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.

Mocking for testing starts off easy, but once you scale to multiple teams and AI agents, handcrafted mocks become a serious form of technical liability. Instead of treating mocking as an individual software engineering task, shift your mindset to treat it as a platform engineering task focused on automation and continuously refreshed modern data. Watch to see how adopting technologies like traffic replay to simulate realistic backend sandboxes can transform your modern testing workflow!

Human biology is extraordinarily complex, and researchers often have only fragments of information to work with. Brian Martin compares it to looking at a skyscraper through a keyhole: you can see something clearly, but only a tiny piece of the whole. Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Brian Martin, CTO of Applied AI at Cloudera and co-founder of Rare Hopes NFP, to explore what one of the world’s most data-intensive industries can teach us about AI and decision-making.

Discover how Onlyfy by XING, an all-in-one recruiting and applicant management solution partnered with ThoughtSpot, a leader in search and AI-driven analytics that empowers businesses with intuitive, self-service data experiences.

As companies adopt AI coding tools, code review processes are breaking down. Traditional compliance methods are slowing teams down, but human engineers aren't going to spend hours reading AI-generated low-level code forever. How will SOC 2 adapt to the era of AI-driven development? Drop your thoughts in the comments and subscribe for more tech insights! Learn more: speedscale.com.

This video shows how Astera ReportMiner extracts data from documents that don't share a common layout, with no template to design and no fields to mark. Traditional pattern matching depends on documents landing in the same shape every time. Invoices from hundreds of different suppliers don't, and this demo uses that as the test case: mixed layouts, multiple languages, multi-page files, and scans. The walkthrough covers.