Viewing profile — tonyhschu
tonyhschu
HN member- Joined
- Mon, Aug 15, 2011, 7:45 PM UTC
- HN karma
- 153
- Public activity
- 14 items
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About tonyhschu
https://bsky.app/profile/tonyhschu.bsky.social
Recent public activity
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Comment #47391775
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 fi…
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Comment #47390181
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 easi…
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Comment #47388389
One of the creators of R2D3 here. Funny to wake up to this today! Happy to answer questions here or on bsky
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Comment #44752807
That workflow sounds amazing. How do you set that up? Got any code for it that we can look at?
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Comment #30628311
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_...
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Comment #30628295
Second this. Idyll is the closest thing I know that makes this sort of thing approachable. Those dot animations are unfortunately very custom.
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Comment #30628265
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…
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Comment #17343780
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 We…
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Comment #9958028
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.
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Comment #9957333
Thanks Brandon! We're certainly thinking about it. We'll work out way up there.
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Comment #9957302
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/statist…
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Comment #9955983
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.
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