What is Vibecoding?

Vibecoding is everywhere, whether on social media channels, YouTube, or LinkedIn. Job boards are also increasingly offering jobs in the field of Vibe or AI Coding.

Vibecoding in a nutshell

In short, Vibe Programming/Coding is an interesting way to build code with AI, where you simply describe what you want and the AI handles the rest. As an application developer, I use it daily to prototype faster. My last project was with Streamlit, a Python framework—in just a few minutes I was able to build a functioning prototype that already had many features implemented. The company could immediately see what the end goal was and quickly approved the project. In the end, I implemented it with FastAPI and React, and here too I used AI to integrate existing projects.

However, even though it’s as simple as it sounds, it carries many risks if you’ve never programmed properly before. Without planning, the AI may create too many methods and functions, especially if the application has the same workflows multiple times. This makes the software difficult to maintain, but you can be careful to prevent this. Many pitfalls are often only noticed if you have knowledge of them.

Basically, 90% of AI/AI applications want to make you happy quickly and cheaply. For that, it just needs to look good on the outside; the code doesn’t necessarily have to be good or secure. When you write code yourself, it can happen that the code gets touched and changed during debugging.

In the end, you get ripped off and end up with buggy software that can only be maintained with great difficulty or not at all.

Therefore, here’s a small guide on how to avoid many pitfalls:

My Compact Vibecoding Guide

What is Vibe Programming?

Vibe Programming means you provide the AI with natural language instructions, like “build me a Todo app with React,” and it spits out finished code. You then iterate through feedback without typing every line yourself. The term comes from Andrej Karpathy and revolves around rapid prototyping rather than perfect code.

You stay in the flow, test, and say make that better until it works. It makes coding accessible, even for non-professionals.

Different Users and Types of Integration (CLI or IDE)

The market is currently divided among a few good providers.

Differences in IDE Coding with User Tools

Yes, there are clear differences between tools like Windsurf AI, Cursor AI, Replit AI, and Claude Code, Microsoft Copilot, and GitHub CoPilot.

Windsurf and Cursor are AI IDEs based on VS Code with Cascade agents for multi-file edits and autocomplete.

Claude Code runs as CLI, console, or web, integrates into IDEs, and is great for terminal tasks.

Replit AI is web-based with agents for quick apps.

IDE tools feel like normal coding, CLI and web are more flexible for remote work.

Comparison of the Most Important Tools

Tool	Type	Strengths	Weaknesses
Windsurf AI	IDE	Multi-file, Debugging	More expensive ($15 per month)
Cursor AI	IDE	Context understanding	Model switching needed
Replit AI	Web, IDE	Quick deployment	Less local
Claude Code	CLI, Web, IDE	Agentic, Opus models	Learning curve

Best Practices with Models

Claude Code: always use /plan or /think step for overview before code comes. Important files are README.md for context, package.json, main app file, and tests.

Perfect Setup

1. Define persona: you are a Senior React Dev
2. State the problem clearly
3. Give context: tech stack
4. Request a plan
5. Iterate

Best Prompt

Plan an app with features. Tech: React, Node. Create structure, then code. Test everything. Break small tasks down, use checkpoints. With Claude, use /init for setup.

Costs for Small Projects

A small project like a web app costs $20 to $50 with Claude or Cursor, depending on model usage—for example, Opus is more expensive. Monthly subscription: €10 to €20 for basics, €100 or more for intensive use. Watch out for token limits, choose cheap models like SWE 1 Lite, and review code to avoid hallucinations. Track costs via dashboards.

Most Important Keywords

From research: Vibe Coding, Agentic Coding, Prompting, Cascade Agent, SWE bench, Checkpoints. These topics lead to different approaches for AI-assisted programming.

How do I Plan Prompts?

Planning prompts is crucial for successful vibecoding. Always start with a clear description of your goal, provide context about your tech stack, and define the desired outcomes. Iterate step by step and give precise feedback to the AI.

Agents vs. Vibe

Agents are autonomous AI systems that execute tasks independently, while Vibe Coding is more of an iterative process where you retain control. Agents can handle complex multi-step tasks, while Vibe Coding is better suited for rapid prototyping and creative coding.

Optimize Costs

To optimize costs with AI tools, use cheaper models for simple tasks and only switch to more expensive models for complex problems. Watch out for token limits and use checkpoints to avoid unnecessary repetitions. Many tools also offer free limits or trial versions.

