Cursor AI is a full fork of VS Code with AI built directly into the core. What drew me to the tool first wasn’t the interface, but its bold claim – to understand my entire codebase, not just the file I am working in.
This was too powerful to ignore. Plus, the fact that I can give natural-language instructions, reference files with @, and hand off multi-file tasks to an AI agent is astonishing.
In this Cursor AI review, I’ll share my hands-on experience building a real Django project on the Cursor editor, how easy the setup and learning curve felt, and where it delivered on its claims.
What Is Cursor AI?
That deep integration means Cursor understands your entire codebase, can rewrite whole functions or classes in plain English, and even suggest multi-line edits that match the style of your project.
With features like error detection, natural language debugging, and an “Agent Mode” that can carry out tasks end-to-end, Cursor cuts repetitive work and lets you focus on problem-solving.
Who is It For?
Cursor AI code editor is built for developers who want to move faster without sacrificing accuracy. If you’re already comfortable with VS Code, you’ll feel right at home, but the AI-powered upgrades make it especially valuable for:
- Individual developers and freelancers who want an extra set of hands to speed up coding, debugging, and learning unfamiliar frameworks.
- Startup teams looking to build and ship features quickly without getting bogged down in repetitive edits or boilerplate code.
- Experienced engineers who handle large codebases and need an editor that can understand context across multiple files and suggest meaningful improvements.
- New programmers who benefit from natural language explanations, inline edits, and quick answers that help flatten the learning curve.
In short, Cursor AI is for anyone who codes, but it excels most when productivity, speed, and accuracy matter.
Cursor AI Pros and Cons
- Accurate multi-line code suggestions
- Powerful inline edit with Ctrl K
- Works with most VS Code extensions
- Built-in documentation reference with @docs
- Supports frameworks like Django and DRF
- Saves time on boilerplate configuration
- Privacy mode with SOC 2 compliance
- Requires credit card for free trial
- Occasional errors in package handling
- Learning curve for absolute beginners
- Needs stable internet for best results
- Not yet as lightweight as VS Code
- Limited free tier functionality
Cursor AI Features
- AI Agent Mode for multi-file tasks
- Deep project-wide codebase understanding
- Integrated AI chat with @ references
- Natural language code editing and generation
- Intelligent autocompletion with multi-line context
- Built-in terminal with AI command support
- Automated linting error detection and fixes
- Custom coding rules using .cursorrules
- Persistent AI memory across coding sessions
- Familiar VS Code-inspired interface
- Support for multiple large language models
- Privacy Mode to safeguard code locally
- Full access to VS Code extensions
My Hands-On Experience with Cursor AI: A Step-by-Step Guide
Not depending on the marketing claims, I decided to use Cursor myself to find out how it really works for users.
Here’s how it went:
1. Setup and Installation
I started my journey on Cursor’s official website. Right at the top of the page, a big button jumped out: “Download for Windows.”
Unlike most AI coding tools that run in the browser, Cursor is a full desktop application. That means you have to actually download and install it, similar to VS Code.
I liked that the call-to-action was clear, but I immediately noticed there wasn’t a “try it in the browser” option. So, if you want to use Cursor, you’re committing to a local install from the start.
Once I clicked the download button, the installer came down quickly, and within a couple of minutes I had Cursor installed. Launching the app for the first time greeted me with a “Welcome to Cursor” screen.
Here I had the option to Sign Up or Log In. Since I was new, I chose to sign up. This directed me to the browser. The signup flow provided me with several options: email, sign in with Google, GitHub, and Apple. I personally prefer GitHub for dev tools, so I clicked “Continue with GitHub.”
That redirected me to GitHub’s familiar authorization page. Cursor asked for permission to read my email address (read-only). I reviewed the request and hit “Authorize.” Within seconds, I was bounced back into Cursor.
So far, the flow felt straightforward and developer-friendly. I didn’t hit any confusing verification loops or slowdowns.
