GitHub Copilot vs Cursor (GPT-5) – My Honest 6-Hour Portfolio Build Review

Introduction

AI-powered coding assistants are evolving fast, and two of the big names in this space today are GitHub Copilot and Cursor AI (with GPT-5). Recently, I decided to put these tools to the test by building my personal portfolio website from scratch in just 6 hours using both. The result was impressive – I managed to create a fully functional site in a single afternoon, thanks to the help of these AI pair programming tools. In this blog post, I’ll share my honest review and comparison of GitHub Copilot vs Cursor (GPT-5) based on this experience, including which tool excelled in speed, how their features differ, and which might give you the best value. If you’re a developer curious about using AI to boost your productivity, read on for a first-hand look at what each of these tools offers.

What Are GitHub Copilot and Cursor (GPT-5)?

Before diving into the comparison, let me briefly explain what these tools are and how they differ in approach:

GitHub Copilot is an AI coding assistant developed by GitHub (and powered by OpenAI models) that integrates directly into popular code editors like VS Code, Visual Studio, and others. It works alongside you as you write code, suggesting lines or blocks of code, functions, tests, and more based on the context. In fact, Copilot is now built right into VS Code Insiders, feeling like a native part of the IDE. Because of this deep integration, Copilot can leverage the full power of the VS Code ecosystem – it has direct access to the editor’s API and benefits from GitHub’s vast dataset and integrations. Essentially, Copilot acts like an AI pair programmer inside your existing workflow, with minimal setup (just install the extension and you’re ready to go).
Cursor is an AI-powered code editor of its own. It’s a standalone application (a fork of VS Code) that comes with AI features built-in from the ground up. Cursor includes a powerful generative model (currently offering OpenAI’s GPT-5 as a preview option) and additional capabilities beyond just autocompletion. It provides a VS Code-like experience, but with extra AI superpowers: for example, Cursor can generate and execute terminal commands with safety checks, apply code changes across multiple files in your project at once, and continuously update its suggestions based on your entire project context in real-time. In short, Cursor isn’t just an extension – it’s its own IDE tailored for AI-assisted coding.

GPT-5 in Cursor: A big highlight of Cursor is that it gives you access to OpenAI’s latest model, GPT-5, which is known to be extremely powerful for coding tasks. This model was made available in Cursor (as a preview) and I was excited to try it out. GPT-5 is OpenAI’s most advanced model at the moment and has shown itself to be highly effective at understanding complex coding instructions and generating solutions. In theory, using GPT-5 via Cursor could provide smarter and more context-aware code assistance than Copilot (which, at the time of this writing, primarily uses the GPT-4 based models for generation). I was curious to see how much difference this would make in practice during my project.

Now that you know the basics of each tool, let’s talk about my experience using them to build a project, and then break down how they compare in various aspects like speed, features, and cost.

Building a Portfolio in 6 Hours with AI Pair Programmers

To test these tools, I set out to build a brand-new portfolio website (with a blog section) in one sitting. My plan was to use GitHub Copilot within VS Code for some parts of development, and also use Cursor’s GPT-5 agent for other parts – effectively having two AI “pair programmers” to assist me. Here’s how the process went and how each tool came into play:

Project Setup: I started by initializing a basic project (using a JavaScript/TypeScript stack for my website). Copilot immediately helped by suggesting commands and boilerplate code. For example, when I began writing a README.md or setting up a package.json, Copilot auto-completed a lot of the repetitive bits (scripts, dependencies) based on context. Meanwhile, I also tried Cursor’s capabilities here. Cursor has a feature called “Composer” agent which can run commands on your behalf. I let Cursor handle some setup tasks by simply instructing it in natural language (e.g. “initialize a new Next.js app” or “set up Tailwind CSS”). Cursor’s agent actually went ahead and executed the necessary steps (running npx create-next-app, installing Tailwind, etc.) with minimal intervention from me. I was impressed to see the Cursor agent take direct action – it even looped on errors or adjustments automatically until the commands succeeded. This saved me time compared to manually running setup commands, and it felt like I had a junior developer executing my setup instructions quickly.
Writing Code (Frontend & Backend): Once the scaffolding was in place, I started building out the components and pages of the portfolio. Here I used GitHub Copilot mostly within VS Code for quick code completions as I typed. Copilot excels at inline code suggestions; for instance, as I created a React component for the header, Copilot suggested the entire JSX structure after I wrote a few lines, which was pretty spot-on. It also suggested utility functions and even simple unit tests without me having to prompt it explicitly. On the other hand, I switched to Cursor for more complex or multi-step coding tasks. With Cursor (GPT-5), I could ask it in plain English to implement a feature (like “create a responsive navigation bar with a dark mode toggle”) and it would generate the necessary code across multiple files – e.g., it edited the CSS/Tailwind config and the React component together. This is something vanilla Copilot typically doesn’t do, as Copilot usually focuses on one file at a time. In Cursor, the AI had the whole project context and could apply changes in several files in one go based on my single instruction. I found this incredibly powerful. In one case, I needed to update a data model in my backend and propagate the changes to related frontend code; I simply described the change once, and Cursor’s agent modified the model file, the API route, and the frontend fetch logic accordingly in a single sweep. That would have taken multiple manual edits with Copilot (and multiple prompts), but Cursor handled it in one prompt.
Debugging & Iteration: During development, I of course hit a few bugs and needed to make adjustments. Here, both tools were useful in different ways. With Copilot, I used the Copilot Chat (the chat interface where you can ask questions about your code). It was helpful for getting quick explanations of error messages and suggestions for fixes, but it generally kept the scope local (e.g., it would suggest how to fix the function I was in). Cursor’s GPT-5 felt more holistic in debugging. I could ask Cursor something like “Why is the contact form not sending emails?” and because it had the context of my entire project, it responded with an analysis that pointed out an issue in my API route file and suggested a fix, while also reminding me to update an environment variable. In terms of speed here, Cursor’s agent responded and even applied the fix faster than I could have done searching through the project manually. Some of Cursor’s agility might be thanks to GPT-5’s advanced reasoning or the way Cursor indexes the whole codebase for context. Copilot was no slouch either – it helped me fix a React state bug quickly by suggesting the correct hook dependency – but it often required me to ask about each issue in isolation. With Cursor, it was like having an AI that was aware of all parts of the app simultaneously, which made certain cross-cutting fixes quicker.

By the end of the session, I had a portfolio site complete with multiple pages, a blog system, and responsive design – all done in around 6 hours (with short breaks included!). It’s a scenario that truly showcased how AI coding assistants can accelerate development. Now, based on this experience, let’s compare Copilot and Cursor in key areas:

Speed and Performance

One of the first differences I noticed was speed – how fast each AI tool provided suggestions or executed tasks:

Cursor felt noticeably faster in generating code suggestions and performing actions. For example, when I paused mid-sentence in code comments to think, Cursor’s autocomplete (GPT-5) would almost instantly suggest the next lines of code or even create an entire function in a flash. Even when using its Composer agent to run multi-step tasks, it completed them quickly. My subjective impression was that Cursor’s responses came with less lag than Copilot’s. In fact, independent tests have measured this too: Cursor’s AI autocomplete was clocked around 320ms to produce suggestions, versus about 890ms for GitHub Copilot for similar tasks. That’s a significant difference. I found that reduced latency kept me in flow – I wasn’t waiting long for the AI to catch up.
GitHub Copilot was a bit slower in comparison, though not so much that it was problematic. Copilot’s suggestions typically appear within a second, which is usually fine, but when you’re in a rapid coding frenzy, those extra milliseconds add up. It’s worth noting that Copilot’s performance is generally lightweight and fast for single-file edits – it’s optimized well for suggesting the next chunk of code as you type. However, when it comes to running larger tasks (like refactoring multiple files or understanding a big codebase), Copilot doesn’t operate on the whole-project level in one go, so you might need multiple steps which feels slower overall. In my case, when I needed to apply a change across many files, Copilot would handle it piece by piece (slower, and requiring me to coordinate the changes), whereas Cursor did it in one sweep (faster from the user perspective).
Performance in large projects: My portfolio project is relatively small (a few dozen files). Both Copilot and Cursor performed well in this context. I was curious, though, about how Cursor handles truly large codebases, since it loads the entire project for context. Some reports from other developers indicate that Cursor can experience performance issues on very large repositories – for instance, slight lag or UI freezes when dealing with a huge number of files or packages. Copilot, integrated in VS Code, is pretty lightweight and doesn’t slow down the editor itself regardless of project size (since it’s mostly querying the AI on-the-fly for the current file). So, if you’re working in a massive monorepo, Copilot might actually feel smoother, whereas Cursor’s all-in-one approach could introduce some slowness when scaling up. I didn’t hit those limits in my project, but it’s a point to keep in mind for enterprise-scale codebases.

