PyData London 2026

Oreolorun Olu-Ipinlaye

Oreolorun Olu-Ipinlaye is a Machine Learning/AI Engineer at Crowdhelix in London, where he builds production AI systems end-to-end for a platform connecting researchers with EU funding opportunities. As the lead engineer behind ReviewIQ; a self-hosted-LLM proposal review tool used by researchers across dozens of organisations; he has helped researchers in assessing their proposals before submission leading to more competitive proposals.

His work spans the full stack of applied ML: self-hosted LLM infrastructure, recommender systems, semantic and hybrid search, and the event-driven pipelines underneath, built largely in Python. He's particularly drawn to taking ML products from idea to adoption with measurable impact, and to making AI capabilities legible to non-technical stakeholders.

He holds an MSc in Artificial Intelligence and Data Science from the University of Hull, where he earned the award for Best Overall Performance.


Session

06-05
14:10
90min
Building a Browser Agent from Scratch: Teach an LLM to Navigate the Web
Richard, Oreolorun Olu-Ipinlaye

AI systems that can autonomously navigate websites, fill forms, extract data, and complete multi-step workflows; are one of the most exciting and practical applications of large language models in 2026. Libraries like browser-use (60k+ GitHub stars) and Skyvern have demonstrated their potential, but their abstractions can obscure the surprisingly approachable fundamentals underneath.

In this 90-minute hands-on tutorial, attendees will build a browser agent entirely from scratch using only Python, Playwright, and an LLM API. No agent frameworks, no magic; just the core building blocks: extracting and structuring the DOM into an LLM-friendly representation, capturing screenshots for vision-based reasoning, building the observe-think-act agent loop, and handling real-world challenges like dynamic content, multi-tab navigation, and error recovery.

By building from first principles, attendees will gain a deep understanding of how browser agents actually work; knowledge that transfers directly to using, debugging, and extending any browser agent framework. Every participant will leave with a working agent that can autonomously complete tasks on live websites.

This tutorial is aimed at Python developers and data scientists who are curious about AI-driven browser automation. Basic Python proficiency and familiarity with async/await are expected. No prior experience with Playwright, browser automation, or agent frameworks is required.

Doddington Forum