Free Form AI

In this episode of Free Form AI, we sit down with Lara Rachidi and Maria Zervou, two AI professionals whose paths defy the traditional tech blueprint.Lara began her career in law and economics before pivoting into data science. Maria started at the British Library, digitizing manuscripts and unlocking forgotten datasets. Today, they’re building GenAI systems, leading strategy across regions, and shaping the next chapter of machine learning at OpenAI and beyond.Tune in to Episode 15 for a wide-ranging conversation about:•What actually makes a technical career resilient•Why network-driven learning accelerates growth in AI•How tools like DSPy are revolutionizing prompt optimization for agentic systems•What the future of LLMs may look like More than a career story, this episode is a reminder: some of the most effective voices in tech began outside of it.If you’re looking to pivot into the AI field and want some role models, this episode’s for you.

In this episode of Free Form AI, we sit down with Puneet Jain, Senior Applied AI Engineer at Databricks, to explore what’s really holding back GenAI from reaching production at scale, and why infrastructure, not intelligence, might be the missing piece.From toy clustering algorithms to national-scale LLMs in healthcare, we unpack how secure data access, agentic tooling, and long-term system design are reshaping the GenAI landscape. We also discuss what “AI agency” really means. More importantly, is the road to AGI shorter than we think?Tune in!

In this episode of Free Form AI, we sit down with Mary Moore-Simmons, VP of Engineering at Kibo (formerly GitHub), to explore how career pivots, leadership, and AI are reshaping engineering.From drying Kona coffee beans with radiant heat to leading complex orgs at GitHub, Mary shares how curiosity, efficiency, and a bias for action can accelerate your career. We discuss the hidden cost of inefficiency in scaling teams, why “just doing the thing” often opens new doors, and how AI is transforming cost and performance optimization at scale.

In this episode of Free Form AI, we explore how AI coding assistants are reshaping the development workflow. From spotting bugs that slip past human review to writing context-aware tests and reviewing entire PRs, these tools are changing how engineers build software. We break down the “secret sauce” behind how they scan a repo with targeted search commands, keeping context focused and results accurate without overwhelming the model. The conversation also tackles the trade-offs, from productivity gains to the risk of skill decay and what this means for the future of engineering.

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.

In this episode, Michael Berk and Ben Wilson discuss their experiences at Databricks, focusing on project management, team dynamics, and the importance of product alignment. They explore the challenges of onboarding new team members, the significance of effective communication with stakeholders, and the balance between upskilling and meeting project deadlines. The conversation emphasizes the need for early integration of components, maintaining code quality, and the role of a tech lead in delegating tasks and managing team dynamics.

In this episode, Michael and Ben Wilson discuss the concept of knowledge acquisition, focusing on how to effectively acquire knowledge for practical action rather than for its own sake. They explore the value of note-taking, the role of curiosity in learning, and the importance of hands-on experience in making knowledge intuitive. The conversation highlights the balance between curiosity-driven exploration and just-in-time learning, emphasizing that both approaches can lead to valuable insights and problem-solving skills. In this conversation, Ben and Michael explore the significance of practical experience in learning, the balance between curiosity and just-in-time learning, and the importance of physical fitness in the tech industry. They discuss the nature versus nurture debate in engineering skills and how AI is evolving to reflect societal changes in knowledge acquisition. The dialogue emphasizes that knowledge should be applied for action rather than mere recall, and highlights the value of general knowledge in fostering innovative ideas.

In this episode of Freeform AI, hosts Michael and Ben discuss the importance of understanding ROI in feature development, emphasizing the need for effective prioritization and the implementation of a Minimum Viable Product (MVP). They explore how to analyze demand, identify root causes of inefficiencies, and utilize the MoSCoW method for prioritization. The conversation also touches on the challenges of iterative development, legacy code, and the significance of communication between stakeholders and developers. The hosts share personal experiences and lessons learned from past mistakes, highlighting the value of simplicity in solutions and the importance of continuous improvement.