Right after creating my account, Cursor offered me a 14-day Pro Trial. Here’s where I hit the first bit of friction: to activate the trial, I had to enter my credit card details. Some tools let you try them without payment info, but Cursor requires it upfront.
The subscription after the trial was $20/month (or $16/month if billed annually). I decided to continue and went through the Stripe checkout form. It asked for my billing name, address, city, and postal code, all the usual details.
Once I filled them out and clicked “Start trial,” the payment was processed, and my trial was active. While the payment step was smooth, I know some developers will see this as a downside since you can’t explore Cursor fully without handing over card info.
Integrated Development Environment (IDE) Setup
With the trial activated, I jumped into the initial IDE setup. The app asked if I wanted to import my settings from VS Code — extensions, themes, and keybindings. This is a really thoughtful feature, especially since Cursor is built as a fork of VS Code. I decided to skip importing so I could see Cursor in its pure form.
Style & Setting Configuration
Next came theme customization. I had options like Cursor Dark, Cursor Dark Midnight, and Cursor Dark High Contrast. I clicked through each preview before settling on Cursor Dark.
Then I was introduced to the Quick Start guide. This was a key moment because it highlighted Cursor’s main features and shortcuts right away:
- Ctrl + L for Agent Mode (plan and build anything with AI).
- Tab for predictive completions (Cursor’s “Tab, Tab, Tab” feature).
- Ctrl + K for Inline Edits (rewrite or generate code in context).
I appreciated this onboarding step. It made me feel like I could start taking advantage of the AI features immediately, without guessing what commands to use.
Before entering the main editor, Cursor also asked about data sharing. By default, Cursor learns from your code to improve its suggestions, but you’re in control. I got a checkbox saying I could let Cursor learn from my code, or disable it anytime in settings. I decided to leave it on, knowing I could always turn it off later.
The last setup screen let me review my settings. I set my AI response language to English and installed the cursor terminal command so I could launch projects from the terminal. With that done, I hit Continue, and the setup was complete.
Interface Appearance: Built on a Familiar Design
When the main interface finally appeared, it felt instantly familiar — a VS Code-like layout with a sidebar on the left, a top menu bar, and a central workspace. The sidebar had the usual Explorer and Extensions icons, but at the bottom, I noticed something new: an Agents icon, unique to Cursor.
On the right-hand side was a chat panel where I could start interacting with the AI. It defaulted to Agent Mode (Ctrl + L), and I could add context like files or symbols from my codebase. There were even example prompts like “Write documentation,” “Optimize performance,” or “Find and fix 3 bugs.”
2. Building a Project with Cursor AI
For me, the best way to judge an editor is to build an actual project. Instead of something trivial, I picked a Django app with multiple moving parts: a custom user model, several apps (accounts, core, billing, reports), plus Celery and Redis for background tasks.
This was a full, production-style structure that usually takes hours to set up by hand.
This is where Cursor really had to prove itself: could it understand a complex prompt, execute the steps, adapt when things went wrong, and still get me to a working Django project? Here’s exactly how that played out.
Kicking Things Off in Agent Mode
I opened the Agent Mode panel (Ctrl + L) and typed in a big, ambitious request:
Cursor didn’t just spit out a wall of code. Instead, it first broke my request into a clear checklist in the chat: create the Django project, configure settings, add apps, set up Celery, create the .env, and even generate documentation.
That was my first “wow” moment. It was like pairing with a senior engineer who maps the job before touching code.
Creating the Project Structure
The first command Cursor suggested was django-admin startproject project_pulse.
It, however, didn’t run it automatically. Instead, the IDE paused and asked for my approval before executing it in the integrated terminal. This meant I stayed in control of what was happening under the hood. It also gave me a chance to double-check the command, making sure it wouldn’t overwrite or create files in the wrong place.
However, after running the command, nothing happened. Before I even reacted, Cursor flagged the issue, checking the Django version and pointing out that I was on 4.2.7 while my request was for Django 5.
That awareness was impressive. It not only spotted the mismatch but immediately suggested creating the structure manually to keep us moving forward.
