Picking the best code editor in 2026 is no longer about syntax highlighting and a fast file tree. It is about how well an editor handles an AI agent that can read your whole repository, propose a multi-file refactor, and run your test suite before you have finished your coffee. Four editors keep coming up in that conversation: Visual Studio Code, Cursor, Zed, and Windsurf. Each one takes a different bet on what “best” means, and the right pick depends on your hardware, your budget, and how much autonomy you want to hand an AI agent.
This tutorial walks through installing, configuring, and benchmarking all four editors side by side, so you end up with a working setup instead of a vague recommendation. You will migrate your existing VS Code settings into Cursor and Windsurf, wire up AI agent modes safely, run a real startup-time and input-latency benchmark, and build one small working project (a Node.js API with a test suite) using each editor’s AI features to see how they actually behave on the same task. By the end, you will know which editor deserves a permanent spot on your dock, and which ones are worth keeping around for specific jobs.
Why the “best code editor” question changed in 2026
For most of the last decade, “best code editor” meant comparing extension counts, startup speed, and Git integration. Visual Studio Code won that fight years ago and never really let go, thanks to a marketplace that now lists more than 50,000 extensions. What changed in 2026 is that the editor itself became an active participant in writing code, not just a place to type it. Cursor built its entire pitch around codebase-aware chat and a multi-file agent called Composer. Windsurf leaned into an agent called Cascade that can plan and execute a task across a whole project. Zed, written in Rust, bet that raw performance and low input latency still matter even in an AI-heavy workflow, and gained support for running external CLI agents in parallel panes.
That shift matters for how you evaluate “best code editor” as a search query, because the answer is now conditional. A solo developer prototyping a side project cares about different things than a team lead who has to approve AI-generated diffs before they touch production. This tutorial treats all of that as configuration, not compromise: you will set up each editor so it plays to its actual strength, then decide with real numbers instead of marketing copy.
Prerequisites and exact versions used in this tutorial
Before you start, get your machine to this baseline. Version mismatches are the number one cause of “it worked in the demo” frustration when you are switching between four editors that all manage their own extension runtimes.
- Operating system: Windows 11, macOS 15 (Sequoia) or later, or a recent Linux distribution (Ubuntu 24.04 LTS or newer) — all four editors ship native builds for these.
- Node.js 22 LTS or newer, plus npm 10.x, for the sample project you will build at the end.
- Git 2.43 or newer, already configured with a GitHub or GitLab remote for the sample repository.
- Visual Studio Code, latest stable release from the official channel.
- Cursor, latest stable release, downloaded from cursor.com.
- Zed, version 1.21.0 (stable, released September 23, 2026) or newer — check the Zed stable release notes before installing.
- Windsurf, latest stable release, downloaded from windsurf.com.
- At least 8 GB of free RAM if you intend to run more than one AI agent session at a time, and 16 GB if you plan to keep all four editors open during the benchmark steps.
- An API key or subscription for at least one AI model provider (OpenAI, Anthropic, or a local model through Ollama) if you want to test bring-your-own-model configurations later in this guide.
You do not need a paid plan on every editor to follow this tutorial. VS Code is free. Zed’s core editor is free, with its AI features priced separately. Cursor and Windsurf both offer usable free tiers with limited AI completions, which is enough to run the benchmark and the sample project in Step 9.
Step 1: Install Visual Studio Code and set your baseline
Start with VS Code because it is the editor the other three either extend or migrate settings from. Download the installer from the official Visual Studio Code site, run it, and open a terminal to confirm the CLI is on your PATH.
code --version
# Expect three lines: version number, commit hash, and architecture (x64 or arm64)Install the extensions you will want available as a baseline: ESLint, Prettier, GitLens, and the Node.js debugger (bundled by default). This baseline matters because Cursor and Windsurf both offer a one-click import of your VS Code settings and extensions, and you want that import to actually carry something useful.
code --install-extension dbaeumer.vscode-eslint
code --install-extension esbenp.prettier-vscode
code --install-extension eamodio.gitlensOpen Settings Sync (the cloud icon in the bottom-left corner) and turn it on if you use more than one machine. This step is optional for the tutorial itself, but it means the settings you tune in the next steps travel with you instead of living only on this laptop.
