Cellular Automata Machine (CAM6) Simulator (by SimHacker)

CAM6 Alternatives

Similar projects and alternatives to CAM6

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better CAM6 alternative or higher similarity.

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CAM6 reviews and mentions

Posts with mentions or reviews of CAM6. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-11-21.
  • Programming the CAM-6 Cellular Automata Machine Hardware in Forth (CAM6 Simulator demo)
    1 project | /r/Forth | 14 Dec 2022
    Github Repo:
  • Ask HN: What weird technical scene are you fond/part of?
    25 projects | | 21 Nov 2022

    I hate it when a program I wrote mocks me. In Lex Fridman's interview of Steven Wolfram, he demonstrates the machine learning functions in Mathematica by taking a photo of himself, which identifies him as a .... (I won't give it away):

    Here's a video I recently recorded of the CAM-6 simulator I implemented decades ago, and rewrote in JavaScript a few years ago.

    I recorded that demo to show to Norman Margolus, who co-wrote the book and wrote the CAM6 PC Forth code and many rules, so it's pretty long and technical and starts out showing lots of code, but I'm sure you'll totally get and appreciate it. I linked to a pdf copy of the book in the comments, as well as the source code and playable app.

    Demo of Don Hopkins' CAM6 Cellular Automata Machine simulator.

    Live App:

    Github Repo:

    Javacript Source Code:

    PDF of CAM6 Book:

    Comments from the code:

        // This code originally started life as a CAM6 simulator written in C
  • Theory of Self Reproducing Automata [pdf]
    1 project | | 24 Sep 2022

    DonHopkins on March 31, 2020 | parent | context | favorite | on: Von Neumann Universal Constructor

    Here's some stuff about that I posted in an earlier discussion, and transcribed from his book, "Theory of Self-Reproducing Automata".

    His concept of self-reproducing mutating probabilistic quantum mechanical machine evolution is quite fascinating and terrifying at the same time (or outside of time), potentially much more powerful and dangerous than mere physical nanotechnology "gray goo" and universe-infesting self replicating von Neumann probes:

    Can Programming Be Liberated from the von Neumann Style? (1977) [pdf] (

    John von Neuman's 29 state cellular automata machine is (ironically) a classical decidedly "non von Neumann architecture".

    He wrote the book on "Theory of Self-Reproducing Automata":

    He designed a 29 state cellular automata architecture to implement a universal constructor that could reproduce itself (which he worked out on paper, amazingly):

    He actually philosophized about three different kinds of universal constructors at different levels of reality:

    First, the purely deterministic and relatively harmless mathematical kind referenced above, an idealized abstract 29 state cellular automata, which could reproduce itself with a Universal Constructor, but was quite brittle, synchronous, and intolerant of errors. These have been digitally implemented in the real world on modern computing machinery, and they make great virtual pets, kind of like digital tribbles, but not as cute and fuzzy.

    Second, the physical mechanical and potentially dangerous kind, which is robust and error tolerant enough to work in the real world (given enough resources), and is now a popular theme in sci-fi: the self reproducing robot swarms called "Von Neumann Probes" on the astronomical scale, or "Gray Goo" on the nanotech scale.

    >The von Neumann probe, nicknamed the Goo, was a self-replicating nanomass capable of traversing through keyholes, which are wormholes in space. The probe was named after Hungarian-American scientist John von Neumann, who popularized the idea of self-replicating machines.

    Third, the probabilistic quantum mechanical kind, which could mutate and model evolutionary processes, and rip holes in the space-time continuum, which he unfortunately (or fortunately, the the sake of humanity) didn't have time to fully explore before his tragic death.

    p. 99 of "Theory of Self-Reproducing Automata":

    >Von Neumann had been interested in the applications of probability theory throughout his career; his work on the foundations of quantum mechanics and his theory of games are examples. When he became interested in automata, it was natural for him to apply probability theory here also. The Third Lecture of Part I of the present work is devoted to this subject. His "Probabilistic Logics and the Synthesis of Reliable Organisms from Unreliable Components" is the first work on probabilistic automata, that is, automata in which the transitions between states are probabilistic rather than deterministic. Whenever he discussed self-reproduction, he mentioned mutations, which are random changes of elements (cf. p. 86 above and Sec. below). In Section above and Section 1.8 below he posed the problems of modeling evolutionary processes in the framework of automata theory, of quantizing natural selection, and of explaining how highly efficient, complex, powerful automata can evolve from inefficient, simple, weak automata. A complete solution to these problems would give us a probabilistic model of self-reproduction and evolution. [9]

    [9] For some related work, see J. H. Holland, "Outline for a Logical Theory of Adaptive Systems", and "Concerning Efficient Adaptive Systems".

    Ericson2314 3 months ago [-]

    > Although I refer to conventional languages as "von Neumann languages" to take note of their origin and style, I do not, of course, blame the great mathematician for their complexity. In fact, some might say that I bear some responsibility for that problem.

    From the paper. Whew.

  • Show HN: Making a Falling Sand Simulator
    3 projects | | 8 May 2022
    Typically a cellular automata simulation will have some edge condition like wrapping or mirroring an adjacent cell.

    A nice optimization trick is to make the cell buffers 2 cells wider and taller (or two times whatever the neighborhood radius is), and then before each generation you update the "gutter" by copying just the wrapped (or mirrored) pixels. Then your run the rule on the inset rectangle, and the code (in the inner loop) doesn't have to do bounds checking, and can assume there's a valid cell to read in all directions. That saves a hell of a lot of tests and branches in the inner loop.

    Also, the Margolus neighborhood can be defined in terms of the Moore neighborhood + vertical phase (even/odd row) + horizontal phase (even/odd column) + time phase (even/odd time). Then you can tell if you're at an even or odd step, and which of the four squares of the grid you're in, to know what to do.

    That's how the CAM6 worked in hardware: it used the x/y/time phases as additional bits of the index table lookup.

    Here's how my CAM6 emulator computes the Margolus lookup table index, based on the 9 Moore neighbors + phaseTime, phaseX, and phaseY:

                        function getTableIndexUnrotated(
  • Ask HN: What book changed your life?
    1 project | | 19 Mar 2022
  • It's always been you, Canvas2D
    12 projects | | 4 Mar 2022
    Oh, nicely done! Trying to code up cellular automata simulations are pretty much guaranteed to push my brains through my nostrils - I've never progressed far beyond classic Conway. Your CAM6 library[1] may be about to steal my weekend from me!

    [1] -

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