It guided me through creating the root folder, navigating into it, and prepping the environment. I wasn’t left guessing. Cursor adapted to the hiccup and explained each adjustment in plain language.
Dependencies and Environment Setup
Next, Cursor generated a requirements.txt with everything I’d need: Django 5, DRF, Celery, Redis, Pillow, psycopg2, Whitenoise, CORS headers, and more. When a permissions error stopped the file from saving, it rewrote the command with the full path, and it worked flawlessly.
For the .env.template, the first attempt was blocked, but again, Cursor switched tactics. This time, writing the file via echo commands.
Within seconds, I had a full environment template with all the right keys: DATABASE_URL, REDIS_URL, CELERY_BROKER_URL, email settings, and security toggles.
Building the Accounts App
With the basics in place, Cursor created the accounts app and scaffolded the entire user system. It extended AbstractUser with fields like phone number, date of birth, and profile picture, plus a separate UserProfile for things like bio, location, and job title.
It also generated serializers, admin registrations with search and filters, and a DRF integration for authentication.
Every proposed change came with a diff preview. I could accept or reject each block of code, giving me control without having to manually write boilerplate. This balance, AI doing the heavy lifting while I stayed in charge, is where Cursor started to feel like a genuine coding partner.
Overhauling Settings
Cursor then rewrote settings.py, and this was massive. It reorganized everything into sections for Django apps, third-party apps, and local apps.
It set up environment variables with django-environ, added DRF defaults, configured Celery with Redis, included static and media file handling, enabled CORS, and even added logging and email configurations.
Note: This is the kind of work I usually copy-paste from old projects or documentation, tweaking line by line. Cursor did it in one shot, and all I had to do was scan and approve.
Adding Core, Billing, and Reports
One by one, Cursor scaffolded the other apps.
- In core, it generated models for Clients, Projects, Tasks, and Time Entries, complete with serializers and views.
- In billing, it created Invoices and Payment Methods with endpoints for managing them.
- In reports, it set up a simple Report model and views for generating summaries.
These weren’t empty placeholders. The fields and relationships actually made sense for the project I described.
Wiring It All Together
Once the apps were ready, Cursor updated urls.py with clean routes for admin, accounts, core, billing, and reports. It also populated the .env with all the required keys so nothing would break when running the project.
To finish things off, it created a professional README.md with setup instructions, generated a proper .gitignore, and even created folders for static, media, logs, and templates.
At this point, the project already looked like something I’d normally spend a few hours bootstrapping.
Debugging with Cursor AI
When I tried running migrations, I hit two common errors: a missing django-environ package and a Unicode issue in the .env file.
Instead of me hunting for fixes, Cursor spotted both problems, guided me through reinstalling the missing package, and recreated the .env without encoding issues.
After that, migrations ran smoothly. I created a superuser, started the dev server, and launched the Celery worker. Everything worked as expected.
My Overall Experience & Key Highlights
By the time I was done, I had a fully running Django project with accounts, core functionality, billing, reports, and Celery workers. Normally, that setup alone would eat up hours of boilerplate and troubleshooting. Cursor cut it down to a fraction of the time.
What stood out most was how Cursor:
- Broke big prompts into organized steps.
- Adapted quickly when something went wrong.
- Generated meaningful code, not just snippets.
- Provided diff previews so I stayed in control.
- Even wrote documentation and a README.
Yes, I still had to stay engaged, approving changes, checking fixes, but that’s the point.
For a framework as heavy as Django, that’s exactly the kind of help I want.
3. AI Accuracy and Context Awareness
One thing that quickly became clear while building my Django project was that Cursor isn’t just an autocomplete tool.
It has a genuine understanding of your codebase, context, and workflow. This section is where I felt Cursor’s real advantage over a standard editor like VS Code with an AI extension.
Referencing My Codebase with @files and @symbols
As my project grew, I didn’t always want to paste large chunks of code into the chat. Cursor has a smarter way: I could just type @ in the chat box and instantly see a list of files, classes, and functions from my codebase.
