02-12-2025 - 👤 Andrés Cruz

The Ultimate Guide to Artificial Intelligence for Developers: From Theory to Practice with Local Models and Modern Tools

Artificial intelligence has stopped being a science fiction concept and has become one of the most disruptive and transformative forces of our era, especially in the field of software development. For programmers, AI is not a threat coming to take our jobs, but a revolution that is redefining our tools, workflows, and, ultimately, our capabilities.

At DesarrolloLibre, we have closely followed this evolution, not from a theoretical perspective, but from the trenches of daily development. This pillar guide is the result of that deep dive.

This is not a manual on machine learning algorithms, but a practical guide for the modern developer who seeks to understand and leverage the power of AI. Here, we will explore the real impact of AI on the job market, demystifying the panic and focusing on the skills that truly matter. We'll teach you how to use AI as an efficient "copilot," how to write prompts that generate useful code, and how to avoid the pitfalls of "magic code."

We will immerse ourselves in the exciting world of Local Language Models (LLMs), showing you step-by-step how to set up your own AI ecosystem with tools like Ollama and Jan.ai. And finally, we will analyze the Google tools ecosystem, such as Gemini CLI and MCPs, which are designed to integrate directly into our workflow.

Prepare for a journey that will transform you from a passive AI consumer into an active architect of intelligent solutions.

Section 1: AI and the Future of Software Development

The rise of artificial intelligence has unleashed a wave of speculation and anxiety in the tech industry. Are our jobs at risk? Is it still worth learning to code? This section tackles these questions head-on, analyzing the true impact of AI on the job market for developers, why Python has become the language of this revolution, and how the landscape is changing for junior and senior profiles.

The Debate: Will AI Leave Us Without Work?

The popular narrative often paints AI as an imminent replacement for programmers. However, the reality is much more nuanced. AI is not an autonomous programmer; it's an extremely powerful productivity tool. It won't replace us, but it will radically change the nature of our work.

Senior Developers vs. Junior Developers

My opinion, forged in daily practice, is that AI is not going to put the experienced developer out of a job, at least not in the short or medium term. A senior developer doesn't just write code; they design architectures, solve complex problems, debug systems, and understand the business. AI, in its current state, cannot do any of that. It acts as an incredibly fast assistant, but without direction, guidance, and validation, its output is, at best, unreliable.

However, the situation for junior profiles is different and more precarious. The tasks traditionally assigned to entry-level developers—creating simple UI components, writing basic scripts, implementing well-defined functions—are precisely the tasks in which AI excels. A senior developer, equipped with a tool like GitHub Copilot or Gemini, can generate this kind of code in seconds, reducing the need to delegate it.

Before, if I worked alone, I would have appreciated someone to help me with code or writing. But now, AI covers that need in seconds and, often, with better results. The biggest impact, then, falls on entry-level positions.

This does not mean that junior positions will disappear, but that the bar for entering the industry has been raised. Knowing the basics is no longer enough; new developers are expected to bring value that goes beyond what an AI can automatically generate: critical thinking, the capacity for learning, and a deeper understanding of systems.

The Industry Perspective: Not Replacing, but Elevating

Industry leaders, such as Amazon's cloud director, have called the idea of replacing junior employees with AI "one of the dumbest things they've ever heard." The logic is simple: if you eliminate entry-level positions, where will the senior developers of the future come from? Who will gain the necessary experience to lead complex projects in 10 years?

The consensus is that AI is not a substitute for human talent, but an enhancer. The most valuable skills in this new era are not the mechanical ones, but the cognitive ones: critical reasoning, creativity, and, above all, the ability to adapt to a constantly evolving technology. The future belongs not to those who compete with AI, but to those who learn to collaborate with it.

Delve into this vision in: Replacing JUNIOR employees with AI is "one of the dumbest things I've heard".

Python: The Language Driving the AI Revolution

It's impossible to talk about the rise of artificial intelligence without talking about Python. Although it's a language with more than 30 years of history, its popularity has exploded, positioning itself as the most used programming language in the world. The main reason for this dominance is its perfect symbiosis with the AI and Machine Learning ecosystem.

Why Python?

