Vibe Coding with Windsurf

Imagine bringing your coding ideas to life effortlessly, with AI guiding you every step of the way. Codeium’s Windsurf turns software development into a smooth, intuitive process by streamlining coding, debugging, and optimization. The Windsurf Editor enhances productivity with code completion tools and smart IDE features, helping you refine code and solve problems before they even arise. More than just an editor, it acts as an AI-powered creative assistant, making coding faster and more efficient. In this article, we will explore Windsurf, learn how to set it up, and try out some hands-on tasks to experience how it helps with vibe coding.

What is Vibe Coding?

Vibe coding is about effortless AI-assisted development. You focus on problem-solving while AI handles repetitive coding tasks. Instead of writing every line manually, you describe what you need. AI then generates, refines, and optimizes it in real time.

What is Windsurf Editor?

Windsurf Editor is developed by Codeium. It is an AI-powered Integrated Development Environment (IDE) designed to enhance coding. It integrates advanced artificial intelligence features directly into the development workflow.

How Windsurf Enhances Vibe Coding

Here’s how Windsurf facilitates vibe coding:

• AI-Driven Development: Provides proactive code suggestions and debugging without interrupting your workflow.
• Cascade AI Chat: Understands relationships across files, enabling intelligent, multi-file code edits.
• Supercomplete & Inline AI: Predicts intent and refines code precisely where needed.
• Multi-Modal Capabilities: Converts images into code and pulls live web data for problem-solving.
• Local Indexing & Memory: Scans entire projects for context-aware suggestions and retains knowledge across sessions.
• AI-Powered Terminal & Debugging: Fixes errors autonomously and executes code within the IDE.
• Custom Rules & Modes: Enforces coding styles and offers automation levels from full autonomy to manual control.
• Performance & Privacy: Optimized for low-latency AI responses while ensuring data security.

Also Read: I Tried Vibe Coding with Cursor AI and It’s Amazing!

Now, let’s set up Windsurf and explore some hands-on projects.

Setting Up Windsurf: Your Gateway to Vibe Coding

Before diving into AI-powered coding with Windsurf, ensure your environment is set up correctly. This step will help you unlock seamless coding, real-time debugging, and AI-driven assistance effortlessly.

Steps to Install Windsurf

Follow these steps to ensure a seamless installation and setup experience.

Step 1. Download the Installer

• Visit the official Windsurf website, choose your operating system, and download the appropriate installer for Windows, macOS, or Linux.
  • Windows and macOS: For Windows and macOS, simply open the downloaded installer file and follow the on-screen instructions.
  • Linux: Copy the command and run it in your terminal to download and install Windsurf Editor on your system.

Step 2. Run the Installation

• Open the downloaded file and follow the setup wizard.
• Select installation preferences, such as creating a desktop shortcut or integrating Windsurf into your system’s PATH for command-line access.

Step 3. Initial Setup & Configuration

• Import your existing settings for a seamless transition if you’re migrating from VS Code or Cursor.

• Else, choose default settings for a clean setup, or pick VS Code-style, Vim mode, and a color theme that suits your workflow.

Step 4. Sign In & Unlock Features

• Create a Codeium account or log in to enable cloud features, AI assistance, and a personalized coding experience.

• Experiment with AI Flows, Cascade, and Supercomplete to optimize your IDE. Open your folder and start coding efficiently.

Pricing Plans for Windsurf Editor

Windsurf Editor offers flexible pricing for individuals and teams, ranging from a free tier to enterprise solutions for seamless AI-powered development.

• The Free Plan includes a 14-day Pro trial, 50 bonus User Prompt credits, and 5 slow premium requests per month. However, extra credit purchases are not allowed.
• The Pro Plan ($15/month) provides 500 User Prompts and 1,500 Flow Action credits. It offers priority access to Cascade Base and larger models. Extra credits are available for $10 per 300 credits.
• It also offers a Pro Ultimate Plan for advanced users priced at $60/month, which includes unlimited User Prompt credits and 3,000 Flow Action credits. It also provides faster AI responses. Additional credits can be purchased at $10 per 400.