For example, when I wanted to extend the Task model to include priority levels, I typed:
Cursor pulled the exact model into context, made the change, and showed me a diff preview before committing. I didn’t have to explain the entire file — Cursor already knew where the Task class lived and how it was structured.
The same worked for functions. When I needed to tweak the generate_invoice function in billing/services.py, I simply referenced @generate_invoice, and Cursor edited only that function, without touching unrelated code.
This made my prompts shorter, my results more accurate, and my workflow faster.
Using @docs for External References
Cursor also let me pull in documentation. While setting up DRF, I used @DRF to reference the official Django REST Framework docs. That meant I could ask things like:
“Configure pagination for @core/views.py using @DRF default pagination class, page size 20.”
Instead of hallucinating, Cursor pulled the right syntax from the docs and applied it correctly. This mix of my codebase and official documentation in the same context is something I’ve never had in a regular editor.
Multi-Line Suggestions and Tab-Through
Cursor’s autocomplete goes beyond filling in the next word. While writing serializers, it often suggested entire multi-line blocks. For example, when I typed class ClientSerializer(serializers.ModelSerializer):, Cursor immediately ghost-wrote the full Meta class with model = Client and fields = “__all__”.
I could hit Tab once to accept, or tap again to cycle through alternatives. This “Tab, Tab, Tab” feature became addictive. It was like having boilerplate written for me, but still under my control.
It wasn’t just serializers. While writing views, Cursor often filled out import statements, queryset filters, and response structures without me asking. The suggestions were based on the rest of my project, so they felt relevant, not generic.
Inline Edits with Ctrl + K
The feature I used most was inline edits. I’d highlight a chunk of code, press Ctrl + K, and type plain English instructions.
- On my Project model, I highlighted the whole class and asked: “Add a method that calculates total billable hours from related tasks’ time entries.” Cursor wrote the method and even imported what I needed.
- In settings.py, I highlighted the email config and typed: “Switch this to use console backend for development.” Cursor rewrote just that section.
- When I made a careless typo in a serializer, I didn’t have to manually debug it. I highlighted the block and asked Cursor to “fix errors and ensure fields match the model.”
Each time, I got a different preview and could decide whether to accept. This was much faster than editing line by line.
Staying Context-Aware
What impressed me most was how Cursor remembered the state of my project. When I asked it to update views, it already knew which serializers and models existed. When I generated new migrations, it kept track of my database config from the .env.
Even during errors — like the Unicode issue with .env — Cursor explained what had gone wrong and adapted the fix. It wasn’t just reactive, it was proactive.
5 Cursor AI Code Editor Features That Stood Out to Me
These features (@files, @symbols, @docs, multi-line edits, and inline edits) are what made Cursor feel less like a “code generator” and more like a pair programmer. I didn’t have to constantly re-explain context or copy-paste files into chat.
Instead, Cursor worked directly with my project, understood its structure, and made changes exactly where I needed them.
For me, this is the real difference between Cursor and a normal AI extension. It’s not bolted onto the editor — it is the editor. And that deep integration shows every time you use it.
4. Productivity Gains: How Cursor AI Changed My Workflow
Once I got into the rhythm of using Cursor, the biggest difference I noticed was in how much smoother my overall workflow became. Instead of bouncing between documentation, Stack Overflow, and my editor, I could stay inside Cursor and let the AI handle the repetitive or boilerplate tasks.
Normally, setting up a Django project with multiple apps, a custom user model, Celery, and Redis is an afternoon’s work. With Cursor, I had the structure ready in under an hour.
I can’t overstate how much time Cursor saved me when errors popped up. For example, when migrations failed because of the Unicode issue in my .env, I didn’t have to Google cryptic errors or scroll through GitHub issues.
Cursor spotted the problem, explained it, and suggested recreating the file with the right encoding. The same thing happened with missing dependencies — it identified the problem and guided me through installing fixes.
