Japanese language helper (WASM, PWA, no back-end)

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My web application makes it easier to read Japanese sentences. Where does a word begin and end? (there's no spaces) How do I pronounce the word? (phonetics are missing) How do I look up the word in the dictionary? (it's necessary to know how to type it, and how to deconjugate verbs) We can overcome these gaps with good software. MeCab (compiled to WebAssembly) provides morphological analysis (guesses where words start and end, and what kind of word it is) Dictionaries are embedded, for client-side searching. As a result: there is no backend. The application is a Progressive Web Application, so it can be saved for offline use (141MB). https://birchlabs.co.uk/mecab-web/ (Warning: 37MB webpage) Technical notes: There's a serious amount of dictionary included. I culled Kanjidic from 15.5MB to 0.7MB. Remaining dictionaries gzip pretty well (138MB -> 36MB). Apache is configured for streaming compilation and pre-computes gzips. I wanted to explore whether we actually _need_ a bundler in 2019. I used @pika/web to grab libraries as ES modules. HTTP/2 + gzip used instad of bundler. Source _is_ distribution; old school. No backend, so application can be served statically from a CDN. Preact/htm/unistore are used instead of React/JSX/Redux. Libraries weigh <100KB. Workbox is used to generate a service-worker. Saves source code and assets so that the webpage can be saved as a PWA and used offline. Offline dictionaries have been done before (e.g. apps), but this is a particularly small one, and perhaps the first to provide sentence tokenization via MeCab. I'd love to hear your feedback, be it on language concerns, technology, or user experience.

Visit product View on Hacker NewsApr 14, 2019

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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: apps, user, notes · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, 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

45%45% 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 · Strong signals: apps · Missing: mobile apps, ios, personal

39%39% 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

26%26% 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.

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