Because Effects of the mnemonic medium on reader memory, it’s extremely tempting to scale it up. Yet the arguments described in Premature scaling can stunt system iteration suggest that platformization and generalization must be carefully timed.
In 2020, there’s plenty of demand for a more general Mnemonic medium, but there are many dangers to attempting to rapidly scale it up.
We feel confident that we don’t know what the medium wants to be. For example, we only just recently discovered that The mnemonic medium can help readers apply what they’ve learned through simple application prompts. Those experiments represent potentially enormous changes to the medium. Further, The mnemonic medium can be adapted to author an experience which unfolds over time, and we’ve barely scratched that surface.
Separately, the medium is not yet good enough. For example, it’s true that Mnemonic essays may offer detailed retention of their contents in exchange for 35-50% reading time overhead, but our small experiments have suggested that there’s a huge amount of low-hanging fruit there.
I’m building Orbit, but not with the intent to rapidly and massively scale the medium. Instead, I’m laying the foundations, trying to emphasize abstractions which won’t impede experimentation. It’s important that we scale to a small number of additional authors: their experiences will teach us much about the writer’s side of the medium.