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If you recently saw the advertisement “Stop hiring humans” on the streets of San Francisco, don’t scoff—this might be a metaphor for the trend of the times. The rise of AI is not just a gimmick; it’s a movement that is changing the labor landscape. From order-takers in fast food restaurants to the “human cogs” in traditional software development, certain aspects of human labor are gradually yielding to machines. As a young person, especially a student without a technical background, you might feel confused: are we destined to be eliminated in this wave?
The answer is: not necessarily. There is a new generation of AI tools that provide you with a shortcut to quickly iterate and move forward, allowing you to compete with large companies without needing to master programming. Tools like Devin and Cursor can not only improve your execution efficiency but also help you evolve from a mechanical task executor to a deep thinker. In this article, we will delve into a comparison of Devin and Cursor, as well as similar AI tools like Windsurf, to see how they are changing work methods and innovation pathways!🌟
The Three Evolutions of AI Tools 🛠️
First Generation AI: Q&A AI (e.g., ChatGPT and Claude)
Early AI tools were more like “smart teleprompters”. You ask them questions, and they provide answers. Want to learn about a piece of history or quickly look up information? No problem. These tools are useful but more like encyclopedias with search functions.
Second Generation AI: Task Execution Tools (Cursor)
Then came Cursor and Agent Cursor. They not only answer questions but also directly “do” things. Give them instructions, and they can write code, process files, and even call a series of local tools for automation. Cursor has a somewhat “technician” style: it executes clear instructions with precision and efficiency but struggles in complex and ambiguous situations.
Third Generation AI: Intelligent Planning and Self-Learning (Devin)
Then there is Devin. It is not just an executor but more like an experienced, thoughtful intern engineer. In complex tasks like data scraping and project management, Devin can autonomously plan and optimize execution methods, significantly reducing human intervention. When you give Devin a complex project, it won’t immediately dive into work; instead, it will outline a high-level plan, analyze local issues, and then implement gradually. When it encounters difficulties, it will adjust its strategy and continuously self-optimize. Devin does not just mechanically complete tasks but can grow and learn through iteration.
Cursor vs Devin: A Watershed in Design Philosophy 🔍
I have personally experienced both tools. Although Devin has a monthly subscription fee of $500, honestly, it is quite expensive. However, its design philosophy is completely different from Cursor. Interacting with Cursor feels like working with a set of tools; whereas working with Devin feels more like hiring a smart, albeit sometimes a bit “headstrong”, intern.
Cursor: Clear Tasks + Specific Execution
Cursor (including its Agent mode) excels at completing clear task instructions quickly. If you ask it to scrape data from a website, it will attempt to write a scraping code and provide results as quickly as possible. However, if the task is complex and requires high-level planning, it can easily get confused. It might “hallucinate” nonexistent webpage structures or overlook critical details. In short, Cursor is an efficient soldier but struggles with long-term thinking.
Devin: Global Planning + Intelligent Task Management
Devin starts by formulating a project strategy. If you ask it to clone a website, it will first consider whether it needs to download the webpage locally, analyze each page, component, and style before starting to act. It behaves like a seasoned professional: think clearly before acting, and can dynamically adjust strategies during execution. For complex and variable tasks, Devin’s methodology and thinking are reassuring.
Flexibility of Tool Usage: Who Can Do More? ⚙️
Cursor has a streamlined toolkit: file systems, code generation, and command execution. It can smoothly handle clear technical tasks. In contrast, Devin has more capabilities. It can launch a browser for front-end analysis, data scraping, and even analyze webpage structures using visual patterns, and it can call its own large language model “brain” to fix webpage link errors or identify unclear bugs without requiring you to write extra code.
For example, when you have a bunch of disordered webpage links, Cursor struggles to find patterns and program a solution, but Devin acts more like a smart intern: it opens the file, checks each link one by one, and directly helps me fix them. No extra code, no rigid logic, but relies on its understanding to “manually” correct. This humanized, flexible handling is something Cursor cannot easily match.
However, this flexibility can sometimes be a double-edged sword. For instance, when asking Devin to scrape the titles and links of 2000 papers from the CVPR website, Cursor quickly wrote a scraper to finish it. Devin, however, chose to manually open the browser and output results three by three. When I reminded it that there was a large data volume, it only then realized it needed to change strategies. It’s like having a diligent but inexperienced intern, needing constant feedback and training.
Cursor relies more on clear task instructions to complete work. It is suitable for handling simple file generation, code execution tasks, but when faced with tasks that require intelligent judgment, it struggles. For example, during the webpage scraping process, Cursor might overlook some details, while Devin can better understand the complexity of the task and adjust flexibly.
The Dynamic Growth of AI: Who Can Learn Faster? 📚
1. Devin: Self-Learning and Optimization
Devin emphasizes knowledge and feedback loops. If you point out issues from the last task, it will remember and improve next time. Like training a new engineer, your feedback becomes its experience to avoid mistakes in the future. Over time, it becomes proficient in the context, dependencies, and testing processes of a project. For example, if you tell Devin that a task did not meet expectations, it will remember the error and make improvements the next time it executes. Imagine if you tell Devin, “Your last scraping program did not finish processing all webpages”; it will automatically adjust its processing method.
2. Cursor: Simpler Optimization
In contrast, Cursor has weaker self-optimization capabilities. Its optimization relies more on manual input, such as adjusting tasks manually through the .cursorrules file, but it cannot dynamically adjust its strategies like Devin based on feedback. It still relies on you to tell it what to do. It has not yet internalized experience like Devin in terms of self-growth.
Task Difficulty and Scenario Suitability: Which is Right for You? 🤔
1.Devin: Suitable for Complex Tasks and Long-Term Projects
Devin excels in complex, multi-round iterative projects. It is suitable for engineering work that requires high-level strategies and flexibility. The downside is that it is expensive (500 USD/month) and slow. Sometimes it may “zone out” for a long time, making you wonder if it is slacking off. But if your goal is to build a complex system, its methodology and growth potential are irreplaceable.
2. Cursor: Suitable for Quick Tasks
If you need a tool for simple, clear tasks, Cursor is the ideal choice. It can complete generating code snippets, processing files, and small scripts in five minutes, tasks that Devin might take half an hour or even several hours to accomplish. For daily tasks, Cursor is a low-cost, high-speed, and reliable tool.
3. Windsurf and Agent Cursor
Similar tools like Windsurf and Agent Cursor try to become smarter based on Cursor, calling more local tools to achieve multi-round iterations. However, overall, they still resemble a finely-tuned toolkit rather than a growing intern.
Looking Ahead to the Future of AI Tools: Co-Creation
When we talk about Devin and Cursor, we are not just discussing two products; we are exploring the future paradigm of work. Future AI tools will not just be mechanical task executors; they will explore, iterate, and grow alongside us like colleagues.
In this era, everyone could potentially be partially replaced by machines and AI. But we also have the opportunity to collaborate with AI to create together. This is not fate but a choice. We can choose to be a human tool that only executes mechanically, or upgrade our way of thinking, harness AI, and become the masters of this transformation. For those willing to invest time, money, and patience, Devin offers a path into the deep waters of technology. For those who want quick results, Cursor is a tool that can deliver immediate effects.
Ultimately, the future belongs to those who can dance with AI. Let us seize the moment, using these tools to improve ourselves and break through limitations. Whether it’s an intelligent planner like Devin or a reliable soldier like Cursor, we stand at the threshold of a new world driven by AI, waiting for us to make decisions.
Seize the moment, use AI tools to enhance yourself, and embrace the future transformation.
🌟 The future belongs to those who dare to co-create with AI.
