Systems | Development | Analytics | API | Testing

AI writes code faster than any team can check it. Diego Molina thought he had a tooling problem. He didn't. In 37 minutes he shows the workflow mistake most engineering teams are making right now, the one bottleneck that decides whether AI speeds you up or buries you, and the principles that drive his own workflow scale without the noise. Chapters Learn more at saucelabs.com.

Rovo, Atlassian’s AI assistant, can help you generate test cases directly inside Jira through the SmartBear Zephyr Agent for Rovo. This demo offers a practical look at AI-powered testing with Rovo in Jira, from requirements to reviewed test cases, all within the Jira experience, without switching tools.

Most AI pilots never make it past the demo phase. AWS Machine Learning Lead Praveen Jayakumar has seen plenty of promising AI projects get stuck between a successful demo and production. Teams often define what success looks like without deciding what failure looks like, leaving underperforming projects alive long after they should have been shut down. As Praveen puts it, they become “zombie” AI projects.

This session takes a practical look inside LLM context windows and token consumption, exploring what happens when context enters a model - from tokenization, embeddings, attention, QKV, prefill, and decode to KV caching. It also examines how context windows are allocated and why simply increasing context length doesn’t always lead to better model performance.

This video shows how Astera ReportMiner extracts structured data from PDFs in two ways: a reusable template for consistent layouts, and an AI-driven pipeline for documents that vary. Working from a mix of PDFs, digital and scanned, that vary in layout and quality, you can build a custom extraction logic in ReportMiner: Whether a PDF arrives with a known layout or a new one, it runs through the same pipeline into the same output, with fewer manual exceptions and less setup.