> I don't think his Usonian concepts have had much impact on society.
The word Usonain has vanished, but the style's influence has not.
> In 2024, I spent $750,000 on a 1200 sqft rancher built in 1962.
The Jacobs First House [1] in Madison, WI was the first Usonian house; it is credited with many features that became common in the mid-century ranches of the 50's and 60's. Stewart Hicks has a good deep dive [2] into Wright's influence on 20th century architecture.
Wasserstein distance (Earth Mover’s Distance) measures how far apart two distributions are — the ‘work’ needed to reshape one pile of dirt into another. The concept extends to multiple distributions via a linear program, which under mild conditions can be solved with a linear-time greedy algorithm [1]. It’s an active research area with applications in clustering, computing Wasserstein barycenters (averaging distributions), and large-scale machine learning.
There is an analogue of the CLT for extreme values. The Fisher–Tippett–Gnedenko theorem is the extreme-values analogue of the CLT: if the properly normalized maximum of an i.i.d. sample converges, it must be Gumbel, Fréchet, or Weibull—unified as the Generalized Extreme Value distribution. Unlike the CLT, whose assumptions (in my experience) rarely hold in practice, this result is extremely general and underpins methods like wavelet thresholding and signal denoising—easy to demonstrate with a quick simulation.
I’ll share an anecdote I witnessed in my extended family–it was horrible. When US Air went bankrupt, employees with decades of service, expecting high five-figure to low six-figure annual income, learned they would get roughly $0.20 on the dollar. For many who were entering retirement, the impact was life-changing, and the stress and disruption it caused could well be argued as life-shortening.
PBGC did take over, but that did not solve the problem.
I believe the root cause was mismanagement of the pension, with the bankruptcy merely exposing this. But I wouldn’t be surprised if, at every opportunity during the bankruptcy process, changes were made that eroded the program’s health.
This is an instance where Conway's Law applies: state and county systems were kept separate so that maintenance and repairs crews wouldn’t accidentally duplicate work. https://en.wikipedia.org/wiki/Conway%27s_law
I've seen several, Planet Trek in Wisconsin is a good bikeable one with high quality signage. The sun is downtown, the moon is the size of a peach pit, Pluto is ~20 miles away.
how helpful was ai for this? The paper is light on details, but it says the agent was used to generate a kind of seed set (rank-1 bilinear products) that were then fed into the subsequent steps. Evidently this idea succeeded. Curious if anyone here has insight into if this is a common technique, how this agent's output would compare to random or a simple heuristic that attempts the same. Also interested to see how the training objective gets defined since the final task is a couple of steps downstream from what the agent generates.
My hope is that a lib like this one or similar could rally mindshare and become integrated as the new standard, and adopted by the wider developer community. In near term, it comes down to trade-offs. I see no decision that works for all use cases. Dependencies introduce ticking time bombs, stdlibs should be correct and intuitive, but at least when not they are usually well tested and maintained, but when stdlib don't meet urgent production needs you have to do something.
I found a bill from the Weimar hyperinflation era. Its face value was several billion (Milliarden). Its only value was as a curiosity.