David Huggins-Daines#WhatTheSongIsReallyAbout Satisfaction - a job well done
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Computer engineering professor. Researches security. Fluent in English, German, French.
@rrb @suzannealdrich @mattkenworthy @jwz a timeless classic: https://youtube.com/shorts/8-_ZA8dlRRE?si=mtmAvSVfr__-GoXv
Cultissime Daniel Prevost à montcuq. #culte #comédie #humour #rire #television
#WhatTheSongIsReallyAbout Black Dog - Labrador retriever
#WhatTheSongIsReallyAbout #HashtagGames
I Get Around - buying friends drinks
Looking back in 5 years, I think we will all agree that AI will have been almost as much a universal game-changer for society as NFTs have shown themselves to be.
Hacker Memes
tante“It's making me dumber for sure,” the fintech software developer told me. “It's like when we got cellphones and stopped remembering phone numbers, but it's grown to me mentally outsourcing ‘thinking’ in general. I feel my critical thinking and ability to sit and reason about a problem or a design has degraded because the all-knowing-dalai-llama is just a question away from giving me his take. And supposedly I tell myself ill just use it for inspiration but it ends up being my only thought. It gives you the illusion of productivity and expertise but at the end of the day you are more divorced from the output you submit than before.”
https://www.404media.co/software-developers-say-ai-is-rotting-their-brains/
https://www.404media.co/software-developers-say-ai-is-rotting-their-brains/
Ulrike Hahnnew preprint on the spread of hallucinated citations in the scientific literature:
- origin primarily from small and early-career author teams
- hallucinated references disproportionately assign credit to already prominent and male scholars
- 𝟴𝟱.𝟯% of hallucinations in preprints persist into the published version
- hallucinations appear across the range of journals, including high impact ones
- fake citations are becoming listed in search engines like Google scholar
LLM hallucinations in the wild: Large-scale evidence from non-existent citations
Large language models (LLMs) are known to generate plausible but false information across a wide range of contexts, yet the real-world magnitude and consequences of this hallucination problem remain poorly understood. Here we leverage a uniquely verifiable object - scientific citations - to audit 111 million references across 2.5 million papers in arXiv, bioRxiv, SSRN, and PubMed Central. We find a sharp rise in non-existent references following widespread LLM adoption, with a conservative estimate of 146,932 hallucinated citations in 2025 alone. These errors are diffusely embedded across many papers but especially pronounced in fields with rapid AI uptake, in manuscripts with linguistic signatures of AI-assisted writing, and among small and early-career author teams. At the same time, hallucinated references disproportionately assign credit to already prominent and male scholars, suggesting that LLM-generated errors may reinforce existing inequities in scientific recognition. Preprint moderation and journal publication processes capture only a fraction of these errors, suggesting that the spread of hallucinated content has outpaced existing safeguards. Together, these findings demonstrate that LLM hallucinations are infiltrating knowledge production at scale, threatening both the reliability and equity of future scientific discovery as human and AI systems draw on the existing literature.
Effin' Birds
Estarriol, Terrorist Dragonsee also education....