epitrochoid413

9 ms·

1. 1.▲
   by epitrochoid413 2mo ago

   Yes I agree with you here. LLMs tend to be a bit random, but are still more consistent and predictable than slot-machines. Also, gambling tends to exert lower effort and higher dopamine hits than vibe-coding, making it way more addictive.
2. 2.▲
   by epitrochoid413 4mo ago

   That’s a fair concern. It’s a hybrid system, so dictionaries and rules handle the high-confidence cases, and the context model handles selected ambiguous words. 今日 is extremely common, but in the vast majority of ordinary text it’s read きょう
3. 3.▲
   by epitrochoid413 4mo ago

   Thanks, this kind of report is very useful. 如何 is context-dependent, and I hadn’t come across this case yet. I’ll add it to the model soon. Really appreciate the report and the kind words.
4. 4.▲
   by epitrochoid413 4mo ago

   Thank you this is very helpful, especially from a native speaker. 今日 is a tradeoff I made intentionally: I disabled the fallback model for it because most cases are きょう, while こんにち is much rarer. But yes, this is one of the cases that gets
5. 5.▲
   by epitrochoid413 4mo ago

   Thanks, good catch. This is a known class of edge case I am trying to improve: adjacent tokens sometimes get over-merged into a phrase reading when they should stay separate. 今 and 何 should be handled separately here, not as こんなに. I appreci
6. 6.▲
   by epitrochoid413 4mo ago

   Thanks, I really appreciate that. No signup was a deliberate choice, to keep the barrier to trying it as low as possible. I’ve thought about pitch accent, but it feels like a whole separate beast. The datasets are less comprehensive, and pi
7. 7.▲
   by epitrochoid413 4mo ago

   Thanks for sharing this. It looks like a really cool project, and making the data public domain is especially generous. I especially like the dictionary + example sentence format. I haven’t found a really good Japanese-English dictionary fo
8. 8.▲
   by epitrochoid413 4mo ago

   Thanks, that’s great to hear. Thanks for the vouch too, I didn’t realize the comment was dead.
9. 9.▲
   by epitrochoid413 4mo ago

   Thank you, that means a lot. I’ve been working on it for about a year now, so it’s really encouraging to hear it’s useful. I’m hoping to keep pushing the accuracy further, especially on the remaining hard cases like rendaku and person/
10. 10.▲
    by epitrochoid413 4mo ago

    I built a context-aware furigana converter for Japanese text, files, and web pages. The main problem I wanted to solve was that simple dictionary-based furigana works well for common cases, but breaks on words where the reading depends on c
11. 11.▲
    Show HN: Context-aware Japanese furigana using Sudachi and ModernBERT(ezfurigana.com)

    37 points by epitrochoid413 4mo ago|20 comments
12. 12.▲
    by epitrochoid413 4mo ago

    Same here. I use codex for planning and deepseek v4 flash for implementation. Its worked really well so far.
13. 13.▲
    Anthropic reaches $965B valuation, surpassing OpenAI as most valuable AI firm(theguardian.com)

    4 points by epitrochoid413 4mo ago|0 comments
14. 14.▲
    by epitrochoid413 4mo ago

    Meanwhile Deepseek is cutting inference costs to mere cents. Thats the real AI revolution for you.
15. 15.▲
    by epitrochoid413 5mo ago

    Lets see how OpenAI holds up. They prolly shitify or dumb down their models like Anthropic to finally turn their massive loss streak into a profit.
16. 16.▲
    by epitrochoid413 5mo ago

    Another round of lets dumb down the previous model so the new model feels "game changing" and "OP".
17. 17.▲
    by epitrochoid413 5mo ago

    OpenAI is being squeezed from both sides. ChatGPT's chat quality has recently dropped hard. While Claude is pricier, it actually takes the effort to think through complex tasks. All the while, Chinese models are providing cheaper alter