Over the past year, I've built a workflow that combines multiple AI tools to speed up development, improve code quality, and reduce friction between idea and execution.
In this guide, I'll break down:
- The exact tools I use
- How they fit together
- Why each one matters
- And how you can build your own AI-powered dev stack
What Is an AI-Powered Web Development Stack?
An AI-powered stack combines:
- • Code editors
- • AI coding assistants
- • Model providers
- • Local AI runtimes
- • Rapid app builders
- • Deployment & version control
Instead of relying on one platform alone, you create a unique system where each tool does what it's best at.
This approach gives you:
Better Performance
Optimized tools for specific tasks
More Flexibility
Switch between tools as needed
Faster Iteration
Quick prototyping and testing
The Core Tools in My AI Development Stack
Here are the tools that power my workflow:
Let's break them down by role.
1. Best Code Editors for AI Web Development
Visual Studio Code
Best for: Stability, extensions, and full control
VS Code is the backbone of my workflow. Even with newer AI-native tools, it remains essential.
Why I use it:
- Massive extension ecosystem
- Strong debugging tools
- Git integration
- Customizable environment
Windsurf
Best for: AI-first development
Windsurf is designed around AI collaboration, not just AI assistance.
Why it stands out:
- AI-native interface
- Faster prototyping
- Less manual coding overhead
2. AI Coding Assistants That Actually Improve Overall Productivity
Claude Code
Best for: Deep reasoning and structured code generation
Claude Code is incredibly strong when working through complex logic.
Use it for:
- Refactoring
- Debugging
- Explaining large codebases
OpenCode
Best for: Speed and rapid output
OpenCode is more lightweight and fast-moving.
Use it for:
- Generating boilerplate (standardized reusable code)
- Quick iterations
- Reducing friction
3. AI Model Access Platforms (Flexibility Matters)
OpenRouter
Best for: Accessing multiple AI models in one place
OpenRouter lets you switch between models without changing your workflow.
Why it matters:
- Compare outputs easily
- Optimize for cost/performance
- Avoid vendor lock-in
Hugging Face
Best for: Exploring and experimenting with models
Hugging Face is where I go beyond mainstream tools.
Use it for:
- Discovering new models
- Testing capabilities
- Learning how models work
4. Running AI Locally (Privacy + Speed)
Ollama
Best for: Local LLM development
Ollama allows you to run AI models on your own machine.
Why this is powerful:
- No API limits
- Better privacy
- Offline development
5. Rapid App Development with AI
Lovable
Best for: Building MVPs fast
Lovable AI helps you go from idea to working app quickly.
Use it for:
- Prototyping
- Validating ideas
- Speeding up development cycles
6. Deployment & Version Control
Vercel
Best for: Fast, modern deployment
Vercel makes deploying web apps incredibly simple, especially for frontend frameworks.
Why I use it:
- Instant deployments
- Built-in performance optimization
- Seamless integration with GitHub
- Ideal for modern frameworks like Next.js
GitHub
Best for: Version control and collaboration
GitHub is the backbone of how I manage and share code.
Why it's essential:
- Version control with Git
- Collaboration and pull requests
- Integration with deployment tools like Vercel
- Huge developer ecosystem
How to Combine These Tools Into a Workflow
Here's a simplified version of my AI development workflow:
This workflow helps:
- Reduce development time
- Improve output quality
- Keep flexibility across tools
Why You Shouldn't Rely on Just One AI Tool
A common mistake is trying to find a single "best" tool.
That approach limits you.
Different tools excel at different things:
- Some are better at reasoning
- Others are faster
- Some give more control
The advantage comes from combining them strategically.
How This Setup Helps with SEO and Developer Productivity
This stack doesn't just improve coding—it also helps with:
- Faster content creation
- Better technical writing
- Building projects worth sharing (and linking to)
Which is key if you're trying to:
- Grow a blog
- Attract backlinks
- Build authority in the development space
What's Next (Deep Dives Coming)
This is the pillar article.
Next, I'll be publishing detailed guides on:
Windsurf vs VS Code
How to use OpenRouter effectively
Running local AI with Ollama
Building apps with Lovable AI
Deploying apps with Vercel
Managing projects with GitHub
Each guide will go deeper and link back here as the central hub.
Final Thoughts: The Future of Web Development
AI isn't replacing developers—it's amplifying them.
The real shift is:
- Faster iteration
- More experimentation
- Better tooling
If you want to stay competitive, it's not about picking the "best" tool.
It's about building the right stack.