Tootfinder

This does put me in mind of the time that #JuliaChild (she of “Mastering the Art of French Cooking” fame) was publicly outed as an agent of #DeltaGreen (qv,

The French Chef and Lovecraft: Or How I Discovered a Historical Writer had Used the Cthulhu Mythos in her Book.
I have a forty-five minute drive each way from my home to work, and my car radio doesn’t work. Fortunately the CD player does, so I often raid the Estacada Public Library for books on CD to listen …

Ex-Bills coach Sean McDermott taking year off from coaching, aims to improve for next opportunity https://www.nfl.com/news/sean-mcdermott-taking-year-off-from-coaching-aims-to-improve-for-next-opportunity

Just wrote a fun little algorithm for iterating through all grid positions within X euclidean distance from a center, roughly in order of distance but also mostly ensuring each position yielded is at least diagonally adjacent to the previous. It's a fun problem to think about with lots of design tradeoffs and many valid solutions.
My solution requires that the origin be on the grid (I think?) and uses a bunch of caching instead of a bajillion distance checks, although I don't actually know what the memory/speed tradeoff is like or whether there are any gains at all (could be net losses, which I'm starting to wonder about more as I write this post). Thankfully I don't expect performance to be very critical here anyways, and doing the caching actually helped separate some logic in a useful way.
A fun little diversion from bigger stuff in the project and a reminder of some of the joys of programming design at a small scope. Feels kinda like mastering the local town's minigame while you're stuck on the main quest in an RPG.
#programming

A special 79-year-old man has received unparalleled access to Eli Lilly’s obesity drug.
Millions of Americans are eagerly awaiting access to retatrutide,
a powerful new drug from the pharmaceutical company.
But one unidentified person has been able to gain premature access to the drug via the FDA’s “compassionate use” program, STAT reported Tuesday.
The FDA program is designed to prioritize access to experimental drugs for patients with grave or life-threatening medi…

Eli Lilly Approved Obesity Drug for Mystery 79-Year-Old Patient
Questions are swirling over Donald Trump’s health after a bombshell report revealed a mystery man was getting unprecedented access to a weight-loss drug.

Exploring cooperation mechanisms via reinforcement learning in network common-pool resource games
Yihang Qin, Lin Wang
https://arxiv.org/abs/2606.05867 https://arxiv.org/pdf/2606.05867 https://arxiv.org/html/2606.05867
arXiv:2606.05867v1 Announce Type: new
Abstract: Sustaining cooperation in resource-constrained populations requires allocation mechanisms that balance individual incentives, resource sustainability, and distributional fairness. This paper proposes a network common-pool resource game in which individuals are embedded in complex networks, participate in multiple overlapping local resource pools, and face endogenous resource constraints during strategy evolution. Within this framework, we first examine two representative allocation mechanisms, equal allocation and proportional allocation. The results show that equal allocation produces fair but inefficient outcomes by weakening contribution incentives, whereas proportional allocation can temporarily promote cooperation but amplifies accumulated advantages and leads to severe inequality. To overcome these limitations, we develop a graph neural network-based reinforcement learning framework in which a learned social planner allocates local pool resources without directly controlling individual strategies. Simulation results under four representative network topologies show that the learned planner sustains higher cooperation levels and average accumulated resources, and reduces inequality compared with the baselines. Furthermore, we interpret the learned policy and distill it into two simpler mechanisms: a resource-dependent mixture mechanism for regular networks and a degree-conditioned mixture mechanism for heterogeneous networks. These mechanisms reveal that effective allocation should adapt to both local resource states and structural positions, providing an interpretable route from reinforcement learning policy search to mechanism design in networked resource-sharing systems.
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