Esto es lo que hace un programador moderno en 2026

Introduction to AI Tools for Programmers

The Evolution of Programming Tools

• The speaker emphasizes that programmers using only Visual Studio Code and basic tools like Chat GPT are falling behind as new, more advanced coding generation programs emerge.

• A roadmap is presented to summarize the various AI tools available for programmers, explaining their evolution from simple autocompletion features to sophisticated AI agents capable of generating complete code.

• The focus is on programming tools rather than "no-code" solutions, highlighting the need for technical knowledge among programmers compared to those using simpler applications.

Current Landscape of AI in Coding

• Programmers often face challenges when launching projects, such as finding a suitable domain name; the speaker recommends using Don Dominio for quick domain availability checks and professional email setup.

• A brief overview of traditional development practices is provided, illustrating how coding was done manually with editors like Visual Studio Code before the advent of intelligent autocompletion.

Impact of Intelligent Autocompletion

Transition from Manual Coding to AI Assistance

• The introduction of intelligent autocompletion marked a significant shift in programming workflows, with GitHub Copilot being one of the first notable services offering this feature.

• While initial versions focused on simple code completion, newer alternatives have emerged that provide more comprehensive support beyond just filling in code blocks.

Limitations and Advancements

• Autocompletion alone has become less impressive due to advancements in chat-based AI tools like Chat GPT, which offer more robust capabilities than mere code suggestions.

• Many developers initially combined Chat GPT with Visual Studio Code and basic autocompletion but found limitations in functionality until plugins integrated these technologies directly into their workflow.

The Future of Programming with AI

Open Source Innovations

• The discussion hints at upcoming changes driven by forks of Visual Studio Code that leverage open-source technology to enhance coding experiences further.

Cursor: The AI-Powered Code Editor

Introduction to Cursor

• Cursor is an AI code editor built on the open-source foundation of VS Code, enhancing it with intelligent features.

• It offers a user experience that significantly differs from traditional coding environments, making development more comfortable and efficient.

Features of Cursor

• Unlike previous tools, Cursor provides a complete workflow for project creation, including file execution and management.

• The term "agent" refers to its ability to execute commands and manipulate files directly within the system.

Market Positioning

• Despite updates to VS Code, Cursor has captured a significant market share due to its advanced capabilities compared to GitHub Copilot.

• The popularity of Cursor has led to the emergence of other VS Code forks offering similar functionalities.

Competing Editors in the Market

Direct Competitors

• Winsor is highlighted as a direct competitor; it also builds on VS Code but operates as a proprietary service with intelligent editing features.

Alternative Options

• Trae, developed by TikTok's creators, focuses on providing an integrated IDE experience where AI handles most tasks autonomously.

• Kiro, backed by AWS, introduces unique planning capabilities alongside standard coding features.

Unique Offerings

• Set stands out as it is built from scratch using native languages like Rust rather than being a fork of VS Code. This results in better performance and speed without compatibility issues with extensions.

The Evolution of Coding Editors

Trends in Development Tools

• Many new editors are emerging that either offer lower prices or distinct experiences while maintaining core functionalities similar to existing editors.

Conclusion on Editor Functionality

• These alternatives aim at providing comparable coding experiences while integrating smart features that enhance productivity across various platforms.

Understanding AI Models in Code Editors

The Role of Intelligent Models in Code Editors

• The utility of code editors stems from the intelligent models they employ, which enhance features like smart autocompletion and AI capabilities.

• Visual Studio Code utilizes GitHub Copilot, an AI model based on GPT Codex, developed with significant investment from Microsoft in OpenAI's technology.

• Other competitors have emerged alongside GPT Codex, such as Cloudsonet and Antropic, each offering their own intelligent models for coding assistance.

Competition Among AI Coding Models

• Google has introduced its own coding model called Gemini, competing with existing models like Cloudsonet and Grog by X.

• Some code editors are backed by investments in these AI models; for instance, Kiro has invested heavily in Antropic to access Cloudsonet early.

• Winsorf was a paid editor whose developers were hired by Google to work on Gemini, leading to the creation of Antigravity—a new editor leveraging Google's resources.

Features and Accessibility of New Tools

• Antigravity offers a free plan with ample tokens for project use, making it attractive compared to other tools due to its budget backing from Google.

• The relationship between Winsorf and Antigravity is notable since both share development teams despite Winsorf continuing as a separate brand.

