A Dark Mode Toggle Without JavaScript

Hacker News

Today I scratched my itch of implementing a dark mode toggle without JacaScript. The implementations I saw typically revolved about adding a 'dark' class to body via JS and then controlling it with JS handlers. Thanks to `:has()` selector [0], that passed 90% browser compatibility [1], it is possible to avoid JS. My implementation uses CSS variables combined with a checkbox, `:checked`, and `:has()`. It is simple, yet supports color scheme preference and updates toggle's label based on active mode. [0]: https://developer.mozilla.org/en-US/docs/Web/CSS/:has [1]: https://caniuse.com/css-has

Visit product View on Hacker NewsJan 11, 2024

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Did not reach leaderboard

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

85%85% predicted probability of success on Indie Hackers, 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: io · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

29%29% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

26%26% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

22%22% 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

6%6% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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