Debugging Tips

When errors occur, describe the problem precisely and give the AI context about your code. Use the debugging features of your tool and let the AI analyze the error. Often it helps to reduce the code step by step to find the source of the error.

How do I Debug Code Best?

When debugging with vibecoding, you should proceed systematically. Start with a clear error description and provide the AI with the relevant code context. Describe not just the error message, but also the expected behavior and the steps that led to the error.

Use breakpoints and debugging features in your IDE to execute the code step by step. The AI can help you understand the logic and identify potential error sources. It’s often helpful to break the code into smaller parts and test them separately.

When the AI suggests a solution, test it thoroughly and provide feedback on whether it worked or if further adjustments are needed. This iterative process often leads to a solution faster than purely manual debugging.

Should I Outsource Debugging by Default and Include Log Outputs?

Yes, it’s recommended to outsource debug logic and build in log outputs from the start. This not only makes troubleshooting easier, but also maintenance and team collaboration.

Use a logging framework that supports different log levels, such as DEBUG, INFO, WARNING, and ERROR. This way you can adjust log output depending on the environment—for example, more details in development and less in production.

Outsource debug logic to separate functions or modules so you can easily enable or disable it without changing the main code. This makes your code cleaner and more maintainable.

Log outputs should be meaningful and not just contain the word “error,” but also context, such as what value a variable had and at what point in the code the error occurred.

What is a Logging Framework and How Do You Set It Up?

A Logging Framework is a library that helps you create structured and controlled log outputs. It offers various log levels, formatting options, and the ability to write logs to files or external services.

In FastAPI with Python:

FastAPI uses the Python Logging Framework by default. You can configure it like this:

import logging
from fastapi import FastAPI

# Logging konfigurieren
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    handlers=[
        logging.FileHandler('app.log'),
        logging.StreamHandler()
    ]
)

logger = logging.getLogger(__name__)

app = FastAPI()

@app.get("/")
async def root():
    logger.info("Root endpoint aufgerufen")
    return {"message": "Hello World"}

@app.get("/items/{item_id}")
async def read_item(item_id: int):
    logger.debug(f"Item ID: {item_id}")
    try:
        # Dein Code hier
        logger.info(f"Item {item_id} erfolgreich geladen")
        return {"item_id": item_id}
    except Exception as e:
        logger.error(f"Fehler beim Laden von Item {item_id}: {str(e)}", exc_info=True)
        raise

In Node.js with Express:

For Node.js there are various Logging Frameworks like Winston or Bunyan. Here’s an example with Winston:

const winston = require('winston');
const express = require('express');
const app = express();

// Logger konfigurieren
const logger = winston.createLogger({
  level: 'info',
  format: winston.format.combine(
    winston.format.timestamp(),
    winston.format.json()
  ),
  transports: [
    new winston.transports.File({ filename: 'error.log', level: 'error' }),
    new winston.transports.File({ filename: 'combined.log' })
  ]
});

// In Entwicklung auch Console Logs
if (process.env.NODE_ENV !== 'production') {
  logger.add(new winston.transports.Console({
    format: winston.format.simple()
  }));
}

app.get('/', (req, res) => {
  logger.info('Root endpoint aufgerufen');
  res.json({ message: 'Hello World' });
});

app.get('/items/:id', (req, res) => {
  const itemId = req.params.id;
  logger.debug(`Item ID: ${itemId}`);
  try {
    // Dein Code hier
    logger.info(`Item ${itemId} erfolgreich geladen`);
    res.json({ itemId });
  } catch (error) {
    logger.error(`Fehler beim Laden von Item ${itemId}: ${error.message}`, { stack: error.stack });
    res.status(500).json({ error: 'Internal Server Error' });
  }
});

The most important log levels are:

• DEBUG - Detailed information for diagnosis
• INFO - Confirmation that things are running as expected
• WARNING - Unexpected event, but it doesn’t cause problems
• ERROR - Error that occurred and prevented an operation
• CRITICAL - Serious error that terminates the application

With a Logging Framework you can adjust the log level depending on the environment, for example DEBUG in development and WARNING in production.

Code Preparation

Yes, you should create certain files and folder structures so everything goes according to plan and you can see the plan yourself. A good project structure is the key to success in Vibecoding.

Start with a clear project folder that contains the following standard files: README.md for documentation, package.json or requirements.txt for dependencies, a .gitignore file for Git configuration, and a LICENSE file for licensing.