Distinction Between User Interfaces

• Beyond visual editors, there are terminal-based tools like Cloudsonet that focus solely on coding without graphical interfaces.

• Cloudsonet is considered superior for writing code across various languages due to its dedicated design for this purpose rather than multi-functional capabilities.

Minimalist Approach of Terminal Tools

• Unlike other companies that create full-fledged editors, Antropic developed a minimal console tool called cloud code that interacts directly with their model without GUI elements.

• This approach categorizes tools into Graphical User Interface (GUI) and Terminal User Interface (TUI), highlighting the versatility available for users depending on their preferences.

Cloud Code and Terminal Tools Overview

Introduction to Cloud Code

• Cloud Code is a versatile tool that can generate various types of programs, including web applications and database systems. Its popularity significantly contributes to the company's revenue.

Efficiency of Cloud Code

• The tool minimizes manual coding by automating file creation and navigation, allowing users to focus on larger contexts without resource-heavy editors.

Functionality Without GUI

• Cloud Code operates directly through a terminal interface, eliminating the need for graphical tools while still generating necessary files efficiently.

Competitors in Terminal Tools

• Google offers its own terminal tool called Gemini Clean, which utilizes the Gemini model, positioning it as a competitor to Cloud Code.

• OpenAI's Codex also provides a terminal tool that competes with Cloud Code. Additionally, there are open-source alternatives like Open Code that allow users to choose models behind their operations.

Features of Open Code

• Open Code stands out for its user-friendly interface and speed. It supports multiple sessions within the same terminal and integrates well with code editors designed for terminal use.

Popularity of Terminal Editors

• While several code editors have terminal versions (e.g., Gemini Cly), they tend to be less popular compared to those with graphical interfaces due to user preferences.

Importance of Quality Terminals

• A good terminal is essential for executing numerous programs effectively. Windows Terminal, Linux emulators, and Mac options are available; Warp is highlighted as an innovative choice offering advanced features.

Features of Warp Terminal

• Warp includes functionalities such as session multiplexing and native performance optimization. It integrates AI models similar to code editors while allowing direct code editing within the terminal environment.

Conclusion on Terminal Ecosystem

• Warp has gained traction despite being a paid service because it combines essential features into one ecosystem. The free plan allows basic usage while premium features enhance functionality further.

Terminal Emulators and AI Integration

Popular Terminal Emulators

• Several terminal emulators have gained popularity, including Kitty, Alacrity, and Westernm. These tools allow users to execute commands in a highly configurable environment with GPU support for accelerated outputs.

• Ghosty is highlighted as another popular open-source terminal emulator that can be installed on Linux and Mac (Windows version not available yet). It offers excellent performance and configurability.

• Ghosty stands out due to its innovative features and ease of configuration, allowing users to manage their settings through Git repositories.

Role of Terminal Emulators in AI Development

• Terminal emulators serve as the environment where AI agents operate, particularly in terminal format. They are essential tools for developing programs utilizing AI technologies.

• The discussion shifts from specific tools to broader techniques and protocols used in AI integration across various environments.

Understanding Model Context Protocol (MCP)

• The Model Context Protocol (MCP) is introduced as a key protocol facilitating communication between AI models and external services. It simplifies interactions by allowing any interface—terminal or graphical—to connect with external APIs.

• MCP enables seamless data retrieval from databases like Postgres or file management on platforms such as Google Drive without manual coding efforts.

Practical Applications of MCP

• Various applications can utilize MCP for tasks such as querying databases or managing files across different services like Slack, Gmail, Notion, etc., showcasing its versatility.

• Companies develop their own MCP implementations for specific services (e.g., Google Drive), while open-source alternatives exist for broader compatibility with other systems.

Installation and Usage of MCP

• Installing an MCP typically involves downloading code from repositories; however, it can often be done with a single command depending on the tool being used.

• For instance, integrating CloudCode with PayPal allows users to query sales data directly through the AI model's capabilities using simple commands.

Expanding Connectivity Through MCP

• Numerous MSP options exist for various services like Stripe or Sentry. This connectivity enhances automation by enabling the AI to create workflows without user intervention.

• Users are encouraged to refer to documentation when connecting services via MCP to ensure security and reliability in their implementations.

Understanding MCPs and Their Role in AI Development

Introduction to MCPs

• The discussion begins with concerns about third-party developers potentially injecting malicious code into open-source projects, highlighting the need for reliable tools.

