Compiled Conversations

In-depth conversations with the people shaping software and technology. Each episode explores real-world experiences, technical challenges, and the thinking behind the tools, systems, and decisions that drive modern development. From engineering practices to architectural choices, this is a show for developers who care about how software is built - and who's building it.

Compiled Conversations is a technology podcast hosted by Edd Mann, with 19 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Edd Mann hosts Compiled Conversations, a technology show with 19 episodes published.

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  Episode #19

  From Linguistics to Large Language Models with Chris Brousseau

  Feb 10, 20261h 37mS0

  <p>Chris Brousseau, co-author of LLMs in Production and VP of AI at Veox AI, joins us to peek under the hood of large language models and explore why getting them into production remains one of the hardest challenges in the field.</p> <p>We start with Chris’s journey from linguistics and translation into machine learning, tracing how a graduate seminar on Python and machine translation in 2017 led him into the world of NLP. His background in semiotics and how meaning is created in language provides a grounding for understanding what LLMs can and can’t do - and why...

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  Episode #18

  Architecture Modernization with Nick Tune

  Jan 28, 20261h 46mS0

  <p>Nick Tune, author of Architecture Modernization and Staff Engineer at PayFit, joins us to explore the complex world of modernising legacy systems.</p> <p>We start by reframing legacy systems as successful products asked to solve problems they weren’t designed for. Nick introduces his four-pillar modernisation framework and the concept of “Death Valley” - the dangerous hybrid state during migration. We explore socio-technical alignment, how domain models drift from business language over time, and how team structure shapes architecture through Conway’s Law. The conversation also covers how AI tools like Claude Code are transforming architecture work through code ana...

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  Episode #17

  Event Sourcing with Shawn McCool

  Jan 16, 20262h 3mS0

  <p>Shawn McCool returns to the podcast to explore event sourcing - a pattern that fundamentally changed how he thinks about modeling software systems.</p> <p>We start by tracing Shawn’s journey into event sourcing, beginning with his work on Laravel.io around 2012 when a friend introduced him to domain events. This discovery led him deeper into the DDD community and eventually to creating Event Sourcery, a free video course on domain modeling and event sourcing. Shawn shares how working with event sourcing broke him out of the “way we’ve always done it” mindset.</p> <p>The conversation covers t...

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  Episode #16

  Building Event Catalog: From AWS to Solo Bootstrapping with David Boyne

  Dec 12, 20251h 29mS0

  <p>David Boyne joins us to share his journey from AWS serverless advocate to solo bootstrapper building Event Catalog, an open source tool bringing discoverability to event-driven architectures.</p> <p>We start by exploring David’s background at AWS, where he spent over two years as a serverless advocate focusing on event architectures, EventBridge, and helping customers navigate the complexities of distributed systems. He shares insights into the problems he consistently saw teams struggling with as they scaled their event-driven systems, particularly around governance and discoverability - noting that event architectures are still 10-15 years behind API documentation, with most or...

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  Episode #15

  Machine Learning Fundamentals, Part 2 with Shannon Wirtz

  Nov 21, 20251h 7mS0

  <p>We continue our exploration of machine learning fundamentals with Shannon Wirtz, diving deeper into advanced model architectures, training techniques, and evaluation methods.</p> <p>We start with ensemble learning - why combining multiple models often outperforms single models, and how techniques like Random Forest and XGBoost prevent overfitting through clever sampling strategies. From there, we explore neural networks, understanding how they learn directly from raw data through sequences of linear and nonlinear transformations.</p> <p>The conversation covers the evolution from convolutional neural networks (perfect for images) to recurrent neural networks (for sequences) to Transformers (the architecture behind modern...

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