Tootfinder

Two dormant Bitcoin wallets, each holding 10,000 "Satoshi era" BTC since 2011, transferred the coins to new wallets using a modern, lower-fee address format (Shaurya Malwa/CoinDesk)
https://www.coindesk.com/markets/2025/07/0…

Eight Bitcoin Wallets Move 80,000 BTC in Largest Ever ‘Satoshi Era’ Transfers
All of these moved coins are among the rarest class of BTC: mined or transacted during the “Satoshi era,” a loosely defined period from bitcoin’s launch in 2009 through 2011, when its pseudonymous creator was still active online.

Fedi meta-musings.
Just went to look at a Mastodon account I interacted with a little while back. Their follow requests require approval (which is fair enough) and the bio states
Got a blank or nonsensical avatar, no visible activity, no pointers to your identity? I'll ignore your follow request.
Well I guess that's me out. I have a good reason for wanting to be pseudonymous, as do many others here I imagine.
Of course it's every user's right to set whatever conditions they want on who follows them, but a blanket refusal on anyone not featuring a "real name" and a human-appearing avatar feels quite over-sensitive to me.
#fediverse #mastodon

Inequality in the Age of Pseudonymity
Aviv Yaish, Nir Chemaya, Lin William Cong, Dahlia Malkhi
https://arxiv.org/abs/2508.04668 https://arxiv.org/pdf/2508.…

Inequality in the Age of Pseudonymity
Inequality measures such as the Gini coefficient are used to inform and motivate policymaking, and are increasingly applied to digital platforms. We analyze how measures fare in pseudonymous settings, as common to internet-based or blockchain-based platforms. One key challenge that arises is the ability of actors to create multiple fake identities under fictitious false names, also known as ``Sybils.'' While some actors may do so to preserve their privacy, we show that this can inadvertently di…

Who Gets Cited? Gender- and Majority-Bias in LLM-Driven Reference Selection
Jiangen He
https://arxiv.org/abs/2508.02740 https://arxiv.org/pdf/2508.02740

Who Gets Cited? Gender- and Majority-Bias in LLM-Driven Reference Selection
Large language models (LLMs) are rapidly being adopted as research assistants, particularly for literature review and reference recommendation, yet little is known about whether they introduce demographic bias into citation workflows. This study systematically investigates gender bias in LLM-driven reference selection using controlled experiments with pseudonymous author names. We evaluate several LLMs (GPT-4o, GPT-4o-mini, Claude Sonnet, and Claude Haiku) by varying gender composition within c…