Cursor AI: Not Another VSCode Fork — 5 Key Features That Set It Apart

There’s a common misconception that Cursor AI is simply a VSCode fork with GitHub Copilot-like features. This view underestimates the work Cursor’s team has put into advancing the software development experience using AI, which they recently discussed in Lex Fridman podcast.

Here are the five key innovations we’ll explore:

1. Custom LLM Architecture: The Mixture of Experts (MoE) Model
2. Diff-Aware Predictions: More Than Token-Level Suggestions
3. Predictive Editing: Anticipating Your Next Move
4. Performance-Optimized Caching: Ensuring Real-Time Responsiveness
5. Cursor Composer: Multi-File Editing and Application Generation

Let’s start by examining the core of Cursor AI: its custom language model architecture.

1. Custom LLM Architecture: Allowing Long-context processing

At the core of Cursor AI is a custom Language Model (LLM) which enables long-context processing. It means that it can understand and process large portions of your project at once, not just isolated snippets. This allows it to offer more accurate suggestions large-scale changed, and maintain consistency with your project’s coding style.

Cursor achieves this using a Mixture of Experts model, which works like a team of specialized programmers. Each “expert” in the model focuses on a specific topic. For example one focuses on React components, others on database queries, or error handling. A central component, called gating network, then decides which expert’s output is most relevant for the task at hand.

2. Diff-Aware Predictions: More Than Token-Level Suggestions

A key feature that sets Cursor AI apart is its ability to generate diff-aware predictions. Unlike standard language models that predict individual tokens, Cursor AI is able to generate multi-line code diffs. This feature, combined with predictive editing, has had the most significant impact on my daily development workflow.

In my experience, Cursor AI’s predictive diff-based autocomplete feature is often faster than manually navigating the code. Here’s how it works in practice:

1. As you’re making changes to code, Cursor AI analyzes the context and tries to predict the next likely change to the code nearby.
2. It then proposes these changes as a diff, allowing you to see exactly what will be added, modified, or removed.
3. You can use the “Tab” key to apply the suggested changes.

This feature can be very useful during refactoring. For example, when renaming a variable or changing a function signature, Cursor AI can suggest the necessary changes across multiple files.

The speed at which Cursor AI provides these suggestions is impressive. In many cases, by the time I’ve thought about where I need to make the next edit, Cursor AI has already suggested it.

3. Predictive Editing: Anticipating Your Next Move

Cursor AI goes beyond simple autocomplete. This feature anticipates next likely code changes or where you would want edit next, enabling you to use “Tab” key to move across the file while making changes.

Predictive editing uses speculative edits, a technique that anticipates low-entropy actions (highly predictable code changes) and minimizes user input by reducing repetitive tasks. In practice, this means Cursor can:

1. Automatically jump to the next logical editing position in the code.
2. Suggest entire blocks of code that are likely to follow the current code structure.
3. Predict and offer to implement common refactoring patterns.

The result is a more fluid development experience where the AI assistant feels less like a tool and more like an intuitive extension of my thought process.

4. Performance-Optimized Caching: Ensuring Real-Time Responsiveness

In order to not sacrifice responsiveness, Cursor AI has implemented advanced caching techniques. These optimizations ensure almost real-time responsiveness, even when dealing with large codebases or complex operations.

Key Caching Strategies

1. Reusing Computed Keys and Values: By caching and reusing previously computed keys and values across requests, Cursor minimizes redundant computations. This is particularly noticeable when working with large files or complex data structures.
2. Cache Warming: Cursor preemptively loads likely-to-be-used data into the cache, reducing latency for common operations.
3. Speculative Pre-fetching: Based on predicted user actions, Cursor pre-fetches potentially relevant data, further reducing response times.

5. Cursor Composer: Multi-File Editing and Application Generation

Cursor Composer enables multi-file editing and application generation. Developers can provide high-level instructions to create or modify multiple files simultaneously.

Unlike GitHub Copilot Chat, Cursor Composer can create new files and perform edits across multiple files in a single operation. This capability allows for more comprehensive project-wide changes and initial setup tasks.

Conclusion

Cursor AI stands out with features that go beyond a typical VSCode fork. With just a press of the “Tab” key, you can apply multi-line code changes and have your next edit automatically suggested. You can even set up entire projects using a single prompt. These advanced capabilities show how Cursor’s team is reimagining the software development experience, giving us a glimpse of where the future of coding is headed.

For a deeper dive into AI-powered development tools, including discussions on Cursor, v0, Continue.dev, and Ollama, check out my recent appearance on the Map for Engineers podcast. You can find the full episode (Ep. 3) on YouTube or in your podcast app (search for “Map for Engineers).

Have you tried Cursor AI or other AI-assisted development tools? I’m curious to hear about your experiences. How have they affected your development workflow? Share your thoughts in the comments below.