Create a logical folder structure with src for source code, tests for unit tests, docs for documentation, and config for configuration files. This way you keep track and the AI can better understand the structure.

A project plan is also important. Create a file like PLAN.md or TODO.md where you document your goals, milestones, and current progress. This way you always see what needs to be done, and the AI can take the plan into account in their suggestions.

How Can I Focus on Security?

Security should be part of your development process from the start. Use AI tools not only for code generation, but also for security checks.

Explicitly ask the AI to check the generated code for security vulnerabilities, such as SQL injection, XSS, or buffer overflows. Many modern AI tools are able to identify and fix such issues.

Use static code analysis tools like ESLint, Pylint, or SonarQube that can automatically identify security problems. These can be well integrated into your development process.

Implement authentication and authorization according to best practices. Use established libraries instead of implementing roles yourself, as these are already tested and secure.

Keep your dependencies up to date, as security vulnerabilities are often found in older versions. Use dependency scanners to identify known security issues in your libraries.

What Does the Future Look Like for Application Developers?

The future of application developers will be heavily shaped by AI. Vibecoding and Agentic Coding will become standard tools that make developers more productive.

Developers will focus more on architecture, design, and problem-solving, while routine tasks like boilerplate code and simple implementations will increasingly be taken over by AI.

The role of the developer will shift from a pure programmer to an AI coordinator and architect. You will need to learn how to effectively guide AI and evaluate its results.

Skills like prompt engineering, system design, and architecture will become more important, while pure syntax knowledge will lose importance. The ability to translate complex problems into clear instructions for AI will become a core competency.

However, AI will not replace developers, but rather complement them. The human aspect, creativity, understanding of business requirements, and ethical decisions will continue to be important.

What Do I Need to Learn as a Developer to Be Well Prepared?

As a developer, you should focus on the following core areas to be well prepared for the future. Prompt engineering is one of the most important skills you should acquire. Learn how to give clear and precise instructions to AI to get optimal results.

Fundamentals of system architecture and software design are becoming increasingly important, as AI takes over routine tasks and you should focus more on the big picture. Understand design patterns, architectural principles, and how to design scalable systems.

Automation and DevOps knowledge are essential. Learn CI/CD pipelines, containerization with Docker, and orchestration with Kubernetes. These technologies will be standard in modern software development.

Database knowledge and SQL remain important. Even if AI can generate SQL queries, you should understand how databases work and how to write performant queries.

Which Skills Are Important?

The most important skills for the future are a mix of technical and soft skills. Creativity and problem-solving skills become increasingly important, as AI takes over routine tasks and you should focus on innovative solutions.

Communication and collaboration are essential. You must be able to explain complex technical concepts and collaborate effectively with other teams. AI can help you with coding, but not with communicating with stakeholders.

Critical thinking and the ability to evaluate AI outputs are essential. Not everything the AI generates is correct or optimal. You need to be able to review code and suggest improvements.

Adaptability and lifelong learning are necessary, as technology is developing rapidly. New tools and frameworks appear constantly, and you must be ready to continuously educate yourself.

How can I best prepare, what tools should I always have active on a test machine to educate myself?

The best preparation is practical experience. Set up a test machine or virtual machine where you can experiment without risking your production environment.

Install Docker and learn to create and manage containers. Containerization is a standard in modern development and will help you run applications consistently across different environments.

Set up a local development environment with Git and learn versioning thoroughly. Git is essential for collaboration and should be integrated into your daily workflow.

Use an IDE like VS Code equipped with AI extensions like GitHub Copilot or Cody. These tools help you become more productive while learning new techniques.

Experiment with different AI tools like Windsurf AI, Cursor, or Claude Code. Each tool has its strengths, and by trying them out, you’ll learn which one is best suited for your requirements.

Which official channels should I follow to get the most important information quickly enough?

Follow the official blogs and documentation of major AI providers like OpenAI, Anthropic, and Google. They regularly publish updates about new models and capabilities.

Subscribe to newsletters and blogs from leading tech companies like Microsoft, Google, and Amazon. They inform you about new developments in cloud computing, DevOps, and AI integration.

Follow developers and experts on LinkedIn and Twitter who specialize in AI and software development. Platforms like Hacker News and Reddit have active communities that discuss the latest trends.

Join Discord servers and Slack communities dedicated to vibecoding and AI development. These communities are often the first to share new tools and techniques.

