Machine Learning & AI
A Rust-Based Jarvis-Like AI Assistant
Create an AI-powered voice assistant in Rust that can perform complex, low-level tasks on your computer. This goes beyond simple voice commands. Imagine saying, “Search for all .docx files modified last week and move them to my ‘Reports’ folder,” or “Create a bootable USB drive from the ISO in my Downloads folder.” The latter could be achieved by programmatically using tools like Ventoy.
Tech Stack:
- Core: Rust for its performance and safety.
- Voice Recognition: An open-source speech-to-text engine.
- NLP: A library for understanding the intent behind the user’s commands.
- System Interaction: Rust’s standard library and crates for file system manipulation and running command-line tools.
Cricket Prediction with PyTorch
This is a deep dive into sports analytics. The goal is to build a model that can predict ball-by-ball statistics for a cricket match given the data up to a certain point. A Convolutional Neural Network (CNN) could be a good starting point for recognizing patterns in the sequence of game events.
Tech Stack:
- ML Framework: PyTorch.
- Model Architecture: A CNN or a Recurrent Neural Network (RNN) could be suitable for this kind of sequential data.
- Data: You would need to find a source of detailed ball-by-ball cricket data.
A RAG-Based Twitter Bot for Brand Presence
Build a Twitter bot that uses the Retrieval-Augmented Generation (RAG) technique to act as a brand’s presence on the platform. The bot could be fed a knowledge base of the brand’s products, documentation, and common customer questions. When a user mentions the brand with a query, the bot can retrieve the relevant information and generate a helpful, context-aware response.
- Tech Stack:
- Core: RAG pipeline using LangChain.
- LLM: A powerful open-source model suitable for RAG applications.
- Vector Database: ChromaDB for storing and retrieving information from your knowledge base.
- Social Media Integration: The Twitter API to read mentions and post replies.
A High-Throughput Resume Analyzer
This is a more systems-level ML project. The goal is to build a tool that can analyze a large number of resumes in bulk and find the most relevant candidates for a specific job description. This would involve more than just a simple script; you’d need to think about how to process these resumes at scale.
Tech Stack:
- Core: Python.
- ML/NLP: Natural Language Processing techniques to extract skills, experience, and other relevant information from resumes. You could even fine-tune a model like Gemini to better understand the nuances of your specific requirements.
- Architecture: Use a queuing system (like RabbitMQ or Kafka) to handle the influx of resumes. Implement multi-processing or multi-threading to analyze multiple resumes concurrently.
- Optimization: Use caching (like Redis) to store intermediate results and speed up processing.
