Origin, our code hosting platform, is now live. It's fast, easy to use, and deeply integrated with Cursor. Get started by syncing your repos from GitHub.

Cursor Community Chat on xchat We will accept as many as possible until capacity hits nitter.net/i/chat/group_join/g204… Tell your friends!

its the cursor town square

Cursor has Claude 4 Sonnet with 1M tokens of context! plus a sneak peak at a new Cursor ui

Cursor 1.3 is out! You can now collaborate with Agent in your terminal, clearly see context window usage, and make faster edits.

Do AI models get "dumber" over time? I can see why it might feel that way. But there's actually a simpler explanation: context! Understanding what context is and how to manage it will help you get higher quality output from models. And it's actually more approachable to understand than you might think! You can think about working with AI like cooking. For example, let’s say we’re making a soup. You have many inputs into the cooking process with all of the ingredients. You follow some path or recipe, keeping track of your progress along the way. And at the end, you have a tasty soup as a a result 🍲 Different chefs might add or modify the ingredients to their taste, and even if you follow the same recipe exactly, it might taste slightly different at the end. This is kind of like working with AI models! Let’s look at a similar example for coding with AI: 1. You can have many inputs, like your current codebase and files, and a prompt to tell the AI model what you want to achieve 2. You follow a plan, sometimes human generated or suggested by the model itself, which can then create a todo list and check items off as it completes tasks 3. And the end, you get generated code you can apply to your project Your inputs, as well as the model outputs, all become part of the "context". Think of the context like a long list, where the AI model can keep a working memory for the conversation. At the start of the list is a system prompt. This is how the tool creator can inject some instructions or style for the model to follow. It’s trying to help nudge the output in a certain direction, including defining specific rules to follow. Then you have the user message or prompt. This could be any directions you want to give the model. For example, adding a new route to manage user accounts. You don’t have to use proper spelling or grammar, as AI models are surprisingly good at figuring out what you meant, but it still can’t hurt. This prompt doesn’t have to be just text. Many AI products now support attaching images, where the underlying AI model can read and understand the contents of the image and include that result in the context. For example, tools like Cursor can include other relevant information in the input context based on the state of your codebase. For example, your open files, the output from your terminal, linter errors, and more. After sending the inputs to the model, it generates and returns back some output. For simple questions, this might just be text. For coding use cases, this could be snippets of code to apply to your codebase. Everything returned from the model is part of the output context. Your conversation may go on for many "turns" back and forth between you and the AI model. Every message in the conversation, including both inputs and outputs, is stored as part of the working memory in context. The length of this list grows over time. This is important to note! Just like if you were having a conversation with a human, there’s only so much context you can keep in your brain at one time. As the conversation goes on for a while, it gets harder to remember things people might have said 3 hours ago. This is why understanding and managing context will be an important skill to learn. Every AI model also has a different context limit, where it will no longer accept further messages in the conversation, so many AI tools give the user feedback on how close they are to those limits or provide ways to compress and summarize the current conversation to stay under the limit. Additionally, some models can "think" or reason for longer, which uses more output tokens and thus fills up the context window faster. Generally these models are more expensive and have better quality of responses for more complicated tasks. Okay, that's all for now. I hope this better explains what context is and how it works. Anything missing you would add? Additional things you want me to cover? 👀

I'm joining Cursor to teach the future of coding! There are millions of developers learning how to use AI and they need pragmatic advice: 1. We need to teach new developers strong foundations, so they know what to learn, and how to solve issues when debugging. 2. We need to teach experienced developers how AI can automate the tedious parts of coding, or save them time reading docs and fixing bugs. 3. We need to help developers become even more competent. AI may end up writing most of your code, but you have to review, understand, and maintain that software. This is why some experienced devs are having a great time with AI. They can ask for a pattern like "add an exponential backoff" instead of “make it more robust to errors” which may or may not work. I want to help developers become an order of magnitude more productive, and help more people contribute to building software. This is going to take a *lot* of education and retraining. So expect more videos soon, and if you have ideas for what I should teach, let me know!