Why I Code with Cline
Moving Beyond Simple Code Completion
Remember when tab completion felt revolutionary? Those little suggestions that would pop up as you typed, helping you remember method names and API calls. Then came the era of AI code completion - GitHub Copilot and its contemporaries changed how we think about coding coding assistants.
Then, I spent several months using Cursor IDE, which quickly became the most popular AI coding tool due to its powerful integration with large language models. But after discovering Cline (which was initially called Claude Dev), I realized we're entering a new phase of AI-assisted development. Cline is leading the charge with what I call "thoughtful coding."
Cline was built right after (and inspired by) the Claude 3.5 Sonnet model card addendum that mentioned it’s capabilities in this area.
The Problem with Traditional AI Coding Assistants
Traditional AI coding tools are reactive - they watch what you type and make suggestions. While useful, they often feel like overeager pair programmers who start typing before understanding the full context of what you're trying to achieve. This leads to a lot of trial and error, where you might get syntactically correct code that doesn't quite solve your actual problem.
Cline's Thoughtful Approach
What sets Cline apart is its "think before you code" philosophy. Instead of immediately jumping to code suggestions, Cline first:
Creates a detailed plan for implementing your requested changes
Explains its reasoning and approach
Breaks complex tasks into manageable steps
Shows you exactly what it's going to do before doing it
This methodical approach has transformed how I work with AI coding assistants. Instead of playing "suggestion whack-a-mole," I'm having meaningful discussions about architecture and implementation strategies.
Real Development Environment Integration
One of the most powerful aspects of Cline is its deep integration with the development environment. Unlike other tools that are limited to file contents, Cline has access to:
Direct file creation and editing with diff views
Terminal command execution with real-time output monitoring
Problems panel for immediate error resolution
Full development context awareness
But what really sets Cline apart is its ability to actually use your computer to test and validate solutions with Antrhopic’s Computer Use capabilities.
Computer Use Capabilities
Cline can:
Launch and control a web browser to test your applications
Start up development servers in the background
Monitor server logs in real-time
Run automated tests using Playwright/Puppeteer
Validate that its changes actually work
Here's what it looks like when Cline tests a web application:
This means Cline isn't just suggesting code - it's actively helping you test and validate solutions. It can identify issues that would only be apparent during runtime and help you fix them immediately. This end-to-end testing capability makes Cline feel less like a code completion tool and more like a true development partner.
The Model Context Protocol Advantage
What really excites me about Cline is its implementation of the Model Context Protocol (MCP). As one of the most sophisticated MCP clients available, Cline has become a Swiss Army knife for workflow automation. This open standard means I can:
Create custom tools that extend Cline's capabilities
Use Cline as an MCP server client
Build specialized servers for my specific workflow needs
Automate complex workflows across different tools and services
The MCP integration means Cline isn't just a static tool - it's an extensible platform that can grow with my development needs. I've seen developers use Cline for everything from automated note-taking systems to complex development workflows. For instance, Colin McNamara's excellent article on automated templated note-taking with Cline demonstrates how MCP support enables powerful automation scenarios beyond just coding.
Practical Impact on Development Workflow
In my daily work, Cline has fundamentally changed how I approach coding tasks:
Before Cline:
Write code
Run into issues
Debug
Repeat
With Cline:
Describe the task
Review Cline's proposed implementation plan
Approve or adjust the approach
Watch as changes are implemented systematically
Use checkpoints if we need to roll back
The checkpoint system is particularly valuable - it means I can experiment with different approaches while always having a safety net.
Model Selection and Cost Efficiency
Cline is completely free and open source, but it requires a strong language model to unlock its full potential. You'll need a model with robust agentic capabilities and tool calling support - currently, that means using at least Claude 3.5 Sonnet or equivalent.
As of February 2025, these models are known to work well with Cline (keep an eye on the Discord for the latest compatibility updates):
Claude 3.5 Sonnet - Available through OpenRouter with lower rate limits compared to Anthropic direct
GPT-4 O3-mini
DeepSeek V3
Gemini Flash 2.0
One of Cline's clever features is context-caching of tokens (when supported by the provider), which can significantly reduce costs during extended coding sessions. This means you're not paying for redundant context in every interaction.
Cline allows you to manage the cost and see where you are in your context window directly.
Looking Forward
As we move into 2025, I believe we'll see more tools adopting this thoughtful approach to AI-assisted development. The era of simple code completion is giving way to truly intelligent development assistants. Cline's open-source nature and support for multiple AI models means it can evolve alongside these advances while keeping developers in control.
For those still using traditional code completion tools, I encourage you to give Cline a try. The transition from reactive suggestions to thoughtful planning might feel different at first, but once you experience the benefits of having an AI assistant that truly thinks before it codes, you'll find it hard to go back.
Note: As with any development tool, Cline is most effective when used as part of a thoughtful development process. It's not about replacing developer expertise, but rather augmenting it with AI capabilities in a controlled, transparent way.