Dropflow, a CSS layout engine for node or <canvas>

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For the last 5 years I've been working on a layout engine that targets CSS2 and some more modern properties. Live demo: https://chearon.github.io/dropflow/ It matches browsers in all cases I can find where they agree, and it's fast. It supports `position`, `inline-block`, `z-index`, and complex properties like `float` and `vertical-align`. It doesn't support high-level layout like flexbox or grid yet, but you can get intrinsics to easily divide space yourself and paint multiple layout trees. It has a great text layout implementation, and supporting non-Latin scripts is a top priority. I've wanted this to exist as far back as 2013, and the desire kept coming up: for a way to get detailed intrinsics, for high quality rich text layout to canvas and SVG, and for server-side rich text. We currently use it in CellEngine for our new canvas-based spreadsheet library to layout text in hundreds of thousands of cells, and will be using it soon to render PDFs with thousands of pages in a few seconds.

Visit product View on Hacker NewsMar 21, 2024

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

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

15%15% 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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