

NVIDIA had dedicated itself to developing increasingly powerful chips to achieve impressive advances in artificial intelligence, receiving massive investments.

Investors' concern upon discovering that such advanced and expensive chips weren't really necessary to obtain the same, or at least similar results, has been clear.

Cheaper chips translate to lower return on investment 💸

What can we expect? 🤔

NVIDIA's fall is just the beginning... we're at an inflection point in the AI industry.

Why? Because R1 doesn't just democratize access - it rewrites the rules of the game:

Startups can compete with tech giants
Developers can experiment on their own machines
Small companies can offer AI services without going broke

This means that startups and small organizations, previously limited by operating costs, can now access advanced AI.

The result? A wave of innovation.

We'll see applications that were previously economically unfeasible. New use cases that nobody had imagined, or maybe they had, but couldn't create. Solutions that will change entire industries 🚀

Let's not forget that R1 being Open Source is replicable.

OpenAI will use R1-based models. Anthropic too. Google, Meta. Everyone.

There will be a new hype of AI-powered applications. Much bigger than what we've seen so far.

The question is no longer "who can afford to develop AI?" Now it's "what will YOU do with it?"

Advanced artificial intelligence models will no longer be a privilege...

OpenAI with ChatGPT and other giants won't disappear. It would be naive to think they will.

The market will be weird for a while. Yes.

There will continue to be new advances. R1 won't be trending forever, nor the only alternative we'll have.

But that's exactly what happens when a technology goes from being exclusive to being accessible.

Don't believe me? In the time it took me to write this article, Amazon announced that DeepSeek-R1 is already available on AWS

Remember when Linux came out? Android? The web we know today?

The future is promising.

🚀 Boost Your Presentations with Claude + Slidev

José D. Gutiérrez — Fri, 24 Jan 2025 17:51:37 +0000

Ever spent hours wrestling with presentation tools? I've discovered a game-changing workflow combining Claude AI and Slidev. Think of it as having a technical co-pilot for your Slides.

Claude is Anthropic's AI assistant known for in-depth technical understanding and natural language interactions.

Slidev is an open-source presentation tool designed specifically for developers, allowing them to create slides using Markdown and Vue components with live coding capabilities.

Together, they form a powerful combination for creating presentations efficiently.

Why This Matters 💡

Creating technical presentations is often time-consuming. You need clear explanations, code examples, and engaging visuals. This workflow cuts that time dramatically while improving quality.

I'm not a big fan of PowerPoint 😅

My Workflow 🎯

1. Prompt Engineering

Start by crafting effective prompts. Here's a real example:

❌ Basic Prompt:
"Create slides about How the Web Works"

✅ Enhanced Prompt:
"Create comprehensive educational content in 
Markdown format explaining 'How the Web Works', 
structured for conversion into presentation 
slides. The content should be highly accessible 
for complete beginners with no technical 
background."

💡TIP: You can ask Claude to do the prompt for you. Just give him your instructions.

Your task is to convert my instructions in a powerfull,
well-structured prompt to be used in Claude.ai later.

--- Start Instructions:
Create a good explanation of “How the Web Works” in Markdown format.
The resulting content will be used to create slides or presentations later.
Content should include:
1. What the Web (WWW) is
2. What HTTP and HTTPS are
3. What TCP/IP (Internet) is
4. Client-server flow, including DNS, ISP, request and response

Use a phone book as an analogy for DNS
Suggest the best order for the content, don't necessarily follow
the order I give you
Target audience: No experience / Beginners
--- End Instructions

The output must be the prompt. AVOID create the content

Claude helps refine these prompts. It suggests improvements and points out missing context.

2. Content Generation Phase 📝

Feed the refined prompt back to Claude. The key here is iteration:

Start with an outline
Generate section-by-section content
Request specific examples
Ask for clarification on concepts

3. Technical Implementation with Slidev 🛠️

Since Claude doesn't have an internet connection, his Slidev knowledge base may be limited or outdated. However, it's not a big deal.

Slidev is opensource. All of its documentation is available in Markdown files on GitHub. I've downloaded the docs (just clone the repo) and imported it into a new Claude Project (Claude Projects is a feature of Claude Pro).

Create a new project...

We provide additional instructions (Available in Gist)

We finally uploaded the documentation

Docs will be available as context throughout the entire project. A project in Claude acts as a knowledge base. Each new chat you start will have access to the documentation without you having to re-attach it and you'll have it available to create as many presentations as you want.

This gives Claude context about:

Available layouts
Component syntax
Styling options
Animation capabilities

Now Claude can generate Slidev-specific markdown

4. Get the Code

Create a New Chat using the Slidev Copilot Project. Provide clear and detailed instructions for your presentation. Attach the content you generated in step one and let the magic begin.

NOTE: Don't expect to get a perfect result on the first try. Iterate on the content and code you generate. Once you have a good base/starter, you can quickly modify what you need.

