Are You Looking for an AI Coding Assistant Primarily for Autocompletion, or for Broader Tasks Like Refactoring and Debugging?

Copilot is a strong choice for basic autocompletion. Many Users describe Copilot as a "glorified autocomplete" that excels at boilerplate and repetitive code, making it useful for reducing basic coding effort. "Copilot - Demolished. A normal autocomplete, nothing more, and everything else has long since caught up."

Cursor offers noticeable improvements in contextual suggestions. Some developers find Cursor's tab completion superior to Copilot's for generating code with better contextual awareness. "The tab completion is also noticably better than Copilot for contextual suggestions."

AI aids in rapidly developing front-ends and common tasks. AI is effective for creating functional front-ends from descriptions and UI designs, as well as handling "common" coding tasks. "I basically only use it for rapidly developing front-ends."

Claude Code and Cursor are highly regarded for refactoring. Users frequently recommend Claude Code for its ability to understand and refactor across multiple files, and Cursor for large-scale, multi-file changes. "Claude Code is still probably my fav, but the Fable 5 thing made me a little less comfortable depending on it too much."

AI can assist with cleaning up legacy code. For extensive and redundant legacy code, AI tools can help identify and remove redundancy, even if manual verification is still required. "It is pretty okay for refactoring by "removing redundancy” from super long legacy code."

Agentic AI tools require careful management for refactoring. While powerful, agents like Claude can sometimes over-rewrite code or make mistakes if not given clear, modular instructions and if older, undesirable patterns exist in the codebase. "Claude Code - Really reads the codebase. Refracting, catching bugs in replies that I didn't write, migrations. Cons: Will gladly rewrite 200 lines where five would be enough"

ChatGPT is often used for debugging and architectural discussions. Users find ChatGPT helpful for understanding why issues occur and for navigating unfamiliar codebases. "ChatGPT is probably the one I use most for debugging, architecture discussions, and figuring out unfamiliar codebases."

Tools that reason through problems are preferred for complex debugging. When facing intricate issues, AI tools that can process the entire problem rather than just suggesting snippets are more valuable. "Debugging complex issues → I prefer tools that can reason through the whole problem instead of just suggesting code snippets."

AI still struggles with complex logic and can "hallucinate" solutions. Despite advancements, AI tools can fail when tackling highly complex problems or those requiring nuanced logic, sometimes generating incorrect or suboptimal code. "whenever I try to use AI to solve an actual problem/something I’m stuck on/something that requires logic, it always fails miserably."