Hands-on trainings
These guided projects help you move beyond theory. Design, build, and refine offline AI applications while understanding how real-world AI systems are structured and deployed.
Nutrient Analyzer uses your laptop camera to detect food items in real time with TensorFlow. When you show an item like an apple or banana, the app labels it and sends a prompt to a local Ollama model to return the nutrition summary.
Solution: Help students learn how computer vision and local LLMs work together to turn camera input into useful nutrition guidance.
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Offline AI Chat Assistant is a simple conversational web app that helps students understand how AI-powered chat systems work without relying on the internet. Built using Next.js and a locally running Ollama model, the assistant accepts a user question, sends it to the local AI runtime, and displays a helpful response. The project focuses on understanding request–response flow, prompt handling, and privacy-first AI design. All interactions stay on the student’s laptop, making it safe for classroom use and ideal for learning core AI concepts without external dependencies.
Solution: Help students understand how AI chat systems work while avoiding black-box cloud dependencies.
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