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Odinize now on Android – Generate individual courses on any topic

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Odinize now on Android – Generate individual courses on any topic

Hi everyone, We are looking for beta testers on Android now to provide us with honest feedback on our app idea. The Android preview is brand new, so kindly give it a try. The app allows users to generate comprehensive courses on any topic they would like to learn in any style they prefer. It will also feature a community to browse / share courses and bite-sized daily knowledge for any topic the user is interested in. A chatbot helps with questions during a course. This (actually alpha) version provides the core feature of course generation only, while the full version will feature the above functionalities. In this limited scope you will be able to go back to a previously generated course (if any) to type in "Prev" in the topic field and hitting "Continue". We highly encourage you to provide us with feedback via this thread or any other communication channel. You can also visit our website at https://odinize.app where you can find more information. Thank you in advance and happy learning!

Visit productView on Hacker NewsOct 12, 2024
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
76%76% 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 · Missing: mac, agents, macos
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
44%44% 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, io · Missing: https docs, excited, just released
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive
32%32% 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
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
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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