#16 Adam Tornhill

#17 Don Syme

I sit down with Don Syme, the creator of F#! We chat about language design, the "sweet spot" of type system expressivity, and how A.I. will affect the future of software development. Topics covered: * The origin story of F# * Why types, data modeling, and functions beats type-level wizardry * The Object-Oriented tidal wave of the 90s * Designing programming languages for the real world * The case against type classes, and why simpler systems often win in real engineering. * How task-oriented AI workflows are reshaping developer productivity. * Why writing good constraints and guardrails may become as important as writing good code. * Will programming languages still matter? * Continuous AI for teams * Natural language as a programming model * https://dsyme.net/ * What Kind of Programming is Natural Language Programming? [https://dsyme.net/2025/09/02/what-kind-of-programming-is-natural-language-programming/] * On Natural Language Programming [https://dsyme.net/2025/08/27/on-natural-language-programming/] * The Early History of F# [https://fsharp.org/history/hopl-final/hopl-fsharp.pdf] * Bora's Law [https://chrisbora.substack.com/p/boras-law-intelligence-scales-with] * On Type Classes [https://dsyme.net/2021/09/09/my-position-on-type-classes/] * The Max-Abstraction Impulse, and Everything Else Wrong with Type-Level Genericity [https://dsyme.net/2022/09/01/the-max-abstraction-impulse-and-everything-else-wrong-with-type-level-genericity/]

#16 Adam Tornhill

In this episode I sit down with Adam Tornhill, founder of CodeScene, to talk about technical debt, Clojure, and why it's so hard to write good software. Topics covered * From electrical engineering to software psychology * Why writing good code is so hard * The origin story of CodeScene * What technical debt really is, and why traditional metrics like cyclomatic complexity fall short * Code health: measuring what makes code hard to understand * Visualizing code to align engineering and management * The story behind Your Code as a Crime Scene * Making the business case for refactoring * Lean manufacturing vs. software: the visibility problem * Code quality and business impact (10× slower, 15× more defects) * AI-friendly code: when LLMs break (and why) * How technical debt amplifies AI failure rates * AI as an engineering force multiplier (or multiplier of chaos) * The future developer: AI team lead? * Why Adam chose Clojure for CodeScene * Immutability, REPLs, and iterative problem solving * Test-driven development as cognitive support * Performance myths in dynamic languages * Parallelism made simple with immutability * The real drawbacks of Clojure * Static vs dynamic typing in large codebases * Hiring in niche languages: small pool, strong engineers * Naming, domain modeling, and long-term code health Links * AI-Ready Code: How Code Health Determines AI Performance [https://codescene.com/hubfs/whitepapers/AI-Ready-Code-How-Code-Health-Determines-AI-Performance.pdf] * Code Red: The Business Impact of Code Quality [https://arxiv.org/abs/2203.04374] * Your Code as a Crime Scene [https://pragprog.com/titles/atcrime/your-code-as-a-crime-scene/] * Treat Your Code as a Crime Scene [https://www.youtube.com/watch?v=7FApEq8wum4] * Beating the Averages [https://www.paulgraham.com/avg.html] * CodeScene.com [https://codescene.com/] * AdamTornhill.com [https://www.adamtornhill.com/]

#15 Giacomo Cavalieri

In this episode, I talk to Giacomo Cavalieri, a core Gleam team member. We cover what makes Gleam special and why developers love it, and how it brings modern type safety to the battle-tested Erlang ecosystem. How did Gleam become Stack Overflow's 2nd most admired language in such a short time? Listen to the episode and find out! Topics Covered * Gleam's design philosophy of simplicity and the "one way of doing things" approach * Statically typed functional programming with the Hindley-Milner type system * How Gleam targets the BEAM VM and JavaScript for full-stack development * The history of BEAM and OTP * Who uses Gleam in production * The Lustre framework for building web applications in Gleam * Integrating Gleam with OTP * How Gleam made it to 2nd Most Admired Language in the Stack Overflow 2025 Survey * How to speak at conferences * Why Gleam deliberately excludes type classes and traits * The future of Gleam Links * Gleam.run [https://gleam.run/] * Gleam Language Tour [https://tour.gleam.run/] * Gleam Roadmap [https://gleam.run/roadmap] * Gleam Case Studies [https://gleam.run/case-studies/strand/] * Giacomo Cavalieri's Website [https://giacomocavalieri.me/] * Giacomo's Func Prog Conference Talk [https://www.youtube.com/watch?v=LMrKEaAi4RI] * Giacomo's GitHub [https://github.com/giacomocavalieri] * Giacomo's BlueSky [https://bsky.app/profile/giacomocavalieri.me] * Giacomo's Twitch [https://www.twitch.tv/giacomo_cavalieri] * Lustre Web Framework [https://github.com/lustre-labs/lustre] * Gleam OTP Library [https://hexdocs.pm/gleam_otp/] * Stack Overflow Developer Survey 2025 [https://survey.stackoverflow.co/2025/] * Func Prog Conference [https://funcprogconf.com/] * Code BEAM Europe [https://codebeameurope.com/] * GOTO Copenhagen [https://gotocph.com/2024] * Lambda Days Conference [https://www.lambdadays.org/lambdadays2025] * Strand Case Study [https://gleam.run/case-studies/strand/] * Uncover [https://www.uncover.co/] * The Zen of Python (PEP 20) [https://peps.python.org/pep-0020/] * Paul Graham's Blub Paradox [https://www.paulgraham.com/avg.html]

#14 Robert Kreuzer

In this episode, I sit down with Robert Kreuzer, co-founder and CTO at Channable, to hear what it's like using Haskell in production. You will hear the story of Channable, and how Haskell worked its way into their code base. Topics covered: How Channable got started How pivoting from their initial idea made Channable a success Using Haskell in production What are the pros and cons of using Haskell? Haskell VS Rust Hiring for Haskell Links: Channable [https://www.channable.com/] Channable jobs [https://jobs.channable.com/] The origin story of Haskell at Channable [https://www.channable.com/tech/how-we-secretly-introduced-haskell-and-got-away-with-it] Effectful [https://hackage.haskell.org/package/effectful] David Christensen - Coding for Types [https://www.youtube.com/watch?v=AWeT_G04a0A] Robert Kreuzer on LinkedIn [https://www.linkedin.com/in/robert-kreuzer-9a729027/]

#13 Matthías Páll Gissurarson

What is the future of Haskell, program generation and AI? I sit down with Matthías Páll Gissurarson and try to figure this out, along with the optimal development setup... and of course, deadlifts! Topics covered: Haskell Typed holes Using AI for code generation Lift weights, not just monads Links: Starting Strength [https://startingstrength.com/] Pumping Iron [https://www.imdb.com/title/tt0076578/] You come to me at runtime, to tell me the code you are executing does not compile [https://x.com/tritlo/status/1368384569720905732] The Lambda Cube [https://en.wikipedia.org/wiki/Lambda_cube] Ghost in the Haskell [https://www.youtube.com/watch?v=hh0axmFH1j8] Matthias website [https://mpg.is/] CSI: Haskell [https://mpg.is/papers/gissurarson2023csi.pdf] Synthesis and Repair for Functional Programming: A Type- and Test-Driven Approach [https://mpg.is/thesis.html] The Spectacular paper [https://mpg.is/papers/gissurarson2023spectacular-extended.pdf] PropR: Property-Based Automatic Program Repair [https://mpg.is/papers/gissurarson2022propr.pdf]