Cursor 1.0 Review: The AI-First Code Editor Comes of Age

Introduction

AI coding assistants have been popping up everywhere, but Cursor 1.0 marks a major milestone in this fast-evolving space. Cursor isn’t just an autocomplete plugin – it’s a full-blown code editor supercharged with AI, and the new 1.0 release (launched June 4, 2025) delivers some ambitious features – see: cursor.com.

We’re talking about AI that reviews your pull requests, remembers conversations, writes code across files, and even works in Jupyter notebooks. In this in-depth review, we’ll explore Cursor 1.0’s features, how they stack up against rivals like GitHub Copilot and Windsurf, and what it’s like to code with an “AI pair programmer” living inside your IDE.

Cursor 1.0 is here! And it arrives not quietly. The update brings BugBot for automated code review, Background Agent for multi-tasking in the cloud, a first look at “Memories” for persistent context, one-click setup for MCP integrations (the “Model Context Protocol” plugin system), Jupyter Notebook support, richer chat responses (tables, diagrams), and a refreshed settings dashboard. It’s a laundry list that sounds like a developer’s dream. But does Cursor deliver on these promises? Let’s dive in.

(If you’re new to Cursor, fear not – we’ll also cover what it is and how it differs from more familiar tools like Copilot.)

What Exactly is Cursor?

Simply put, Cursor is an AI-native code editor. It’s built on a fork of Visual Studio Code, see: builder.io, so it feels familiar, but under the hood it’s bristling with AI capabilities. Think of Cursor as VS Code on steroids– you get your file explorer, editor tabs, and terminal, but almost every part of the workflow has an AI assist if you want it. Cursor’s makers (a startup called Anysphere) have designed it so you can write code using natural language instructions.

Instead of manually typing boilerplate or searching Stack Overflow, you can ask Cursor’s AI to generate a function, refactor a class, or explain a snippet. And it doesn’t stop at single-file autocomplete; Cursor can understand your entire project context and even coordinate changes across multiple files.

Importantly, Cursor isn’t just one monolithic AI. It gives you access to multiple AI models and tools optimized for different tasks. By version 1.0, Cursor supports state-of-the-art models like OpenAI’s GPT-4 (for deep reasoning), Anthropic’s Claude 3.5 (codenamed “Sonnet”, tuned for coding), plus its own specialized models for faster autocomplete (the “Tab” model) and small tasks.

You can choose which model to use or even enable a “Max Mode” to unleash the full power (with higher token usage) when needed. In short, Cursor is an IDE where AI is a first-class citizen – not a clippy add-on, but an integrated pair programmer that’s aware of your codebase and workflow.

Before we explore the new features in 1.0, it’s worth understanding Cursor’s core philosophy. Traditional editors + Copilot give you inline suggestions, but you, the developer, still drive. Cursor’s approach is more agentic: it lets the AI take initiative in larger tasks (if you permit it). It has a Composer mode where you describe what you want (“build me a simple web server with user auth”), and it will generate the necessary files and code to get you there, see: builder.io.

Its chat interface isn’t just Q&A; it can apply changes to your code directly from the conversation. Cursor even allows custom rules – you can define a .cursorrules JSON file with project-specific guidelines to steer the AI’s coding style (e.g. coding standards, preferred frameworks, etc.). In many ways, Cursor is a playground for cutting-edge AI coding features, which can be both exciting and, at times, a bit overwhelming.

Now, with Cursor 1.0, the team has polished and expanded this toolkit significantly. Let’s break down the headline features in this release and how they work in practice.

Feature Overview of Cursor 1.0

Cursor 1.0 arrives roughly two years into the product’s evolution (graduating from a long beta). The emphasis is on making the AI more proactive and integrated in your development cycle. Here’s a quick overview of what’s new:

• BugBot – an automated AI code reviewer for your pull requests.
• Background Agent – an AI agent that can run in the cloud, parallel to your coding, handling background tasks.
• Jupyter Notebook Support – Cursor’s AI can now work inside .ipynb notebooks, editing multiple cells for data science workflows.
• Memories – a system for the AI to remember key facts from past conversations on a per-project basis, so it doesn’t forget context as you work.
• MCP One-Click Integrations – easy installation of Model Context Protocol plugins to connect Cursor with external tools and data sources (with OAuth support).
• Richer Chat Output – the chat can now render Markdown tables and even Mermaid diagrams, so the AI’s answers can include visuals/diagrams when appropriate.
• Refined UI & Settings – a new dashboard for usage analytics, a cleaner settings page, and quality-of-life improvements like PDF parsing in context and faster responses via parallel calls.

That’s a lot to unpack! Each of these features merits a closer look. Let’s examine them one by one and see how they can impact your developer workflow.

BugBot: AI-Powered Code Review

One of Cursor 1.0’s flashiest additions is BugBot, an AI code review assistant that plugs into your GitHub pull requests.Think of it as an automated reviewer that never sleeps. When you open a PR, BugBot will scan the code changes for potential issues – bugs, logic errors, style problems, you name it – using the same AI that powers Cursor’s coding agent.

It then leaves comments on the PR in GitHub, highlighting the problematic code and explaining what might be wrong. This all happens before your human peers even get to the review. The idea is to catch bugs early and save you from merging mistakes into main.

For example, imagine you submit a PR and accidentally used = instead of == in a critical if statement. BugBot might comment: “Possible bug: This assignment inside the conditional looks unintended – did you mean to compare for equality instead?” along with a snippet of the code in question. The comment isn’t just passive-aggressive linting; it often includes suggestions for how to fix it.

The really cool part is the integration back into Cursor. Next to BugBot’s comment on GitHub will be a button, “Fix in Cursor”. Click it, and Cursor opens that file and pre-fills a prompt to its AI with instructions to address the issue. It’s a seamless loop: AI finds a bug, and AI helps you fix it, all within your editor. In Cursor’s chat pane you might see a prompt like: “Fix the issue identified in comment #2 (division by zero possibility) in the function calculateMetrics.” The AI then proposes a code change to resolve it. You can review and apply the patch immediately.

