That makes sense. I was mostly curious about what explicit trade-offs the author chose beyond “generation only” — e.g. fonts, Unicode, images, compression, etc.
Would be interesting to see a concrete “not supported” list from the author.
For me, one signal has been whether the problems remain interesting even when progress is slow.
When working on complex systems (like anything involving long-running automation or agents),
most of the real work happens in areas that don’t show up in demos: defining “done”,
handling partial failures, and keeping behavior predictable.
If those problems are still worth thinking about after repeated failures,
I take that as a sign the work itself is worth continuing.
I’ve noticed the same thing: if the shape of the problem is still interesting after the novelty wears off and progress stalls, that’s usually the real signal. The visible demo work is easy; the hard part is exactly what you said—defining “done,” handling edge cases, and making systems behave consistently under stress. If those invisible constraints keep pulling you back, it’s usually because there’s something fundamentally worth building there.
I'm experimenting with a local-first autonomous agent system on Windows.
The interesting part for me hasn't been the UI or demos, but the engineering problems:
how planning compares to step-by-step tool-calling, how state drifts over long tasks,
and how fragile things get once you add retries and recovery logic.
Mostly learning by breaking things and trying to make the system more predictable.