Verdict on Speed: For small-to-medium projects or single tasks, Cursor (with GPT-5) gave me faster responses and a more instantaneous coding flow. Copilot was slightly slower to suggest code and can require more iterative prompts for big changes, but it remained reliable and didn’t hinder my progress. On huge projects, Copilot’s lightweight approach might have an edge in responsiveness, while Cursor’s comprehensive context could introduce lag. In the end, both are reasonably fast, but I’d give Cursor the edge in speed for the scenario I experienced.

Features and Capabilities Comparison

Both Copilot and Cursor offer a suite of AI features, but there are some key differences in capabilities that I observed:

1. Scope of Assistance (Single-file vs Multi-file)

GitHub Copilot typically works on a file-by-file basis. It shines at inline code completion and small-scale suggestions. For example, inside a single JavaScript file, it can suggest the next line or even a whole function implementation effortlessly. Copilot can also do some context-aware tasks like writing tests for a given function or explaining code, but these are usually constrained to the content of the open file or an explicitly given snippet. Even the newer Copilot Chat and “agent” features are somewhat scoped to your current workspace or file in practice. During my project, this meant if I wanted to refactor something across multiple files (say, rename a component and update references project-wide), Copilot alone wasn’t going to just handle that in one prompt – I had to do it file by file (with Copilot assisting each step).
Cursor, on the other hand, was designed for project-wide context. It could take a high-level instruction from me and apply it across many files automatically. As mentioned, I could ask Cursor’s GPT-5 agent to make a change or add a feature that spanned the front-end and back-end, and it would intelligently modify all the relevant files in one go. This is possible because Cursor indexes the entire codebase and its AI operates with a holistic view. One example from my experience: updating the site’s color theme involved changes in the CSS, some React components, and a config file – I described the update once to Cursor, and it handled all three file changes correctly. This kind of multi-file editing by AI command is a standout feature of Cursor. In one benchmark, Cursor had an 83% success rate generating a full React component with all necessary pieces across files on the first try, whereas Copilot achieved about 67% on a similar task (requiring more manual tweaks after). That suggests Cursor can more reliably execute broader changes out-of-the-box.

2. AI “Agent” Actions and Autonomy

Copilot’s Agent (Preview): GitHub has been working on more “agentic” features for Copilot, like the ability to perform multi-step tasks or run test commands as part of Copilot X. In the latest version, Copilot can indeed take some actions – for instance, it might suggest running a build command or creating a PR for you – but these are quite limited and always require your confirmation. There are safety rails: any terminal command Copilot suggests must be manually approved by the user before execution, for obvious security reasons. Also, Copilot’s multi-step abilities are still developing; sometimes the suggestions for larger tasks are off-target (one stat I found: ~42% of Copilot’s “next step” suggestions were irrelevant in one JavaScript testing scenario). In my usage, Copilot’s agent-like behavior was minimal – it’s mostly a smart assistant that suggests, and I execute or apply the suggestions. That’s fine for most cases, but it means Copilot is not fully “hands-off.” I had to drive the process and use Copilot’s output as guidance.
Cursor’s Agent (Composer): Cursor really pushes the envelope with an integrated agent called Composer. This agent can take more autonomous actions. I saw this when Cursor’s agent ran installation commands and fixed errors automatically during my setup. The Cursor agent will actually run in a loop: if the code it generates throws an error, it can catch that and adjust the code in the next iteration, trying again until it works. It’s almost like having a junior developer who will keep debugging their code until it runs. Of course, it doesn’t always get things perfect, but when it works, it’s a magical time-saver. For example, I asked Cursor to “generate a sitemap for my site and save it as XML.” It created a script for the sitemap, ran it, realized there was a minor path issue, fixed the path, ran again, and produced the sitemap – all without me intervening beyond the initial request. This kind of agentic behavior is Cursor’s strong suit, and it felt like the future of AI coding: less about suggesting and more about doing. It’s worth noting you still oversee and approve what it’s doing (it shows the commands and changes it plans), but the workflow is smoother when the AI can just handle the grunt work automatically.

