I built an alternative to product tours

Hacker News

Hey HN! We’ve built a tool that dynamically adjusts the complexity of software interfaces based on a user's interaction with it. It works by hiding non-essential/advanced UI elements for new users, reducing cognitive load and guiding them to their "aha moment" more efficiently. We refer to this as „simple mode“. Non-essential features are progressively revealed when the user is ready to use them, and we’ve also added an option for users to manually switch „simple mode“ off. Every Feature in vykee consists of Elements. Elements can be selected via UI picker or via code by adding the vykee attribute. Our analytics tool allows you to easily measure the usage of Features and Elements over time. You can identify the ones that are (a) not used or (b) used a few times in the beginning but not again. Those non-essential Features can be hidden by applying conditions for revealing them. As we’re still very early, I’d love to get your feedback on the tool and thoughts re simplifying complex software more broadly.

Visit product View on Hacker NewsFeb 17, 2024

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

93%93% 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.

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

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, efficient, users · Missing: plus, platform, intuitive

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

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

8%8% 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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