Photo Credit: Elan Rushkin

Starting over alone, anew, afresh, in California a half-lifetime ago, I came to find out that the maw of the weekend can swallow you whole if you’re not careful. There are so many hours that you can easily do nothing thanks to the embarrassing riches of having time enough to do anything in a state being chock-full of everything. In order to etch numbers in my sundial and create speedbumps on the slide to Monday, I invented orienting rituals like my Saturday night radio ritual.

So, on San Jose’s warm Summer-into-Fall nights, I found myself whiling away Saturday night with KQED playing. Honestly, I’ve spent time more poorly.

But episode #74 of “This American Life” changed my life.

• I laughed (Making change at the math convention).
• I roared (“Dark Shadows Lives/Rules!”).
• I groaned (“Any advice for all the dishwashers?”)
• I found an omen of my future ("NeXT was one of the only computers…where the whole notion of design was really important to the product — elegance of design")
• …and then I wept (“It’s like, this is where I left her. And she could be in any one of these seats…just sleeping.”).

I had never been so powerfully moved by radio, and I haven’t been so moved since.

The promise of computing was that it would take drudgery away. And it has: I no longer have to squeeze in a bank visit on a lunch break and I can get disinformation pumped into my home without having to go outside to listen to the town idiot. Up next: flying cars.

But computing, in doing so, has created digital chores that the more obsessive of us (👋) need help automating away. Ironically then, the automation needs, uh, automation. While tools like AppleScript/Automator suggested a way forward, I’ve not seen their use become commonplace.

The drudgery has not been dispelled. Most workflow automation remains “None,” as best that I can tell. Maybe in a push, the wizard class might put in some light coding (👋) to help out. At the outset (2024-current moment), LLMs suggested to me that at last automating workflow development without specialized knowledge could be democratized.

The results were disappointing as ChatGPT-4o bungled the simple task of processing images again and again.¹

Let’s use the following lenses:

1. Generalized knowledge
2. Domain-specific knowledge
3. Operating System / Platform ease-of-use or integration

to examine how automation workflow software with Apple’s Automator or LLMs might yet help us bid farewell to our digital chores.

I’ve been watching discussions around AI systems since last winter, and I’ve noticed a divide in how people perceive their capabilities. The same technology that leaves some users genuinely amazed leaves others profoundly frustrated. What’s even more interesting, is that the frustrated ones are often the technically sophisticated; the amazed, the technically unsophisticated.

1. Token prediction can save lots of work – and that’s magical!
2. The inability to build reliable workflows feels like failure to the technically sophisticated

Understanding this divide helps explain why AI discussions often feel like people are talking past each other, and what it means for the future of these technologies.