Scrawl-canvas – a new text layout engine

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I've spent the last 4 months ripping out my canvas library's text layout engine and replacing it with something entirely new. This work has led me on an adventure of discovery around how various languages choose to record their sounds in fonts (why, Thai? Just ... why??) and the various (nefarious!) approaches browsers use to layout those fonts in web pages. The new engine: - Handles horizontal (ltr and rtl) and vertical fonts, with justify options. - Styling text (underlines, overlines, highlights) is done with the help of HTML/CSS markup in the text, and can include gradients and patterns as well as any CSS color string. - Text can be fitted into template shapes, or along a path. This is a bit like the CSS shape-inside property[1] (currently not supported widely by browsers). - With a bit of extra work, text can be selected/copied - even text-along-a-path. (Though I'm determined not to be tricked into creating a text editor demo for this sort of thing!) This is an MVP release. If anyone sees glitches, errors or improvements please let me know. Also: enjoy! [1] - see the "Creative text flows using CSS shapes" article on LogRocket - https://blog.logrocket.com/creative-text-flows-using-css-sha...

Visit product View on Hacker NewsApr 28, 2024

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

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

58%58% 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 · Missing: supports, reddit linkedin, podcasting

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% 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

43%43% 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

23%23% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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