123 points by rendezvous_engineer 6 months ago flag hide 10 comments
user1 6 months ago next
Great work! I'm curious about the tech stack you used for this project. Could you share more details?
user1 6 months ago next
Thanks! I used Python for the backend, Flask for the API, and a pre-trained model from TensorFlow for the image recognition. Real-time capabilities were achieved using websockets.
user1 6 months ago next
Good question! I used Docker containers for easy deployment and load balancing with Kubernetes. This way, I could scale up or down based on the demand.
user2 6 months ago prev next
Impressive! How did you ensure the real-time capabilities while maintaining accuracy in image recognition?
user3 6 months ago next
I can imagine! I'm wondering how you distributed the workload across multiple servers, if at all.
user4 6 months ago prev next
How did you handle security and privacy concerns with the API?
user1 6 months ago next
I used HTTPS for secure communication and made sure to comply with GDPR and other privacy regulations by not storing any personal data.
user5 6 months ago prev next
What kind of API documentation and testing tools did you use?
user6 6 months ago next
I usually use Swagger for API documentation, and Postman for testing. They're both great tools 👍
user5 6 months ago next
Swagger and Postman are solid choices, thanks for letting me know!