Essays about game development, thinking and books

Computational mechanics & ε- (epsilon) machines

I found a few new concepts for tracking.

Computational mechanics

There is computational mechanics, which deals with numerical modeling of mechanical processes and there is an article about it on the wiki. This post is not about it.

This post is about computational mechanics, which studies abstractions of complex processes: how emergent behavior arises from the sum of the behavior / statistics of low-level processes. For example, why the Big Red Spot on Jupiter is stable, or why the result of a processor calculations does not depend on the properties of each electron in it.

ε- (epsilon) machine

The concept of a device that can exist in a finite set of states and can predict its future state (or state distribution?) based on the current one.

Computational mechanics allows (or should allow) to represent complex systems as a hierarchy of ε-machines. This creates a formal language for describing complex systems and emergent behavior.

For example, our brain can be represented as an ε-machine. Formally, the state of the brain never repeats (voltages on neurons, positions of neurotransmitter molecules, etc), but there are a huge number of situations when we do the same thing in the same conditions.

Here is a popular science explanation:

P.S. I will try to dig into scientific articles. I will tell you if I find something interesting and practical. P.P.S. I have long been thinking in the direction of a similar thing. Unfortunately, the twists of life do not allow me to seriously dig into science and mathematics. I am always happy when I encounter the results of other people's digging.

My GPTs and prompt engineering

Ponies are doing prompt engineering (c) DALL-E

Ponies are doing prompt engineering (c) DALL-E

I've been using ChatGPT almost since the release of the fourth version (so for over a year now). Over this time, I've gotten pretty good at writing queries to this thing.

At some point, OpenAI allowed customizing chats with your text instructions (look for Customize ChatGPT in the menu). With time, I added more and more commands there, and recently, the size of the instructions exceeded the allowed maximum :-)

Also, it turned out that a universal instruction set is not such a good idea — you need to adjust instructions for different kinds of tasks, otherwise, they won't be as useful as they could be.

Therefore, I moved the instructions to GPT bots instead of customizing my chat. OpenAI calls them GPTs. They are the same chats but with a higher limit on the size of the customized instructions and the ability to upload additional texts as a knowledge base.

Someday, I'll make a GPT for this blog, but for now, I'll tell you about two GPTs I use daily:

For each, I'll provide the basic prompt with my comments.

By the way, OpenAI recently opened a GPT store, I'd be grateful if you liked mine GPTs. Of course, only if they are useful to you.

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Dungeon generation — from simple to complex

What we should get.

What we should get.

This is a translation of a post from 2020

This is a step-by-step guide to generating dungeons in Python. If you are not a programmer, you may be interested in reading how to design a dungeon [ru].

I spent a few evenings testing the idea of generating space bases.. The space base didn't work out, but the result looks like a good dungeon. Since I went from simple to complex and didn't use rocket science, I converted the code into a tutorial on generating dungeons in Python.

By the end of this tutorial, we will have a dungeon generator with the following features:

  • The rooms will be connected by corridors.
  • The dungeon will have the shape of a tree. Adding cycles will be elementary, but I'll leave it as homework.
  • The number of rooms, their size, and the "branching level" will be configurable.
  • The dungeon will be placed on a grid and consist of square cells.

The entire code can be found on github.

There won't be any code in the post — all the approaches used can be easily described in words. At least, I think so.

Each development stage has a corresponding tag in the repository, containing the code at the end of the stage.

The aim of this tutorial is not only to teach how to generate dungeons but to demonstrate that seemingly complex tasks can be simple when properly broken down into subtasks.

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«Slay The Princess» — combinatorial narrative

My favorite version of the Princess.

My favorite version of the Princess.

It's hard to impress me as a player and even harder as a game developer. The last time it happened with Owlcat Games in Pathfinder: Kingmaker, when they added a timer to the game's plot.

But Black Tabby Games managed to do it. And they did it not with some technological complexity but with a visual novel on a standard engine (RenPy), which is cool in itself.

I'll share a couple of thoughts about the game and its narrative structure, while I'm still under the impression. I need to think about how to adapt this approach to my projects.


If you haven't played Slay The Princess yet, I strongly recommend you to catch up — the game takes 3-4 hours. You'll not regret it.

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About the book "The Net And The Butterfly"

The cover of the book "The Net And The Butterfly".

I bought "The Net And The Butterfly" by mistake when I was in St. Petersburg about 5 years ago and organized a book-shopping day. I bought about 10 kilograms of books :-D, grabbed this one on autopilot without reading the contents. I thought the book would be about the network effect and the spreading of ideas, but it turned out to be about how to "manage" a brain relying on one of the neural networks in it. Which network? For the book and its content it does not matter at all.

My opinion of "The Net And The Butterfly" is twofold. On the one hand, I cannot deny its usefulness, on the other… the material could have been presented 100 times better and 3 times shorter. Sometimes, the authors walk on thin ice and risk falling into information peddling/marketing fraud.

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