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TextGlitch – A Glitch Text Generator]

Hacker News

TextGlitch – A Glitch Text Generator]

I made a simple tool called TextGlitch.com that lets you generate “glitch text” (also known as Zalgo text). It works by stacking Unicode combining characters on top of your text so it looks distorted, eerie, or chaotic. You can control the intensity of the glitch, copy the result instantly, and use it on social media, memes, or just for fun. I built it as a side project because most glitch text sites were either overloaded with ads, outdated, or clunky. My goal was to make something clean, fast, and distraction-free. Would love feedback on: - How smooth/usable it feels compared to similar tools - Ideas for features (e.g., styles, presets etc) Thanks for checking it out!

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Actual performance

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Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% 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
68%68% 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
41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: code · Missing: mac, agents, macos
39%39% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · 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
21%21% 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.

Correct prediction on native model

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