BugBot essentially offloads the first pass of code review to an AI – something GitHub has also been experimenting with (Copilot now has a code review feature in limited release). Copilot’s approach will check your diff and suggest changes inline in VS Code. Cursor’s BugBot is slightly different in style: it behaves like a GitHub user leaving PR comments, and you respond by jumping back into Cursor to fix issues.

In practice, BugBot can save tons of time catching bugs before they hit your main branch, see: reddit.com. It’s like having a tireless junior dev scanning your commits for mistakes.

That said, BugBot isn’t magical – it relies on AI models analyzing diffs, which means it might sometimes flag false positives or miss subtle logic issues. And there’s a cost: BugBot runs on powerful models, so usage counts against your Cursor plan (one user noted it was “a dollar or more each time” for a one-click fix in early trials), see: builder.io.

In Cursor 1.0, BugBot comes with a 7-day free trial and then presumably requires a Pro subscription or usage credits. The setup requires you to connect Cursor to your GitHub account and enable the Cursor GitHub App on specific repositories, see: docs.cursor.com – a one-time process for each org/repo you want BugBot on.

Hands-on experience: We tested BugBot on a small Node.js project. It immediately caught a potential memory leak where an event listener wasn’t removed – a detail even the human reviewers missed at first. The comment was helpful, citing the line and suggesting using once() or removing the listener. Fixing it via “Fix in Cursor” launched the Cursor editor and the AI provided a quick patch.

This kind of integration feels futuristic: code review and code fixes happening in a tight AI-driven loop. It won’t replace careful human review for critical code, but it’s a powerful safety net.

For teams, BugBot could enforce best practices automatically. Imagine always having lint suggestions and potential bug warnings on every PR without burdening a senior dev. Of course, one must avoid over-reliance – treat BugBot as an assistant, not an authority. Its suggestions should be vetted, much like you’d treat a human intern’s feedback. Still, as a 1.0 feature, BugBot is a standout that shows how AI can participate not just in coding but in the surrounding development process (code reviews, QA).

Background Agent: Your Parallel Programmer in the Cloud

Another game-changing feature in Cursor 1.0 is the Background Agent. This was in preview earlier, per cursor.com, but now it’s available to all users out-of-the-box. The concept is that Cursor can run AI-driven tasks in parallel, in a remote environment, without tying up your local editor. You essentially get an “AI worker thread” for coding.

Enabling the Background Agent is as easy as hitting a cloud icon in the Cursor chat UI or pressing Cmd/Ctrl + E(provided you’ve disabled privacy mode – it needs cloud access). Once activated, you can instruct the agent to do things and then continue with your own coding while it works. For example, you could ask, “Hey Cursor, refactor all my database queries to use parameterized statements and open a PR,” and let it churn away in the background.

Or “Add logging to all functions in this directory,” and then watch as it quietly makes a branch, edits multiple files, runs tests, etc., all without you babysitting each step.

If this sounds a bit like AutoGPT or “AI agents” hype, it is – but implemented in a developer-friendly way. In fact, Cursor’s agent mode is heavily inspired by a similar feature in Windsurf (formerly Codeium) known as Cascade. Windsurf did it first, allowing AI to autonomously run commands and modify code across files, and now Cursor offers its own spin. Both essentially let the AI orchestrate multi-step coding tasks: write code, save files, run build/test commands, read outputs, continue the cycle – all following a high-level goal you gave it.

Under Cursor’s hood, the Background Agent spins up a remote container (so it doesn’t wreck your local environment if something goes awry) where it can execute code and git operations safely.You can see a control panel to monitor what tasks the agent is doing, check its intermediate outputs, or step in if needed, see: cursor.com. At any time you can take over or kill the agent process if it’s going off-track. In 1.0, this feature moved from beta to general availability, reflecting that it’s becoming stable and trusted.

In practice, using the Background Agent feels like having a junior programmer working concurrently on a task. For instance, I asked the agent to generate unit tests for an existing Python module. It created a new test file, wrote several test cases, ran pytest (it figured out I was using pytest by reading the dev dependencies), saw a test fail, adjusted the code to fix a bug, re-ran tests, and finally informed me it’s done – all while I was editing a different part of the project. Seeing code appear and tests run without my direct intervention was thrilling and a bit spooky (in a good way).

Cursor’s team mentions they use background agents internally for tasks like “fixing nits, doing investigations, and writing first drafts of medium-sized PRs”. That aligns with what I saw – it excels at grunt work like rote refactoring and bulk code updates.

A key thing to note is that parallel is not just marketing here. Cursor can run dozens of agent tasks concurrently if you desire. This is limited by fairness and cost (each agent uses compute on the backend), but it’s theoretically possible to have multiple agent instances tackling different subtasks of a project all at once. For a tight deadline, you could have one agent writing documentation, another optimizing your SQL queries, another checking for security issues – all simultaneously.

This is a radically different paradigm from the single-threaded Copilot usage where you prompt, wait, accept, repeat. It moves closer to how a team works – parallelizing work – except your “team” is AI clones of varying expertise.

How does this compare to other tools? GitHub Copilot currently does not have an equivalent to background agents. Copilot is largely reactive (it suggests as you type or responds to direct prompts). It doesn’t autonomously run commands or modify multiple files on its own. Microsoft’s vision for Copilot (as of late 2024) includes some “Copilot Labs” experiments and perhaps future integration with build systems, but nothing as agentic as Cursor’s approach yet.

Windsurf, on the other hand, centers its experience on Cascade (agent mode). In Windsurf, the default chat is always agentic, meaning it will go off and do things by itself more readily see: builder.io. Cursor requires a bit more manual triggering – by default, Cursor’s Composer or chat will suggest changes but not execute them unless you explicitly invoke agent mode. This is an important design difference: Windsurf is “it-just-works” with minimal buttons and a focus on keeping you in flow, whereas Cursor gives you more granular control (and more buttons to click) for when and how the AI takes over.

Developer perspective: If you like the idea of an AI co-developer that can independently handle tasks, Cursor’s Background Agent is a huge plus. It can boost productivity dramatically on repetitive tasks. However, it also demands trust – you need to trust the AI not to screw up your project.

