Hello everyone. Last week, we developed an AI model that predicts greyhound race results. This model analyzes and learns from the race results of the past year to predict the likely winner of the next race and calculates the break-even point based on the odds. Developed using AutoGen, this model predicted the leading contenders with 87% accuracy, which is a notable achievement in the betting industry. Are there any of you currently working on similar projects or have any ideas? Modec is currently researching ways to increase the probability of recommending leading contenders to over 93%.

Hello everyone, It’s a pleasure to meet you, members of the Skool Community. I’m a Senior Full-Stack AI/ML Engineer with over 10 years of experience, and I’m excited to introduce myself and explore opportunities to collaborate. I build AI-driven products using Python (FastAPI, PyTorch) and TypeScript (Node.js, Next.js), along with scalable cloud-based architectures. My experience includes developing LLM applications, agent systems, SaaS platforms, and real-time data pipelines. On the backend, I work with microservices using FastAPI, Django, Express, Docker, and Kubernetes. On the frontend, I create clean, responsive interfaces with React, Next.js, and modern UI frameworks. Some of my past work includes AI chatbot systems, automation tools, fintech dashboards, and mobile apps built with React Native. In the AI/ML space, I focus on building reliable production environments by integrating APIs, vector databases, and custom models. I’m committed to developing scalable, high-performance systems with clean architecture that support long-term growth. Thank you for taking the time to read my introduction. If anything resonates with you or you’d like to connect, please feel free to reach out anytime.