Simplicity and Readability: Python's clean and expressive syntax allows researchers and developers to focus on algorithms and models, not on the complexities of the language. Writing code to manipulate data or define a neural network feels natural.
Vast Library Ecosystem: Python has an arsenal of specialized libraries that have become the industry standard:
- NumPy and Pandas: For high-performance data manipulation and numerical computation.
- Scikit-learn: For classic machine learning algorithms.
- TensorFlow and PyTorch: The two deep learning giants, which allow building and training complex neural networks.
- Keras: A high-level API that simplifies the prototyping of neural networks.
Community and Support: Python's immense community has created an overwhelming amount of tutorials, courses, and help forums, which greatly facilitates the learning curve.

Python vs. Other Languages like C++

Low-level languages like C++ remain crucial for applications where performance is the highest priority, such as in video game engines. C++ offers granular control over memory and unmatched execution speed. However, this power comes with a much steeper learning curve and a greater propensity for errors.

Python, being a higher-level language, democratizes access to programming. It abstracts complexity, allowing developers to be productive much faster. In the context of AI, where the speed of experimentation is key, this is a decisive advantage.

Explore Python's dominance in the AI era in: The Unstoppable Advance of Python, Python and Artificial Intelligence and Python Democratizes Access to Programming.

Section 2: Using AI as a Programming Tool

AI is not a magic solution that writes perfect applications with a single click. It's a tool, a "copilot," and like any tool, its effectiveness depends on the artisan's skill. In this section, we will learn the best practices for collaborating with AI, how to formulate prompts that generate useful code, and how to evaluate the quality of what it returns to us.

Principles for Correct and Responsible Use of AI

The biggest mistake a developer can make is treating AI as an infallible black box. Believing that a complete application can be built with a single prompt, without supervision and without understanding the code, is a recipe for disaster.

AI is an Assistant, not a Substitute

The name "GitHub Copilot" is revealing: AI is your copilot, but you are the pilot. The final responsibility for the code is always yours. This implies:

Don't blindly accept what the model produces: You must always review, supervise, and adjust the generated code. Make sure it complies with your project's conventions, that it's readable, and that it makes sense in the context of your application.
Prioritize maintainability: AI can generate code that "works" but is hell to maintain. It's your job to refactor it so that it is scalable and reusable.
Verify the quality and currency of the code: AI models are trained on enormous amounts of code from the internet, including obsolete, insecure, or poorly practiced code. It is your duty as a developer to filter and correct this.

In summary: I don't care how the code got to your IDE, but I want you to care. The quality, security, and maintainability of the software remain human responsibility.

Adopt a mindset of responsible collaboration with AI by reading: Correct Use vs. Incorrect Use of AI, How NOT to use AI when programming and I don't care how the code got to your IDE, but I want you to care.

The Art of Writing Effective AI Prompts

The quality of an AI's response is directly proportional to the quality of the prompt you give it. A "vague prompt" will generate a vague and useless response. A well-designed prompt is the key to unlocking the true potential of your AI assistant.

Ten Tricks for Better Prompts

Be Concrete and Provide Context: Instead of "make a note app," specify: "Create a Pydantic model for a 'Note' that has an id (integer), a title (string, maximum 50 characters), content (string, optional), and a creation date (datetime)."
Work in Small Iterations: Don't ask for a complete application. Ask for a function, a class, a unit test. "Add a method to this class to validate the password, ensuring it has at least 8 characters and one number."
Define the Output Format: Be explicit about what you want. "Give me the result in JSON format," "Write a Python class," "Generate a list of steps."
Use Examples: "A code snippet is worth a thousand words". Pass the AI an example of your own code and ask it to adapt it or generate something similar following the same style. "I have this class in Laravel, translate it to an equivalent class in Django."
Assign a Role: "Act as an expert Python developer and review this code for possible performance improvements."
Provide Conversation History: Keep the conversation in the same thread so the AI has context of what you've already discussed.
Limit Length: "Give me a concise answer," "Explain it in less than 100 words."
Use Separators: Use triple quotes (`"""`), XML, or Markdown to clearly separate the instructions from the context or code examples.
Ask Open-Ended Questions (with caution): "Give me ideas to optimize this function" can be useful for brainstorming.
Tune and Refine: If the first response isn't ideal, don't give up. Tell the AI what was wrong and how to improve it: "That's good, but now make sure it handles the case where the user is null."

