AI Coding Tools at the End of 2025: A Working Engineer's Verdict
Cursor, Claude Code, Copilot, Aider - I use all of them. Here's where each fits.
I've spent more time with AI coding tools in 2025 than I have with any IDE in my career. The differences between tools are significant and often misreported. Here's how I actually use each.
If you're not using AI tools to write code in 2025, you're slower than the engineers who are. That's no longer controversial. The interesting questions are: which tools, for which tasks?
My current setup
- Cursor - main IDE. Inline edits, multi-file refactors, codebase chat.
- Claude Code (CLI) - for big "do this whole feature" tasks where I want a controlled agent loop.
- GitHub Copilot - for tab-completion. Cheap. Always on.
- Aider - for offline work and when I want explicit git-commit-per-change control.
What each one wins at
Cursor: best fit for everyday coding. The Cmd-K inline edit + multi-file Cmd-I are the killer features. Strong autocomplete. Good codebase indexing.
Claude Code: when I want to say "implement this whole feature" and walk away for 20 minutes. The CLI gives me an audit trail (every command logged) which Cursor doesn't.
Copilot: for when I just want autocomplete and nothing else. Less smart than Cursor's autocomplete but cheaper and quieter.
Aider: for sensitive code where I want git granularity. Every change is a commit. Easy to revert. Best for refactoring legacy code where you want to bisect aggressively.
What changed in 2025
- Claude 3.5 Sonnet → Sonnet 4 → Sonnet 4.5 → Sonnet 4.6 each meaningfully improved coding quality
- Tools learned to use tools (search, read_file, run_tests) instead of just emitting code blobs
- Long context made "explain this entire repo" actually viable
- Costs dropped enough that "always-on AI" is normal
What I'd warn against
- Don't trust generated tests. Models write tests that pass; that's not the same as tests that catch bugs. Always read what they generated.
- Don't generate without reading. Skim the diff before you accept it. The "obvious" generated change is wrong about 5% of the time, and 5% of the time you accept it without reading is enough to break production.
- Don't lose your taste. AI tools amplify whoever's driving. If you've never written 10K lines of production Java, AI will help you write bad Java faster. Build the taste first.
Where this is going
By mid-2026, every IDE will be an AI IDE. The differentiation will be on agent loops, codebase memory, and integration breadth (MCP, etc.) - not on autocomplete quality.
The engineers who are getting better at directing these tools - clear specs, good test coverage, intentional prompting - are pulling away from the engineers who aren't.