"Hypothesis: Chart Hits Before Maybe 2015 Are Less Likely to Be Remembered Than Hits from Previous Decades"
https://Karl-Voit.at/2026/04/19/hypothesis-chart-hits-popularity-decay
To check my assumption, I asked Claude what it "knows"…
It's a misleading title. But a bold hypothesis ->
Canada to Join EU - First Non-European Member, €150B Defence Access, Treaty Can Be Amended
https://www.youtube.com/watch?v=l_MozOqbeGM
Weekend #Plankton Factoid 🦠🦐
The equinox has returned, so let's talk spring phytoplankton blooms. Temperature and light has increased to create stratification (warm water layer floats over a denser one). Algae is kept near the surface, growing quickly without sinking into the depths. This is the Sverdrup (1953) Critical Depth Hypothesis.
Dr. David Shull at Western Washington Univ…
Lasst uns den gescheiterten, toxischen Trumpismus doch als Chance für einen Aufbruch sehen, für eine neue Weltordnung! Darüber sprach Pedro Sšnchez an der Tsinghua Universität in Peking am 13. April 2026.
2 Zitate aus lesensenswerter Rede:
"Zum 1. Mal in der modernen Geschichte keimt der Fortschritt an vielen Orten der Welt gleichzeitig auf."
"Eine Multipolarität ohne Regeln führt zu Rivalität und aus Rivalität entstehen nur Kriege, Handelskonflikte und Unte…
Hi #fediverse. We need to talk about something.
While talking to a colleague about how I recently learned most people have never sat on a crow it came up that she has never been sat on by a cat. Like, not even once during childhood.
Another colleague admitted they also have never been sat on by a cat.
My hypothesis is that most people have at one point in their life sat on b…
"The most recent orbital computations make it increasingly likely that the object [the new #Kreutz comet MAPS] is a fragment of one of the comets observed by Ammianus Marcellinus in AD 363, thereby strengthening evidence in support of the contact-binary hypothesis of the Kreutz system," writes Zdenek Sekanina in https://arxiv.org/abs/2602.17626: "In this context, the comet is the only second-generation fragment of Aristotle's comet that we are aware of to appear after the 12th century. It does not look like a major fragment, but rather like an outlying fragment of a much larger sungrazer."
Hypothesis: Every sufficiently large, complex, or performance sensitive application will eventually end up with a custom memory pool or allocator of some sort.
A complete set of canonical nucleobases in the carbonaceous asteroid (162173) #Ryugu: https://www.nature.com/articles/s41550-026-02791-z -> "The detection of diverse nucleobases in asteroid and meteorite materials demonstrates their widespread presence throughout the Solar System and reinforces the hypothesis that carbonaceous asteroids contributed to the prebiotic chemical inventory of early Earth."
Interesting observation from today (a beautiful, cloudless day): We got *less* solar power than a few days ago, when it was partly cloudy. Significantly less, by almost a factor of two vs the previous peak (and closer to what we got on fairly rainy days).
My working hypothesis at this point: the sun is low in the sky because it's winter, and spends a significant fraction of its time partly occluded by trees.
On a cloudy day, shading isn't really a thing because you have t…
Fast $k$-means Seeding Under The Manifold Hypothesis
Poojan Shah, Shashwat Agrawal, Ragesh Jaiswal
https://arxiv.org/abs/2602.01104 https://arxiv.org/pdf/2602.01104 https://arxiv.org/html/2602.01104
arXiv:2602.01104v1 Announce Type: new
Abstract: We study beyond worst case analysis for the $k$-means problem where the goal is to model typical instances of $k$-means arising in practice. Existing theoretical approaches provide guarantees under certain assumptions on the optimal solutions to $k$-means, making them difficult to validate in practice. We propose the manifold hypothesis, where data obtained in ambient dimension $D$ concentrates around a low dimensional manifold of intrinsic dimension $d$, as a reasonable assumption to model real world clustering instances. We identify key geometric properties of datasets which have theoretically predictable scaling laws depending on the quantization exponent $\varepsilon = 2/d$ using techniques from optimum quantization theory. We show how to exploit these regularities to design a fast seeding method called $\operatorname{Qkmeans}$ which provides $O(\rho^{-2} \log k)$ approximate solutions to the $k$-means problem in time $O(nD) \widetilde{O}(\varepsilon^{1 \rho}\rho^{-1}k^{1 \gamma})$; where the exponent $\gamma = \varepsilon \rho$ for an input parameter $\rho < 1$. This allows us to obtain new runtime - quality tradeoffs. We perform a large scale empirical study across various domains to validate our theoretical predictions and algorithm performance to bridge theory and practice for beyond worst case data clustering.
toXiv_bot_toot
Say you have a machine that spits out hundreds of very long solution attempts to the Riemann hypothesis. Each is tedious and hard and then humans start reading it all until they refute most and finally find one that is correct. It’s a gigantic effort for the humans, but now they have a proof.
Did the machine then _solve_ the Riemann hypothesis?
#math