These small saves add up. Instead of breaking my flow to research every issue, I could stay inside the editor and keep building.
Staying in the Zone
Another big productivity gain was not having to context-switch. Normally, coding involves switching between the editor, docs, terminal, and browser tabs. With Cursor, I could open the chat panel, reference files with @, and run commands in the integrated terminal — all without leaving the IDE.
Even when I needed help with syntax or best practices, I didn’t need to leave Cursor to search online. Typing a quick question or referencing @docs gave me the answer right in context. That meant I stayed “in the zone” much longer.
Balancing Speed with Control
What I liked most was that Cursor didn’t take over. Every major change came with a diff view where I could accept or reject its edits. That meant I could move quickly without losing oversight of my project. I always knew what was being added or changed, and I could step in if something didn’t look right.
For me, that’s the real productivity gain: not just writing code faster, but spending more of my time on the parts of development that actually matter.
5. Integrations and Extensions
At the end of the day, a coding tool is only as powerful as the ecosystem it fits into.
One of the things I always look at with a new editor is how well it plays with the tools I already use.
To my relief, Cursor handled this part surprisingly well.
Because Cursor is built as a fork of VS Code, it inherits compatibility with most extensions out of the box. When I opened the Extensions panel, I saw all the familiar options: Remote SSH, Dev Containers, WSL, even small quality-of-life tools I use like auto-formatters and GitLens. Installing them was as straightforward as in VS Code. You can also import all your VS Code extensions.
What impressed me most was that my workflow didn’t break. I could still lint with my preferred tools, manage Git history visually, and even connect to remote environments, all inside Cursor.
The difference was that the AI features were layered seamlessly on top. Instead of replacing my extensions, Cursor felt like it was enhancing them.
How Cursor Integrated Extensions on the Django Project
For my Django project, Cursor worked smoothly with the frameworks and libraries I rely on: Django REST Framework, Celery, Redis, and even smaller packages like django-environ and corsheaders. Whenever I asked Cursor to add configuration or update serializers, it wrote code that respected the conventions of those frameworks.
What made this even better was the ability to pull in external documentation with @docs. I could reference Django or DRF directly in my prompts, and Cursor would blend that knowledge with my existing codebase.
If Cursor had forced me to abandon the VS Code extensions I depend on, or if it struggled with common frameworks, it wouldn’t have lasted long in my workflow. But by supporting the same ecosystem I’m already comfortable with, Cursor avoided the “lock-in” problem.
Cursor AI Pricing & Plan
Cursor isn’t trying to be completely free, and that’s important to understand up front.
Large language models cost real money to run, and Cursor is positioning itself as a professional-grade editor. Still, the plans are flexible enough that you can start free and only pay if you really need the extra power.
Cursor offers four main categories of plans:
- Hobby (Free): This tier includes a limited number of Agent requests and Tab completions. It’s enough to test the waters and see how Cursor fits into your workflow. It also comes with a two-week free Pro trial automatically.
- Pro: Unlimited Tab completions, extended Agent limits, access to Background Agents, Bugbot, and maximum context windows. This is the sweet spot for most individual developers.
- Ultra: Everything in Pro, plus 20x higher usage across all supported models (Claude, GPT, Gemini, etc.) and priority access to new features.
- Team: Includes all Pro features plus team-focused tools like enforced Privacy Mode, an Admin Dashboard, centralized billing, and SAML/OIDC SSO.
- Enterprise (Custom): Adds more usage, SCIM seat management, access control, and priority support. Built for large engineering orgs that need control and scale.
Cursor AI Website Builder-abonnementen
Is Cursor Worth the Price?
In my experience, yes. For $20/month, you get a tool that sets up projects, configures frameworks, fixes bugs, and even writes documentation. If I compare that to the time I’d normally spend doing boilerplate work or chasing down errors, Cursor pays for itself quickly.
The Ultra plan at $200/month is steep, but it’s designed for developers or teams who push model usage to extremes and need the highest possible context windows. For most solo devs, I don’t think Ultra is necessary.
