I built a tool to add noise texture to your images

Hacker News

I'm excited to introduce Noise Tools – a simple yet powerful tool that lets you effortlessly add noise textures to your images. Whether you're a designer, artist, or just experimenting with aesthetics, Noise Tools helps you enhance your visuals with just a few clicks. Why I built this? I often found myself needing high-quality noise textures for design projects but struggled to find a quick and easy solution. So, I built Noise Tools to make the process easy for everyone! Features: Generate noise textures instantly Adjust intensity & styles No downloads or complicated settings Would love to hear your thoughts! Try it out and let me know what you think. Check it out here: noisetools.vercel.app

Visit product View on Hacker NewsMar 27, 2025

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Actual performance

53points

40comments

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Launch Intel predictions

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

80%80% 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.

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

64%64% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

40%40% 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 · Missing: mobile apps, ios, personal

27%27% 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

14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: introduce · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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