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AI Coding Assistants: The 70/30 Rule and the Enduring Human Role

2025-03-14

AI coding assistants automate roughly 70% of software development, handling boilerplate and routine tasks. However, the remaining 30%—complex requirements, architecture, edge cases, and ensuring correctness—demands human expertise. This article explores the crucial skills engineers need to thrive alongside AI, including system design, handling edge cases, code review, debugging, communication, and continuous learning. Senior engineers should leverage their experience to guide AI and mentor junior developers, while junior developers should focus on fundamentals, problem-solving, and testing. AI accelerates development but doesn't replace human judgment; the article emphasizes the enduring importance of critical thinking, design, quality assurance, and problem-solving in the age of AI.

Cline: A Game-Changing AI Coding Assistant for Serious Engineering

2025-02-04

The AI coding assistant market is flooded with tools, but Cline, a free VSCode plugin, stands out for its system-level integration and model flexibility. Unlike code-generation-focused tools, Cline interacts with your entire development environment, excelling in complex debugging, refactoring, and testing. It supports various models (Anthropic, OpenAI, Google Gemini, etc.), boasts intelligent context management, real-time cost tracking, and a robust checkpoint system. Its unique 'Plan/Act' mode and Model Context Protocol (MCP) enhance efficiency and extensibility, making it ideal for complex systems and large codebases. While limitations exist, Cline's system-level integration, model flexibility, and respect for engineering principles make it a powerful tool for serious development work.