MockingBot - an online iOS mock-up tool

Hacker News

Hi all, this is my latest side project: MockingBot, it's a pure JS/HTML based online iOS mockup tool. It only supports iPhone at now, but iPad will be added soon. http://mockingbot.com we build this tool mainly for UI/UX designers and project managers, but as I'm a developer, so I'm glad to hear your opinions about our product and i'll be very appreciate if you can help answering below question: which prototype tool do you use to mock-up your mobile app? and which feature attract you most? any comments are welcome.

Visit product View on Hacker NewsMay 16, 2012

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Actual performance

13points

10comments

Made the leaderboard

Launch Intel predictions

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

79%79% 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: soon · Missing: plus, platform, intuitive

52%52% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · 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: ide, io · Missing: https docs, excited, just released

27%27% 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 · Missing: arr, mrr, revenue

13%13% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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