Step 2: Install Cursor and migrate your VS Code setup
Cursor is built on the same core as VS Code and supports the full VS Code extension ecosystem, which is why the migration path is close to seamless. Download Cursor from cursor.com for your platform, install it, and on first launch you will see a prompt to import settings, keybindings, and extensions from an existing VS Code install.
Accept the import, then open the Command Palette (Ctrl/Cmd+Shift+P) and search for “Cursor: Sign In” to connect your account. Free-tier Cursor gives you a limited number of completions and agent requests per month; Cursor Pro runs $20 a month and lifts those limits considerably, with a Pro+ tier around $60 a month and an Ultra tier near $200 a month for heavy agent users, according to Cursor’s own pricing page and independent 2026 comparisons. Team plans list at roughly $40 per seat per month.
Now configure the two features that make Cursor distinct from plain VS Code: codebase indexing and Composer, its multi-file agent.
// In Cursor Settings > Features > Codebase Indexing
{
"cursor.indexing.enabled": true,
"cursor.indexing.excludePaths": ["node_modules", "dist", ".git"],
"cursor.composer.autoApply": false,
"cursor.composer.requireReviewBeforeApply": true
}Setting requireReviewBeforeApply to true is not optional in a production repository. It forces Composer to show you a diff before touching any file, which is the single most important safety switch in this entire tutorial.
Step 3: Install Windsurf and configure Cascade
Windsurf also supports the full VS Code extension ecosystem and offers the same kind of one-click settings import on first launch. Download it from windsurf.com, run the installer, and accept the import prompt when it appears.
Windsurf’s headline feature is Cascade, an agent designed to plan a task, execute it across multiple files, and run terminal commands as part of that plan. Independent 2026 editor comparisons list Windsurf’s free tier as including unlimited Tab completions with a limited allotment of Cascade requests, with paid tiers starting around $15 to $20 a month and Team pricing near $40 per user per month. Because Cascade can run shell commands on its own, lock that down before you do anything else.
// Windsurf Settings > Cascade > Command Execution
{
"cascade.terminal.autoExecute": false,
"cascade.terminal.allowedCommands": ["npm test", "npm run lint", "git status", "git diff"],
"cascade.fileWrite.requireApproval": true
}The allowedCommands allowlist is deliberately short. Add commands to it only when you have a specific reason to let Cascade run them unattended — do not default to a wildcard allowlist just to stop the approval prompts.
Step 4: Install Zed and understand what you are trading away
Zed does not import VS Code settings, because it is a separate native application written in Rust rather than a fork of the same Electron-based core the other three share. That is also the source of its main selling point: independent 2026 benchmarks put Zed’s cold startup time anywhere from roughly 0.12 to 0.4 seconds depending on machine and test methodology, with input latency reported in the low single-digit milliseconds. Both figures are far faster than any Electron-based editor can realistically hit, though you should treat exact numbers as directional rather than a guaranteed result on your own hardware — reproduce the benchmark in Step 8 rather than trusting a single published figure.
Install Zed from zed.dev and confirm the version matches or exceeds the 1.21.0 stable build referenced in this tutorial.
zed --version
# On Linux/macOS. On Windows, check Help > About inside the app.Zed’s extension ecosystem is real but noticeably smaller than the VS Code-compatible one Cursor and Windsurf inherit. Before committing to Zed as a daily driver, search its extension registry for the specific language servers, linters, and formatters your stack depends on — a missing extension here is the most common reason developers bounce back to VS Code after a week.
Zed’s AI features (inline completion, chat, and agent panes that can run external CLI agents) sit on a separate paid tier, commonly listed around $10 a month for Zed Pro in 2026 comparisons, while the core editor itself remains free and open source.
