Scrawl-canvas filter functionality demo

Hacker News

The Scrawl-canvas library's filters functionality is inspired by SVG filters. Filter operations can be ordered in ways to combine different sets of operations to achieve complex effects. The library comes with an extensive set of pre-built filters, including: color and alpha channel manipulations; matrix, pixellate, blur; compositing and blending operations; palette reduction and dithering; image and noise asset upload (for use with composites and blends); gradient, and displacement, mapping. Filters can be applied to individual graphical objects, to Groups of such objects, and also to Cell displays. Additional links: - A gallery of SC compound filter effects - https://scrawl-v8.rikweb.org.uk/demo/filters-103.html - SC stencil (background) filter functionality - https://scrawl-v8.rikweb.org.uk/demo/filters-028.html

Visit product View on Hacker NewsApr 27, 2023

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2points

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

51%51% 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 HackersIH features products with proven revenue · Strong signals: including · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

44%44% predicted probability of success on Product Hunt, 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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