Data Science Bulletin is a podcast about news in space of data science, artificial intelligence and machine learning. We bring you views of people "on the ground", implementing these methods into industry setting. Podcast in Czech, accompanying posts and newsletter in English.
In the 24th episode of the Data Science Bulletin podcast, hosts delve into a Netflix paper focusing on the GenRec model, an LLM-backed recommendation ranker designed to optimize content recommendations on the platform. They explore how Netflix has expanded its offerings beyond films to include podcasts and games, and discuss the challenges in recommendation systems as well as the implications of reverse engineering in the software industry. The episode concludes with a lively discussion on the potential impacts of reverse engineering and open-source alternatives on major software companies.
blog post (https://datasciencebulletin.com/?p=1641)
4 Oct 2026
DSB Podcast #23 [CZ] - Graph Engineering and Jev
In this episode of the Data Science Bulletin podcast, hosts discuss the evolving concept of graph engineering, exploring how it can provide a more structured and deterministic framework for working with AI models compared to traditional methods. They also delve into the introduction of Jev, a new language model from Typesafe AI, which boasts rapid decision-making capabilities and cost efficiency for classification tasks. The episode concludes with a discussion on the potential risks of AI development, highlighting recent industry concerns about AI safety and the implications of rapid technological advancements.
Blog post (https://datasciencebulletin.com/uncategorized/dsb-podcast-23-graph-engineering-and-jev/)
10 Sept 2026
DSB Podcast #22 [CZ] - Minimal description Lenght and Harnesses
In this episode of the Data Science Bulletin podcast, hosts delve into the historical significance of AI research by exploring a list of recommended papers, including Geoffrey Hinton's "Keeping Neural Networks Simple by Minimizing the Description Length of the Weights," which addresses overfitting in neural networks. They discuss the concept of harness engineering, which involves creating supportive infrastructures around AI models, particularly in programming and productivity contexts, highlighting the differences between coding agents and broader productivity tools. The episode concludes with an exploration of new trends in AI engineering, such as graph engineering, and encourages listeners to stay informed about the evolving landscape of AI technology.
blog post (https://datasciencebulletin.com/dsb-podcast/dsb-podcast-22-mdl-and-harness-engineering/)
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