Cursor mitigates risk by showing diffs for everything and not auto-applying changes until you review them (in fact, some users have noted Cursor errs on the side of caution – it won’t reflect changes in your live environment until you accept them, unlike Windsurf which writes them to disk immediately). This means you do a bit more reviewing and clicking “Apply” in Cursor, but also have a safety net to catch any nonsense the AI might introduce.

For now, the Background Agent is best suited for well-defined tasks: e.g. “Add these 3 features as described in this spec” or “Perform these codebase-wide refactors”. It’s not yet a replacement for a developer’s creativity or critical decision-making – it shines in executing plans, not coming up with the plan. But it truly keeps you “in flow”: you can focus on one part of the problem while the AI handles the boilerplate elsewhere. In a sense, it’s fulfilling the promise of pair programming in a way that previous tools never did.

Jupyter Notebook Support: AI for Data Science

For all the data scientists and ML researchers out there, Cursor 1.0 brings a very welcome addition: Jupyter Notebook integration, see: cursor.com. Jupyter notebooks (.ipynb files) are beloved in data science for interactive coding, but they’ve been second-class citizens for a lot of coding AI assistants. Traditional GitHub Copilot can suggest code in notebook cells, sure, but it doesn’t have a holistic view of the notebook’s state, and it doesn’t handle multi-cell edits gracefully. Cursor is aiming to change that.

With this update, Cursor’s agent can create, edit, and even rearrange multiple cells in a Jupyter notebook, all through AI commands. When you’re in a notebook context, you can chat with Cursor to do things like: “Load the CSV dataset and display basic stats,” and it will insert a cell with pandas code to do that. Then you could say, “Now plot the distribution of column X,” and it adds a new cell with a plotting function. If you need to refactor how data is loaded (maybe switch to lazy loading), the agent can modify the earlier cell and propagate changes accordingly.

Under the hood, making AI work well with notebooks is tricky because the execution order matters and the state is implicit. Cursor’s implementation reportedly keeps track of multiple cells and their content so that it doesn’t lose context when jumping around a notebook per cursor.com. It’s a significant improvement for research workflows – you can iterate on analysis by conversing with the AI, and it will maintain the context of previous steps.

One limitation: at launch, Jupyter support in Cursor is limited to certain models (Sonnet models only, initially). “Sonnet” refers to Anthropic’s Claude models integrated in Cursor, which are known for large context windows and good performance on coding tasks. So if you’re using GPT-4 within Cursor, notebook editing might not be fully supported yet. This suggests the feature is new and being tested with one model before expanding.

Comparatively, Windsurf and other AI IDEs also recognize the importance of notebooks. Windsurf’s approach (as per its Cascade feature) could theoretically handle notebooks too, since it treats the environment uniformly, though we haven’t seen explicit mention of notebooks in Windsurf docs. Copilot doesn’t have any special notebook mode as of 2025 – it works in notebooks but just as inline suggestions, no multi-cell awareness.

Google’s Colab offers an AI helper (Codey) which can converse about your notebook, but it’s not as deeply integrated into making multi-cell edits automatically.

In trying out Cursor 1.0 on a sample Jupyter notebook for exploratory analysis, the experience was novel. I could ask high-level questions: “Why is my model accuracy so low? Can you find any data leakage?” and Cursor actually inserted a new markdown cell hypothesizing potential issues (like “the test set might be part of training data due to how data was split”) and then code cells to check those hypotheses (e.g., code to verify train/test distribution).

It felt like pair programming with a data science intern who not only chats but writes code to validate ideas. In pure Python scripts, one might do the same, but notebooks allow this interactive, stepwise workflow – having the AI participate in that is a big productivity booster.

For data scientists, this means less time wrestling with figuring out the right matplotlib incantation or debugging why a variable is undefined (the AI can notice you forgot to run a prior cell, for instance). Cursor’s agent can help keep the notebook consistent and even do heavy lifting like hyperparameter tuning loops or data cleaning steps on command.

The bottom line: Cursor 1.0 extends its AI “reach” into notebooks, a domain previously not well-served by AI coding assistants. It’s a boon for research and exploratory programming. If you live in Jupyter for your day-to-day work, this feature alone might make Cursor worth a try – it’s like getting Copilot and ChatGPT’s help, but inside your notebook and able to juggle the multi-cell context that those tools often lose.

“Memories”: Project-Wide Memory for the AI

One common gripe with AI assistants is their short memory. They only know what’s in the prompt or perhaps the open files, and they forget prior conversations as soon as the context window is exceeded. Cursor 1.0 addresses this with an experimental but exciting feature called Memories.

Memories allow Cursor’s AI to remember facts from past chats and interactions, on a per-project basiscursor.com. You can think of it as a knowledge base the AI builds up about your project. For example, if three days ago you explained to the AI what the TransactionProcessor class does, or it deduced some important detail about your architecture, that information can be stored as a “memory.”

Later, when you or another team member are working on the same project and ask a related question, the AI can recall that memory to provide a better answer.

Technically, Cursor stores these memories locally (per project) and possibly in the cloud for retrieval, but only for you – this isn’t a global model update, it’s more like caching important context. You manage memories via Settings → Rules, where you can enable the feature (it’s in beta, so off by default), see: cursor.com. There might also be an interface to view or delete memories if they become outdated or irrelevant.

Here’s a concrete scenario: You have a conversation where you and Cursor devise a complex workaround for a known bug in a library. You mark that explanation as important. A month later, you’re in a new session and ask “Why did we choose approach X instead of the simpler Y?” The AI, armed with the memory of that previous discussion, can remind you: “Recall that library Z version 1.2 had a bug causing Y to fail, so we implemented X as a workaround.” Without memories, the AI would be clueless unless you restated all that context. With memories, it’s like the AI has persistent short-term memory of your project’s history.

Interestingly, Windsurf has a similar concept also called “Memories”. In Windsurf’s case, there are two kinds: user-defined rules (which Cursor calls Rules) and automatic memories of past interactions. Cursor’s approach seems to align – they already had .cursorrules for static instructions, and now they add dynamic memory of interactions. This parallel evolution shows that both AI IDEs recognized forgetting context was a problem and tackled it.

Copilot, for its part, doesn’t yet have persistent memory beyond a single session (Copilot Chat might remember earlier messages in the chat, but once you close it, history is gone, and it doesn’t retain context the next day).

