AISocietyIITJ/FileNest

AI Summary: Implement a basic UDP messaging client in Go with a CLI for sending and receiving text messages over a local network using a single static port. The client should take the target IP address and port as input and not use ephemeral ports.

AI Summary: Implement a MNIST digit classifier in PyTorch with under 10,000 parameters, achieving over 98.5% accuracy within 20 epochs. The solution must utilize wandb for logging, hydra for configuration management, ray for hyperparameter tuning, and uv for package management. Modular code, PEP8/black formatting, and optional Dockerization and mixed-precision training are also required.

AI Summary: Develop a local-only Go backend prototype for FileNest's file indexing. This involves concurrently processing files, generating simulated embeddings, assigning them to the nearest Depth 1 Tagging Vector (D1TV) using cosine similarity, and storing the results in a PostgreSQL database. The prototype should utilize a worker pool concurrency model, channel-based job distribution, context cancellation and timeouts, and demonstrate code modularity and testability.

AI Summary: Implement a basic CRUD (Create, Read, Update, Delete) REST API in Go for managing file metadata within the FileNest project. This involves creating API endpoints, integrating with a MongoDB Atlas database, handling JSON requests/responses, implementing error handling and validation, and testing the endpoints using Postman. This is a foundational backend task before implementing the full P2P network.