Follow conferences and meetups focused on AI and software development. Many of these events are streamed online and offer valuable insights into current trends and best practices.

How important is coding in the field of cybersecurity?

Coding is extremely important in the field of cybersecurity. While AI tools can help analyze security vulnerabilities, deep understanding of programming is necessary to understand and fix security problems.

Secure coding requires knowledge of buffer overflows, SQL injection, XSS, and other attack vectors. You must understand how these attacks work to implement effective countermeasures.

Penetration testing and security audits often require custom scripts and tools that you need to write yourself. AI can help you, but you must understand the fundamentals.

Incident response and forensics require coding skills to analyze log files, reconstruct attacks, and develop automated response systems.

The cybersecurity landscape is constantly evolving, and new attack techniques emerge regularly. As a security developer, you must be able to quickly develop new tools and techniques to respond to threats.

Recommended books for further education

Here are some recommended books that will help you improve your skills in vibecoding and modern software development:

Typical pitfalls in programming

There are also typical pitfalls in vibe coding that you should avoid. One of the most common mistakes is trusting AI too quickly and not understanding what the code actually does. The AI can generate working code, but without your understanding, it will be difficult to fix errors or make changes.

When programming, there are many pitfalls you should avoid. One of the most common mistakes is poor planning. Many developers start coding immediately without analyzing the requirements first. This often leads to faulty code and wasted time.

Another common mistake is ignoring error handling. Many forget to catch exceptions or implement error messages. This makes debugging difficult and leads to poor user experience.

Security is often neglected, especially with quick prototypes. SQL injection, XSS, and other security vulnerabilities occur when developers don’t focus on security. Always validate user input and use parameterized queries.

Copying and pasting code from the internet without understanding it is another pitfall. You don’t understand the code and can’t fix errors if something doesn’t work. Learn the fundamentals before using code.

Poor documentation leads to problems when others or you yourself need to read the code later. Write clear comments and document important decisions.

Which programming languages should I learn in 2026?

Here are the top 10 programming languages for 2026 that offer you optimal career opportunities:

1. Python - Versatile for AI, data science, web development, and automation
2. JavaScript - Essential for web development, frontend and backend with Node.js
3. TypeScript - Type-safe alternative to JavaScript, growing strongly in enterprise
4. Rust - Systems language with focus on security and performance, rising strongly
5. Go - Easy to learn, ideal for cloud native and microservices
6. Java - Stable and widely used in enterprise development
7. C# - Strong in the Microsoft ecosystem, gaming, and enterprise
8. Swift - For iOS and macOS development, Apple ecosystem
9. Kotlin - Modern alternative to Java, Android and backend
10. SQL - Essential for databases, every language needs SQL knowledge

Which frameworks should I use for websites?

For websites, you should use the following frameworks to be optimally prepared for interfaces:

Frontend:

• React - The most popular frontend library, large community and many jobs
• Vue.js - Easier to learn than React, very performant
• Next.js - React framework with SSR and API routes, ideal for SEO
• Svelte - Modern alternative, more compact and faster than React

Backend:

• FastAPI - Modern Python framework, ideal for APIs with automatic documentation
• Express.js - Minimalist Node.js framework, very flexible
• Django - Complete Python framework with many features out of the box
• Spring Boot - Java framework, standard in enterprise development

Full Stack:

• Nuxt.js - Vue.js framework with SSR, similar to Next.js
• Remix - Modern React framework with focus on web standards
• Astro - Content-oriented framework, ideal for blogs and documentation

These frameworks have good interface support, large communities, and many job opportunities.

What do UML, class diagrams, use case diagrams, and sequence diagrams still accomplish?

Yes, they usually serve to make your problem visible to other people or AI. If your boss doesn’t know what you’re planning to do, AI will probably misunderstand it too. All these diagrams, even if old-school, form good architecture if you write them down correctly.

UML diagrams help you visualize and communicate complex systems. Class diagrams show the structure of your software, what classes exist, and how they’re connected. Use case diagrams describe which actors use which functions, which is particularly important for communicating with stakeholders.

Sequence diagrams visualize the flow of interactions between different components. They are particularly helpful for understanding temporal sequences and dependencies. Even though these diagrams are considered old-fashioned, they are extremely valuable for documentation and communication.