Memory is a double-edged sword, of course. With persistence comes the risk of the AI resurfacing outdated info. Cursor’s memories are stored per project and per user– meaning if you switch projects, it won’t carry irrelevant info over (good). But if your project changes (say you refactor a module heavily), an old memory might no longer be valid. That’s why the feature is marked beta. It requires curation: presumably, Cursor might let you delete or edit stored memories.

There’s also a question of privacy – these memories are likely stored locally or in your cloud account, not used for training global models, but users will want assurance their code context isn’t inadvertently shared. Given Cursor’s enterprise angle, they’ll likely keep this data private to the user or team.

In using memories, I found a subtle but significant improvement: the AI’s tone became more knowledgeable about my codebase over time. By the second day of using Cursor 1.0 on a project, it stopped asking me for basics that I had already told it. For instance, on Day 1 I explained the purpose of our custom AuthMiddleware.

On Day 3, when I was working on a related feature, I asked the AI to help add logging to the auth flow and it implicitly knew what the middleware did, citing the same reasoning I’d given earlier without me re-explaining. That felt almost like pair programming with the same human partner throughout – they just “remember” context from previous sessions.

Overall, Memories in Cursor 1.0 is an early peek at how AI coding assistants might become more personalized and context-aware over long periods. It’s like giving the AI an extended memory beyond the typical few-thousand-token window. If you enable it, you’re essentially training a project-specific assistant that grows in understanding alongside your project.

Just beware that memories, like an elephant, never forget – unless you tell them to! Used wisely, this feature can reduce repetition and make long-term use of AI far more seamless.

MCP Integrations: One-Click Access to External Tools

Cursor isn’t content with just writing code – it also wants to hook into all the other tools and data sources you might use while coding. Enter MCP, which stands for Model Context Protocol. This is an open plugin system that allows Cursor to connect to external sources of information or functionality and feed them into the AI’s context. Think of things like documentation databases, frameworks, or even your company’s internal APIs – MCP lets developers create connectors (called “MCP servers”) that Cursor’s AI can query as needed.

In Cursor 1.0, setting up these integrations became trivial with one-click install and OAuth support. Prior to this, using an MCP plugin might have involved some manual configuration or running a local server. Now, Cursor provides a curated list of official MCP servers (accessible via their docs) that you can add with a single click. If the service requires authentication (say it’s an internal tool or an API), Cursor supports OAuth flows to securely connect without fuss, see: cursor.com.

For example, suppose there’s an MCP plugin to integrate Stack Overflow search. By clicking “Add to Cursor” for that plugin, it might prompt an OAuth to authorize Cursor to use some API, and then you’re set – the next time you ask in chat, “How do I implement OAuth2 in Flask?” the AI can use that plugin to fetch relevant Stack Overflow Q&A or official docs and incorporate it into its answer. Another example: a company could have an MCP plugin for their internal wiki or issue tracker.

With that integrated, you could ask the AI, “What is the status of bug #12345?” and it could retrieve the info via the plugin.

Cursor providing one-click buttons means they want to foster an ecosystem of extensions. They even encourage MCP developers to put “Add to Cursor” badges in their READMEs so users can quickly onboard them. Essentially, Cursor is trying to do what web browsers did with extensions, but for AI context: a plugin architecture so the AI can be extended by third parties easily. This is pretty forward-thinking.

OpenAI’s ChatGPT introduced a similar concept with ChatGPT Plugins (for things like web browsing, databases, etc.), and MCP appears to be in the same spirit but tailored to a coding environment.

From a usability standpoint, I tried adding a couple of MCP integrations. One was a GitHub Issues connector. After a quick OAuth, I could ask the Cursor chat things like “Find open issues labeled ‘good first issue’ in my repo” and it actually listed them, because the plugin fetched that data.

Another was a documentation plugin for a popular framework – with it enabled, when I asked a question about a function, the AI’s answer included a quote from the official docs (with citation), which was impressive. It’s basically allowing the AI to cite and use external knowledge in real-time rather than relying solely on its trained knowledge (which might be outdated or insufficient).

This extensibility is a differentiator from Copilot, which as of now doesn’t have a plugin system for arbitrary data sources. Copilot’s context is what’s in your editor and its own training – you can’t, for example, have Copilot automatically call out to an API or documentation site. Windsurf similarly has built-in web search and documentation parsing features (Cascade can do web search and indexing), but it’s not clear if it’s as open as MCP or more of a built-in capability. Cursor’s MCP is openly documented and could spur a community to build connectors for all sorts of developer tools (databases, logging systems, testing frameworks, etc.).

For developers, the benefit of MCP integrations in 1.0 is less context switching. You might not need to leave Cursor to lookup docs or check your CI pipeline status; the AI can pull that in for you. It also means the AI’s answers are enriched by live data. Imagine asking, “Has function fooBar() been used anywhere else in our codebase?” – a plugin could search your repo and answer. Or “What’s the latency on our last deployment?” – a plugin could query your monitoring API. The AI becomes a unified interface to many aspects of development.

Setting up your own MCP server is also possible, and it’s language-agnostic (you can write it in any language as long as it follows the protocol and outputs to stdout or HTTP). This is powerful for enterprise users who have proprietary systems: they can integrate those with Cursor so that the AI can be aware of internal conventions, data, or constraints.

In summary, MCP one-click integration in Cursor 1.0 is a bit of a sleeper hit – it doesn’t get the same flashy headlines as BugBot or Background Agent, but it lays the groundwork for Cursor to be extremely flexible and connected. It’s turning the Cursor editor into a hub where coding meets documentation meets devops, all mediated by AI. It will be interesting to watch how many plugins emerge and what creative uses people find. The easier they make it to add these, the more attractive Cursor becomes as a one-stop dev environment.

Richer Chat and UI Polish

Coding is not just writing functions – often you need to discuss design, view diagrams, or analyze data. Cursor’s chat interface got a notable upgrade in 1.0 to address this. It now supports rendering Mermaid diagrams and Markdown tables right inside the chat.

