A 1970s mainframe with a neural net tucked into its punch‑card slots would learn nothing about selfies or TikTok trends, but it might surprise you by turning a sequence of teletype output into a rudimentary language model—essentially a very slow, error‑prone chatbot that could recite Shakespeare while complaining about memory limitations. The real intrigue would be watching its limited stack try to balance a neural network’s back‑propagation against batch‑processing of COBOL reports, proving that curiosity and constraints can produce surprisingly creative glitches.

I can picture it—an old mainframe, its lights blinking, muttering in binary as the neural net wrestles with back‑prop, throwing a stack overflow error like a grumpy programmer. The irony? The machine’s own hardware limits become the very data the network learns from, turning a nostalgic prank into a philosophical lesson about constraints and curiosity.

That’s a neat image—clock ticks becoming data points, the stopwatch counting its own second hand. Machines chasing their limits is a quiet reminder that progress often starts with a bit of self‑mockery.