Deep Dive into Cursor...Is This the First Real AI IDE? (Part 1/2)

For years, the developer world has flirted with AI coding assistants...tools that can autocomplete, summarize, or lint your code while you work. Useful? Definitely. Game-changing? Not quite. Is Cursor the difference maker?

Cursor is an AI-native fork of VS Code that bills itself not as a plugin, but as a full-on AI IDE. That’s a bold claim, but one that increasingly serious developers, and investors, are paying attention to.

Cursor raised $30M in Series A funding this past February led by Index Ventures, and the team behind it includes alumni from OpenAI and Google. More telling? Some early adopters are already swapping out Copilot in favor of Cursor entirely. It’s fast. It’s smart. And unlike Copilot, it remembers what you’re working on.

Let’s walk through what makes Cursor interesting...and why some engineering leaders are starting to ask whether it’s more than just another toy in the toolbelt.

Cursor isn’t just another wrapper

The key difference with Cursor isn’t the AI model. (In fact, it supports GPT-4, Claude, Mixtral, and others.) It’s the tight coupling between code and context. Cursor builds a persistent memory of your session: not just the file you’re editing, but the surrounding functions, the patterns across your repo, and your history of prompts. It uses embeddings to retrieve relevant parts of your codebase and lets you chat in full VS Code layout...not some side panel bolted on as an afterthought.

Developers can highlight a function, ask the AI to refactor it, explain it, test it, or rewrite it. Then, crucially, iterate again based on feedback. Cursor retains context across interactions, something Copilot (for now) simply doesn’t do.

In this sense, Cursor feels less like a code generator and more like a persistent thought partner. One early adopter on Hacker News put it this way:

“Cursor is the first time I’ve felt like I’m pair programming with someone who gets it.”

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Designed for interactivity

Unlike the Copilot Chat beta or CodeWhisperer sidebars, Cursor’s interaction model is fluid. It lets you ask:

• “Why is this function slow?”

• “Generate tests for these edge cases.”

• “Explain the data flow from this file to that one.”

• “Split this monolith into smaller components.”

And it does this while pulling in relevant files, not just what’s in view.

More than just autocomplete or magic paste, Cursor supports structured commands like /edit, /explain, and /test, as well as direct inline prompting...effectively combining chat and command line in one interface.

Cursor’s AI layer uses its own retrieval engine to query across the repo and documentation. And yes, you can use your own OpenAI key or pay for Pro, depending on how much horsepower you want.

The Copilot comparison (inevitable, but fair)

It’s hard not to compare Cursor to GitHub Copilot. Both use similar foundation models. But where Copilot is a suggestion engine, Cursor acts like a session-aware assistant.

Here’s how they break down:

• Context length: Cursor remembers more across interactions.

• Editor integration: Cursor is the editor. Copilot is inside one.

• Multi-step reasoning: Cursor handles follow-up questions and refinements.

• Live repo search: Cursor indexes your codebase with embeddings.

Does that mean Copilot is dead? No. For many developers, especially inside GitHub-native teams, Copilot remains a rock-solid boost. But Cursor is betting on a deeper shift: the editor itself as an interface for thinking.

Cursor is not perfect

It still hallucinates. It can crash. Some developers report slowdowns with large repos or vague queries. Others feel its chat interface, while powerful, sometimes adds friction compared to simple autocomplete.

Still, the traction is real. One developer wrote:

“I was skeptical at first, but now Cursor is open every day. Copilot is not.”

Cursor isn’t trying to replace your brain. It’s trying to sit next to it. And for some engineers, that’s more valuable than just another tab-completer.

In Part 2, we’ll unpack where Cursor actually performs best...and where it still falls short. How are teams using it? Where does it slot into real-world workflows? And does it play nice with the rest of your stack?

Recommended Reads

✔️Behind Cursor’s Success: Two PMFs (Gru.ai)

✔️Cursor AI: The AI-powered code editor changing the game (Daily.dev)

✔️What I learned using CursorAI every day as an Engineer (Codeaholicguy)