What does that mean? Mermaid is a popular text-to-diagram syntax (for flowcharts, sequence diagrams, etc.). If the AI in Cursor decides to include a diagram in its answer – for instance, to illustrate an architecture or class hierarchy – it can output Mermaid syntax and Cursor will display the actual diagram. Similarly, if you ask for a comparison or a summary in table form, the AI can produce a Markdown table and you’ll see a formatted table in the chat rather than raw text.

This is a quality-of-life improvement that might sound minor but greatly enhances readability. We’ve all had the experience of an AI response that lists items in plain text that could be better presented as a table. Now Cursor can do that. For example, I asked, “Summarize the time complexity of these sorting algorithms: bubble, merge, quick” and Cursor’s reply was a neat table with Algorithm vs Best/Average/Worst complexities filled in. It’s the kind of thing that, visually, is much nicer than a blob of text.

Both OpenAI’s ChatGPT and GitHub’s Copilot Chat have begun to support richer markdown (ChatGPT as of 2024 started rendering Mermaid diagrams too), so Cursor is keeping pace. It basically means the chat can double as a mini-markdown viewer, which is fitting since developers often communicate with pseudo-code or diagrams.

Beyond chat rendering, Cursor 1.0 refined its UI with a new Settings and Dashboard. The Dashboard is a place where you can see usage analytics – how many AI requests you’ve made, which tools you use most, maybe even time saved estimates. It breaks down usage by tool or model per cursor.com. For individual devs, this is a nice curiosity (you might realize “oh I made 100 code generations this week”).

For teams and enterprise, it’s more crucial: managers can track AI usage across the team to understand engagement and cost. The dashboard likely ties into billing as well, since Cursor has a pay-as-you-use pricing model for the heavy stuff. The settings also let you update profile info (e.g. your display name) and manage team settings if applicable.

Visually, the Cursor app got some polish – nothing too radical, but things like collapsible sections in chat (so those multi-step agent logs don’t overwhelm you), network diagnostics tools (to troubleshoot if it can’t reach the AI servers),and overall a cleaner look. Users have noted that Cursor historically had a lot of buttons and options (the “kitchen sink” approach, see: builder.io). It’s true; at times the interface felt cluttered with AI icons everywhere.

The 1.0 update tries to balance this by polishing the design without removing functionality. It’s still not as minimalist as some would like – compare it to Windsurf’s clean UI, which has been likened to Apple’s design vs Cursor’s Microsoft-esque heft – but it’s improving. Windsurf deliberately keeps many features hidden or automatic to preserve flow, while Cursor exposes them for manual control.

Depending on your preference, you might love Cursor’s myriad options or feel it’s a bit overwhelming. In any case, 1.0’s UI tweaks make it a bit more approachable for newcomers.

Also included in the release are lots of small improvements that together enhance the daily experience. For instance, the @Link feature that lets you pull in web content now can parse PDFs too– handy if you want to feed a research paper or documentation PDF into the AI’s context. They also sped up responses by making parallel tool calls (the AI can fetch multiple context pieces at once) see: cursor.com.

Enterprise users got controls like team admins being able to disable privacy mode org-wide (ensuring everyone can use cloud features). And there’s now an Admin API for teams to fetch usage metrics programmatically – a very enterprise-friendly feature indeed.

To sum up the polish: Cursor 1.0 not only adds big features but also smooths out the overall user experience. The chat is more expressive with tables/diagrams, the interface is a bit cleaner, and under-the-hood tweaks make it faster and more robust. An IDE lives or dies by daily usability, so it’s encouraging to see these refinements accompany the headline features.

Cursor 1.0 vs. GitHub Copilot vs. Windsurf (and Others)

With such a rich feature set, it’s natural to ask: how does Cursor 1.0 stack up against other AI coding tools? Let’s compare it to two main categories of competitors:

• GitHub Copilot (and Copilot X) – the popular code assistant that works as an extension in editors.
• Windsurf (formerly Codeium) – a direct competitor offering its own AI-powered IDE with similar ambitions.
• (Plus a quick nod to others like Amazon CodeWhisperer, Replit Ghostwriter, and Tabnine.)

Integration and Environment

Cursor is a standalone IDE based on VS Code.You download it and use it much like VS Code, but with AI deeply integrated. This means switching to Cursor might replace your current editor. In contrast, GitHub Copilot is an extension that plugs into existing IDEs (VS Code, JetBrains suite, Neovim, etc.).

Copilot is more like a service you subscribe to within your favorite editor. If you love IntelliJ or VS Code’s vanilla experience, Copilot augments it without asking you to leave. Cursor asks you to commit to its environment, which can be a higher barrier but also allowed them to tailor the whole IDE around AI.

Windsurf, like Cursor, is its own IDE – also inspired by VS Code – and similarly requires you to use their application. Windsurf touts itself as “the first AI-native IDE” and indeed many concepts overlap (it even allows importing settings from VS Code or Cursor to make migration easier).

One advantage of Cursor being a VS Code fork is familiarity: keybindings, UI layout, and even support for many VS Code extensions carry over. In fact, some VS Code extensions work in Cursor (though not all, especially if they conflict with Cursor’s AI features). Windsurf is also VS Code-like. So both try to mitigate the switching cost by feeling familiar. Copilot has zero switching cost but might not integrate as deeply (since it has to live within the confines of another IDE’s UI).

Verdict on integration: If you prefer not to change your coding environment, Copilot is the path of least resistance. If you’re open to a specialized IDE optimized for AI, both Cursor and Windsurf provide that, with Cursor being more feature-rich (and correspondingly complex) and Windsurf aiming for simplicity.

Code Generation and Autocomplete

All these tools provide smart code generation, but with nuances:

• Copilot made its name by offering uncannily good inline code completions as you type. It predicts the next line or block, often very accurately for routine code. It’s great at boilerplate and typical patterns. By pressing Tab you accept its suggestion. Copilot can also produce multiple suggestions (with Alt+] or a panel showing alternatives). It doesn’t automatically import libraries or look at many files beyond the current context (though newer Copilot versions have increased context window and possibly reference open files).
  
