CSSGrid – 2.7k LOC CSS-Grid layout engine targetting embedded (Nim)

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Hey I've been working on this project for a few years. It's a smallish (~ 2.7k loc w/ tests) implementation of CSS grid. My ultimate goal with this is to be able to make full featured UIs on embedded devices like the ESP32. I just did a fairly big update which adds a bunch of features that make it into a reasonably complete CSS Grid implementation: - basic html/css style layout for nodes not in a grid - basic html layout using constraints like percent parent (20'pp), auto (cx"auto"), content-min (cx"content-min") - support for constraint operators including min, max, - and + (limited to single level, e.g. no `min(min(x,y),z)`) - basic and grid layouts now support min-size and max-size constraints (e.g. like CSS min-width) It's a weekend project. It's been pretty fun figuring out how CSS Grid works and how browsers implement parts of their layout routines. Especially how auto and frac works in CSS Grid. The core implementation turns out simple but subtle. This project is similar to Taffy in Rust, however, the implementation is a bit simpler and much less thorough. It doesn't support CSS flexbox either. Feedback or suggestions would be awesome!

Visit product View on Hacker NewsFeb 15, 2025

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Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting

69%69% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, using · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io, including · Missing: https docs, excited, just released

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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