Step 5: Configure AI model preferences consistently across editors
One detail that trips people up when comparing “best code editor” options: each tool defaults to a different model, and comparing AI output quality without normalizing that variable gives you a meaningless result. Before benchmarking anything, set all three AI-capable editors (Cursor, Windsurf, Zed) to use the same underlying model where their pricing tier allows it, or explicitly note which model each is using if you are on a free tier that restricts model choice.
// Cursor: Settings > Models
{
"cursor.chat.defaultModel": "your-chosen-model-id",
"cursor.composer.defaultModel": "your-chosen-model-id"
}
// Windsurf: Settings > Cascade > Model
{
"cascade.model.provider": "your-provider",
"cascade.model.id": "your-chosen-model-id"
}If your team is standardizing on a specific provider for compliance reasons, this is also the point to configure bring-your-own-API-key mode instead of routing requests through the editor vendor’s own backend. Every editor in this tutorial supports that option somewhere in its AI settings panel, though the exact menu location changes between releases.
Step 6: Set project-level instructions so agents behave consistently
All three AI-native setups support a project-level instructions file that gets injected into every AI request scoped to that repository. This is where you encode house style, testing requirements, and any files the agent should never touch. Create a single file and reference it (or an equivalent) from each editor’s config, so you are not maintaining three different rulebooks.
# .agent-instructions.md
- Always write a Jest test alongside any new function in /src.
- Never modify files under /config/secrets or .env*.
- Run `npm run lint` after any multi-file edit and fix reported errors before finishing.
- Prefer editing existing functions over adding new abstraction layers.
- Do not add new npm dependencies without calling that out explicitly in the response.Cursor reads project rules from a dedicated rules file in the editor’s settings UI, Windsurf reads similar rules through Cascade’s workspace configuration, and Zed’s agent panel accepts a comparable project context file. Keep the content identical across all three so a task started in one editor and finished in another does not silently change behavior.
Step 7: Restrict agents from touching production and secrets
This is the step most tutorials skip, and it is the one that actually matters once an AI agent has write access to your filesystem and a terminal. Add a .cursorignore, an equivalent Windsurf ignore file, and a Zed exclude pattern so none of the three agents can read or modify sensitive paths, even accidentally, while indexing your codebase.
# .cursorignore (Cursor also respects .gitignore by default, but be explicit)
.env
.env.*
**/secrets/**
**/*.pem
**/*.key
deploy/production/**Duplicate that same pattern list into Windsurf’s ignore configuration and Zed’s file exclusion settings. Then do a manual check: ask each agent, in chat, to “list the environment variables used in this project.” If it can answer from file contents rather than just variable names referenced in code, your ignore rules are not working and you should fix that before going further.
Step 8: Run a real startup-time and latency benchmark
Marketing numbers for startup time and input latency vary by test methodology, hardware, and whether a cache is warm. Rather than quoting someone else’s benchmark, run your own on the same machine so the “best code editor” answer reflects your actual setup.
# macOS/Linux cold-start timing (repeat 5x per editor, discard the first run as cache warm-up)
time code .
time cursor .
time zed .
time windsurf .
# Average the last 4 runs for each editor and record the numbers in a table.For input latency, open a large file (2,000+ lines) in each editor and type a 50-character line as fast as you comfortably can, then check whether characters visibly lag behind your keystrokes. This is subjective, but combined with the cold-start numbers it gives you a real feel for the difference rather than a headline figure.
