Notes

I had this idea that a “Recap” button would be a nice addition to an ereader. Too many times, I start a book only to put it down for weeks or months. Coming back to it, I often have no clue what’s happening.

The idea here is that a recap button appears when opening the book. After clicking it, we pass the context of the previous chapters to an LLM for summarization. To save on costs, I think we’d be smart about what context is passed: e.g. maybe only a few chapters if it hasn’t been too long since they last picked it up.

While building this, I started thinking the inverse would be nice. For boring, long-winded chapters, having a way to skip to the end and summarize would be helpful.

This proof of concept was built using Vercel’s AI SDK. It took a little extra work to get it running in Protobox. Eventually, I figured out how to use Edge Functions with Vite. It required adding a functions block to vercel.json.

Spending time prototyping an action menu for Whimsical. The current idea is to offer a mix of stochastic and deterministic actions, based upon the context. Context is usually the page but can also be the selection.

Many AI actions benefit from dialogue. It’d be nice to also offer suggested next steps when appropriate, for one-click convenience.

The menu can be docked out of the way too. I’m thinking it’d be magnetically attracted to the corners, so that it docks nicely. The shape could get thinner & taller to suit. Adding physics, so that it could be flicked into a corner, would be 💅🏽.

One thing I’m on the fence about: should the actions be treated as messages to the user, or suggestions in the textbox?

Climbers often want a way to gauge the intensity of their training sessions. The folks at ClimbStrong had developed a simple way to quantify this, but it involves a lot of manual tallying and calculating.

This seemed like a perfect problem to apply some of my SwiftUI learnings. Points is an iOS app that improves this experience.

Users can log climbs during their sessions and get simple stats about their experience. A graph offers visceral feedback about their training pyramid and where to allot time.

When the session ends, users can export the results to journaling tools like Notion, Notes, or Google Docs.