DevNest
Code, Collab and Contribute Together!
The problem DevNest solves
Competitive programming and technical interviews are challenging environments where every second counts. With the pressure of solving complex problems quickly and efficiently, distractions can slow down your progress. Current platforms often force users to toggle between different coding tools, messaging apps, and debugging environments—leading to loss of focus and productivity.
In live coding interviews or team-based challenges, coordinating work across multiple people while staying productive can be overwhelming. Often, the lack of integrated tools for collaboration, debugging, and real-time change tracking makes things more complicated and less efficient.
Challenges we ran into
The primary issue stemmed from connecting the chatbot’s backend to our frontend in real-time. We needed the chatbot to interact seamlessly with the user interface, providing suggestions and feedback instantly as the user worked through code. However, ensuring that the chatbot’s responses were contextually accurate and relevant to the user’s actions in real-time was tricky. We also had to ensure that the chatbot was responsive and didn't slow down the user experience, especially when there were multiple users collaborating simultaneously.
API Optimization:
The first step was optimizing the communication between the chatbot’s backend (where the AI model resides) and the frontend (where the user interacts). We streamlined the API calls to reduce latency and ensure faster response times. By implementing a more efficient request-response cycle, we could keep the experience smooth for users, even when multiple users were interacting with the platform.
Testing and Iteration:
Finally, we ran multiple tests to ensure that the chatbot’s responses were accurate, timely, and helpful. We gathered feedback from users during the beta testing phase, which helped us fine-tune the chatbot’s behavior and optimize its performance.
Tracks Applied (1)
Main Track
LLMWare