With vibecoding, it’s especially important to create these diagrams before working with AI. The AI needs clear context and understanding of the architecture to generate good code. When you visualize your architecture in UML diagrams, the AI can better understand it and generate consistent code.

Modern tools like Mermaid, PlantUML, or Draw.io make it easy to create these diagrams quickly. Many IDEs have integrated plugins that help you create diagrams directly in your code.

GitHub Copilot

GitHub Copilot is an AI-powered code assistant available as an extension for various IDEs. It suggests code completions and entire code blocks based on your context.

Copilot uses the Codex technology developed by OpenAI and was developed jointly by GitHub and Microsoft. It analyzes your code and comments to generate relevant suggestions.

The advantage of Copilot is that it integrates seamlessly into your workflow. You don’t have to switch between different tools; instead, you get suggestions directly while coding. Copilot learns from your coding style and adapts its suggestions accordingly.

For Vibecoding, Copilot is particularly useful because it helps you code faster and complete fewer repetitive tasks. However, it can also recognize complex code patterns and make corresponding suggestions.

Microsoft Copilot

Microsoft Copilot is a broader AI ecosystem that encompasses various applications and services. There is Copilot for Microsoft 365, Copilot for Windows, and specialized developer tools.

Copilot for Microsoft 365 helps you work with Office applications like Word, Excel, and PowerPoint. It can generate text, analyze data, and create presentations.

For developers, there is Copilot in Visual Studio and Azure, which helps with code development and cloud infrastructure. These tools are deeply integrated into the Microsoft ecosystem.

The connection between GitHub Copilot and Microsoft Copilot is that both are based on similar AI technology and were developed by Microsoft. GitHub Copilot is specialized for code, while Microsoft Copilot is a broader ecosystem for various applications.

Perplexity

Perplexity is an AI-powered search assistant that works differently from traditional search engines. Instead of simply providing links, Perplexity analyzes your question and generates a direct answer with source citations.

Perplexity uses various AI models and searches the internet in real time to find current and relevant information. It can also search specific sources such as academic papers or technical documentation.

For developers, Perplexity is particularly useful for answering technical questions, finding documentation, and understanding complex concepts. It can also provide code examples and help with debugging.

The advantage of Perplexity over traditional search engines is that it summarizes the information and gives you a direct answer instead of overwhelming you with a list of links. This saves time and helps you get to solutions faster.

What certificates in AI and Vibecoding would you recommend?

In the field of AI and Vibecoding, there are various certificates that help you validate your knowledge and make yourself attractive to employers.

Microsoft Certificates:

• AI-900 Microsoft Azure AI Fundamentals - Entry-level certificate for AI fundamentals on Azure
• DP-100 Designing and Implementing a Microsoft Azure AI Solution - Advanced certificate for AI solutions on Azure
• AZ-900 Microsoft Azure Fundamentals - Basic Azure certificate, important for Cloud AI

Google Certificates:

• TensorFlow Developer Certificate - Specialized in TensorFlow and Machine Learning
• Google Professional Machine Learning Engineer - Focus on ML Engineering on Google Cloud

AWS Certificates:

• AWS Certified Machine Learning Specialty - ML on AWS, highly sought after in the industry
• AWS Certified Solutions Architect - Cloud architecture, important for AI infrastructure

Specialized AI Certificates:

• IBM AI Engineering Professional Certificate - Comprehensive program for AI Engineering
• DeepLearning.AI TensorFlow Developer - Practical TensorFlow certificate
• Coursera AI for Everyone - Non-technical entry certificate for everyone

Cloud and DevOps Certificates:

• CKA Certified Kubernetes Administrator - Kubernetes, important for AI deployment
• Docker Certified Associate - Containerization, foundation for modern AI infrastructure
• AWS Certified Developer Associate - Cloud development, important for AI applications

For getting started, I recommend beginning with the AI-900 or TensorFlow Developer Certificate. These are well-structured and give you a solid foundation. After that, you can focus on more specialized certificates depending on which direction you want to develop.

Security with Agents

When using AI agents, you should always be careful. Thoroughly review the generated code, especially if it handles sensitive data or contains security functions. Do not use agents for critical systems without human review.

Best Practice for Windsurf AI

Windsurf AI is one of the most powerful tools for Vibecoding. Here are my best tips for optimal use:

1. Project Setup

Always start with a clear project setup. Define your tech stack, project structure, and coding standards before the first Vibecoding step. Windsurf AI learns from your project context and thus delivers better results.

