A Hydra.

HyperCard is one of my early loves as a computer program and computational tool for thought. For those who are familiar with HyperCard, which was bundled onto early Macintoshes, it needs no introduction. But for everyone else, here’s how I described it in an essay I wrote several years ago:

Bill Atkinson, its developer, described HyperCard as "an erector set for building applications." Simply put, you could build your own software using HyperCard, with each program made up of "stacks" of "cards". Each card could contain text and images, as well as interactive elements like buttons, with the ability to interconnect between other cards. Think of these stacks as rudimentary websites of sorts that exist entirely on a single machine, with each card as a page.

It was an incredibly open-ended piece of software, allowing those without much expertise in programming (or even any at all) to build a wide variety of computational tools. To borrow a formulation from Seymour Papert (who co-created the programming language Logo), HyperCard had low floors and high ceilings.

But I recently discovered an orthogonal formulation for how to think of this open-ended and democratizing piece of software. And it’s found in an early exploration of HyperCard within MacUser magazine by none other than Douglas Adams. Douglas Adams, in addition to being a science fiction humorist, was also a big fan of the Macintosh. As per Wikipedia, "Adams was a Macintosh user from the time they first came out in 1984 until his death in 2001. He was the first person to buy a Mac in Europe, the second being Stephen Fry."(!)

In this MacUser article from 1987, Adams writes the following about HyperCard:

I think it occupies the same niche in the evolution of software as human beings do in the evolution of life. A human can’t run as fast as a horse, can’t climb trees as well as a monkey, can’t swim as well as a fish, hear as well as a dog or see as well as a cat. But we can swim better than a monkey and run faster than a fish. If we need to go as fast as a horse, we can ride one. If we need to go faster still we can build a car or an aeroplane or use one that someone else as built. We can find a way of doing just about anything we want to do. We can invent Velcro.

It’s the fact that we are unspecialised but infinitely adaptable that has been the secret of our stupendous success as a species. A cheetah may, after millions of years of evolution, be perfectly designed to run at a phenomenal 70 miles per hour, but it cannot use the phone—which, as we know, is often a more effective way of getting something done quickly. A giraffe that knew where the ladder was kept could dispense with a lot of troublesome vertebrae.

In other words HyperCard is a program that functions in the same that human beings do. It can turns its hand to any kind of task at any moment and do it as well as most tasks actually need. And if that task is beyond it, HyperCard can use the phone, it can go for a ride on Excel, and it knows where Illustrator is kept.

…

The work we do rarely consists of single Herculean tasks, but rather is made up of a large array of comparatively small and simple tasks all of which relate to each other. It’s the relationships between all these little tasks that makes the work that each of us does unique, and which makes the business of trying to find software that actually fits the way we work such a bitch.

Of course, this property of acting as a sort of Swiss Army Knife plus "glue for tasks" is no longer only the province of HyperCard. We have IFTTT and Zapier and Val Town. But Adams makes a powerful point: the general-purpose nature—as well as composability—of a piece of software is just as important as its low floors and its approachability.

Ultimately, we need all of these features in order to do, in the words of Douglas Adams, "all those things that people who don’t know anything about computers assume they ought to be able to do." ■

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I recently made a list of new educational models that are worth considering:

While universities and traditional learning models are powerful mechanisms for education, at the edges of these lurks the potential for new institutional forms. Whether it's novel upstart schools (or, as per Simon DeDeo, "parallel academies") or simply better forms of continuing adult education, there is so much left to explore.

The entire list is here (and my list of lists is here).

A few links worth checking out:

• "In upstate New York, two algorithmic artists make wooden puzzles inspired by natural forms."

• The Strange equation, the AI analogue of the Drake equation

• The history of infinite canvases

Until next time.