Key Benefits 🎉

Time Efficiency: Cut presentation creation time by 60%
Technical Accuracy: Claude understands complex technical concepts
Consistency: Maintains style and depth throughout
Flexibility: Easy updates and iterations

Best Practices 📌

Keep prompt history for reuse
Use Claude Projects for documentation context
Test generated code snippets
Review technical accuracy
Iterate on specific sections

Common Pitfalls to Avoid ⚠️

Don't rely on Claude for exact Slidev syntax without context
Always verify technical claims
Keep backups of working prompts
Test presentations in presenter mode

Conclusion

The magic of combining Claude's AI capabilities with Slidev's developer-first approach goes way beyond just saving time. It transforms how we craft technical presentations, making them more engaging, accurate, and impactful.

Give it a try – your future self (and your audience) will thank you 😉

My Favorite VSCode Extensions for Modern Development

José D. Gutiérrez — Wed, 22 Jan 2025 20:36:45 +0000

VSCode extensions transform your development experience. Even though VSCode is more considered an editor rather than an IDE, with the right extensions we can get a lot of potential out of it.

Here's a curated list of extensions that significantly improve my daily workflow.

🛠️ Core Extensions

Remote Development

Remote Development in Marketplace

A pack including Remote WSL, SSH, Containers, and Tunnels. Perfect for WSL2 development environments. Seamless integration between Windows and Linux subsystems.

Auto Rename Tag

Auto Rename Tag in Marketplace

Updates matching HTML/JSX tags automatically. Saves time when working with complex component structures.

Code Spell Checker

Code Spell Checker in Marketplace

Catches spelling mistakes across your codebase. Supports multiple language dictionaries. Have you had to push a commit to fix a typo? It's annoying, right? Never again.

Pretty TypeScript Errors

Pretty TypeScript Errors in Marketplace

Makes TypeScript errors readable and actionable. Speeds up debugging with clear, understandable error messages.

⚡ Productivity Tools

Turbo Console Log

Turbo Console Log in Marketplace

Smart debugging with auto-generated console logs. Includes variable names and locations automatically. Perfect for quick debugging sessions.

Official Documentation

GitHub Copilot

GitHub Copilot in Marketplace

AI pair programmer that understands your code context. Increases productivity with smart code suggestions and completions. Now with Claude 3.5 support and Free Tier.

ESLint + Prettier-ESLint

ESLint in Marketplace + Prettier-ESLint in Marketplace

Code consistency made simple. Prettier-ESLint ensures formatting rules align with your ESLint configuration. No more formatting conflicts.

🎨 Visual Enhancement

Dracula Theme

Dracula Theme in Marketplace

High-contrast theme optimized for long coding sessions. Clear syntax highlighting reduces eye strain.

Fluent Icons + Symbols

Fluent Icons in Marketplace + Symbols in Marketplace

Improved file navigation and code readability. Intuitive icons for quick file type identification.

🔧 Tech Stack Specific

Cloud & Infrastructure

AWS Toolkit: Direct AWS service management
Docker: Full Dockerfile and compose support
Kubernetes: Manifest validation and autocompletion
Hashicorp Terraform: IaC syntax support

Development

Astro: Complete meta-framework support. More on Astro.build
Prisma: Schema intellisense and validation for Prisma ORM

💪 Workflow Impact

This extension setup gives a total boost to my development workflow. Real-time error prevention, faster development through smart automations, shortcuts, and smooth integration of cloud tools.

🎯 Conclusion

These extensions create a powerful development environment. Each one solves specific challenges in modern development workflows.

What VSCode extensions do you consider essential? Share your recommendations below 😉

Best productivity subscriptions worth paying for in 2025 🚀

José D. Gutiérrez — Fri, 17 Jan 2025 18:20:38 +0000

It's crucial to invest in tools that truly enhance our productivity and experience, as they make our lives easier. These days, I've been thoroughly reviewing which services are worth keeping, and I'll explain it below.

🤖 Claude Pro

Claude, especially with its Claude 3.5 Sonnet model, has transformed my workflow. Claude stands out for its deep understanding of technical context and its ability to generate precise responses in infrastructure and development topics.

I've been testing both ChatGPT and Claude in their free versions, and Claude not only has a much friendlier interface, but it also generated better responses in my opinion, thanks to its ethical and secure approach. Regarding code generation, I also noticed more accurate responses.

But don't just take my word for it. Check out a detailed objective comparison here

Advantages over the free plan:

Higher daily message limit
Priority access during peak hours
Ability to process longer documents
Faster and more consistent responses

The difference in my workflow is particularly noticeable in tasks like debugging, pipeline optimization, and infrastructure analysis or design.

💻 GitHub Copilot

GitHub Copilot has become a default extension in my development process. The recent integration with Claude 3.5 Sonnet, a model I've grown accustomed to using, has significantly improved its contextual understanding, and I'm confident it will continue receiving improvements.

