I'm a Korean currently learning Japanese, and while I do understand that this mash of Chinese characters and kana writing system can be appreciated for its exoticness, as a learner I can't help but feel it's more of a hassle resulting from it being a not yet fully optimized writing system. (I mean, do we really need both hiragana and katakana?)
I'm definitely not claiming that Korean is a more "optimized" language overall, but at least when it comes to the writing system, we had exactly the same problem as the Japanese (if you look at Korean newspapers just a few decades ago they are littered with Chinese characters), and at some point we fully ditched Chinese characters and have no problem going on with our lives. In fact, it made our lives easier in many cases, especially in keyboard typing.
As a side note, we obviously have some side effects from switching to entirely phonetic alphabet system. For example, the words "tea" and "car" have the same pronunciation (차=cha) and so they are indistinguishable in writing, but it wasn't the case when Chinese characters were used (茶/車). I'm not sure how this side effect propagates into some sort of sociolinguistic phenomenon, but at least for average people it doesn't seem to have much significance.
Watching Dr. Eric Lander's lectures in MIT OCW Biology course was a watershed moment in my life. I felt exactly the same way as this blog post after finishing the course, two years after graduating from college. Highly recommended if you haven't seen it yet: https://youtu.be/P-Ry4rRdDbk?si=Hp2SEkEKGeeui-pP
May I ask what library you're using for kanji to hiragana transliteration? I'm working on a language product as well and I'm using pykakasi which is certainly prone to errors. I tried your app and noticed similar errors as well (大いばり should show ooibari instead of daiibari, for example). Wonder if we can do better on transliteration.
Given enough advances in hardware and software optimization, isn't it reasonable to think that if we connect this level of language model to speech-to-text + image-to-text models on the input side and robotic control system on the output side, and set up an online end-to-end reinforcement learning system, the product will be a convincingly sentient robot, at least on the surface? Or am I underestimating the difficulty of connecting these different models? Would like to hear from the experts on this.
This is a very short-sighted view. Globalization requires certain level of operational efficiencies, such as speed and cost of communication and transportation. We have just recently reached a level where governments and corporations can support demands from billions of population. Technology will keep on progressing unless we end up in an extinction scenario, and at certain level globalization will be just another step in humanity's perpetual drive towards centralization.