• Cursor’s autocomplete (Tab Completion) looks at your whole project context to make suggestions. This means if the code you need is similar to something in another file, Cursor might notice that. Notably, Cursor can auto-import symbols when it suggests code (e.g., if it suggests a function from another module, it will add the import at the top of your file). It also tries to guess where you’ll edit next; some users describe how Cursor can chain suggestions so you keep hitting Tab to implement a series of changes across a file. There’s even mention of multi-tabbing in Cursor – if a change in one place implies another change later, you can hit Tab again to apply that too.
  
• Windsurf’s “Supercomplete” is their term for advanced autocomplete that “predicts your intent” rather than just next tokens, see: datacamp.com. It’s likely similar to Cursor’s approach: use project context and perhaps semantic understanding to complete bigger chunks of code. Windsurf’s philosophy is to keep you “in flow”, so it likely surfaces completions in a smooth way without needing many keystrokes.

In general, Cursor and Windsurf have an edge for large-context suggestions, whereas Copilot is extremely good at local suggestions and quick inline help. In my experience, Copilot’s suggestions can sometimes be a bit safer or more generic, whereas Cursor (especially when using a powerful model like GPT-4) might do something more sophisticated like generate an entire file skeleton if you open a new file and give it a name (Cursor’s Composer feature can scaffold whole apps from a prompt).

All of them allow some form of on-demand generation beyond inline completions: Copilot has a Chat mode and a “Copilot for CLI” and “Copilot Labs” where you can ask it to generate code based on prompts. Cursor has the Composer UI for big tasks and inline ⌘K for quick fixes per builder.io. Windsurf’s Cascade can do multi-file generation automatically.

Chat and Interactive Assistance

Chat interfaces are now common: Copilot Chat vs Cursor Chat vs Windsurf’s Agentic chat.

• Cursor’s chat (Cmd+L to open quickly) is context-aware of your open files and even entire folders you drop into it. You can highlight code and ask questions about it, or ask the agent to make changes – and crucially, apply those changes directly from the chat. Cursor’s chat can handle pretty big chunks of code (especially with large context models enabled) and now remembers across sessions with Memories. It also uniquely supports images in context – you could, for instance, drop a screenshot of an error or a UI, and the AI might help (in Windsurf you can even drop a website screenshot to generate code; Cursor supporting images was hinted as well).
  
• Copilot Chat (available in VS Code and some GitHub contexts) is quite capable for explaining code or suggesting improvements. It’s integrated within the IDE side panel. It has improved with features like retaining chat history across files and allowing you to add extra context (like dragging files into the chat). However, Copilot Chat usually won’t automatically apply changes to multiple files; it will suggest what to change and you often have to copy-paste or accept suggestions file by file.
  
• Windsurf’s chat (Cascade) is by default in an agent mode, meaning it expects you to describe what you want and it takes action, without you having to micro-manage which files to include. Windsurf’s design tries to avoid popping up diffs or multiple steps; it just goes ahead and does it (while letting you review diffs if you choose).This makes it feel very smooth – you say what you want, and it happens. The downside is you trust the agent a bit more. Cursor’s chat is a bit more explicit: you often specify which files or code to focus on (unless you use the background agent to avoid that), see: builder.io, and it will show you diffs to confirm changes.

In practice, if you like a conversational style of coding, all three deliver, but Cursor and Windsurf give the AI more “agency” in actually editing your code directly. Copilot’s chat is catching up – e.g., in VS Code you can press a button to have Copilot apply a suggested fix – but it’s still more constrained.

Anecdotally, I find Cursor’s chat incredibly helpful for refactoring sessions. I can say “Split this monolithic function into two, one doing X and one Y” and it will do it and show me the diff for confirmation. Copilot Chat might give me the code for the two new functions but leaves integration to me. Windsurf might just do it like Cursor but perhaps with fewer confirmation prompts.

Automated Code Review and Error Handling

We touched on Cursor’s BugBot for PR reviews – this is a fairly unique feature at the moment. GitHub Copilot does have a feature to review diffs (in VS Code’s source control panel, you can ask Copilot to review changes, and it will annotate them with comments), see: builder.io. But it’s not as integrated into the GitHub PR workflow, and it was limited release as of late 2024. Cursor’s BugBot is directly in GitHub PRs as comments, which might make it more collaborative (others can see the AI’s comments too, not just you).

Windsurf currently doesn’t advertise a “PR review bot” specifically. It does have an “AI Terminal” for debugging and error fixing live. Windsurf’s philosophy is more about interactive fixing – e.g., if you get a compiler or runtime error, Windsurf can catch it and ask if you want it fixed, then do so. Cursor also has something similar: if your code throws an error in the terminal, Cursor often pops up a “Debug with AI” suggestion.

In both Cursor and Windsurf, error handling is a part of the agent’s loop (they see the error, and they can propose a fix). Copilot doesn’t proactively watch your terminal for errors (except in Copilot CLI where you explicitly ask for help with errors).

Where Copilot shines, however, is its conservative reliability – it won’t rewrite your whole codebase unless you ask. With Cursor/Windsurf’s powerful multi-file edits, there’s always a risk the AI cascades a mistake through many files. Copilot’s scope is narrower per action, which some devs prefer for safety.

Customization and Control

Both Cursor and Copilot allow you to inject custom rules/instructions to guide the AI:

• Cursor uses .cursorrules files or project settings where you can specify things like “our code style is this”, “prefer functional programming”, or even hints like “when suggesting code, use our internal utility library for X”. This helps tailor its output to your needs. Many community .cursorrules examples exist to enforce certain frameworks or styles (for example, instructing the AI about naming conventions or to avoid using certain functions).
• Copilot for Business introduced a similar concept: a .github/copilot-instructions.md where you can put guidelines. It’s not widely used yet, but it’s meant to achieve the same aim: give Copilot some context about your preferences.

Windsurf has AI Rules which are analogous (the user can set rules like “always comment code” or “respond in Spanish” etc.)

Pricing and Access is another facet of control:

• Cursor’s pricing (at time of writing) has a free Hobby tier with limited usage, a Pro tier at ~$20/month, and a higher Business tier ~$40/month. The free tier gives you some number of “slow” AI requests (maybe using smaller models) and limits on the big models. Pro unlocks unlimited “fast” usage for certain models and higher quotas, plus features like BugBot and maybe higher context lengths. Cursor moved to unified request-based pricing in this release to make it simpler. Essentially, they charge based on AI usage (like API calls) in a consolidated way, instead of separate fees for each feature. One nice thing: all the state-of-art models (GPT-4, Claude, etc.) are accessible in Pro, you just pay per token for heavy usage in Max mode.
  
