How do always-on, proactive agents like @Bot work? Watch my lecture at Stanford CS146S: 2:35 How we got here 6:13 What changed in the models 10:06 Inside an always-on agent 18:07 The harness 27:26 Context engineering 34:26 Where this is going

I've been reflecting on this. I'm generally a very optimistic person, but lately I've been feeling a strange sense of sadness that coding has been "solved". I can, and have, made many caveats to "solved"... but I also know that in five years none of them will matter. It will be solved. Building things is still fun, but a different kind of fun. It doesn't activate the same parts of my brain as trad coding did. I think it's okay to mourn the thing we loved doing being different now. Software was the first job to go through the five AI stages of grief, which means we're also some of the first to internalize that this will happen to many other jobs. It was the best of times, it was the worst of times.

We just rolled out CursorBench 4.0! It includes new tasks for how well models follow instructions, work on challenging projects over time, and is more difficult than before (so all models score lower).

The similarities between teaching my daughters to be good humans and teaching machines to think and learn new skills continue to amaze me. And yet, watching my oldest run through our yard, laughing and experiencing life, I can't see how machine intelligence could ever compare. It's already hard to imagine life without AI, much like life without the internet. But the more time I spend talking to machines, the more I appreciate the human condition and the messiness of the real world. Being human, with all its imperfections, is awesome.

Chat → Code → @Bot​s I was skeptical at first, but I'm now Bot-pilled. It is hard to give up "the old way" of prompting agents, but I strongly believe always-running bots are the future of where all work on a computer is headed. For example, it still feels *weird* to not see the response stream back to me, or not look at model pickers, and trust in the model/harness to do compaction in these very long conversations. But once you see how simple the product UX can be, and see the results, this way of working is amazing (even with it being in early beta). Models will continue to improve exponentially in the next 3-6 months, getting better and better at proactively doing real work for you in the background. And this feels like the right interface and UX for that work. I started by using Bot for personal tasks instead of work things, but it quickly took over the majority of my tokens. Very excited about the future of Bot!

We just increased the included usage of Grok models in Cursor! Demand has been increasing with the launch of Grok 4.6. We already doubled limits last month with more compute. And now we're raising limits again permanently. Enjoy!

How can you trust @Bot​s to operate autonomously? This is something we've carefully designed. First, we use an LLM to automatically review every action your bot takes. If anything is unexpected, it will ask for your approval. But this isn't enough. We also have customizable rules to allow or deny actions. For example, I have bots that are connected to my X, Gmail, etc. I explicitly block any write/destructive actions and have it ask for my approval first. This is a good balance between YOLO mode and reviewing everything by hand. Although, we do support YOLO if you like to live dangerously!

We're making Grok Bot more widely available. All SuperGrok Plus, Cursor Pro+, and Cursor Teams subscribers now have access. We're also offering a free trial with limited usage for all other users.