Mastering "prompt engineering" is a new fundamental skill for the modern developer.

Become a master of prompts with our guide: Ten Tricks for Writing Better AI Prompts and A Code Snippet is Worth More Than a Thousand Words.

Evaluating AI-Generated Code with CodeSpy

Given that AI-generated code can be problematic, how can we verify its origin? Tools like CodeSpy.AI are emerging to address this need.

CodeSpy.AI is a tool that analyzes a code snippet and, with high precision, determines the probability that it was generated by an AI. This is useful in several scenarios:

Education: For teachers to verify that students are writing their own code.
Software Clients: For companies to audit the quality of code they receive from vendors.

-Self-evaluation: For developers to measure how much of their own work is being assisted by AI and reflect on their dependence.

Although these tools are useful, the final validation always rests with a human developer who can evaluate not only the origin but also the quality, security, and maintainability of the code.

Learn more about this tool in: CodeSpy, your tool for detecting AI-generated code.

Section 3: AI Tool Ecosystem for Developers

The true AI revolution for developers is not just in using cloud APIs, but in the ability to execute and control our own models and tools. This section is a practical guide to building your own local AI ecosystem, using open-source tools like Jan and exploring Google's solutions designed specifically for the programmer's workflow, such as Gemini CLI and its extensions.

Running Language Models (LLMs) Locally with Jan.ai and LM Studio

Running an LLM on your own machine offers you two crucial advantages: absolute privacy (your data never leaves your computer) and economic independence (no subscriptions or usage costs). Tools like Jan.ai (an open-source alternative to LM Studio or Ollama) simplify this process.

What You Need

Decent Hardware: Although you can run small models on almost any machine, for larger models and acceptable performance, a dedicated GPU (NVIDIA is best supported) with at least 8GB of VRAM and 16GB of RAM is recommended.
Jan.ai: Download the application from its official website (jan.ai).

Setup Steps

Install Jan.ai: It's a desktop application for Windows, macOS, and Linux.
Explore and Download Models: From the application's "Hub," you can search and download a wide variety of open-source models in GGUF format. Some recommended ones to start with are:
- Mistral 7B: An excellent and lightweight model.
- LLaMA 3 8B: Meta's latest generation of very powerful models.
- Code Llama: A model specialized in code generation and understanding.
Start the Local Server: Jan.ai is not only a chat interface; it can also start a local server compatible with the OpenAI API. This is incredibly powerful, as it allows you to make requests to your local model from your own code (for example, from a Flask application) as if you were calling the OpenAI API.

 # Example of a Flask app consuming the local Jan server import requests def query_local_llm(prompt): response = requests.post( "http://localhost:1337/v1/chat/completions", headers={"Content-Type": "application/json"}, json={ "model": "mistral-7b-instruct-v0.2.Q4_K_M.gguf", "messages": [{"role": "user", "content": prompt}] } ) return response.json()['choices'][0]['message']['content']

Having an LLM running locally gives you unprecedented control and flexibility to integrate AI into your projects in a secure and personalized way.

Follow our complete guide to setting up your own AI server in: Install, Configure, and Consume your LLMs Locally with FREE Jan AI.

Google Gemini for Developers: CLI vs. Agent

Google offers two main tools for developers to interact with its Gemini model: Gemini Agent for VS Code and Gemini CLI for the terminal. Both are powerful, but they are designed for different use cases.

Gemini Agent for VS Code

It is an extension that integrates directly into your editor, similar to GitHub Copilot. It allows you to:

Contextual Chat: Select a code snippet and ask questions about it (`Ctrl+I`).
Inline Code Generation: Write a comment and have the AI generate the corresponding code.
Explanations and Refactoring: Ask it to explain a function or suggest improvements.

It is ideal for coding assistance at the moment, within your regular workflow.

Gemini CLI (Command Line Interface)

This is a terminal tool that allows you to interact with Gemini and its capabilities in a more programmatic and automated way. It's not just about chatting, but about using Gemini as part of a script or a larger process.

