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Prepare for exams with your own set of questions

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Prepare for exams with your own set of questions

What does this app do? If you are preparing for exams and have a whole set of questions, you can organize your questions as per a simple structure, load them into the app and start revising. Why this app? I recently started preparing for AWS certifications. For revising the topics I used AI and AWS documentation to generate a huge set of questions. But then I didn't find any tool/app into which I could load these questions easily. Even the one that I found was a little complicated and not serving my purpose (sorry I forgot that app's name). So I quickly put together this app using plotly dash. What was my requirement based on which this app is designed? I wanted a simple interface where the app 1. shows the question with choices 2. I make a choice 3. The app shows the correct answer 4. Along with the correct answer, explanation and reference url are provided. That's it. Simple process to memorize and master some topics. I'm going to be using this extensively to prepare for my exams. I also plan to publish my question sets once my preparation is complete for each certificate. Hope someone finds this useful. Initially I had plans to have score, analytics and such. But then I didn't want to spend too much time on this app. Maybe in future I will add those as well when I'm relatively free.

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Product HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
64%64% 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.
TrustMRRFits verified-revenue profile · Strong signals: para · Missing: mobile apps, ios, personal
58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: started, para · Missing: supports, reddit linkedin, podcasting
55%55% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: interface · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, 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
30%30% 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
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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