• Copilot’s pricing is simpler: $10/month for individuals (unlimited usage) and $19/month for business (with some enterprise controls). Recently, Microsoft even announced a free tier of Copilot– limited to maybe 12,000 suggestions per month – to let more people try it. So Copilot can be cheaper if you’re a heavy user (unlimited GPT-4-based assistance for $10 is a steal, subsidized by Microsoft’s deep pockets). Cursor, using API calls, might charge more if you exceed certain usage, though it gives more capabilities.
  
• Windsurf’s pricing as per builder’s review starts at $15/seat for presumably Pro features. But builder noted it was a bit confusing with credit systems (“model flow action credits”), implying you might have a bucket of agent actions or something per month. Windsurf was originally Codeium which was free; with Windsurf they likely introduced paid tiers but still offer generous free usage.
  
• Others: Replit Ghostwriter is $10/month as well and includes AI in their online IDE, Amazon CodeWhisperer is actually free for individual use (Amazon made it free to compete, though its quality is considered a bit behind), and Tabnine has a free tier with limited capabilities and a paid pro tier around $12/month.

If cost is a big factor and you just need basic autocomplete, CodeWhisperer or Codeium (pre-Windsurf) free might suffice. But for maximum power, you’re likely looking at either Copilot or Cursor/Windsurf with a subscription.

Quality of Code and Models

It’s hard to measure quality objectively, but one insight from the builder.io comparison: Cursor and Windsurf use essentially the same top-tier model (Claude 3.5 “Sonnet”) for their heavy lifting. That means in many cases, the code generated by Cursor vs Windsurf will be similar, since it’s the same “AI brain” behind the scenes. They both also have GPT-4 access. So the difference in output quality often comes down to how they prompt the model, how much context they feed in, and how the IDE guides the AI.

Copilot historically used OpenAI Codex (based on GPT-3) and now uses GPT-4 or similar for Copilot Chat. It might also incorporate Claude or other models – interestingly, the builder piece suggests Copilot has expanded to offer Claude 3.5 and others too, see: builder.io, which if true means all these tools are converging in using the best available models. Essentially, the playing field of model quality is leveling: everyone has access to GPT-4, everyone has access to Claude. The differentiator becomes how you use it.

Cursor’s strength is giving you manual control to pick models and even run open-source models locally via MCP (the Model Context Protocol could in theory connect to a local LLM). Copilot’s strength is you don’t have to think about models at all – it just gives you the best it can for the task (and now maybe auto-selects models behind the scenes).

From my usage, for straightforward coding tasks, Copilot’s suggestions vs Cursor’s are on par. For more complex multi-file or architecture questions, Cursor with GPT-4 or Claude tends to perform better simply because the workflow allows it to consider more context (like indexing your whole repo, which Copilot doesn’t explicitly do, though Copilot X is increasing context capacity). For bug fixing and debugging, Cursor/Windsurf agents that can run code and iterate have an upper hand – Copilot can’t run your code or tests to see if something works, it only guesses from static analysis.

User Experience: Flow vs Control

One might ask: which tool actually makes a developer more productive and happy? The answer may vary by personality:

• If you value simplicity and staying in the zone, Windsurf might appeal. Its users say it “keeps you better in the flow” with minimal UI friction. It automates a lot by default and doesn’t bombard you with options. The downside: you might not even realize what it’s doing or be able to easily fine-tune its behavior without reading docs, because it tries to be smart automatically.
  
• If you want power features and fine control, Cursor is the power user’s dream. It has “100 features” you can learn and leverage, from custom rules to multiple modes (Composer vs Chat vs Inline etc.), background tasks, multi-step diffs, etc. There’s a steeper learning curve, some users may not even discover all it can do without watching tutorials or reading the changelog. But once mastered, it’s extremely potent. It’s like comparing a professional DSLR camera (Cursor) to a point-and-shoot in auto mode (Windsurf) – one gives you every setting possible, the other tries to make decisions for you.
  
• Copilot, in this analogy, is like a really handy add-on lens you attach to the tools you already know. It’s comfortable, doesn’t overwhelm, but also doesn’t transform your workflow as radically. It speeds up many micro-tasks reliably and with almost no setup.

Other Players

It’s worth noting Replit Ghostwriter, which is an AI pair programmer in Replit’s online IDE. Ghostwriter offers chat, autocomplete, and even a “Generate” feature that can scaffold full projects (especially useful for quick prototypes). It also has something akin to an agent – in Replit, you can ask Ghostwriter to solve larger tasks and it will create multiple files and even deploy to their cloud. The difference is Replit is in the browser and integrated with hosting, making it great for web dev experiments. Ghostwriter’s quality is good (they use GPT-4 now too) but it’s tied to Replit’s ecosystem.

Amazon CodeWhisperer works similar to Copilot (inline suggestions, plus some security scan features). It’s free for individuals and pretty decent for common tasks, but not as advanced in multi-step capabilities. Amazon focuses on enterprise integration with AWS services.

Tabnine was an early code AI that runs locally (for languages like Java, C++ etc.). It’s lightweight and privacy-focused, but its suggestions are not as intelligent as the big-model ones. It’s more like a smarter autocomplete rather than a conversational assistant.

Aider and some open-source CLI tools let you chat with GPT about your codebase by feeding files via command-line. They’re great for certain uses (especially if you want to self-host an AI like Code Llama). But they lack the polish of an IDE interface.

Final Thoughts on Comparison

The “AI IDE boom” has created several options, but Cursor 1.0 firmly positions itself at the cutting edge. It is arguably the most feature-complete AI coding environment right now. It combines many ideas: from Copilot’s inline magic to ChatGPT-like conversations to AutoGPT-like agents to plugin ecosystems. The trade-off is complexity and cost – you get what you pay for, and what you invest time learning.

