In this episode we discuss the Monte Carlo method, both the technique and its place in the history of computing. The Monte Carlo method is a set of tools that use probability to solve problems that are otherwise intractable. Programs using Monte Carlo were among the first programs created on digital computers.

The Monte Carlo Method Nicholas Metropolis; S. Ulam Journal of the American Statistical Association, Vol. 44, No. 247. (Sep.

Equation of State Calculations by Fast Computing Machines

Hitting the Jackpot: The Birth of the Monte Carlo Method | LANL

Monte Carlo Sampling Methods Using Markov Chains and Their Applications W. K. Hastings Biometrika, Vol. 57, No. 1. (Apr., 1970),

2014HaighPriestleyRope-LosAlamosBetsonENIAC.pdf

The Unheralded Contributions of Klara Dan von Neumann

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In this episode we discuss the 5 most-cited papers from the flagship journal of the Association for Computing Machinery, the JACM. The papers include everything from distributed computing to real-time scheduling.

Direct Search'' Solution of Numerical and Statistical Problems | Journal of the ACM

Impossibility of distributed consensus with one faulty process | Journal of the ACM

Robust principal component analysis? | Journal of the ACM

Authoritative sources in a hyperlinked environment | Journal of the ACM

Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment | Journal of the ACM

What is HITS Algorithm (Hyperlink-Induced Topic Search)?

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This episode started with the question: who first had the idea to connect computers together to make a network? We explore JCR Licklider's ideas about an "Intergalactic Network", some of the earliest attempts at networking, and the ideas that lead to packet switching and the Arpanet.

SAGE: Semi-Automatic Ground Environment Air Defense System | MIT Lincoln Laboratory

Memorandum for Members and Affiliates of the Intergalactic Computer Network

The Arpanet and Computer Networks

60 Years of Networking - by Bruce Davies - Systems Approach

Information Flow in Large Communication Nets (1961, Leonard Kleinrock, MIT)

On Distributed Communications Networks (1962, Paul Baran, RAND)

National Physical Laboratory Proposal for a Digital Communication Network by D. W. Davies June, 1966

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In this episode we discuss a couple of recent papers on just-in-time development and agentic systems and how we think they suggest trends that will affect software development for years to come. We boldly predict that in 2046 people will still not want robot dentists.

The Time is Here for Just-in-Time Systems: Challenges and Opportunities

MDASH: The MS Security Thing

Services: The New Software | Sequoia Capital

Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl

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For the 100th episode of Picture Me Coding we talk about numbers. Mike talks about his favorite numbers, Erik talks about his favorite ports, and we discuss the surprising phenomenon of Benford's Law.

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This week we were joined by our friend Doug Burke, who runs a company and has been working on his "digital life workout partner", a voice-activated LLM tool. Doug's story is interesting because he does not have a background in software development, and we wanted to learn more about what he's building.

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In this episode we invite back our friend Bob Farzin to discuss our personal experiences with using teams of agents to write software, and we try to parse out the experiences we're seeing on the interwebs, including Steve Yegge's GasTown and Wes McKinney's discussion of agentic development in the context of Brooks's _Mythical Man Month_.

• "Introducing Beads: A coding agent memory system"
• Welcome to Gas Town — Steve Yegge's original essay
• The Future of Coding Agents — Yegge's follow-up
• Gas Town on GitHub — The actual tool
• How to Think About Gas Town — Steve Klabnik's analysis
• A Day in Gas Town — DoltHub's practical walkthrough
• Gas Town, Beads, and the Rise of Agentic Development — Software Engineering Daily interview with Yegge
• Top Coding Agents 2025 — Benched.ai comparison guide
• [April 8, Wes McKinney] AI Agents, The Mythical Agent Month, My Wild AI Coding Setup

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In this episode we attempt to explain query optimization and where it came from. In particular we discuss Patricia Selinger's 1979 SIGMOD paper Access Path Selection in a Relational Database Management System.

• Access Path Selection paper (PDF)
• A Conversation with Pat Selinger — ACM Queue (2006)
• Database Dialogue with Pat Selinger — CACM (2008)
• Pat Selinger Speaks Out — SIGMOD Interview (PDF)
• Patricia Selinger — IBM History
• System R: Database Research Retrospective — TODS 1981
• Graefe, G. (1995). The Cascades Framework for Query Optimization
• Leis et al. (2015). How Good Are Query Optimizers, Really? PVLDB Vol. 9 — introduces the Join Order Benchmark (JOB) and empirically audits modern optimizers.

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