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CSV Driller

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CSV Driller

Hey guys, http://www.amelyn.com/csvdriller/csvdriller.htm CSV Driller is a simple webapp for drilling through information that you need to learn. Simply paste csv information into the textarea in the middle of the page. Each line in the csv should be in the format: question,answer It provides four random answers for the user to select for each question (one of which is the correct answer) and provides a running score, along with a final score at the end. The advantage of this webapp, compared to other websites that offer quizzes on different subjects, is that the user can build a library of his/her own csv files, filled with the stuff he/she needs to learn, and use them with the webapp to learn a bunch of stuff specific to his/her current learning requirements. :D I'm thinking of extending this and adding extra features, such as: - saving previous scores and providing a list of the last x scores, so the user can see how his/her performance is improving/degrading over time - a button to reverse questions and answers in the csv text before drilling - a countdown where the user can select 5/10/20/30 seconds to answer each question - alternative answer formats other than multiple choice (e.g. type in the answer, numeric etc.) or adjust the number of multiple answers. Any feedback or thoughts about what features people would be interested in seeing added to it is much appreciated. Please post feedback to this thread or post it to bigbadmick2000@hotmail.com. Cheers! Miktor

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Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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.
TrustMRRFits verified-revenue profile · Strong signals: answers · Missing: mobile apps, ios, personal
56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, 000, io · Missing: https docs, excited, just released
46%46% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos
45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
31%31% 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.

Correct prediction on native model

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