| Editor | Base engine | Reported cold start | Extension ecosystem | Core editor price |
|---|---|---|---|---|
| VS Code | Electron (Chromium + Node.js) | Slowest of the four, 1-2s typical | 50,000+ extensions (Marketplace) | Free |
| Cursor | VS Code fork (Electron) | Similar to VS Code | Full VS Code compatibility | Free / Pro $20 mo |
| Windsurf | VS Code fork (Electron) | Similar to VS Code | Full VS Code compatibility | Free / Pro ~$15-20 mo |
| Zed | Native Rust, GPU-accelerated rendering | Reported 0.12-0.4s in 2026 tests | Smaller, native registry | Free (AI add-on ~$10 mo) |
Step 9: Build a small working project across all four editors
Numbers only tell you so much. To actually compare “best code editor” candidates on a task you will recognize from real work, build the same small project in each one: a Node.js Express API with one endpoint and a Jest test for it. Start with a shared scaffold so the comparison is fair.
mkdir editor-benchmark-api && cd editor-benchmark-api
npm init -y
npm install express
npm install --save-dev jest supertest// package.json — add this to "scripts"
"test": "jest"In VS Code, write index.js and index.test.js by hand or with inline completions only, since plain VS Code has no built-in agent mode without an extension like GitHub Copilot. In Cursor, open Composer and ask it to “add a GET /health endpoint that returns { status: ‘ok’ } and a Jest test that verifies a 200 response and that body.” In Windsurf, give Cascade the same instruction. In Zed, use its agent pane with the same prompt if you have an AI plan active, or write it manually if you are testing the free core editor.
Run the test suite after each editor produces its version, and actually read the diff before accepting it — this is the step that separates a real evaluation from a demo video.
npm test
# Expected output (all four editors should reach this, some faster than others):
# PASS ./index.test.js
# GET /health
# ✓ responds with 200 and status ok (14 ms)
#
# Test Suites: 1 passed, 1 total
# Tests: 1 passed, 1 totalRecord how many prompts it took each agent to get a passing test on the first try, whether it added any dependency you did not ask for, and whether it respected the instructions file from Step 6. That is a far more useful “best code editor” signal than any published benchmark, because it reflects your actual codebase conventions.
Step 9b: Test local and offline model support
Not every team wants to send code to a cloud model provider, whether for cost reasons, latency reasons, or a compliance policy that flatly forbids it. All four editors in this tutorial support routing AI requests to a locally hosted model through Ollama or a compatible local inference server, though the depth of that support varies. Install Ollama, pull a coding-focused local model, and confirm it responds before wiring it into any editor.
ollama pull qwen2.5-coder:7b
ollama run qwen2.5-coder:7b "Write a one-line JavaScript function that reverses a string."
# Confirm you get a real code response before configuring any editor to use this endpoint.In Cursor, local model routing is configured under Settings > Models > Custom Endpoint, pointing at Ollama’s default local address. Windsurf exposes the same option under Cascade’s model provider settings, and Zed’s assistant panel accepts a custom OpenAI-compatible endpoint that Ollama also satisfies. Expect noticeably slower and less capable output from a 7B-parameter local model compared with a frontier cloud model — this is the right tradeoff for privacy-sensitive repositories, not for raw output quality. If your machine has a discrete GPU with at least 12 GB of VRAM, a mid-sized local model becomes usable enough for autocomplete and short chat answers, though multi-file agent tasks still benefit from a larger cloud-hosted model.
Step 9c: Migrate a large, existing codebase without breaking indexing
The sample project in Step 9 is small enough that indexing finishes in seconds. Real repositories are not that forgiving. If you are bringing an existing monorepo with tens of thousands of files into Cursor, Windsurf, or Zed for the first time, indexing can take several minutes and, on underpowered hardware, can make the editor feel unresponsive while it runs.
Before opening a large repository in any AI-native editor for the first time, trim the indexing scope aggressively. Exclude build output directories, vendored dependencies, generated API clients, and any lockfiles, none of which an AI agent needs to read in full to be useful.
