In this episode of Free Form AI, Michael sits down with Gino Ferrand, CEO of Tecla, to explore how traditional tech hiring misses the mark, especially when it comes to LATAM engineering talent. We break down flawed “rockstar” hiring, AI-proofing your interview process and what successful onboarding actually looks like in a distributed, GenAI-driven world.

Music: “Intro” by DJ RADIK, used under CC BY-SA 4.0. Edited for duration and volume by Free Form AI.

In this episode, Michael and Ben discuss the intricacies of building a video clipping service and recommendation engine. They explore the challenges of defining interesting clips, the importance of feature requirements, and the cold start problem when launching without user data. The conversation delves into tagging and metadata extraction, hierarchical models for recommendations, and the significance of user telemetry data. A/B testing is highlighted as a crucial method for optimizing user experience and recommendations.

In this episode, Michael Berk and Ben Wilson discuss the various levels of a data science career, from internships to senior leadership roles. They explore the expectations, responsibilities, and best practices for each level, emphasizing the importance of collaboration, continuous learning, and maintaining a positive team culture. The conversation provides valuable insights for both aspiring data scientists and seasoned professionals looking to navigate their careers effectively.

In this episode, Michael and Ben speak with Mark Fussell, CEO of Diagrid, about Dapr, a runtime for building distributed applications. Mark shares insights on the challenges of microservices development, the importance of durable workflows, and the actor model. The conversation also touches on API design, the future of AI in software engineering, and how Dapr can accelerate development processes. Mark emphasizes the need for best practices in the rapidly evolving AI landscape and how Dapr aims to provide a robust solution for developers.

In this episode, Michael and Ben explore the world of open source projects in AI, focusing on MLflow and its various flavors. They discuss popular machine learning libraries like Scikit-learn, SparkML, and tree-based algorithms such as XGBoost and LightGBM. The conversation also delves into time series forecasting techniques and the rapidly evolving landscape of generative AI, emphasizing the importance of understanding the strengths and weaknesses of different tools in the open source community.

In this episode, Michael and Ben discuss the challenges of decision-making in software engineering, particularly focusing on the sunk cost fallacy, emotional attachment to work, and the importance of prototyping. They explore how to recognize when to pivot or bail on a project, the complexities of ROI calculations, and how to effectively communicate with stakeholders about project scope and trade-offs. The conversation emphasizes the need for a balanced approach to emotional investment in work, the value of iterative development, and the biases that can affect decision-making.

In this episode, Michael Berk and Ben Johnson discuss the essential traits of effective engineering leadership, the importance of customer-centric software development, and the challenges of integrating AI into business processes. They emphasize the need for clear communication, trust, and vulnerability in leadership, as well as the potential risks and ethical considerations surrounding AI technology. The conversation also touches on the evolving nature of human relationships in the age of AI and the importance of fostering a creative and empowered team culture.

In this episode, Michael Berk and Ben discuss the intricacies of transferring code, project ownership, and knowledge within teams, particularly in the context of machine learning and software engineering. They explore the importance of documentation, bridging skill gaps, and ensuring effective knowledge transfer to maintain and extend codebases. The conversation also touches on the challenges of generative AI and the need for continuous upskilling in a rapidly evolving field.