I made a screenshot beautifier

Hacker News

I’ve always seen talented designers and developers post their work, only for it to get lost in the noise. I realized a major reason is that they often just post a simple, unstyled screenshot. As a result, nobody pays attention. My solution is a tool where you can drop in a screenshot, pick from a collection of hand-picked gradients, style it, and export it—all in a few seconds. I’ve carefully designed every element of the app to be efficient, simple, and intuitive. On making it "Free, Forever": I don't plan on ever charging a subscription for Moocup. I dislike the subscription model as much as you do. Instead, my plan is to include a donation option for those who wish to support the project. This allows me to keep my promise of making the app free and useful for everyone, without being burdened by hosting and maintenance costs. My goal is simple: to provide a high-quality tool that is genuinely useful and doesn't waste your time. If you find it helpful, the best thing you can do is share it with others. I've included all my contact links for feedback and suggestions. Happy designing :)

Visit product View on Hacker NewsJun 22, 2025

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

74%74% 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 · Strong signals: intuitive, host, efficient · Missing: plus, platform, reviews

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

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

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

39%39% 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: ide, io · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: subscription · 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.

Incorrect prediction on native model

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