IMNMV

@svpino @Yuchenj_UW This file: ~/.claude/settings.json

@Metacogmission @Yuchenj_UW Where do I do this?

I noticed something interesting: Claude Code auto-adds itself as a co-author on every git commit. Codex doesn’t. That’s why you see Claude everywhere on GitHub, but not Codex. I wonder why OpenAI is not doing that. Feels like an obvious branding strategy OpenAI is skipping.

@svpino @Yuchenj_UW edit the json { "includeCoAuthoredBy": false, "gitAttribution": false }

@Yuchenj_UW I really hate that Claude does this. I had to write my own skill + hook to prevent it from doing this.

Interested in identifying and modeling emotions in videos, images, and text data via AI models?Check out our new transforEmotion: An Open-Source R Package for Emotion Analysis Using Transformer-Based Generative AI Models Aleksandar Tomašević (@atomasevic), Hudson Golino, & Alexander Christensen Computational Communication Research, Volume 8, Issue 2, Jan 2026, p. 1 DOI: doi.org/10.5117/CCR202…

We are no longer living in a purely human society. We are entering a hybrid system where humans and machines continuously interact and influence each other. Where does this system evolve? In a new perspective piece, we brought together leading experts to address this using the lens of evolutionary game theory. We outline six core research directions: 1) Evolution of social behaviour. How cooperation, fairness, and trust evolve in mixed human–AI populations. 2) Machine culture. How AI systems generate, transmit, and select cultural traits. 3) Language–behaviour co-evolution. How LLMs, by framing decisions, reshape preferences, norms, and actions. 4) Delegation dynamics. How control, responsibility, and agency shift between humans and machines. 5) Epistemic pipelines. How different cognitive processes generate human vs AI judgments, and how these co-evolve. 6) AI–regulation co-evolution. How firms, institutions, and users strategically shape—and are shaped by—AI development. We hope this framework sparks new work at the intersection of AI, behaviour, and society. * Paper in the first reply Joint with @T_A_Han, @jzl86, Tom Lenaerts, @iyadrahwan, @fernandopsantos, @matjazperc

@GaryMarcus While sycophancy is an issue, this is a small sample of a group of people who self-selected. Perhaps I missed it in the paper where they indicate if they had previous mental health struggles - but it seems as though these results shouldn't be extrapolated to the general public.

Holy crap. I knew about sycophancy. But the 37% number below blows my mind. This from an analysis of chat logs in people who experienced chatbot-associated delusions. In over a third of the messages to those users, the LLMs told the users they had (eg) “multi-billion-dollar-IP”. That is wild. And utterly irresponsible.

Disturbing anecdotal reports of "AI psychosis" and negative psychological effects have been emerging in the news. But what actually happens during these lengthy delusional "spirals"? In our preprint, we analyze chat logs from 19 users who experienced severe psychological harm🧵👇

@justalexoki @SUPERcranky247 imagine a local llama 3.1 8b doing ur taxes

@SUPERcranky247 if we don't have internet I have much bigger issues than my taxes

claude actually just did all my taxes. baffled. accountants are so done man

Out of context reasoning is one of the most fascinating developments in the science of how LLMs work. This primer by @OwainEvans_UK, one of the main discoverers of the phenomena, is a great introduction

@miniapeur If you have a pre-reg I'd include it to make sure it has context. Make sure not to lead it or tell it its your paper. Try to be as neutral and objective when you explain what you want and have a series of well-defined criteria you want it to rate it on. Try GPT Pro, Opus & Gemini

I have a draft of a paper, and I am thinking of using a chatbot to review it (grammar, general flow/structure, mistakes, and suggestions). Does anyone have experience with this, and which chatbot is best for this?

Imagining and building wise machines: the centrality of AI metacognition By Samuel Johnson @amirhkarimi_ @Yoshua_Bengio @NickJChater @tobigerstenberg Kate Larson @sydneymlevine @MelMitchell1 @iyadrahwan @bschoelkopf @psywisdom Free access until 4/17: tinyurl.com/36yc6ant

All in all, I found this to be a really fun project. A lot of detail was omitted in this thread to keep things concise, but I'm happy to explain anything in more detail. If you'd like to download the data and analyze it for yourself it can be found here github.com/IMNMV/Claude-P…

I tested 3 Bayesian models with different levels of informative priors. False belief was one of the strongest predictors. Even under strong priors, a false belief tag was associated with ~15 point higher predicted probability of anthropzn. In the weak/moderate models, even more.

Last year, Anthropic released a fun benchmark - Claude playing Pokemon. While it was useful to see how an LLM could perform in a noisy digital environment with short and long-term goals, I was more curious to explore how people talked about Claude within the chat. Let's dive in.