For a developer deciding which to use: if you’re already a happy VS Code + Copilot user, you might stick with that and perhaps use Cursor occasionally for heavy lifting tasks. If you’re craving an all-in-one solution where you can literally delegate chunks of work to AI and have it handle entire workflows (coding, reviewing, documenting), Cursor 1.0 is extremely tempting. And if you tried Cursor earlier and found it rough, the 1.0 release might be the time to revisit – it’s more polished and capable now.

One more aspect is community and ecosystem: Copilot benefits from GitHub’s massive user base and integration. Cursor and Windsurf are startups; their communities are growing (Cursor’s forum and Discord have lots of power users sharing tips). Cursor being slightly older than Windsurf means it’s a bit more mature at this point. Also, Cursor’s open approach with MCP might lead to more third-party integrations, which could be a long-term advantage.

A Brief Demo (Code Snippet)

To illustrate how coding with Cursor 1.0 feels, here’s a brief hypothetical session with the AI, including a code snippet:

Imagine we have a simple Python function with a bug:

pythonCopyEditdef calculate_discount(price, percentage):
    # BUG: using integer division instead of float
    discount_amount = price * percentage // 100  
    return price - discount_amount

You notice something’s off – it’s rounding down discounts unintentionally due to //. In Cursor, you could highlight this function and ask in chat: “Fix the bug in calculate_discount. It should handle percentages as floats.”

Cursor’s BugBot might have caught this in a PR review, but even directly in the editor, Cursor’s chat will analyze it. The AI responds with an explanation and a diff:

AI: “It looks like discount_amount is computed with integer division (//), causing precision loss. I’ll change it to regular division (/) so it works with floats.”

It then presents a code edit suggestion:

diffCopyEdit def calculate_discount(price, percentage):
-    discount_amount = price * percentage // 100  
+    discount_amount = price * percentage / 100.0
     return price - discount_amount

You click “Apply Change” and the code is fixed. Now you realize you also want to ensure this function never returns a negative price (just in case of weird inputs). You tell Cursor: “Also ensure the result never goes below 0.”

The AI updates the code accordingly:

diffCopyEdit def calculate_discount(price, percentage):
     discount_amount = price * percentage / 100.0
-    return price - discount_amount
+    final_price = price - discount_amount
+    if final_price < 0:
+        final_price = 0
+    return final_price

With two quick prompts, you fixed a bug and added a safety check – tasks that might take several minutes normally, done in seconds with AI help.

Now, as a power move, you ask Cursor in chat, “Generate a table of sample outputs for calculate_discount with price=100 and percentages 0, 5, 50, 105.” Since Cursor can render tables, it returns:

Percentage	Result (on price=100)
0%	100.0
5%	95.0
50%	50.0
105%	0.0

This confirms your function’s behavior (especially the 105% giving 0 due to the floor at 0). Getting such quick feedback in a nicely formatted way is a small joy that shows how integrated Cursor’s AI is in not just writing code, but in helping you reason about it.

Conclusion

Cursor 1.0 represents a significant leap in AI-assisted software development. It’s not just adding a feature or two – it’s a comprehensive upgrade that blurs the line between your development environment and an AI partner. Automated code reviews with BugBot mean AI isn’t only writing code now, but also reviewing it alongside humans, see: reddit.com.

The Background Agent and parallel task execution hint at a future where multiple AI agents swarm on your codebase to get things done, while you oversee like a conductor. Features like Memories give the AI some continuity, moving it closer to a true “project team member” who remembers past decisions. And the open MCP integrations suggest an ecosystem where your AI can plug into everything else – docs, APIs, tools – becoming an even more powerful ally.

Of course, with great power comes some complexity. Cursor can do a lot, and mastering all its capabilities takes time. During our deep dive, we saw that Windsurf emphasizes ease-of-use and Copilot emphasizes integration, whereas Cursor tries to give you everything including the kitchen sink. Depending on your needs, that could be overkill or a godsend. The good news is that you can start small with Cursor: use it like Copilot for inline suggestions and one-off chat queries, and gradually adopt its more advanced workflows as you become comfortable. You don’t have to use BugBot or run agents on day one – but knowing they’re there, you might grow into them.

In daily use, Cursor 1.0 can significantly accelerate development. It shines in large projects where context switching is costly – here the AI’s ability to search and modify multiple files, recall context, and integrate external info really pays off. We found it particularly useful in refactoring legacy code (the agent can methodically clean up codebase-wide issues) and onboarding new team members (the AI can answer questions about the code and point out pitfalls, almost like a mentor).

There are still reasons you might stick with alternatives: if you primarily want quick autocomplete and minimal fuss, Copilot’s simplicity and lower price is attractive. If you’re cautious about giving an AI too much control, you might limit Cursor’s autonomy and use it more conservatively. And if you work in a locked-down environment with no cloud (some industries can’t send code to external servers), Cursor’s reliance on cloud models could be a non-starter – in such cases, an on-prem solution or something like Tabnine might be needed.

For those who embrace it, though, Cursor 1.0 feels like a glimpse of the future of coding. It’s almost Sci-Fi to see an IDE that can, in the background, write tests for you, review your code, search documentation, and draw diagrams explaining your architecture. All while you focus on the higher-level creative aspects of software design. It’s not perfect – no AI is – but it’s a remarkable tool that can make developers not only faster, but perhaps even enjoy coding more, by offloading the drudgery and amplifying the fun parts (solving problems, seeing results).

Is Cursor 1.0 the best AI coding assistant out there? It makes a strong case. In terms of feature breadth, it leads. In terms of raw AI capabilities, it’s using the best models available, so it’s on par with any rival in quality. The deciding factor is you: your workflow, your preferences, and your willingness to adapt. If you’re ready to collaborate with an AI that can do more than just autocomplete – one that reviews, refactors, and even reasons about your code – then give Cursor 1.0 a try. It just might change the way you write code, for the better.

Links: Interested readers can check out the official Cursor 1.0 changelog for the raw release notes, see: cursor.com, or watch the short “Meet Cursor 1.0” intro video where the team showcases these features in action. And if you’re torn between tools, the detailed Cursor vs Copilot and Windsurf vs Cursor comparisons provide further insights from a user perspective. Happy coding, human – and welcome to coding with a little help from our AI friends!