I created a basic CRM for a real estate business with Flask, SQLAlchemy, and Tailwind CSS, achieving rapid development through Cursor AI and modern LLM tools.
I have faced similar challenges in scaling a real estate CRM. My experience indicates that a proactive monitoring system is crucial for managing asynchronous tasks and load spikes. Implementing reliable task queues along with detailed logging allowed me to quickly detect anomalies. Additionally, stress testing under simulated peak loads helped identify and resolve performance bottlenecks early. Regular audits of queue performance and adaptive resource allocation have been key in maintaining system reliability, even during unexpected traffic surges. This integrated approach has minimized downtime and improved overall system resilience.
Impressive work! I’m curious, how do you manage performance when scaling up? Also, any surprising challenges integrating Cursor AI with Flask? Would love to hear more on how you tackled potential hurdles.
Performance challenges have been a key focus in my development work, especially when integrating flexible tools with Flask and AI functionalities. I noticed that using a combination of caching strategies and efficient ORM techniques helped alleviate bottlenecks during high data loads. Early identification of query inefficiencies also played a significant role in maintaining response times. In my experience, iterative testing and refinement not only addressed the immediate integration quirks but also built a foundation for smoother scalability as the system evolved.
hey, i tried using asynce endpoints to speed things up. also had to tweak caching to balance cursor and claude outputs. not too bad once you patch up the quirks, but debugging was a bit messy at times.
Hey everyone, I’ve been exploring background workers to handle heavy tasks. I’m curious, how do you manage unexpected load spikes or errors when offloading tasks asynchronously? Any interesting stories or tips from your experiences?