In this episode, we explore how OLX improved discovery by combining keyword search and vector search instead of forcing a choice between the two. Keyword systems remain excellent for precision, while vector systems add semantic understanding. Together, they create a smarter and more user-friendly marketplace experience.
For more details, you can refer to their published tech blog, linked here for your reference: https://tech.olx.com/hybrid-search-where-keywords-meet-vectors-enabling-classifieds-discovery-b7c383fe4fc4

In this episode, we explore how Udemy built a multilingual AI platform to bring its generative AI features to learners around the world. The team approached localization across three levels: a translation-first approach for broad and fast coverage, a fully native multilingual system for markets where fluency and cultural precision are essential, and a hybrid solution in between that intelligently routes between the two depending on the situation
For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/udemy-engineering/from-zero-to-hero-localization-led-generative-ai-at-udemy-a422e4f968d4

In this episode, we explore how Meta uses the “Ladder of Evidence” framework to evaluate the effectiveness of new product features. Instead of relying on a single analytical method, this framework helps teams choose the right type of evidence based on real-world constraints, leading to better and more informed product decisions.

For more details, you can refer to their published tech blog, linked here for your reference: 

https://medium.com/@AnalyticsAtMeta/ladder-of-evidence-in-understanding-effectiveness-of-new-products-part-i-ad8dee70906c

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

In this episode, we explore how Faire built its ads system from scratch. On the business side, we discuss why ads matter for a growing marketplace: enabling brand discovery, creating a new revenue stream, and strengthening the overall ecosystem. On the technical side, we break down the three core components—Ads Delivery, Ads Manager, and Ads Foundation—and examine key considerations such as optimizing for long-term brand–retailer relationships and shipping a complex system within just six months.

For more details, you can refer to their published tech blog, which is linked in the episode description: https://craft.faire.com/building-faires-ads-system-from-scratch-5c24fc916995

In this episode, we explore how Agoda tackled a costly engineering bottleneck by integrating GPT into their CI/CD pipeline to analyze failing SQL stored procedures automatically and suggest optimizations — complete with rewritten queries, index recommendations, and side-by-side performance comparisons. The result is a human-in-the-loop system where AI handles the heavy lifting and engineers make the final call, leading to significant improvements in engineering productivity.

For more details, you can refer to their published tech blog, linked here for your reference: https://medium.com/agoda-engineering/how-agoda-uses-gpt-to-optimize-sql-stored-procedures-in-ci-cd-29caf730c46c

In this episode, we explore how LinkedIn is reimagining job search with AI and large language models — evolving from rigid, keyword-based systems to flexible, intent-aware experiences that feel more conversational and personalized.

For more details, you can refer to their published tech blog, linked here for your reference: https://www.linkedin.com/blog/engineering/ai/building-the-next-generation-of-job-search-at-linkedin

In this episode, we explore how Uber tackled the challenge of personalizing CRM communications at scale through contextual bandit strategies enhanced with generative AI embeddings, lightweight and powerful models like LinUCB and XGBoost, and smart decision augmentation with SquareCB. This work shows how data science can take a core business need—delivering relevant user communications—and build systems that adapt in near real time to maximize impact.
For more details, you can refer to their published tech blog, linked here for your reference: https://www.uber.com/blog/enhancing-personalized-crm