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.

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.

From there, we dive into the fundamentals - transformers, tokenization, word embeddings, context windows, and the training pipeline from self-supervised learning through reinforcement learning (PPO and GRPO) to supervised fine-tuning.

We also explore hallucinations, the evolution from prompt engineering to context engineering, how logic manipulation and context-free grammars can make a 2023-era 7B model outperform frontier models at math, and where agents and code-based tool calling are heading in 2026.

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Throughout the conversation, Chris demonstrates how a linguistics background provides a unique lens for understanding both the strengths and fundamental limitations of large language models - and why bridging the gap between language research and computer science could unlock the next wave of progress.

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Nick Tune, author of Architecture Modernization and Staff Engineer at PayFit, joins us to explore the complex world of modernising legacy systems.

We start by reframing legacy systems as successful products asked to solve problems they weren’t designed for.

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Throughout the conversation, Nick demonstrates how architecture modernisation is fundamentally about aligning business needs, team structures, and technical systems - and how AI tools are becoming essential for understanding, documenting, and evolving complex codebases.

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Shawn McCool returns to the podcast to explore event sourcing - a pattern that fundamentally changed how he thinks about modeling software systems.

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.

The conversation covers the core concepts - what event sourcing actually means (using events as the source of your model state), how it differs from event streaming and event-driven architectures, and when you might choose this approach.

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Shawn emphasizes how building systems around short-lived aggregates - where event streams naturally terminate - eliminates entire classes of versioning and data retention problems.

Throughout the conversation, Shawn demonstrates how stepping away from noun-centric thinking and embracing events as the core building block leads to systems that are easier to understand, test, and evolve.

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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.

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.

Seeing these governance challenges repeatedly, David made the decision to leave AWS and pursue Event Catalog full-time as a solo bootstrapper.

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Finally, we explore how AI is changing architecture documentation, with David sharing his experiments with MCP tools that provide context-aware assistance to developers working with event architectures; while recognizing that business context, domains, and ubiquitous language must come from human understanding.

Throughout the conversation, David emphasizes the importance of being human, focusing on problems over features, and advocating for the space rather than just the product.

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We continue our exploration of machine learning fundamentals with Shannon Wirtz, diving deeper into advanced model architectures, training techniques, and evaluation methods.

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.

The conversation covers the evolution from convolutional neural networks (perfect for images) to recurrent neural networks (for sequences) to Transformers (the architecture behind modern LLMs).

We then shift to the critical topic of model evaluation - exploring loss functions, gradient descent, learning rates, and the importance of proper train/validation/test splits.

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Shannon also shares his personal learning journey, from rote learning to practical hands-on experience, and discusses how he learns most effectively through immediate feedback and engaging projects.

Whether you’re looking to understand how modern AI systems work or seeking practical guidance on getting started with machine learning, this episode provides both theoretical depth and practical strategies for building and evaluating ML models.

This is Part 2 of a 2-part series. In

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Shannon Wirtz, product analyst at Angi, joins us to explore the foundations of machine learning - breaking down the terminology, concepts, and approaches that form the bedrock of modern ML systems.

We start by understanding what machine learning actually means in practice, how it differs from traditional rules-based programming, and where it fits within the broader landscape of AI and deep learning.

The conversation covers everything from the fundamental building blocks (models, features, training sets) to the different paradigms of learning - supervised, unsupervised, semi-supervised, self-supervised, and reinforcement learning.

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Shannon also shares practical examples from his work, including predicting tradesperson behavior, handling missing data, and the importance of understanding your data’s context and history before training models.

Whether you’re new to machine learning or looking to solidify your understanding of the fundamentals, this episode provides a comprehensive foundation for understanding how ML systems work and why certain approaches are chosen for different problems.

This is Part 1 of a 2-part series. In

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We continue our exploration of Erlang and Elixir with Shawn McCool, diving deep into the Elixir ecosystem and its powerful tools and frameworks.

Shawn shares practical examples from his experience, including building home automation systems with Nerves for monitoring energy usage.

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We wrap up by exploring the paradigm shift from object-oriented to functional programming, with Shawn sharing his recommended learning path including books from the Pragmatic Bookshelf.

This is Part 2 of a 2-part series. In

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Shawn McCool joins us to explore the unique and powerful world of Erlang and Elixir.

We dive deep into what makes Erlang and the BEAM virtual machine special, exploring how its process-based concurrency model and “let it crash” philosophy enable building highly reliable systems.

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Throughout the episode, Shawn demonstrates how Erlang and Elixir’s unique approach fundamentally changes how we think about building systems, from state management to error handling, making certain patterns natural and elegant that would be impractical or complex in traditional object-oriented languages.

This is Part 1 of a 2-part series. In

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Bruce Momjian returns to complete our exploration of PostgreSQL internals through a table’s lifecycle.

We examine how Postgres manages everything from transaction isolation to reclaiming space.

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Bruce also shares insights into performance optimisation, operational considerations, and the evolution of PostgreSQL’s features over time.

This is Part 2 of a 2-part series. In Part 1, we explored table creation, data insertion, and query execution in PostgreSQL.

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Bruce generously shared links to all the presentations (with direct references to the discussed slides) and blog articles mentioned in the episode. You’ll find them below:

Bruce Momjian returns to explore the internals of PostgreSQL by way of a table’s lifecycle.

We examine how Postgres handles everything from physical storage to transaction visibility.

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Bruce also shares insights into why certain design decisions were made, how they compare to other databases, and practical tips for working with Postgres effectively.

This is Part 1 of a 2-part series. In Part 2, we’ll explore the remaining lifecycle of a PostgreSQL table - including updating rows, altering table structure, row deletion, and finally dropping tables.

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Bruce generously shared links to all the presentations (with direct references to the discussed slides) and blog articles mentioned in the episode. You’ll find them below: