Mu

Multi-Language Manga Reader

Hacker News

Multi-Language Manga Reader

A webapp that lets you read manga/comics and switch languages at any time easily. Growing up learning English as a second language, watching Family Guy (and later other shows) has helped me improve a lot. Now learning Japanese, I tried to replicate the same but had to realize that I'm not at the right level yet. I still had to pause and look up words too many times for it to work well, so I moved to reading manga. In cases where I am still unable to understand after looking up every word and researching grammar, I check the same speech bubble in the official translation. Even if the translation sometimes takes some liberties, it helps me understand the general intent and allows me to move on. I've built this app, so that I don't have to manually keep two manga readers (the original and translation) in sync. You can load two versions of the same manga and it will synchronise them, so you can switch the language with a click of a button. I use an image registration algorithm from OpenCV to match and adjust the images of pages, in order to be able to switch languages without jitter. It is done entirely in the front-end, so I more or less don't have to worry about infrastructure. Because of this, it can also be used offline once the webapp has loaded, which was an important feature for me. It still has many rough edges that I would love to polish, but for now, it works well enough for me and I'm the only one using it as far as I know. Still, it would be awesome if this helped people with similar needs. Try it out here: https://aligned.pictures/ Or see how it works in the README of the project through a few gifs I recorded a while back: https://github.com/EDVTAZ/MLMR

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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: using, open · Missing: mac, agents, macos
76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: 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.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
24%24% 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 · 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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