Why Copilot over alternatives:

Seamless integration with VSCode and GitHub
More precise and contextual suggestions thanks to Claude
Faster response time (?)
Production-proven stability

Cursor offers a really interesting proposition with its "tab tab tab" style. It's quite impressive to use, I must admit. If you haven't tried it yet, I recommend giving it a shot.

However, it's still a VSCode fork in early stages. It feels "sluggish" and still has many bugs, especially during long sessions.

It's great for experimenting, but for day-to-day work, I prefer a more stable solution.

💡Tip: Recently, GitHub Copilot has added a free plan, obviously with limitations. If you haven't tried Copilot before, now's a good opportunity to do so.

🔑 1Password

1Password is the most comprehensive password manager I've tried. Its versatility goes beyond just storing passwords by accepting a wide variety of items to always have them at hand. Ever had to go look for your passport just to see and copy the number? I have.

Key features:

Full PassKeys support
Windows Hello integration
SSH key management with OpenSSH replacement
Secure storage for sensitive documents
Password generator and review with Watchtower

What I like most is its developer-focused approach. It allows you to store database access, SSH keys, and server credentials. By replacing OpenSSH with its own agent, you can easily connect to remote servers.

🌎 Kagi

Kagi is a search engine, like Google or Bing, but with a different approach: Showing relevant results (sounds ironic, doesn't it?). Currently, the top results are usually ads and promoted sites, not to mention that search tools might not be very user-friendly.

I've been testing Kagi for a while, and it's really a productivity boost by helping me find what I actually want quickly. Plus, it's highly customizable.

Strong points:

Highly accurate results without ads
Intuitive advanced search tools
Customization of result sources
Focus on privacy and user experience

It also includes AI features like Global Summarizer to give you a website summary (works with YouTube videos too), ask questions based on a blog post, or translate sites.

If you're still not clear on why pay for search, they explain it better.

Conclusion

There are thousands of tools and services to cover our needs, and many of them are free. It's worth analyzing which tasks are the most important in your routine and identifying which services can really give you a "Power Up".

Investing in tools is necessary. You're investing in time, and time is invaluable...

Which services do you think are worth paying for?

My Terminal Setup for 2025 🚀

José D. Gutiérrez — Tue, 14 Jan 2025 18:37:42 +0000

Here I show you how I have configured my terminal to start 2025

Key Features

🎨 Is Cool
📁 Relevant Information about the current project or environment
🤖 Auto-completion for tools (git, aws, kubernetes, etc)
🚀 Fast

Disclaimer: This setup is specifically designed for my requirements and use cases, but I'm sure you'll be able to get some use out of it.

🪟 Windows Terminal

Windows Terminal has evolved a lot, nothing to do with the old CMD. It makes a very good match when using WSL as a development environment.

It is included in recent versions of Windows, if you don't have it, it is also available from the store.

Why?

Native integration with WSL
Support for multiple tabs and panels
Customization
Ease of use
No a Mac User

Tip: Looks great with JetBrains Nerd Font

🎨 Dracula Theme

I've always been a fan of this project. Although this is a matter of taste, Dracula Theme is the one that offers me the most consistency in having a theme for a wide variety of tools. Many of them are part of my daily life.

Its color schemes are designed to reduce eye fatigue during long coding sessions.

Key Features

Optimized contrast for long readings
Visual consistency between different tools
Color palette that improves code readability

More information on their website: draculatheme.com

🚀 ZSH + Oh My ZSH

ZSH and Oh My ZSH offer superpowers to your terminal thanks to its customization and wide variety of plugins.

I have tried other alternatives like Fish, but I didn't feel comfortable.

My essential plugins:

git: Aliases for Git
zsh-autosuggestions: History-based suggestions
zsh-syntax-highlighting: Real-time syntax highlighting
aws: Autocompletion for AWS commands
terraform: Autocompletion for Terraform commands
kubectl: Aliases and completion for Kubernetes

I recommend the first three for general use, no matter what. The rest are for specific use of other tools.

⭐ Starship

The default prompt is, meh...

Starship isn't just a pretty prompt. It's an informative interface that shows you exactly what you need to know at any given moment.

It is very extensible and configurable. Here is mine, from Gist

Key Features

Contextual information from Git, AWS, Kubernetes, or other tools you use
Exceptional performance
Single-file configuration

More instructions on their official site

💡 Conclusion

This isn't a perfect setup, or even a final version. It works perfectly for my current needs but the ideal terminal is one that adapts to your workflow, not the other way around.

Whether you use Linux or Mac, a list of different plugins, or prefer other tools, the key is to build an environment that boosts your productivity. Every developer has their own style.

Do you have a setup that works especially well for you? Did you discover a new tool that improve your workflow? Share your experience.

I'll keep improving this list. I hope you do too.