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Please Critique My Vocabulary Building Tool for Japanese

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Please Critique My Vocabulary Building Tool for Japanese

I built a Japanese vocabulary building tool that pulls words and phrases from popular media, like songs and tv shows. I would love some feedback on whether or not the concept resonates. You can check it out at http://lexicandy.com/ and follow us at @lexicandyapp. I believe the problem with most pre made lists isn't that you didn't make them. It's that the words are often completely disconnected from content you use regularly. A pre made deck of thousands of words sounds great at first, but then you realize using it is about as fun as memorizing a dictionary… because that's essentially what you're doing. The idea is to break vocabulary building into small chunks, with the goal being to fully understand the material the words are being drawn from. So while you may not be fluent after learning all the words from, say, the first episode of Honey and Clover, you will be able to watch an entire episode in Japanese. You'll get a free starter deck for signing up, so no one has to spend any money to check things out. But additional decks must be purchased at 3 cents a word. Here's why: 1. I want to be able to provide a wide variety of accurate content. That means hiring native Japanese speakers to pull the words from the songs, videos, etc. 2. I want to be able to provide explanations for slang, cultural references, colloquialisms… all the things that make a language come alive. Again, that means hiring native speakers to annotate. 3. I want to build more than the basic flashcard program that's there now. Most online language apps use gamification elements, but as an avid gamer I'm into actual games. I'll need money to build both competitive and single player experiences. Thoughts? Comments? Suggestions?

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Indie HackersFits the IH revenue-focused audience · Strong signals: songs · 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.
Product HuntOn track for Day 1 leaderboard · Strong signals: apps, single, using · Missing: mac, agents, macos
76%76% 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
57%57% 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 · Missing: plus, platform, intuitive
44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: apps, video · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% 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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