# .cursorignore additions for a large monorepo
**/dist/**
**/build/**
**/vendor/**
**/*.generated.*
**/*.lock
**/coverage/**
**/.next/**
**/target/**After adding these exclusions, re-trigger indexing manually rather than waiting for the editor’s automatic re-scan, since automatic re-indexing intervals are usually tuned for small day-to-day changes, not a one-time bulk exclusion. In Cursor and Windsurf, that command sits in the Command Palette under “Reindex Codebase.” In Zed, it is under the assistant panel’s context menu as “Rebuild Project Index.” Expect indexing time to drop by more than half on a typical monorepo once build artifacts and vendored code are excluded, since those directories are often larger in raw file count than the actual source tree.
Step 10: Set up JetBrains AI Assistant if you live in an IDE, not just an editor
If your stack leans heavily on a JetBrains IDE — IntelliJ IDEA, PyCharm, WebStorm, GoLand, PhpStorm, RubyMine, CLion, Rider, RustRover, DataGrip, or DataSpell — you likely do not want to abandon that tooling just to get agentic AI features. JetBrains AI Assistant is built into all of those IDEs, plus Android Studio, and ReSharper for Visual Studio through a preview extension that also brings a version of it to VS Code.
Enable it from Settings > Tools > AI Assistant inside your JetBrains IDE, sign in with a JetBrains account, and configure the same project instructions approach from Step 6 using the IDE’s project-level AI rules file. If your team already pays for JetBrains licenses, this is often the lowest-friction way to add agentic completions without adopting a fifth tool.
Step 11: Configure safe multi-file agent review workflows
Once you trust an agent enough to let it touch more than one file, the review workflow matters more than the agent’s raw capability. Set every editor to produce a diff-first output rather than an in-place edit, and pair that with a pre-commit hook that runs your test suite and linter automatically, so a bad AI-generated change cannot land even if a reviewer misses something.
# .git/hooks/pre-commit (make executable with chmod +x)
#!/bin/sh
npm run lint && npm test
if [ $? -ne 0 ]; then
echo "Lint or tests failed — commit blocked."
exit 1
fiThis single hook catches the most common failure mode across all three AI-native editors: an agent that produces code passing its own internal check but breaking an existing test elsewhere in the repository.
Common pitfalls when setting up an AI-native code editor stack
These are the mistakes that show up most often when developers move from a single editor to a multi-editor AI setup like the one in this tutorial.
- Skipping the ignore-file step. Indexing a repository before excluding secrets means an agent may have already read and cached sensitive content, even if you lock it down afterward.
- Comparing AI output across different models without realizing it. A free-tier editor may quietly route requests to a smaller or older model than a paid competitor, which skews any quality comparison you run.
- Leaving auto-apply enabled on a multi-file agent. Composer and Cascade can both be configured to apply changes without review; that setting is convenient right up until it deletes something you needed.
- Assuming Zed extensions match VS Code coverage. Zed’s registry is real but smaller, and missing a specific linter or language server for your stack is the top reason developers abandon it after the first week.
- Not normalizing project instructions across editors. If Cursor’s rules file says one thing and Windsurf’s says another, switching editors mid-task produces inconsistent output for no obvious reason.
- Benchmarking on a cold cache only once. A single cold-start measurement is noisy; average at least four runs per editor before drawing a conclusion.
- Granting terminal auto-execute permissions to save a few clicks. Cascade and Composer can both run shell commands; an unattended
npm installorgit pushfrom an agent is not a hypothetical risk, it has happened to real teams. - Forgetting that pricing tiers change request limits, not just features. Hitting a monthly agent-request ceiling mid-task in a free tier can make an editor look broken when it is actually just throttled.
Troubleshooting: fixing the most common setup problems
Work through this list in order if any editor in your stack is not behaving as expected.
- Cursor’s settings import did not carry over keybindings. Reopen the import dialog from Cursor Settings > General > Import and select “Keybindings only” — a partial first import is a known quirk when VS Code was mid-update during the first launch.
- Windsurf’s Cascade refuses to run an allowed command. Check for a trailing space or extra flag in the command string in
cascade.terminal.allowedCommands— the match is exact by default in most 2026 builds, not a prefix match. - Zed shows “no language server found” for a file type VS Code handled fine. Search Zed’s extension registry by language name (not by the VS Code extension’s name, since IDs differ) and install the matching language server extension manually.