It allows you to:

Run prompts from the terminal: `gemini "Summarize this text: ..."`
Interact with local tools and data: Thanks to Model Context Protocols (MCPs), Gemini CLI can read files, search your code, or even connect to databases.
Automation: Integrate Gemini into shell scripts to, for example, classify files, generate log summaries, or create Git commits.

In summary: use Gemini Agent for "in-code" assistance and Gemini CLI for automation and "out-of-code" interaction.

Compare both tools in: Gemini Agent VS Gemini CLI: Which is better for programming? and learn to install the CLI in: Gemini CLI step by step.

Extending Gemini: MCPs and Personalized Assistants ("Gems")

The true power of Gemini CLI is unlocked by extending its capabilities.

Model Context Protocols (MCPs)

MCPs are "bridges" that allow Gemini to interact with external tools and data. You can install MCPs so that Gemini can:

Query a MySQL database: You can ask it "give me the 5 most recent users from the 'users' table."
Search your codebase: "Find the function where the 'User' class is defined in my project."

-Interact with external APIs.

You can install these MCPs in your local environment, giving you total control over how the model accesses your data, a key advantage in terms of privacy and security.

Learn how to configure your first MCP in: Configure your first MySQL MCP in Gemini CLI.

Personalized Assistants: Gemini "Gems"

Similar to OpenAI's custom GPTs, Google allows you to create "Gems": personalized versions of Gemini with specific instructions for a task. The great advantage is that, unlike GPTs, creating and using Gems is completely free.

You can create a Gem to act as:

An expert in a specific framework.
A writing assistant that always uses a specific tone and style.
A travel planner that always asks about your budget and preferences.

The setup is simple: you give it a name, an icon, and, most importantly, a set of detailed instructions about its personality and the task it should perform.

Create your first personalized assistant with our guide: Create your GEM in Gemini: Personalized AI Assistant.

Section 4: The Impact Beyond Code

Artificial intelligence is not only changing the way we program; it is reshaping entire industries and altering the nature of many professions. Understanding this broader context helps us better position ourselves as professionals and anticipate the skills that will be most valuable in the future.

Professions in Transformation

A recurring analysis, both from experts and from the AIs themselves, points out that the professions most vulnerable to disruption are those based on repetitive, predictable tasks that can be reduced to a "script."

Among the careers frequently mentioned are:

Interpreters and Translators: Tools like Google Translate and increasingly sophisticated language models can perform high-quality translations in seconds.
Basic Data Analysis and Reporting: AIs are extremely efficient at processing large volumes of data and generating summaries, tables, and graphs.
Customer Service and Sales Representatives (Level 1): Responses to frequently asked questions can be easily automated with a chatbot trained on the company's knowledge base.
Basic Content Creation: Tasks like writing product descriptions, marketing emails, or article summaries can be accelerated or automated.
Data Entry: One of the most repetitive tasks and, therefore, one of the easiest to automate.

It is crucial to understand that this does not mean the "disappearance" of these professions, but a transformation. For example, a translator can move from manually translating to supervising and correcting an AI's translation, a role that requires a higher level of judgment and cultural knowledge. The focus shifts from mechanical execution to strategic supervision.

Explore the full list and analysis in: The 10 Professions that Artificial Intelligence May Replace and These are the five university careers that AI and experts do not recommend studying.

Conclusion: Navigating the New Era of AI-Assisted Development

We have covered a complete overview of artificial intelligence from the perspective of a software developer. We have demystified the existential fear about the end of our profession, to instead embrace a more pragmatic and powerful vision: AI as the ultimate copilot. We learned that value no longer lies in writing repetitive code, but in architecture, critical thinking, and the ability to guide and validate these new and powerful tools.

We delved into practice, learning to formulate prompts that work, to evaluate the quality of the generated code, and, most importantly, to build our own local AI ecosystem with tools like Jan.ai, giving us unprecedented control and privacy. We explored the tools that Google offers us, such as Gemini CLI and its extensions, designed to integrate seamlessly into our workflow.

The era of the "only" developer is giving way to the era of the "augmented" developer. The skills that will define us in the future will not be those of typing faster, but those of thinking better, asking more intelligent questions, and collaborating more effectively with our silicon assistants. This guide is your starting point. The journey to master this new frontier of development has just begun.