3. Quality of Suggestions and Accuracy

Both tools are using cutting-edge AI models (GPT-4 for Copilot, GPT-5 for Cursor at the time of my test). Both produced high-quality code suggestions overall, but GPT-5 did have an edge in some complex scenarios:

In straightforward tasks (like writing a well-known algorithm or a standard component), I didn’t see a huge difference – both Copilot and Cursor gave me correct and clean code most of the time. Copilot’s suggestions were often a bit more concise, whereas Cursor (GPT-5) sometimes gave a more verbose answer (initially including extra comments or explanation until I guided it to be more succinct). This aligns with some early impressions from others that GPT-5 can be a bit verbose by default, but you can steer it to be concise.
For more complex problems, like debugging a tricky bug or optimizing code, GPT-5 (Cursor) showed its strength. One anecdote: I had an issue with an asynchronous function that was fetching data for my blog. Copilot suggested a solution using async/await that was almost right but missed a race condition. Cursor’s GPT-5 not only suggested the async/await fix but also pointed out the race condition scenario and recommended a fix (using a mutex lock pattern). That extra level of reasoning was impressive. In fact, engineers using GPT-5 have noted it can solve complex bugs that stumped other models. My experience reflected that – GPT-5 (via Cursor) felt like a more knowledgeable assistant, likely because it’s a newer model with a broader training.
I did run some quick informal accuracy checks. For instance, I asked both tools to generate a small function and then wrote unit tests to see if the function worked as expected. In those small tests, both passed similarly. However, when I looked up broader stats, I found one source where Cursor (GPT-5) had about 89% accuracy in a Python debugging task, whereas Copilot (GPT-4) had about 78%. That’s not a scientific proof of anything, but it suggests GPT-5’s extra training data or reasoning ability can translate to solving problems more correctly in certain domains.

4. Integration and Ecosystem

Copilot Integration: This is one area where GitHub Copilot clearly dominates. Copilot is deeply integrated not just in VS Code, but also in multiple IDEs (Visual Studio, JetBrains suite, Neovim, etc.). I was using VS Code, but if tomorrow I switch to say IntelliJ, Copilot has a plugin there too. This flexibility is great. Moreover, Copilot ties into your GitHub account and comes with features like pull request reviews (Copilot can suggest fixes or improvements in PR diffs) and it works with GitHub’s ecosystem (Codespaces, etc.). There’s also a growing ecosystem of Copilot extensions and labs – for example, Copilot can explain code, translate code, or help with tests via official or community add-ons. During my project, I used Copilot for code suggestions and some Copilot Chat Q&A, but knowing that it could also assist in PR reviews or commit message generation down the line is a plus for future use. Essentially, Copilot doesn’t require you to leave your favorite coding environment, and it augments other GitHub features.
Cursor Integration: By contrast, Cursor locks you into its own editor. The Cursor editor is basically VS Code under the hood (so it feels familiar), and it can even use regular VS Code extensions to some extent. However, you have to be in Cursor’s application to use its AI features – it’s not a plugin you can pop into other IDEs. This means if you love IntelliJ or the regular VS Code, you’d have to switch to Cursor’s version to get GPT-5 assistance. In my case, adapting to Cursor’s editor was not difficult (it’s very similar to VS Code), but I did miss some personalization and certain extensions I have in my main VS Code setup. Also, Cursor’s integration with source control or other tools isn’t as deep as GitHub’s. For example, Cursor doesn’t automatically do anything special with my GitHub PRs or issues – it’s primarily focused on the coding part itself. One more point: I’m a Linux user, and I noticed Cursor’s app on Linux is only distributed as an AppImage (no native package), which required some fiddling to integrate well with my system. It worked fine, but clearly the focus of the Cursor team isn’t on multi-IDE support or broad ecosystem integration – it’s on making their one editor as powerful as possible with AI. So, if you’re okay with using a dedicated AI-centric IDE, Cursor is great; if you prefer an AI that plugs into your existing toolchain, Copilot has the advantage.

