tonyhschu
Joined 15 years ago
Karma
153
Submissions
14
Member since
Aug 15
Recent submissions
Comment · 7 months ago
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.
Comment · 7 months ago
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
Comment · 7 months ago
One of the creators of R2D3 here. Funny to wake up to this today! Happy to answer questions here or on bsky
Comment · 1 year ago
That workflow sounds amazing. How do you set that up? Got any code for it that we can look at?
Comment · 4 years ago
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_...
Comment · 4 years ago
Second this. Idyll is the closest thing I know that makes this sort of thing approachable. Those dot animations are unfortunately very custom.
Comment · 4 years ago
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.
Comment · 8 years ago
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)
Comment · 11 years ago
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.
Comment · 11 years ago
Thanks Brandon! We're certainly thinking about it. We'll work out way up there.
Comment · 11 years ago
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
Comment · 11 years ago
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.
https://bsky.app/profile/tonyhschu.bsky.social