AI tools have gone from “cool autocomplete” to agents that can read a whole repo, edit multiple files and run commands. There are dozens now, so here’s how I’d think about it, plus a starter setup.
First, understand the three types
Most confusion comes from comparing tools that do different jobs.
Autocomplete and chat in your editor. It suggests the next line or function and answers questions about your code. It’s fast and low-risk, and it’s the easiest place to start.
AI-first editors. The editor itself is built around AI, so you can say “change this behavior across these five files” and review a diff.
Coding agents. You give them a goal (“migrate this module to the new API and fix the tests”), and they read the repo, edit files, run commands and iterate. This is the biggest shift in the last year or so, and also where you can get burned if you don’t review the output.
What I’d look at in each category
GitHub Copilot is the safe default. It works inside the editor you already use, and there’s a free tier for individuals. It’s great for everyday completion and quick questions. For bigger cross-file changes, the dedicated tools below usually feel stronger.
Cursor is a VS Code fork, so your extensions and keybindings move over. People like it for multi-file edits where you want to stay in control and review changes before accepting them.
Windsurf is in the same space as Cursor and is often compared on price and polish. It’s worth trying both on your own project, because the better one is mostly a matter of taste.
Claude Code runs in your terminal and works as an agent. It’s good for larger tasks like refactors, migrations and tracking down a bug across a codebase. The tradeoff is that you’re delegating more, so you need to read what it did.
Goose is an open-source terminal agent, a good pick if you want something you can inspect and customize.
JetBrains AI Assistant is the obvious choice if you live in IntelliJ, PyCharm or WebStorm and don’t want to leave.
Tabnine and Continue.dev matter if privacy is the constraint. Tabnine focuses on privacy-first completion, and Continue.dev is open source and can run with local models, which helps if your company restricts where code can go.
Qodo and Snyk Code are worth considering as a safety net. Qodo focuses on test generation, and Snyk Code flags security issues as you write. AI writes plausible-looking code very confidently, so a second layer that checks it is useful.
How I’d combine them
You don’t need all of these. A setup that works for many people:
One editor-level tool for daily coding (Copilot, Cursor or Windsurf)
One agent for big or boring tasks (Claude Code, Goose or similar)
Tests and CI as the final judge of whether any of it is correct
Things that make a bigger difference than which tool you pick
Give it context. A short README, your conventions and a couple of example files improve results a lot.
Ask for small steps. “Add this endpoint” works better than “build the backend.”
Read the diff. Treat AI output like a pull request from a new teammate.
Be careful with secrets and private code. Check your company’s policy before using any tool on work repos.
Trial before paying. Pricing and features change quickly, so test on a real task, not a toy demo.
Question for you
What are you using day to day? I’m most curious about two things: whether anyone trusts an agent on a large legacy codebase, and which tool has surprised you, good or bad.
