Unpopular opinion == GitHub Copilot is actually amazing vibe coding tool
EasyProtectedHelp explores a range of AI code generation tools, focusing on why GitHub Copilot excels in their developer workflow. The article outlines practical, step-by-step experiences with Copilot, Claude, and GPT-4.1 for planning, coding, and deploying real-world applications.
Unpopular opinion == GitHub Copilot is actually amazing vibe coding tool
By EasyProtectedHelp
Introduction
Over recent months, I’ve explored a variety of AI-powered code generation tools on numerous software projects — from backend service scaffolding to full production deployments. After trialing many options, GitHub Copilot remains my preferred companion for “vibe coding,” offering efficiency and integration that fits my development style.
Overview of Tools Tested
Here’s a breakdown of notable tools and my impressions:
1. GitHub Copilot
- Models: OpenAI Codex → GPT-4 → now supporting Claude Opus
- Integration: Directly embedded in VS Code and JetBrains IDEs
- Strengths: Real-time completion, strong sequential reasoning, robust agent workflows (notably via Copilot Workspace)
- Usage: Especially handy for modular backend builds, microservices, and complex server structures like the Model Communication Protocol (MCP).
2. Cursor (cursor.sh)
- Features: Whole-document manipulations, “Ask” mode for context-rich code discussions
- Pros/Cons: Superior code understanding in conversation, but less stable and predictable for my workflow compared to Copilot
3. Other Notables
- Cline: Type-aware code generation
- Roo: Lightweight IDE integration
- Augment: Speculative autocompletions
- Windsurf, Claude Code, Atlassian Rovodev: Varied features, but lack the all-in-one workflow and CI/CD readiness of Copilot
Why Copilot Wins (For Me)
While individual tools offer niche benefits, Copilot’s agent mode is particularly effective on well-scoped tasks — such as setting up services, managing routing, and integrating databases. Although Cursor shows advantages in contextual understanding, Copilot consistently finishes tasks, handles agent pipelines, and works well with server protocols (MCP) or task-driven full-stack projects.
My Practical Workflow
Here’s my multi-LLM toolkit for professional development:
- Planning — Claude Opus 4 in Copilot Ask Mode
- For initial structuring, detailed architecture planning, and determining next steps. Claude 4’s Ask Mode within Copilot is clear and highly organized.
- Execution — GPT-4.1 (in Copilot or ChatGPT)
- Once the plan is set, prompting GPT-4.1 yields fast scaffolding, effective code transformations, and clean refactors.
- Post-Scaffold Development & Deployment — Claude Sonnet 4
- After the initial build, Claude Sonnet 4 speeds up subsequent iterations, deployment scripting, and debugging.
AI Tools Breakdown by Company and Model
| Tool | Supported By | Model(s) | Best for |
|---|---|---|---|
| GitHub Copilot | Microsoft + OpenAI | Codex, GPT-4, Claude | Autocomplete, agent workflows |
| Cursor | Independent | GPT-4, Claude | Context-aware code conversations |
| Claude (Opus, Sonnet) | Anthropic | Claude 4 family | Planning, safe deployments |
| GPT-4.1 | OpenAI | GPT-4.1 | Scaffolding, refactoring |
| Augment | Google X alum startup | Gemini-based | Experimental coding |
| Roo | Lightweight IDE tool | Mixed models | Quick context generation |
| Windsurf, Cline, Rovodev | Atlassian / Indie | Custom/GPT-4/Claude | Specific integrations, still testing |
Final Thoughts
These reflections stem from real dev environments and hands-on deployments, particularly using MCP-style agent architectures. While this setup suits my workflow best, I’m interested in learning from other developers managing multi-agent codebases or those experimenting with setups like OpenDevin or SWE-Agent.
Conclusion
AI-powered coding assistants are evolving rapidly. GitHub Copilot stands out for its pragmatic balance of completion, agent support, and real-world workflow integration. However, preferences may vary based on project demands and personal coding style. Your mileage may vary.
This post represents personal experience based on weeks of tool testing. Comments and alternate workflows are welcome!
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