April 2026 docs show Cursor, Claude Code, and GitHub Copilot now split across IDE, CLI, and cloud-agent workflows. Compare commands and setup. Read now.
Three products, three primary surfaces
By April 2026 the docs for Cursor, Claude Code, and GitHub Copilot no longer describe one shared “AI in the editor” pattern. They describe three surfaces that often work together: an IDE for visual editing and multi-file refactors, a CLI for terminal-first sessions and scriptable agent loops, and cloud agents for longer jobs that run away from your local machine. The cheat sheet starts with surface fit, not brand preference. If your day is mostly in a graphical editor with tabs, diffs, and inline accept/reject, an IDE-first tool is the right default. If you live in shell sessions, CI scripts, and remote boxes, a CLI agent is usually faster to wire into your habits. If work needs hours of exploration, large checkouts, or environments you do not want to keep warm on a laptop, cloud agents become the practical layer.
None of the three products is “only” one surface anymore, but each still has a center of gravity. Cursor is still easiest to reason about as an AI-native IDE workflow. Claude Code is easiest to reason about as a terminal agent you drive with commands and session state. GitHub Copilot remains strongest where your work already sits on GitHub: editor completions, PR-aware help, and cloud-side agent runs tied to repos and pull requests. Treat marketing labels lightly; treat where the agent can see files, run commands, and open or update PRs as the real product boundary.
Setup: what you configure once vs every project
Setup work falls into the same buckets for all three: account access, editor or CLI install, repo context, and permission rules for what the agent may read, write, and execute. Do the account and install steps once per machine. Do context and permissions per repo or team template. Repo context means which files are indexed or attached, ignore patterns for secrets and generated noise, and any project instructions that fix conventions (test command, lint command, branch naming). Permission rules mean whether the agent can edit freely, must propose diffs for review, can run shell commands, and which paths are off-limits.
A useful habit is a short project checklist before the first serious session: confirm the correct workspace root is open, confirm ignore rules exclude env files and credentials, set the default branch or worktree the agent should use, and write down the one command that proves a change is good (tests, typecheck, or a smoke script). IDE tools surface much of this in UI panels; CLI tools expect the same decisions via config files and flags. Cloud agents need the same decisions again as environment setup: which branch to clone, which secrets to inject, and how results return as a PR, a log, or a commit you review later.
Commands and workflows worth memorizing
Across IDE, CLI, and cloud, the mental model is the same: start a focused task, give the agent a bounded goal, let it propose or apply edits, then verify with your own command. In the IDE that looks like chat or agent panels attached to the open project, with accept/reject on diffs. In the CLI it looks like launching a session in a repo directory, describing the task, and reviewing terminal output plus file changes. In the cloud it looks like dispatching a job against a repo, watching status, and reviewing the resulting PR or patch.
- Scoped edit: “Change only X; do not touch Y; run Z to verify.”
- Explain then act: ask for a short plan and file list before allowing writes on risky areas.
- Diff-first: prefer reviewing patches over silent multi-file rewrites when you do not fully trust context.
- Hand off surfaces: draft exploration in CLI or cloud, finish review and polish in the IDE where diffs and tests are easy to inspect.
Keep a personal command sheet for the product you use daily: how to start a session, how to attach extra files or folders, how to interrupt a runaway loop, and how to export or open the result as a PR. Those four actions cover most daily use. Product-specific syntax changes; the workflow pattern does not.
How to choose without a full bake-off
Pick the primary surface that matches where you already spend time, then add a second surface only when a real bottleneck appears. Stay IDE-first if you need constant visual review of multi-file changes. Stay CLI-first if you automate tasks, work over SSH, or want agent steps inside scripts. Lean on cloud agents when jobs are long, environment-heavy, or better as reviewable PRs than as live pair sessions. Many teams end up with two: one local driver (IDE or CLI) plus cloud for overnight or large-repo work.
When comparing Cursor, Claude Code, and GitHub Copilot for your stack, ignore feature-list length. Score them on four practical questions: Does it open the right workspace with low friction? Can I constrain edits and shell access cleanly? Can I verify changes with my normal test or build command? Can results land as reviewable diffs or PRs my team already uses? The April 2026 docs frame the market as split across IDE, CLI, and cloud-agent workflows for a reason—your cheat sheet should map tasks to those surfaces first, then pick the product that fits the surface you need most of the week.