2. Use Cascade Agents

Windsurf AI offers Cascade Agents for Multi File Edits. Use these for more complex tasks that affect multiple files. The agents understand the relationships between different files and can make consistent changes.

3. Provide Context

Give Windsurf AI as much context as possible about your project. Don’t just describe the current task, but also the goals of the project and the architecture. The more context the AI has, the better its suggestions will be.

4. Iterative Working

Work iteratively with Windsurf AI. Start with rough requirements and refine step by step. Give constructive feedback and let the AI improve its suggestions.

5. Code Review

Even though Windsurf AI generates good code, you should always perform a code review. Check the code for best practices, security aspects, and performance.

Custom GPTs, Agents, Workspaces

Custom GPTs are custom ChatGPT bots for personal tasks, without actions. Agents are autonomous, perform multi-step tasks, for example editing code, deploying. Workspaces are collaborative spaces like in Cursor, Replit for projects with persistent context.

Difference: GPTs only chat, agents act, workspaces organize files and chats.

What are Agents

Agents are AI systems that plan themselves, use tools, and complete tasks, for example writing code, testing, deploying. In the Vibe context, Claude Code or Aider edit repos autonomously.

They map your codebase and commit via Git.

Software for Agents

Top tools are Aider, Terminal, 100 languages, Claude Code, CLI, Cursor Agent, OpenCode, Replit Agent. For local, Aider with Ollama, for Cloud, Codex.

Start with Vibeprogramming and Agents

For Vibe, install Cursor, Windsurf, start new repo, prompt build Todo App. Iterate. For Agents, install Aider with pip install aider chat, git clone, aider main.py and say add Feature X.

Test locally, deploy via Vercel. Start small.

Connection with ClawBot

ClawBot, or Clawdbot, OpenClaw, is an agentic AI assistant with app access, email, calendar, vibe coded often. It’s related to Vibe since many build that way, but it’s a broader, more personal agent, not pure coding.

Not a pure coding tool, rather a jack-of-all-trades with risks like access permissions.

Important Technical Terms

Glossary of important terms:

• Vibe Coding, Natural language to code
• Agentic Coding, Autonomous agent workflows
• Cascade, Multi file edits in Windsurf
• SWE bench, Benchmark for coding agents
• Checkpoints, Snapshots for undo
• Prompting, Art of addressing AI

FAQ, To Better Understand the Topic

What is Vibe Programming?

Vibe Programming is AI-generated code per description. You describe what you want, and the AI generates the appropriate code.

Who coined the term?

The term was coined by Andrej Karpathy, a renowned AI researcher and former Director of AI at Tesla.

What is the best entry tool?

Cursor AI is often recommended as the best entry point, but Windsurf AI and Claude Code are also excellent options for getting started with vibecoding.

What is the /plan mode?

The /plan mode in Claude Code plans before coding and gives you an overview of the steps before the AI generates code.

What do Vibecoding tools cost per hour?

Costs are approximately 1 to 5 dollars per hour, depending on the model used and the intensity of usage.

What security tips are there?

Always review code, especially for sensitive applications. Check the generated code for security vulnerabilities and best practices.

What is the difference between agents and vibe?

Agents work autonomously and execute multi-step tasks, while vibe coding is more iterative, where you maintain control.

CLI or IDE, what is better?

CLI tools are more flexible for remote work, while IDE tools feel more like normal coding and offer visual support.

Which files are important for context?

README.md for context, package.json for dependencies, main app file and tests are the most important files for AI context.

How does debugging work with AI?

Describe the problem precisely, prompt fix this error and let the AI analyze and fix the error.

What is Replit suitable for?

Replit is ideal for quick development and deployment of web apps directly in the browser.

How do I use Aider?

Aider is a terminal tool for pair programming with AI. Install it with pip install aider chat and use it for git-based projects.

What are Custom GPTs?

Custom GPTs are simple bots for personal tasks without actions, ideal for specific applications.

What are Workspaces?

Workspaces are collaborative project spaces with persistent context, like in Cursor or Replit.

What does the future look like?

The future shows more agent integration, improved AI models and even tighter integration into development workflows.

Further Links Windsurf AI: https://windsurf.ai

Cursor: https://cursor.com

Claude Code Docs: https://code.claude.com

Aider: https://aider.chat

Replit Vibe Guide: https://blog.replit.com/what-is-vibe-coding