• The Teams Plan ($35/user/month) includes 300 User Prompts and 1,200 Flow Action credits per user. It allows team-wide credit pooling and provides organizational analytics.
• The Teams Ultimate Plan ($90/user/month) enhances this with unlimited User Prompt credits and 2,500 Flow Action credits per user. It also includes higher indexing limits.
• Enterprises can opt for the Enterprise Plan, which offers custom pricing and on-premise deployment. It includes enhanced security, private model fine-tuning, and expert training.

All plans include a 14-day Pro trial with unlimited completions and 500 fast requests. However, slow requests may experience significant delays.

Hands-On Projects with Windsurf: Build, Analyze, and Visualize

In this hands-on section, we’ll explore how Windsurf enhances coding efficiency in real-world projects. From game development to data analysis and algorithm visualization, these exercises will demonstrate Windsurf’s AI-driven capabilities. Through these hands-on applications, you’ll see how Windsurf assists in generating code, debugging, and seamlessly interacting with files.

For each task, I will list down my observations, point out any issues, and suggest how to address them with follow-up prompts. We will then see the final implementation and evaluate the result.

So, let’s get started!

You can refer to this Colab notebook for the code, and for a detailed video explanation, check out our YouTube video.

Task 1: Build a Pac-Man Game

Objective: Develop an interactive Pac-Man game where the player navigates a maze while avoiding ghosts.

Prompt: “Create a Pacman game using Python.”

Observations:

• The mouth of Pac-Man was not visible.
• All the ghosts were not moving simultaneously.
• The game speed was too fast.

Follow-up Prompt for Improvement:
“The mouth of Pac-Man was not visible.
Adjust the speed to make gameplay smoother.
Ensure all ghosts move simultaneously.
Introduce levels for progressive difficulty.”

Final Implementation

Result

• The game now has a better visual representation.
• Multiple ghosts move simultaneously, improving gameplay difficulty.
• The speed has been adjusted to make the game playable.
• Some graphical elements still need further refinements.

Result Overview

The game was significantly improved based on iterative prompts. The AI understood the modifications and adjusted the code accordingly. While some refinements, such as Pac-Man’s mouth visibility, were not fully implemented, the overall gameplay experience was enhanced. Further modifications can be made by providing more precise prompts.

Task 2: Titanic Dataset – Exploratory Data Analysis (EDA)

Objective: Create an interactive visualization to perform Exploratory Data Analysis (EDA) on the Titanic dataset.

Prompt: “Create an interactive visualization to do EDA on the Titanic dataset.”

Observations:

• A global rule set Dash and Plotly for interactive visualizations, with Python as the default, unless specified otherwise. Dash and Plotly were used for this task.

Final Implementation

Result

• The dashboard successfully visualized key insights from the Titanic dataset.
• The default chart displayed survival rates based on gender.
• Users could filter data dynamically, e.g., by passenger class or age group.
• The dashboard also provided insights into survival factors like age and demographics.

Result Overview

The AI agent efficiently created an interactive visualization dashboard within minutes, whereas manual coding would have taken significantly longer. The automated approach also ensured proper dataset retrieval and visualization enhancements. The app provided valuable insights into survival rates based on various factors and allowed dynamic data exploration.

Task 3: Gradient Descent Visualization

Objective: Create an interactive visualization to demonstrate the convergence of gradient descent in a U-shaped curve.

Prompt: “Create an interactive visualization to demonstrate gradient descent convergence in a U-shaped curve.”

Observations:

• Some interactive elements, such as adjusting learning rates, Number of iterations, etc., were not functioning.
• The app did not run correctly when the “Run Simulation” button was clicked.

Follow-up Prompt for Improvement:

“Ensure interactive elements like learning rates and iterations function properly, and fix issues with the ‘Run Simulation’ button.”