5. Model Flexibility and Customization

Cursor’s Model Options: An interesting aspect of Cursor is that it doesn’t limit you to a single AI model. While I primarily used GPT-5 (because that’s the headline feature), Cursor actually lets you choose between different AI models for different purposes. In the settings, I noticed options for models like GPT-4, Claude, etc., and even combinations where one model’s output feeds into another. For example, one could use Anthropic’s Claude for some high-level planning or doc generation, and GPT-4 or GPT-5 for actual code writing. This kind of flexibility is great for power users – you can pick a faster model for simple tasks or a more powerful model for complex tasks. It also means if you have your own OpenAI API key or another model API, Cursor might let you configure that (though I didn’t try in my session). Essentially, Cursor is more open in letting the user decide or tune the AI behind the scenes.
Copilot’s Fixed Model: GitHub Copilot, in comparison, is more of a black box in terms of model. It uses OpenAI’s Codex/GPT models under the hood (and recently they’ve likely integrated GPT-4 for Copilot Chat and some of the advanced features). But as a user, you don’t get to choose or swap the model – Copilot picks the best available model for you and that’s what you get. There’s no configuration to use, say, Claude or any other AI; it’s all managed by GitHub/Microsoft’s service. For most users this is fine – the default model is excellent and you don’t need to think about it. But it does mean less flexibility. If, for instance, a new model comes out that you think is better, you can’t plug it into Copilot on your own. With Cursor, theoretically, if they support it, you could. This is a trade-off between simplicity (Copilot’s approach) and flexibility (Cursor’s approach). In my personal use case, I was happy to let Copilot handle the AI selection (it worked well), but I can see advanced users or teams with specific needs preferring Cursor’s customizable model usage.

Cost and Subscription Value

When it comes to choosing any service, pricing can be a deciding factor. There’s a notable difference here:

GitHub Copilot Pricing: Copilot is the more affordable option of the two. As of 2025, an individual Copilot subscription costs about $10 USD per month. For that price, you get unlimited usage (within reason) of the AI suggestions in your editor. GitHub even offers a limited free tier – roughly 2,000 code completions per month for free – which can be enough to try it out or use sparingly. Many students and open-source developers have access to Copilot for free as well (GitHub has initiatives for verified students and maintainers). In a professional context, Copilot also offers a business plan (around $19 per user/month) that adds enterprise features. Given its low cost, Copilot has seen huge adoption – reportedly, 78% of Fortune 500 companies have used GitHub Copilot in some capacity, which speaks to its value proposition. In my case, I find $10/month very reasonable for the boost in productivity I get; it’s like paying for a super smart coding assistant that works 24/7.
Cursor Pricing: Cursor, on the other hand, comes in at about $20 USD per month for the Pro plan, which is double the price of Copilot. There may not be a free tier for Cursor’s full features (aside from maybe a trial or limited “hobby” usage). Essentially, you’re paying a premium – likely because using GPT-5 and offering all those additional features incurs higher costs. For some developers, $20/month might still be a fair price if Cursor dramatically improves their workflow. In my short project, Cursor definitely saved me time in certain areas, but one question to ask is: Does it provide enough extra value to justify costing twice as much? For me, the jury is still out. If I were working on a complex project daily, needing those multi-file AI edits and agent capabilities frequently, I might lean towards paying for Cursor. But if my use is more straightforward (and I’m already happy with VS Code + Copilot), sticking with the cheaper Copilot plan could make more sense. It’s also worth noting that with Cursor, you may end up using your own OpenAI API key for some features (like if you choose custom models), which could have additional costs, whereas Copilot’s flat fee covers all usage without worrying about API charges.
Value for Money: In summary, Copilot is the budget-friendly choice that covers the majority of use cases for an AI coding assistant, while Cursor is a pricier, premium tool that offers more powerful features and the latest models. If you’re price-sensitive or just need the basics, Copilot wins. If you really want the cutting edge (GPT-5, advanced agents) and are willing to pay for it, Cursor might be worth the investment. Just remember that Cursor’s higher cost also ties you to their ecosystem, whereas Copilot’s lower cost integrates into tools you already use, which might also influence the overall value to you.

Pros and Cons Summary

To wrap up the comparison, here’s a quick summary of the pros and cons I found for each tool:

GitHub Copilot – Pros:

Seamless Integration: Works inside popular editors (VS Code, etc.) with deep integration into the GitHub ecosystem (pull requests, multiple IDE support).
Excellent Code Completion: Provides fast and accurate inline suggestions for a wide variety of languages and frameworks. Great for small to medium coding tasks without much setup.
Affordable: Lower cost ($10/month) and even a free tier for light use, making it accessible to individuals and widespread in industry (used by many major companies).
Mature & Evolving: Backed by Microsoft/GitHub, with continuous improvements (e.g. Copilot X features like chat and terminal command suggestions) and a growing community/extension ecosystem.

