Re:Show HN: Real-time system that tracks how news spreads ac...
by supriyo-biswas on Sunday November 30, 2025 @08:56AM
(#46096674)
Thanks for your comment, unfortunately it seems that your comments are primarily LLM-generated (for people looking for evidence, the first comments of this user should provide enough evidence, although they’re getting better by fine tuning the prompt). As HN is primarily a place for humans, please do not do this here. Thanks.
Re:Show HN: Real-time system that tracks how news spreads ac...
by yieldcrv on Sunday November 30, 2025 @10:30AM
(#46097415)
How can I bait this bot?
Re:Show HN: Real-time system that tracks how news spreads ac...
by alchemist1e9 on Sunday November 30, 2025 @01:59PM
(#46099346)
The style of the account comments and “about” definitely give off LLM vibes, but it’s not a particularly active account so I feel not a true bot. It’s also possible the account owner just runs their own comment through an LLM before posting it. I do that for most business emails I send these days but they are still reflecting my own thoughts and details.
Re:Show HN: Real-time system that tracks how news spreads ac...
by nextaccountic on Sunday November 30, 2025 @04:28PM
(#46100611)
this apecific comment shows no sign of LLM authorship
maybe the author uses LLMs in some comments and not others. that is, it's not a bot, just someone manually using LLM tools sometimes
Re:Show HN: Real-time system that tracks how news spreads ac...
by wcallahan on Friday December 05, 2025 @12:21PM
(#46164294)
Bad bot.
‘masterphai’ is evidence of how effective a good LLM and better prompt can be now at evading detection of AI authorship… but there’s no way this authors comments are written by a sane human.
From the comment history it appears it has tricked quite a few humans to-date. Interesting!
A trick that helped in a similar system I built was doing a second-pass “temporal coherence” check: if two articles are close in embedding space but far apart in publish time or share no common entities, keep them in adjacent clusters rather than forcing a merge. It reduced false positives significantly.
Also curious how you handle deduping syndicated content - AP/Reuters can dominate the embedding space unless you weight publisher identity or canonical URLs.
Overall, really nice work. The propagation timeline is especially useful.