There’s something quietly impressive about getting modern AI ideas to run on old hardware (like OP's project or running LLM inference on Windows 3.1 machines). It’s easy to think all the progress is just bigger GPUs and more compute, but moments like that remind you how much of it is just more clever math and algorithms squeezing signal out of limited resources. Feels closer to the spirit of early computing than the current “throw hardware at it” narrative.
There is an absolutely beautiful rendering of the Mona Lisa encoded at some point in the digits of pi. If you know the position, it's really easy to plot the image.
I had no idea your simulator existed. No XCMDs, correct; everything is pure HyperTalk. I just ran a few training steps and they complete in a second or two. Thank you for importing it!
More of a copy-paste process. The scripts are written as .txt files in Nova on my Mac Studio, then pasted one at a time into HyperCard's script editor on the classic Mac. The files are kept separate because SimpleText has a 32 KB text limit.
It's strange to think how modern concepts are only modern because no one thought of them back then. This feels (to me) like the germ theory being transferred back to the ancient greeks.
I think it's incredible to see the potential that is still locked up in old hardware. For example the 8088 MPH demo. Amazing what he was able to do with an 8088 and CGA. All this time the hardware had that potential, but it took decades to figure out how to unlock it, long after the hardware was considered obsolete. Imagine the sort of things that might be done later down the road with hardware of 0-20 years ago if somebody really dug into it to that level.
That 8088 MPH demo is a tour de force. Which tells you that the millions of Apple laptops being bricked right now instead of being recycled could have some amazing use if it were possible to wipe them clean and reuse. Sigh.
Thank you! The constraints made it interesting. HyperCard doesn't have arrays, so the entire model, weights, activations, gradients, is stored as strings in hidden fields. All of the matrix math is done with "item i of field".
Thanks! The quickest way to try it is the HyperCard Simulator link someone just posted in this thread: https://hcsimulator.com/imports/MacMind---Trained-69E0132C — go to the Inference card, click New Random to fill in 8 digits, then click Permute. The model predicts the bit-reversed permutation of all 8 positions. The pre-trained stack gets all inputs correct.
But first you have to find that position.
https://hcsimulator.com/imports/MacMind---Trained-69E0132C
https://www.youtube.com/watch?v=yHXx3orN35Y
On the Greeks, Archimede almost did 'Calculus 0.9'.