Comment by visarga
3 hours ago
Author might have spend days researching it, selecting what to include and what not to, revising the language and graphics, then comes Pangram and slaps 100% AI on it without looking into the substance.
3 hours ago
Author might have spend days researching it, selecting what to include and what not to, revising the language and graphics, then comes Pangram and slaps 100% AI on it without looking into the substance.
Unfortunately, given the sheer magnitude of text on the internet, we need heuristics to gauge whether something is worth reading or not. For authors/blogs that I don't yet know, LLM-generated text is a clear anti-signal for me, and Pangram has been very useful in that regard.
What false positive rate would you regard as still acceptable for your use case?
False positives (Pangram labels something slop that isn't) I'm not that worried about, since the worthwile articles generally circle back to me some way through the communities I'm involved in, so I like to err on the side of not instantly reading something anyways.
For false negatives, that depends on the type of article at hand. If an error leads to me spending 2 minutes reading an article that ended up being not worth reading I'd be fine with an order of magnitude more than if false positives happen on articles that take 30 minutes to go through. Maybe on the order of 1-3% overall?