Kimchi Reader – Immersive Korean Learning with a Popup Dictionary

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Actually, it is yet another tool to learn a language through immersion (classic set of features, watch on youtube/netflix, read a website or book). It has a popup dictionary so you can click on a word and it will show the definition. Nothing new in that regard, except that it has been made with only Korean in mind and does not plan to extends to other languages. I was learning and still am learning Korean (now using my own tool to learn!). I initially made the tool for myself because none of the tools out there could correctly figure out what a word was in the text. And that's where the biggest challenge was: recognizing the lemma. A lemma is the dictionary form of a word. An example in English would be "break, breaks, broke, broken and breaking" all becoming "break". In Korean a word cannot always accurately be returned to the lemma; this might lead to several possibilities. Then the reader, with the context can understand which one ends up being the correct one. Zero IA was used, pure rules based bruteforcing. I do perhaps intend later to use AI, but as a layer on top. I wanted to make sure I can parse massive amounts with cheap computation first. This open me doors to more crazy ideas for later. You can see a live example on the landing page. Any feedback is appreciated!

Visit product View on Hacker NewsOct 29, 2023

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

73%73% 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: new, context, using · Missing: mac, agents, macos

52%52% 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

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: way · Missing: mobile apps, ios, personal

42%42% 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

38%38% 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

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

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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