329 karma · joined August 23, 2020
> Local AI & LLM Ready: By reducing complex pixel streams into structured logical strings, ASCILINE acts as a perfect bridge for AI. Instead of feeding heavy computer vision models, lightweight LLMs can process semantic video summaries.
In what way is this semantic/structured?
> Bypassing Browser Constraints: Modern browsers aggressively throttle autoplay videos, and ad-blockers restrict traditional media frames. To the browser, ASCILINE is simply "JavaScript updating a canvas"—completely invisible to media restrictions.
So... just render the video to a canvas? What does ASCII have to do with it
If true then that indeed seems like an improvement, I think I just need measurements of actual hallucinations. Calling hex random but a selection of words not seems humanly biased? If anything, being random is good because it's saying there's no semantic influence. I'd think that words are more likely to be hallucinated as certain words only follow certain contexts, which is less true for numbers
> Where UUIDs cost ~23 tokens and get hallucinated by LLMs
How does this solve the hallucination problem?
Just removing the - from the example UUID takes it from 26 tokens to 18
Also wrt phone, it's different because I paid for the phone. But also I'd just use a different camera app?
Sure you can represent the same code in fewer tokens but I doubt it'll get those tokens correct as often.
However, I'm happy it's happening because you don't need an LLM to use the protocol.
Arugably this isn't a misuse, but just accepting that Reacts state management isn't powerful enough for what you need. Something akin to useRef + useSyncExternalStore is often what developers want.
For example, there's no guarantee that your useEffect will be invoked with the intermediate state you wanted to observe.