Inline autocomplete is the first thing most developers notice about an AI coding assistant, but it's rarely the feature that ends up saving the most time once the novelty wears off. The bigger wins tend to show up in tasks developers actively avoid, not the ones they're already fast at.
Where it actually helps most
Explaining unfamiliar code โ a legacy function with no comments, or a library you've never used โ is one of the highest-value uses, since reading and re-deriving intent from scratch is genuinely slow. Writing the first draft of a test suite for existing code is another: tedious to start from a blank file, much faster to review and correct a generated draft.
Debugging benefits too, specifically for pasting an error plus surrounding context and getting a plausible cause to check first, rather than guessing. None of this replaces understanding your own code โ a generated fix you don't understand is a liability, not a shortcut โ but it consistently shortens the time between 'stuck' and 'unstuck.'
Picking a tool
The right pick depends heavily on your editor and workflow โ some tools are IDE-native, others are chat-first and editor-agnostic. AlverHub's Code Assistant category lists current options with real ratings, which is a faster way to compare fit than trying each one from scratch.