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I made a simple accessibility widget you can add to your website (free)

Hacker News

I made a simple accessibility widget you can add to your website (free)

Hello everyone! I built a simple accessibility widget, which can be embedded on any website for free. No signup required, just a line of (defer) code to copy & paste. It'll not slow your website down, and once installed, there's a blue accessibility icon fixed to the lower right side of the screen, what can it do? * highlight links and headers * pause all animations * increase font size * change contract * hide images * and more! Any feedback would be great, maybe you miss a feature? Looking forward to your thoughts! Thanks, Simon

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Product HuntOn track for Day 1 leaderboard · Strong signals: code · Missing: mac, agents, macos
53%53% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
50%50% predicted probability of success on Indie Hackers, 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
49%49% 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
45%45% 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
39%39% 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
3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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