Final Implementation

Result

• The visualization successfully demonstrated gradient descent with multiple learning rates.
• Users could modify parameters such as the learning rate.
• The U-shaped curve effectively showcased the effect of different learning rates on convergence.
• Some interactive elements, like selecting specific learning rates, still required refinement.

Result Overview

The AI-generated interactive visualization provided a strong starting point for the gradient descent analysis. While some interactivity issues persisted, the app effectively illustrated how different learning rates influence convergence. Further refinements could enhance user interaction and visualization clarity.

Task 4: File Analysis with Windsurf

Objective: Use Windsurf to analyze a file related to the RAG system.

Prompt: “Analyze the file RAG system for query.”

Final Implementation

Result

• Windsurf quickly processed the file and provided a structured overview with no issues.
• It efficiently analyzed code and extracted key insights without manual intervention.

Task 5: Financial RAG System – Querying Financial Statements

Objective: Develop a visual RAG application using LangChain, CrewAI, and ChromaDB to query uploaded financial statements like annual reports.

Prompt: “Create a visual RAG application using LangChain, CrewAI, and ChromaDB to query uploaded financial statements.”

Observations:

• A version conflict arose between LangChain and CrewAI.
• The retrieval mechanism needed fine-tuning for improved document querying.
• The vector database setup required adjustments to optimize similarity searches.
• The document processor needed modifications to handle different file formats dynamically.

Follow-up Prompt for Improvement:

“Resolve the version conflict between LangChain and CrewAI by updating dependencies.
Fine-tune the retrieval mechanism for better document querying.
Adjust the vector database setup to optimize similarity searches.
Modify the document processor to handle different file formats dynamically.”

Final Implementation

Result

• The system successfully processed and stored financial documents for retrieval.
• Users could query the system to extract relevant insights from uploaded annual reports.
• The application dynamically determined the file type and applied appropriate processing techniques.
• Despite initial installation challenges, the system provided a strong foundation for further improvements.

Result Overview

The AI-generated solution delivered a well-structured and modular RAG-based application. Although the initial execution ran into dependency and versioning issues, a few iterations with Windsurf helped resolve them. The generated boilerplate code sped up development significantly. After refining the setup, the system successfully processed financial reports. It demonstrated the ability to query and extract meaningful insights using a robust document processing pipeline.

Key Takeaways from WindSurf Hands-On Exercises

• Global Rule Feature: WinSurf includes a Global Rule feature, which acts as a governing mechanism to ensure consistency, compliance, and efficiency in interactions across different AI models. This feature helps maintain uniform responses and streamline operations.
• Multiple LLM Support: WinSurf allows the integration of multiple LLMs, enabling users to select models based on their needs.
• Free Credits on Login: New users receive 50 free credits upon login, allowing them to experiment with the latest LLMs. Each model has a specific credit cost, enabling users to optimize usage based on their needs.
• Privacy & Security: WinSurf ensures user data privacy by implementing secure data handling and access control. User interactions are not stored beyond the session, and sensitive information is protected with encryption.

Conclusion

Windsurf is revolutionizing the way developers collaborate with AI, coding more interactively, smoothly, and smartly. With Cascade AI Chat, Supercomplete, and multimodal capabilities, it offers not just assistance but collaboration. Developers can concentrate on innovation as AI does chores, accelerating game development, data analysis, and algorithm visualization. As AI-powered development continues to evolve, Windsurf is changing the game where you contribute ideas, and AI assists in bringing them to life.

Frequently Asked Questions

Soumil Jain

Data Scientist | AWS Certified Solutions Architect | AI & ML Innovator

As a Data Scientist at Analytics Vidhya, I specialize in Machine Learning, Deep Learning, and AI-driven solutions, leveraging NLP, computer vision, and cloud technologies to build scalable applications.

With a B.Tech in Computer Science (Data Science) from VIT and certifications like AWS Certified Solutions Architect and TensorFlow, my work spans Generative AI, Anomaly Detection, Fake News Detection, and Emotion Recognition. Passionate about innovation, I strive to develop intelligent systems that shape the future of AI.