Hover Effects TS – ASCII, Lego, and glitch hover effects using canvas

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Hey HN, I recently built a small TypeScript utility called `hover-effects-ts` — it adds fun and experimental hover effects to images using the HTML canvas. You can apply effects like ASCII art, Lego blocks, glitch distortion, and pixelation on hover. The goal was to break away from boring `:hover` transitions and bring some delightful visual feedback to personal websites, landing pages, or error screens — while keeping it lightweight and performance-friendly. Why I built it: I found most image hovers visually bland, and wanted something weird but still dev-friendly. I used canvas to keep the effects GPU-accelerated and controllable. No dependencies, and devs can tweak intensity, radius, image scope, and more. NPM: https://www.npmjs.com/package/hover-effects-ts Live demo (Vercel): https://hover-effects-ts.vercel.app Video demo: https://youtu.be/YO4R1A6JZ9U GitHub: https://github.com/hsrambo07/hover-effects X/Twitter post: https://x.com/harsh_logs/status/1924739860780519579 Would love feedback, bug reports, or effect ideas. It's still early – planning to add a few more visual modes and expose more dev controls. Thanks for checking it out!

Visit product View on Hacker NewsMay 20, 2025

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

94%94% 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.

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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: friendly · Missing: plus, platform, intuitive

63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

48%48% 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 · Strong signals: personal, video, way · Missing: mobile apps, ios, entrepreneurs

43%43% 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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