tonyhschu

Very cool. I do something like this but with Playwright. It used to be a real token hog though, and got expensive fast. So much so that I built a wrapper to dump results to disk first then let the agent query instead. https://uisnap.dev/

Will check this out to see if they’ve solved the token burn problem.

Sort of like this? https://docs.tecton.ai/docs/introduction/interactive-tour I used https://github.com/xyflow/xyflow for this, with css animations for the edges. It’s probably easier now with coding agents and what not

One of the creators of R2D3 here. Funny to wake up to this today! Happy to answer questions here or on bsky

That workflow sounds amazing. How do you set that up? Got any code for it that we can look at?

Yeah! This was done before CSS scroll-snap is a thing. I'd probably use scroll snap now. See: https://developer.mozilla.org/en-US/docs/Web/CSS/CSS_Scroll_...

Second this. Idyll is the closest thing I know that makes this sort of thing approachable. Those dot animations are unfortunately very custom.

Funny to see this on the homepage today. Part 1 was done so long ago now that looking back on that code is equal parts intimidating and embarrassing.

Maybe this is the motivation I need to actually fix that stupid feet/meter thing.

Author here – Part 2 uses:

- React for DOM rendering

- D3 for visualization geometry

- RxJS for handling state (i.e. turning scroll position into animated properties)

- PIXI for WebGL bits (i.e. all the little circles moving around)

The decision tree model was generated from http://scikit-learn.org in Python. The JS is a complete mess, but I'll try to write up how it works in the next couple days.

Thanks Brandon! We're certainly thinking about it. We'll work out way up there.

OP here. What kinds of sources are you looking for? We built this based on some of the lectures in Stanford's statistical learning class (http://online.stanford.edu/course/statistical-learning-winte...) as well as just hanging out with the ML team at http://siftscience.com

Thanks! Yeah absolutely. We are working our way up to those. Our tentative plan is to tackle bias-variance trade-off next, then random forests, then neural networks.