Michael C Hogan

Agile Product Development & Innovation Strategy


Checkout the source code for the Apollo moon mission

Resharing a link I discovered through the Adafruit blog

The AGC’s source code is an excellent example of organising a large and complex codebase. Written in an assembly language, the code is impressively well-structured. Tasks are split into separate modules and routines for better maintainability, a practice that is even more vital in today’s complex software projects.



Hogan Test for AI

In order for a program to be considered intelligent the following questions must be answerable with a ‘yes’.

  • Is the output of the program passable as human work?
  • Can the program cite sources inline and create a bibliography of works cited?
  • Can the program explain the reasoning behind its conclusions?
  • Can the program respond “I don’t know” when it doesn’t know the answer to a question?
  • Can the program explain its limitations?
  • Can the program propose a conclusion that is not present in the training data used to create it?

Disclaimer: I’m an AI novice and may be completely wrong about the conclusions presented in this article. Over the past few years I’ve dabbled in machine learning. I’ve watched introductory videos explaining the underlying concepts, trained an R/C car to drive itself using computer vision, generated art, and experimented with ChatGPT.


Attractor Landscapes can help visualize complex systems

I enjoyed reading “An interactive introduction to Attractor Landscapes” by Nicky Case, published May 2018.

Case writes about why complex systems can seem stable, then suddenly collapse all at once and demonstrates how a concept called “attractor landscapes” can be used to understand what’s happening.

Why do many complex systems – cultures, environments, economies – seem stuck (or if good, “stable”) despite lots of effort to change them? And why, when change does come, it seems to cascade (or if bad, “collapse”) all at once?

There’s a tool that can help us understand this: attractor landscapes.

So, the next time you’re wondering why things are stuck a certain way, think about:

    • What are the “attractors” of this system?
    • How “deep” are the valleys? (deeper = harder to escape)
    • How “wide” are the valleys? (wider = bigger range of attraction)
    • Can we not just move the ball, but move the hills? (changing the underlying system)

And if you ever find yourself frustrated by the world, remember: for many systems, for long periods of time, nothing much changes. Then, everything changes.

Read the full article & try the examples at https://ncase.me/attractors/.