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Shaded – Learn Japanese with SRS and LLMs

Hacker News

Shaded – Learn Japanese with SRS and LLMs

Hi HN, a good intro to what the app does can be found via the link so instead I will take the time to go over some of the technical aspects instead. Shaded presents the user with a sentence at the start, and asks for a translation. Based on the user's response, it tokenizes it into words/grammar and grades it using structured outputs. Those words are fed into an SRS system. When the user goes to start a new lesson, the system knows which words are difficult, due (to be remembered), and easy and adjusts the exercises to the user's level. Is there anything you want to see? New language options, better beginner level support? Let me know!

Visit product View on Hacker NewsApr 29, 2025

Actual performance

3points

Did not reach leaderboard

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, using · Missing: mac, agents, macos

86%86% 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.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

62%62% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

51%51% 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 · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

30%30% 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

15%15% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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