Xnapper Studio – Web tool for creating attention-grabbing screenshots

Hacker News

Xnapper Studio aims to make it fast and easy to create eye-catching images and mockups, especially for people sharing images their product images on on social media and blogs. Compared to our macOS app, we've doubled down on making the screenshots look better by adding device mockups (i.e. iPhone, Macbook, Safari, etc.), more social media dimensions, and image positioning. Currently we're working on adding an option to share the image with a link (cloud storage and share). Would love to get your feedback on our beta!

Visit product View on Hacker NewsSep 16, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, macos · Missing: agents, agent, cursor

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

58%58% 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

49%49% 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

44%44% 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.

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