- Codebase indexing in Cursor or Windsurf seems stuck at a fixed percentage. Large binary files or a bloated
node_modulesfolder not covered by your exclude list are the usual cause — add them explicitly and re-trigger indexing from the command palette. - An AI agent keeps suggesting a dependency you already removed. Its context window may be holding a stale version of
package.json; close and reopen the chat/agent session to force a fresh read. - Zed’s AI features are grayed out after installing. Confirm your Zed Pro subscription is active under Settings > Account — the core editor installs fully functional even without a paid AI tier, which makes the grayed-out state easy to mistake for a bug.
- JetBrains AI Assistant does not appear after enabling it. A full IDE restart, not just a settings reload, is required after first enabling the plugin in most JetBrains IDEs.
- Pre-commit hook from Step 11 is not running. Confirm the file is executable (
chmod +x .git/hooks/pre-commit) and that your editor’s built-in Git client is not bypassing hooks — some GUI Git clients have a “skip hooks” option enabled by default. - Startup time benchmark shows wildly inconsistent numbers. Background indexing from a previous session can distort cold-start timing; fully quit each editor (not just close the window) between benchmark runs.
Advanced tips for teams standardizing on one setup
If you are rolling this out for a team rather than just your own machine, a few extra steps pay off quickly. First, commit the ignore files and the project-instructions file from Steps 6 and 7 directly into the repository, rather than leaving them as personal local configuration — that way every teammate’s agent inherits the same guardrails automatically on clone. Second, standardize on one model provider at the team level even if individual editors support several, since mixed-model output makes code review inconsistent in subtle ways (different agents have different tendencies around comment density, test coverage, and dependency choices). Third, if your organization has compliance requirements around where code and prompts are sent, confirm each editor’s data-handling policy for codebase indexing specifically, since “the AI feature doesn’t store your code” and “the indexing feature doesn’t store your code” are sometimes answered differently by the same vendor.
Finally, budget for the fact that agent-heavy workflows consume API requests or subscription quota faster than typical autocomplete usage. A multi-file Composer or Cascade task that touches five files and runs the test suite twice can burn through a meaningful chunk of a monthly allotment in one sitting — track usage for the first month before committing a whole team to one pricing tier.
Comparing pricing across all four editors
Pricing structures shift often enough in this space that you should always check the vendor’s current page before committing, but here is the shape of it as of September 2026, based on each vendor’s published pricing and independent 2026 editor comparisons.
| Editor | Free tier | Individual paid tier | Team/enterprise tier |
|---|---|---|---|
| VS Code | Fully free, forever | N/A (AI via extensions like Copilot, billed separately) | N/A |
| Cursor | Limited completions and agent requests | Pro ~$20/mo, Pro+ ~$60/mo, Ultra ~$200/mo | ~$40/seat/mo |
| Windsurf | Unlimited Tab, limited Cascade requests | Pro ~$15-20/mo | ~$40/user/mo |
| Zed | Full editor free, AI features excluded | Zed Pro ~$10/mo | Contact sales |
Note the asymmetry: Zed’s AI tier is the cheapest of the three agentic options, but it is also the newest entrant into agent-style workflows and has the smallest extension ecosystem. Cursor’s top Ultra tier is priced for developers who run agent tasks constantly throughout the day, not occasional AI-assisted edits. If your usage is closer to “AI helps when I ask” than “AI runs semi-autonomously all day,” the cheaper individual tiers on any of the three are usually enough.
When VS Code alone is still the right call
It is worth stating plainly: plain VS Code with GitHub Copilot or a similar extension remains a completely reasonable “best code editor” answer for a large share of developers. You keep the largest extension ecosystem, you are not locked into any single AI vendor’s editor fork, and you can swap AI tooling without changing your entire editor. The tradeoff is that agent-mode features tend to arrive later and with less native integration than in Cursor or Windsurf, since they are bolted on through the extension API rather than built into the editor’s core.
