Guidejar

AppSumo

Guidejar is praised for its intuitive interface, quick guide creation, and customization options. Users appreciate the ability to effortlessly create interactive product demos and the responsive support. However, some users have faced challenges with account setup and team collaboration. Despite these minor drawbacks, the majority of positive feedback indicates that Guidejar is a valuable tool for creating interactive guides and tutorials. Overall, Guidejar seems to be a solid choice for those seeking an efficient solution for creating interactive product demos.

Visit product View on AppSumoDec 13, 2023

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Actual performance

80reviews

Made the leaderboard

Launch Intel predictions

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

71%71% 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

66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Strong signals: intuitive, interface, efficient · Missing: plus, platform, reviews

66%66% predicted probability of success on AppSumo, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · 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 · Missing: web3, chat, crypto

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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