What is Cursor AI? $60M raised in series funding for new coding app

Cursor, an AI firm that is attempting to build a “magical tool that will one day write all the world’s code,” has announced it has raised $60 million in its Series A funding round. The round had the participation of Andreessen Horowitz, Thrive Capital, OpenAI, Jeff Dean, Noam Brown, and the founders of Stripe, Github, Ramp, Perplexity, and OpenAI.

As of August 22, the company had a valuation of $400 million, according to sources cited by TechCrunch. In a post on their site, Cursor stated: “Cursor has become widely recognized as the best way to code with AI. We’ve grown to over 30,000 customers, across some of the world’s largest enterprises, storied research labs, and innovative startups.”

Anysphere is the two-year-old startup that developed the app. Its co-founders are Michael Truell, Sualeh Asif, Arvid Lunnemark and Aman Sanger, who started the company while they were students at MIT.

CEO Truell told Forbes that it was a “Google Docs for programmers,” and that he thought engineers would soon “be able to build systems that are much more complex than what powerful teams can build right now.” The software has reportedly been widely used by engineers at AI startups. Shyamal Anadkat, an OpenAI engineer, said he uses Cursor for fixing bugs and building prototypes.

What is the Cursor AI?

Cursor AI uses GPT-4 and other AI models like Claude 3.5 Sonnet to understand and generate code. Using advanced AI capabilities, it is said to be able to finish, correct, and change AI code through natural language commands. It currently works with JavaScript, Python, and TypeScript, and is free for most uses. The pro plan will set you back $20 per month.

Cursor 0.39.0 comes with much faster autocomplete.

And even more speedups in the next build! pic.twitter.com/5tB7j3L9kd

— Cursor (@cursor_ai) August 5, 2024

The main aim of the app is to speed up the process of coding, which can often be rather laborious. The way it does this is by analyzing your codebase for context using natural language processing and then providing smart code completion and generation.

Some drawbacks include that it can struggle with complex bug detection, misplace generated code, and at times, forget any context after a break.

Featured image: Ideogram

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