Cursor vs Devin UI/UX

MLOps.community · Intermediate ·💻 AI-Assisted Coding ·1y ago

Key Takeaways

The video discusses the differences between Cursor and Devin UI/UX, focusing on the role of AI agents in assisting engineers with tasks such as fielding questions and investigating alerts, using tools like Slack and alert flow systems.

Full Transcript

like an agent that's in your Slack, a teammate essentially. And we actually started on the help desk and we're fielding questions from engineers. So one engineer be like a platform team supporting another engineer coming in with like a question and getting in between engineers was very hard because there's a lot of like chitchat. There's a lot of back and forth. Often the questions are they need immediate answers. They're something that somebody spend a whole day on. And this is a very synchronous engagement where with the alert flow it's a lot more asynchronous. There's not necessar it's a system generating alert. You can investigate that on your own time. And so if you take the cursors and the devons of the world, it's kind of similar, right? With cursor in the loop, it's the most uh important thing of your day that you're trying to solve with cursor. With Devon, it's different because you're saying code this thing for me, but it's like a side task you give to an intern basically. And I think in our case, we're also trying to take the grunt work away from engineers that they're not immediately trying to solve. So, it's more like an ambient background agent that's just doing all this work for you. And if you check in, you're like, "Well, okay, it's solved like 20 alerts for me. I don't have to go and look at those." Um, how do you think about this? Like, they're very different. And you can't rely on the human like hints to work because you're not the premier ID for human attention. Exactly. Cops.

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The video explores the differences between Cursor and Devin UI/UX, highlighting the importance of designing AI agents that can assist engineers with tasks without relying on human hints, and discusses the role of asynchronous engagement in AI-assisted engineering.

Key Takeaways
  1. Identify tasks that can be automated using AI agents
  2. Design UI/UX for AI-assisted engineering tasks
  3. Implement asynchronous engagement systems
  4. Develop ambient background agents for task automation
  5. Integrate AI-powered tools with existing systems like Slack
💡 Asynchronous engagement is crucial for AI-assisted engineering, as it allows engineers to focus on high-priority tasks while AI agents handle lower-priority tasks in the background.

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