Chat apps and coding agents

The familiesCopy Anchor Link

Terminal agents such as Claude Code, OpenAI Codex CLI and Aider read your repo, edit files and run commands, usually with permission prompts. IDE agents such as Cursor, Windsurf and GitHub Copilot sit in the editor with inline edits and an agent mode. OpenCode is an open-source terminal agent, and Replit Agent builds apps in a hosted environment. Cloud agents run tasks on a remote machine and open a pull request when finished, which suits parallel background work. Chat apps are for questions, drafts and analysis.

How to chooseCopy Anchor Link

Try one tool seriously for two weeks before comparing. Learn its memory file, permission model, and how it handles context. The skills transfer between tools: explore, plan, implement, verify, keep context clean, and give the agent checks it can run. Models change quickly, so prefer tools that let you switch models.

A worked example: a two-week trialCopy Anchor Link

Pick one tool. Use it for real tasks for two weeks. Keep a short log: where it saved time, where it wasted time, what you had to correct. After two weeks you know more than any comparison chart can tell you. The habits you build (plan, small steps, tests, review) transfer to any other tool.

Things to checkCopy Anchor Link

Does it let you pick the model? How does it ask permission before running commands? Where does your code go? Can it be run on a branch or worktree so it cannot damage your main work?

What actually differs between toolsCopy Anchor Link

The model underneath matters, but the wrapper around it matters just as much. The wrapper decides what the agent can see, which commands it can run, how it asks permission, and how it edits files. Two tools using the same model can feel very different. Compare on four things: where it runs (your machine or a remote one), how it handles your files, how much you must approve, and whether you can see and undo each change.

A second example: choosing for a small taskCopy Anchor Link

You want to understand what a function does, with no changes. A chat app is enough: paste the function and ask. A coding agent would work too, but it brings permissions and file access you do not need. For a task that spans many files, such as renaming a concept everywhere, the chat app fails because it cannot see the whole project. The size of the job picks the tool.

Trade-offsCopy Anchor Link

Terminal agents give control and scripting but have a learning curve. Editor agents are comfortable and show changes inline, but can encourage quick accepting without reading. Cloud agents work while you are away, but you only see the result at the end, and the code runs on a machine that does not have your local setup. No option is best in every case.

What goes wrongCopy Anchor Link

A common failure is trusting a demo. Marketing shows an agent building an app in minutes on a clean project. Your project is older, bigger and has unusual rules. Symptoms of a bad fit are constant corrections, edits in the wrong files, and tests that the tool cannot run. Those usually mean the tool lacks context about your project, which a memory file can fix, or that the task is too large for one pass.

How to tell it is workingCopy Anchor Link

During your trial, count two things per task: minutes until you had a result you would accept, and number of corrections you made. Compare with how long you would have taken alone. If the numbers do not improve after a week of learning the tool, change your approach before changing the tool.

Common mistakeCopy Anchor Link

Changing tools every week. Depth in one beats shallow tries of five.