Hello everyone,
I’m intrigued by the Cursor Tab feature and its ability to provide fast and accurate code completion. I’d like to discuss the possible reasons behind its performance and would appreciate insights from professionals in the field.
- Use of MoE Architecture: Is the speed and precision of Cursor Tab likely due to the use of the MoE (Mixture of Experts) architecture, similar to what OpenAI or DeepSeek employs? This architecture integrates multiple expert models to handle different tasks, thereby enhancing overall performance and accuracy.
- Engineering Performance Optimization Techniques: On top of the model, are there engineering performance optimization techniques at play, such as KV caching, local caching, speculative decoding, prefix-cache etc.? These techniques could significantly boost the model’s speed without compromising accuracy.
- Collaboration with Local Small Models: Does the Cursor Tab feature also involve the cooperation of local small models? By running small models locally, it could reduce reliance on cloud resources, thus speeding up response times.
I’m curious about the specific implementations of these technologies and how they work together to achieve the efficiency of the Cursor Tab feature. Does anyone have more information or their own insights to share?
Looking forward to your replies and discussions!