I

I built a tool that helps with user onboarding and app understanding

Hacker News

I built a tool that helps with user onboarding and app understanding

Hello HN, In April 2024, I launched my first app, in which people faced difficulties navigating it. Therefore, something had to be done in that regard. My first idea was to make a blog, in which I could present the app’s features so people would get a better understanding of it. However, that did not work, as people simply did not have the time to read all of the long and detailed instructions on how everything works. As this first experiment failed, I decided to add Knowledge Bases, where I took the content of the blogs and basically made them shorter so it would be easier to follow. However, users had difficulties following the instructions as they were simply too boring. It was still a difficult task for the user, as they had to remember each step, and if they forgot something along the way, they had to go back to the Knowledge Base and redo all the steps. At this point, I decided to step things up by creating video tutorials for each of the previous posts I had created. At that time, in my opinion, that had to be the end of the issue: I was literally showing the user what buttons to click step by step. However, despite it being an improvement, it did not solve the problem. People still had issues with finding the same buttons, especially if they were using different devices such as tablets or phones. I realized the solution was close; the only issue now was that the tutorials were hard to follow on different devices. I got tired of googling solutions and decided to create my own tool. The first idea was to directly mark the elements in the code and display pop-ups for each of them. As you may have thought, this was a tedious task because I needed to find the specific elements in my codebase and insert some attributes on them, not to mention that if I needed to use the same element for two different tutorials, I had to mark them differently. That said, I came up with the idea of creating a tool where you had a “Record” button, and once you start a recording, you just click the elements you want to highlight and type a description. With this, I finally had the solution to my problem. It was easy for me to set up, and it was just as easy for the user to follow. After seeing the results, I decided to make a standalone tool that could potentially help others. This is how Tooltip Tutorial was born. It’s very simple to use, it saves a lot of time, and right now it also has comprehensive analytics, which will help with optimizing the onboarding process for your apps. I am eager to see your opinion on this, and if there is anything I can do better, please let me know, and I will do my best to meet those requirements. Thank you!

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
98%98% 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: apps, user, using · Missing: mac, agents, macos
89%89% 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
50%50% 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: apps, video, users · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
38%38% 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
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
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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