2025-11-05 –, AI
Today, the key skill isn’t mastering every line of code - it’s keeping up. This talk shows how understanding core concepts, using AI tools, and writing effective prompts can accelerate learning and development in a fast-moving AI landscape.
The use case will be demonstrated using LangChain and LangGraph LLM frameworks, via Cursor as the IDE with native LLM infrastructure.
In today's fast-evolving AI landscape, one of the biggest challenges isn't just learning what to build—but how to learn to build. In this talk, we'll share our journey of learning how to learn in the world of AI, focusing on understanding the right concepts before jumping into implementation.
We’ll explore how focusing on learning theory and concepts, combining using AI tools and a few good prompts - can help developers navigate the growing AI ecosystem more effectively.
Using Agents as our main use case, we'll walk through how we took an early prototype written in a simple notebook and scaled it into a production-grade code, based on LangChain’s LangGraph framework, wrapping it all up with a ready-made UI using Streamlit – all done fast and simple using Cursor.
Whether you're just starting your AI journey or trying to bring structure to your experimental projects, this talk will give you a clear view of the critical skills and concepts that can help you scale your ideas—with agents as a practical and exciting example
No previous knowledge expected
Data Science and Engineering Team Lead @ LSports, Lecturer at the AI Developers and Data Analytics program @ Hebrew University and WiDS Community manager.
Ortal Ashkenazi is a Data Scientist at Wix, specializing in natural language processing and applied machine learning. She enjoys turning messy, human language into structured, useful insights — bridging the gap between raw data and real-world decisions. With an MSc from the Technion, she combines academic depth with hands-on product thinking to build AI tools that people actually use.