Free vs Pro — Who Should Upgrade?
- Stick with Free if you just want to explore the interface, try out Tab completions, or use it occasionally for small tasks. It’s also a good fit if you’re still learning and don’t need heavy usage.
- Upgrade to Pro if you’re coding daily, building full projects, or want Cursor as your main IDE. The extended Agent limits, Background Agents, and unlimited completions make a real difference once you rely on it.
- Teams and enterprise users should look at the higher tiers — the added privacy and admin features are designed for organizations, not individuals.
Notes:
- The Pro trial lasts 14 days, but you do need to enter a credit card upfront. If you don’t cancel, it rolls into $20/month.
- There’s no explicit money-back guarantee, but since the trial is free, you get two weeks to make up your mind before you’re charged.
- You own all the code that Cursor generates. Whether you’re on free, Pro, or Enterprise, the code is yours to use commercially without restrictions. That’s reassuring compared to some other AI tools with fuzzier terms.
- Cursor hits the right balance. The free tier is suitable for experimentation, but the real value lies in Pro, and at $20/month, it feels like a fair trade for the productivity gains I experienced.
Best Alternative to Cursor AI
A close alternative to Cursor is Windsurf, an AI-first IDE built by Codeium. Like Cursor, it’s based on Visual Studio Code and integrates AI directly into the coding workflow.
The difference is in its focus: Cursor emphasizes control and precision, while Windsurf prioritizes automation, enterprise-readiness, and ease of use for large, complex projects.
Cursor vs Windsurf at a Glance
| Feature | Cursor | Windsurf |
|---|---|---|
| Best For | Experienced developers, fast prototyping, manual AI control | Beginners, enterprise teams, large codebases, autonomous workflows |
| Development Process | Manual control with @ symbols and reviews | Automated Cascade agent handles context and tasks with less intervention |
| Core AI Features | Agent Mode for multi-file tasks, wide AI tooling support | Cascade agent with cross-file edits and Turbo terminal command execution |
| Code Generation Speed | Faster for single-file or isolated tasks | Slower initially, but more contextually accurate for big projects |
| Codebase Context | Developer-driven, manually reference files and symbols | AI-driven, auto-indexing entire codebase (RAG-based) |
| Model Access | Flexible access to frontier models like Claude and GPT-4.1 | BYOK for premium models, promotes in-house options |
| IDE Integration | VS Code fork, limited to its environment | Supports multiple IDEs, including JetBrains via plugins |
| Ease of Use | Slightly steeper learning curve | More beginner-friendly and streamlined |
| Pricing (Pro) | ~$20/month | ~$15/month |
| Deployment | No built-in deployment | One-click deployment directly from IDE |
Cursor vs Windsurf: Best Pick for Beginners & Experienced Developers
Cursor is best suited for experienced developers or smaller, fast-moving teams who value precision and control over AI interactions. If you prefer to hand-pick the files, models, and context for each task — and you enjoy reviewing every diff before applying it — Cursor gives you the power to shape the AI exactly to your workflow. It’s especially strong for rapid prototyping or when you need advanced access to premium AI models.
Windsurf, on the other hand, is the better fit for beginners, enterprise teams, or anyone dealing with massive codebases. Its Cascade agent automates much of the context selection and task execution, which reduces manual overhead and keeps you in flow.
Final Verdict on Cursor
After building a full Django project with Cursor, I can say it feels less like an editor and more like a capable co-pilot. It’s best for developers who already have some coding experience and want to move faster by cutting down on boilerplate, debugging, and repetitive setup. If you enjoy staying in control while still letting AI handle the heavy lifting, Cursor is an excellent fit.
That said, beginners might find the learning curve slightly steeper compared to tools that automate everything for you. Cursor expects you to stay engaged and guide it, which I actually see as a strength — but it may take some getting used to.
For me, Cursor delivers on its promise: it speeds up serious development without taking me out of the driver’s seat.