• Contex 7 is introduced as an MCP (Modular Context Provider) that helps AI access modern code, particularly useful for frameworks like Next.js which frequently update.

Functionality of MCPs

• Contex S updates the context for AI, allowing it to provide current solutions without needing to search online or rely on outdated documentation.

• Other MCPs like Playbright enable AI to manipulate web browsers directly, bridging traditional projects with AI capabilities.

Practical Applications of MCPs

• Playbright allows automated browser control through code execution by the AI, enhancing its functionality significantly.

• Notion MCP is mentioned as a tool that integrates with Notion to create tasks and retrieve data, showcasing how various services can be enhanced using specific MCP tools.

Standardization and Popularity of MCPs

• The concept of MCP was initially developed by Antropic but has since become a standard across many companies due to its effectiveness.

• The conversation shifts towards other extensions such as skills and plugins that enhance functionalities within different agents.

Skills and Enhanced Interactions

• Skills in CloudC are described as markdown-based tools that allow users to request specific actions from the AI more efficiently than typing out detailed instructions each time.

• An example is provided where a skill improves interface generation in Cloud Code by incorporating design rules automatically, leading to better outputs without repetitive adjustments.

Conclusion on Features and Capabilities

• The discussion concludes with mentions of commands and hooks within these systems that further expand their capabilities, emphasizing the growing complexity and utility of these tools.

Open Code and Its Capabilities

Multi-Session Functionality

• Open Code allows for multiple sessions within the same agent, enabling users to delegate various tasks efficiently.

Subagent Feature

• The subagent feature permits the execution of several Cloud Codes simultaneously, each handling different responsibilities such as testing, design, or error checking.

Practical Application vs. Theory

• While theoretically beneficial to have a team of intelligent agents, practical usage may not fully realize this potential; users are encouraged to test these features themselves.

Configuration Files for AI Agents

Importance of Contextual Files

• All agents utilize a specific file (e.g., cloud.md in Cloud Code) that outlines instructions on how they should operate within a project.

File Naming Conventions

• Different agents have distinct naming conventions for their configuration files:

• Cloud Code: cloud.md

• Gemini: gemini.md

• Codex: agent.md

Integration with Development Tools

Rules Configuration Across Editors

• Each code editor requires unique configurations for AI agents; understanding these rules is essential for effective integration.

Unified Configuration Proposal

• A project named .m proposes a single configuration file (ace.md) that can be read by multiple tools, simplifying the setup process across different environments.

Enhancing Web Interaction with AI

Use of lls.txt File

• The lls.txt file serves as a summary document for web pages, aiding AI in understanding site content without parsing complex HTML structures.

Benefits of Simplified Formats

• By providing information in simpler formats like markdown or text files, developers can enhance the context available to AI when it interacts with their websites.

Conclusion on Standards and Suggestions

Suggested Standards for Developers

• It is recommended to adopt standards like agens.md alongside other configuration files to streamline interactions between AI models and development environments.

Considerations for Programmers in Modern Development

Key Considerations for Programmers

• The speaker emphasizes the importance of understanding various platforms available for programmers, particularly those that require coding knowledge.

• Other platforms like no-code and low-code solutions are mentioned, which cater to users without technical skills but are not the focus of this discussion.

• The speaker notes that while these no-code tools can create interfaces quickly, they may limit scalability and deeper system development capabilities.

Advantages of Coding Platforms

• Using traditional coding environments allows developers to manipulate databases and connect with external services effectively.

• The discussion includes code extensions for Visual Studio Code (VS Code), such as Killer Code, which enhance the coding experience by integrating AI models directly into the editor.

Integration of Extensions in Development

• Other similar projects like Kine also provide integration within VS Code, allowing users to leverage advanced features without relying solely on proprietary tools.

• Gemini Code Assist is another extension discussed that adds chat functionality within VS Code, enhancing user interaction with AI models during development.

Practical Use of Extensions

• While some developers prefer using lightweight editors over extensions, integrated tools can offer better error analysis and command execution speed.

• The speaker concludes by summarizing modern web development practices for programmers in 2026 and invites feedback from viewers regarding additional insights or resources.

Community Engagement

• Viewers are encouraged to leave comments or suggestions about missing elements in the discussion.

• Links to social media platforms and a Discord community are provided for further engagement with other developers.