Leslie Lamport - in partnership with ACM Bytecast

In this collaboration with ACM ByteCast and Hanselminutes, Scott welcomes 2013 ACM A.M. Turing Award laureate Leslie Lamport of Microsoft Research, best known for his seminal work in distributed and concurrent systems, and as the initial developer of the document preparation system LaTeX and the author of its first manual. Among his many honors and recognitions, Lamport is a Fellow of ACM and has received the IEEE Emanuel R. Piore Award, the Dijkstra Prize, and the IEEE John von Neumann Medal.

Leslie shares his journey into computing, which started out as something he only did in his spare time as a mathematician. Scott and Leslie discuss the differences and similarities between computer science and software engineering, the math involved in Leslie’s high-level temporal logic of actions (TLA), which can help solve the famous Byzantine Generals Problem, and the algorithms Leslie himself has created. He also reflects on how the building of distributed systems has changes since the 60s and 70s.

Subscribe to the ACM ByteCast at https://learning.acm.org/bytecast

Time-Clocks Paper http://lamport.azurewebsites.net/pubs/time-clocks.pdf

Bakery Algorithm https://en.wikipedia.org/wiki/Lamport%27s\_bakery\_algorithm

Mutual Exclusion Algorithm https://en.wikipedia.org/wiki/Lamport%27s\_distributed\_mutual\_exclusion\_algorithm

Hanselminutes Podcast by Scott Hanselman

The Hanselminutes podcast

Fresh Tech Talk from Fresh Faces

[That's good Mojo - Creating a Programming Language for an AI world with Chris Lattner]

That's good Mojo - Creating a Programming Language for an AI world with Chris Lattner

Show #1037 Feb 19 2026
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What does it take to design a programming language from scratch when the target isn’t just CPUs, but GPUs, accelerators, and the entire AI stack? In this episode, I sit down with legendary language architect Chris Lattner to talk about Mojo — his ambitious attempt to rethink systems programming for the machine learning era.

We trace the arc from LLVM and Clang to Swift and now Mojo, unpacking the lessons Chris has carried forward into this new language. Mojo aims to combine Python’s ergonomics with C-level performance, but the real story is deeper: memory ownership, heterogeneous compute, compile-time metaprogramming, and giving developers precise control over how AI workloads hit silicon.

Chris shares the motivation behind Modular, why today’s AI infrastructure demands new abstractions, and how Mojo fits into a rapidly evolving ecosystem of ML frameworks and hardware backends. We also dig into developer experience, safety vs performance tradeoffs, and what it means to build a language that spans research notebooks all the way down to kernel-level execution.

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