GitHub Copilot – Cons:

Scoped Assistance: Largely focused on one file at a time. Struggles with tasks that involve coordinating changes across an entire project in one go.
Limited Autonomy: Acts as a suggestion engine rather than an autonomous agent. It won’t automatically run tasks (and any command suggestions require manual approval) – you still have to do the driving.
Fixed Model: No control over the AI model used – you get what GitHub provides. If you want to use the “latest and greatest” model beyond what’s offered, you can’t tweak that in Copilot.
Requires Internet & Data Sharing: (This is true for Cursor too) Copilot sends code snippets to the cloud for AI processing. It has privacy measures, but some very sensitive projects might have policies against that.

Cursor (with GPT-5) – Pros:

Project-Wide AI Power: Can handle multi-file edits and understand the full project context, making large-scale refactors or feature additions much faster and easier.
Advanced AI Model (GPT-5): Offers access to the most powerful OpenAI model, which can provide smarter, more context-aware assistance and potentially solve more complex problems.
Agent Capabilities: The Composer agent can execute commands, iterate on errors, and automate tasks beyond just code suggestions. This can save time on setups, running tests, and other repetitive chores.
Customizable & Flexible: Allows choosing different AI models and mixing them for different tasks. You have more control if you want it (and can potentially use your own API keys or models for specific needs).
Innovative Features: Being a newer tool, it’s experimenting with features like using screenshots as input for coding, integrating web search, etc. It feels like an all-in-one AI development environment pushing new ideas.

Cursor – Cons:

Higher Cost: Roughly double the price of Copilot for an individual, which can be hard to justify if you don’t heavily use the extra features.
Single-IDE Lock-In: You must use the Cursor editor to get its benefits. It’s a good editor, but not as universally adopted as VS Code, and the lack of support for other IDEs or a web version means less flexibility in your workflow.
Performance on Large Projects: While great on smaller projects, it can become resource-intensive on huge codebases (some users report slowdowns or memory usage spikes). The all-encompassing approach has a trade-off in heavy scenarios.
Less Established: It’s a newer entrant compared to Copilot. That means smaller community, fewer third-party extensions purely for Cursor (though VS Code extensions mostly work), and potentially more bugs to iron out. Also, platform support quirks (like the Linux AppImage issue) hint it’s not as polished on all fronts.

Conclusion

So, which AI coding assistant is better – GitHub Copilot or Cursor with GPT-5? The answer, in my opinion, depends on your needs and context:

For a typical developer working on day-to-day projects (especially small to medium size), GitHub Copilot is likely the best choice. It’s seamlessly integrated into your workflow, very capable at helping with code, and extremely cost-effective. I was able to get plenty of help from Copilot in building my portfolio – from generating boilerplate to suggesting clever one-liners – all without breaking the bank. Its suggestions might be slightly slower or less “magical” than Cursor’s at times, but they are reliable and convenient. Copilot feels like a natural extension of the coding process if you’re already on VS Code or GitHub.

On the other hand, if you are pushing the boundaries of what AI can do in development – say you want an assistant that can manage entire project modifications, or you’re dealing with very complex coding problems – Cursor (with GPT-5) can be a game-changer. In my 6-hour experiment, Cursor’s ability to act on commands and handle broad changes in one go was a huge productivity boost. The power of GPT-5 showed in how it tackled problems and understood my requests in depth. It genuinely felt like I had an extremely skilled engineer pair-programming with me who could take over tedious tasks. However, that power comes at a literal price (the higher subscription cost) and requires you to adapt to a new tool environment.

For me personally, I plan to use both depending on the situation. Copilot will remain my go-to for quick coding sessions and general use, due to its convenience and low cost. But I’ll fire up Cursor whenever I’m working on something where I need that extra muscle – maybe a big refactor or when I’m stuck on a hard bug and want to see if GPT-5 can crack it. It’s similar to how one might use a lightweight editor for simple tasks and a heavyweight IDE for big projects.

Final takeaway: Both GitHub Copilot and Cursor are excellent examples of how AI can accelerate software development. Copilot is like a trusty assistant that’s by your side all the time, making the routine work easier. Cursor is like a power tool you bring out for heavy lifting, impressing you with what’s possible when AI is fully integrated into the development process. If you have the opportunity, I’d recommend trying both to see which fits your style – and you might find, like I did, that a combination of both is the ultimate productivity hack. No matter which you choose, it’s clear that leveraging these AI tools can help you build and ship projects faster than ever before, as evidenced by my 6-hour website build. Happy coding, and enjoy the AI-assisted development ride!

Last updated on August 10, 2025 at 7:00 AM UTC+7.