Beyond Copilot: Real-World AI Developer Workflows That Boost Productivity

With a new AI developer assistant launching nearly every week, it's becoming challenging to find a setup that genuinely enhances productivity. Instead of settling on a single, all-in-one solution, many developers are finding success by creating hybrid workflows that combine the strengths of multiple tools.

The Rise of the Hybrid Workflow

A recurring theme is the combination of a real-time code completion tool with a powerful, conversational AI chat. The most common pairing is GitHub Copilot running inside the editor for quick, inline suggestions, complemented by Claude for more complex problem-solving, refactoring, and generation tasks. Many users prefer running Claude in a separate, dedicated terminal window, which allows for a focused, conversational workflow without cluttering the main IDE.

Choosing Your Tools: IDEs vs. AI-First Editors

The discussion highlights a key decision point for developers: stick with a mature IDE or switch to a newer, AI-centric editor?

• AI-First Tools: Tools like Cursor and Windsurf are popular for their tight integration of AI. Cursor is frequently praised for its excellent user experience, especially its ability to queue up AI-generated diffs for easy review. Windsurf is noted as being particularly effective for React projects.
• Traditional IDEs: Despite the slick AI features of new editors, many developers aren't willing to sacrifice the robust, mature features of traditional IDEs. Several users advocate for combining an AI tool like Claude with a JetBrains IDE (like GoLand or IntelliJ). They argue that JetBrains' code navigation, refactoring, and debugging capabilities are still far superior to those in VS Code or AI-native editors, creating a "sweet spot" of powerful IDE fundamentals and cutting-edge AI assistance.

Advanced and Self-Hosted Setups

For those concerned with privacy, cost, or control, a self-hosted approach is a viable alternative. One developer shared a sophisticated setup using:

• Open-source models like Qwen (e.g., the 8B parameter version).
• Execution backends like llama.cpp for running models locally.
• Command-line tools like aider to provide project context to the local model.

This approach keeps all code and prompts on a local machine and avoids API costs. The primary benefit cited, however, wasn't just technical; it was psychological. These tools help a developer maintain momentum and push through moments of frustration or distraction, which is a game-changer for productivity.

A Critical Security Tip

One of the most valuable insights shared was a simple but effective security practice: when using AI agents that can execute shell commands, run them on a remote, stateless machine. This isolates the agent from your primary development environment, preventing any chance of privacy leaks from your local files or accidental execution of destructive commands like rm -rf ~.