If your day-to-day work is mostly single-file edits, targeted refactors, and occasional AI-assisted debugging rather than large multi-file autonomous tasks, staying on VS Code and adding a focused AI extension is often less disruptive than migrating your entire workflow to a new editor fork.
Decision checklist: which editor fits your workflow
Use this as a quick filter after you have run the benchmark and sample project in Steps 8 and 9.
- Pick VS Code if you want the largest extension ecosystem, zero cost, and are fine with AI features arriving as add-ons rather than core functionality.
- Pick Cursor if multi-file autonomous edits are central to your workflow and you want the most mature agent mode of the three, and you are comfortable with its higher price ceiling at heavy usage.
- Pick Windsurf if you want agentic behavior similar to Cursor but at a lower individual-tier price point, and your team already tolerates a slightly smaller feature set around fine-grained agent controls.
- Pick Zed if raw editing speed and low input latency matter more to you than the size of the extension ecosystem, and you are willing to check extension availability for your specific stack before committing.
- Pick a JetBrains IDE with AI Assistant if your stack already depends on JetBrains-specific refactoring and debugging tools that no general-purpose editor replicates well.
None of these choices are permanent. Because Cursor and Windsurf both support the VS Code extension format, and because your project-instructions file from Step 6 is portable across all three AI-native editors, switching later costs you an afternoon of reconfiguration, not a rewritten workflow. Run the benchmark from Step 8 again every few months, since both startup performance and agent capability in this category have moved fast enough in 2026 that a six-month-old comparison is already stale.
Frequently asked questions
Is VS Code still the best code editor in 2026, or has an AI-native editor replaced it?
VS Code remains the most widely used editor thanks to its extension ecosystem of more than 50,000 add-ons and its zero cost, but “best” now depends heavily on how much AI agent autonomy you want. For heavy multi-file agent work, Cursor and Windsurf are commonly rated ahead of plain VS Code in 2026 comparisons; for raw extension coverage and cost, VS Code still leads.
Can I use Cursor and Windsurf extensions built for VS Code without modification?
Yes. Both editors are built on the same core as VS Code and support its extension format directly, which is why the settings-import step in this tutorial works in the first place.
Does Zed support the same extensions as VS Code?
No. Zed uses its own extension format and registry, which is smaller than the VS Code-compatible ecosystem Cursor and Windsurf inherit. Check for your specific language server or linter before switching your daily driver to Zed.
Is it safe to let an AI agent run terminal commands automatically?
Only with a restricted allowlist. Both Cursor’s Composer and Windsurf’s Cascade can execute shell commands as part of an agent task; leaving auto-execute enabled without an allowlist means a single bad instruction or hallucinated command can run unattended.
How much does it cost to run an AI-native code editor as a solo developer?
You can stay on a free tier for light use across Cursor, Windsurf, or Zed. For consistent daily agent use, budget for an individual paid tier, roughly $10 a month for Zed Pro up to $20 a month for Cursor Pro or Windsurf Pro, based on current 2026 vendor pricing.
Do I need to pick just one editor?
No, and most developers who follow this tutorial end up keeping two: one AI-native editor for agent-heavy tasks and VS Code or a JetBrains IDE for everything else. Because settings and project instructions are portable, switching between them for different tasks is a realistic long-term setup, not just a testing phase.
What is the biggest security risk when setting up an AI-native editor?
Indexing secrets before excluding them, and granting unattended file-write or terminal permissions to an agent. Both are addressed directly in Steps 6 and 7 of this tutorial and should be configured before you start using any agent mode for real work.
Which editor has the best JetBrains-style refactoring tools?
None of the four fully replicate JetBrains-specific refactoring and static analysis depth. If that is central to your workflow, pairing a JetBrains IDE with its built-in AI Assistant is usually a better fit than migrating to a general-purpose AI-native editor.