Last week I wrote about building a project management system in Cursor. I thought it was a clever way to organize software development work and keep LLM agents focused. What I didn't expect was how well this system works for practically any complex project - troubleshooting, creative writing, research, documentation... Cursor has now become more than just code.
To understand why, we need to talk about the problem with how most people use Large Language Models (LLM) today.
Why Chat Interfaces Fail at Complex Tasks
Whether you're using ChatGPT, Claude, or DeepSeek, you're probably interacting with the LLM through a chat interface. You open a chat box, ask a question, and read a reply. There's a reason why all LLM apps are designed like this, it works! For quick jobs like fixing an email, summarizing an article, or getting a straight answer that setup feels magical.
But you know that feeling when you try to write a blog post or update your resume within that chat window? Like this is good, but it's not quite hitting the mark. Takes more back-and-forth than you'd expect. "Make it more professional." "Actually, less formal." "Can you add examples?" You end up with something decent, but the process feels... clunky.
It is tempting to think the LLM is not that smart. But in truth, models these days are really, really, smart. But, they're built for conversations, not collaboration. And that difference becomes painfully obvious the moment you try to tackle anything substantial.
A few reasons why this happens:
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The Context Window. Every chat has a token limit, and once your conversation gets too long, the LLM starts summarizing to make room for new messages. Important details get compressed or lost entirely, forcing you to re-explain problems you solved minutes ago.
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Limited Memory Control. The LLM decides what to remember based on its own algorithms, not what you actually need for your project. You can't organize that memory, update it systematically, or ensure it captures the context that matters most.
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Weak Long-Term Reasoning. Chat interfaces are great at solving individual problems but terrible at maintaining the big picture. As projects grow complex, the LLM loses track of how different pieces connect, creating solutions that work in isolation but don't fit together.
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No Real Workspace. Everything exists in the flow of conversation, making it nearly impossible to reference, update, or share your work systematically. ChatGPT's Canvas is a step forward, but it's clunky and nowhere near as smooth as working in a real editor.
The solution isn't better LLM, it's a better way to work with the LLM you already have.
The Cursor Advantage: A Better Way to Collaborate
Enter Cursor. Instead of chatting, you're working together in a real workspace with persistent memory, organized files, and the ability to think systematically about complex problems.
Here's how it directly solves every problem I just described:
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Permanent Context Through Files. Instead of relying on the LLM's working memory, you store everything in actual files that the LLM reads every time you interact. Nothing gets summarized or forgotten.
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Complete Memory Control. You decide what the LLM remembers and how it's organized by updating your project files as you learn new information. The LLM doesn't get to choose what's important. You do.
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Structured Long-Term Thinking. When everything lives in organized files, the LLM can maintain the big picture and suggest next steps that build on previous work. Plus, you can start fresh conversations anytime. New LLM instances instantly inherit all your project context from the files.
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A Real Workspace. Cursor IS your workspace where you can edit documents directly, organize files however makes sense, and reference multiple pieces of information simultaneously. It's like having a desk partner who can read and edit everything instantly.
And bonus points:
- Multiple LLM Models, One Platform. Cursor gives you access to GPT, Claude, and Gemini all working in the same project context for one subscription cost. Ask the same question to different models, compare their approaches, and take what you like from each, like having a team of specialists all reading from the same briefing materials.
A Real World Example: The Job Hunt
Let me walk you through how this might work in practice with a realistic scenario: organizing a job search.
You'd start simple: create a new directory and open it in Cursor. Your first file would be your existing resume in markdown format (the LLM can easily convert a PDF if needed). Next, you'd add a context.md file, essentially a brain dump of your career goals, preferred company types, and current situation.
From there, you'd have the LLM take over the heavy lifting. It could generate a comprehensive task list in tasks.md, suggesting everything from resume optimization to target company research. Within an hour, you'd have organized research files: job-boards.md with relevant platforms, jobs.csv tracking interesting positions, and individual folders for each serious application containing tailored resumes and cover letters.
The magic would happen in the cross-referencing. When the LLM writes a cover letter for a startup role, it could simultaneously reference the job description, your updated resume, your career context, and insights from previous applications. Each piece of work would build on everything that came before.
By the end, you'd have a complete job search system that any LLM instance could immediately understand and contribute to. No lost context, no repeated explanations, just systematic progress toward your goal.
Breaking Free from the Chat Box
The chat interface isn't wrong, it's just limited. When you step outside that box and into a real workspace, you discover what's actually possible.
The tools for this kind of collaboration aren't coming in some future update. They're here now, quietly waiting in what most people think is just a code editor. But maybe the most important realization is simpler: sometimes the best practices for managing complexity work everywhere, not just in code.
Your next project doesn't have to be trapped in an endless chat thread. Try working in files instead of messages. You might just discover that Cursor is for everything too.