In this episode, we explore how Vimeo built a customized AI system for subtitle translation—one that goes beyond basic text translation to tackle the much more challenging problem of synchronizing language with timing. We discuss how the team designed a split-brain architecture to separate translation quality from timing constraints, and how they implemented fallback mechanisms to ensure the system remains reliable in real-world scenarios.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/vimeo-engineering-blog/how-we-built-ai-powered-subtitles-at-vimeo-ff11f1d64b2a

In this episode, we explore how the New York Times engineering team used AI agents to scale unit test coverage across their News site. They accomplished this by building a custom coverage measurement tool, designing a two-loop human–AI workflow, and investing heavily in prompt engineering, including strict guardrails to prevent the agent from cheating or drifting. The key takeaway is that AI works best when it is tightly constrained, carefully monitored, and used to amplify human judgment.
For more details, you can refer to their published tech blog, linked here for your reference: https://open.nytimes.com/how-the-new-york-times-is-scaling-unit-test-coverage-using-ai-tools-fa796bf9b8d2

In this episode, we explore how Shopify evolved its product classification system across three major stages: from a traditional logistic regression model with TF-IDF features, to a multi-modal approach combining text and images, and finally to Vision Language Models built on top of a standardized and evolving product taxonomy. We also look at how architectural design and inference optimization are just as important as model accuracy in real-world machine learning systems.
For more details, you can refer to their published tech blog, linked here for your reference: https://shopify.engineering/evolution-product-classification