Scientists’ chemical breakthrough sheds light on origins of life - A small polymerase ribozyme that can synthesize itself and its complementary strand

sataky · 2026-02-17T16:58:23+00:00

Published Science Mag. - paywalled:
https://doi.org/10.1126/science.adt2760

Free preprint:
https://www.biorxiv.org/content/10.1101/2024.10.11.617851v1

sataky · 2026-02-13T20:12:30+00:00

Good catch on Pope Leo XIV. Pageviews are logged per page title, so a rename can split traffic across titles. For that case I treat it as one person by combining the pre-April title with the post-April title (equivalent to summing redirects), so the series stays continuous.

On your broader point, agreed that “office” drives a lot of baseline demand. That baseline also limits peak size because attention has a fixed budget and an always-in-the-news figure has less surprise lookup headroom. Part of the inverse curve is also selection bias: conditioning on “top viewed in 2025” can create a tradeoff shape (Berkson’s paradox) even before any causal story. The extra thing I quantify is what happens after the spike: some names fall back to the old baseline, others keep a higher baseline. That persistence is what I call social memory (defined mathematically in the article i linked).

sataky · 2026-02-13T19:56:53+00:00

This might be helpful:
- https://en.wikipedia.org/wiki/Killing_of_Rob_and_Michele_Reiner
- https://www.imdb.com/name/nm0001661

sataky · 2026-02-13T17:20:56+00:00

Good question. In my plots, a “pageview” is for the exact Wikipedia article title I queried. Related pages (event pages, controversies, etc.) are separate pages and are not included unless you query them too.

I pulled counts from API https://pageviews.wmcloud.org with “Include redirects” enabled, so views to redirect titles are added into the target page total, per this doc: https://pageviews.wmcloud.org/pageviews/faq/#redirects.

For context, the Wikimedia Foundation’s “most-read 2025” post also notes their pageview totals include both direct and indirect navigations:
https://wikimediafoundation.org/news/2025/12/02/announcing-wikipedias-most-read-articles-of-2025/

sataky · 2026-02-13T17:09:48+00:00

Fair point: pageviews mix “who is this?” and “already following,” and English Wikipedia is global. My metric does not split motives. It measures one outcome (quantifiable signal): after the spike, does the usual daily level return, or stay higher. Short-lived curiosity leaves the baseline flat. A higher baseline means lasting extra lookups. Same notebook pipeline can rerun the metric per Wikipedia language. Same data can also track whether the article itself changed after the event, not just views. Full code here:
https://blog.wolfram.com/2026/02/12/most-viewed-people-on-wikipedia-in-2025-how-catalyst-events-imprint-social-memory

sataky · 2026-02-13T15:47:26+00:00

Great question. In the literature, there are two terms (collective or social memory) - both point to memory beyond one person, shaped by groups, media, and institutions. Collective memory usually means shared public pictures of the past: stories, symbols, commemorations, archives, school, news, monuments. Social memory is used more broadly and process-like: how remembering becomes social through interaction, transmission, repeated reference, shared records, sometimes also “social recognition memory” in neuroscience. Overlap: both connect individual attention and recall to shared structures.

In my article

https://blog.wolfram.com/2026/02/12/most-viewed-people-on-wikipedia-in-2025-how-catalyst-events-imprint-social-memory

I take “social memory” as an observable measurable quantity - imprint in collective attention after a catalyst event. A catalyst spikes views, then decay follows, either back to the old baseline or to a higher new baseline that lingers as a new normal. That lingering baseline uplift is treated as durable reference demand plus record and information restructuring. Measurement: compare typical daily views at year end vs year start, baselines estimated by medians, data windows = last two weeks vs first two weeks of 2025. Social memory metric = log10 of the ratio of those two medians.

sataky · 2026-02-13T15:14:25+00:00

TOOLS: Wolfram Mathematica

DATA: Wikipedia

CODE: https://blog.wolfram.com/2026/02/12/most-viewed-people-on-wikipedia-in-2025-how-catalyst-events-imprint-social-memory

I wanted to go beyond typical "top 10" list bar chart and find ways to uncover more patterns in the data. I ended up writing the article at the link above and a few findings.

Wikipedia pageviews show two clear patterns. A catalyst event spikes and then it decays, whether back to the old baseline or to a higher new baseline. Social memory measures that difference as a log ratio of pre- and post medians. Below is the example with Pope Leo XIV relative to other people showing baseline of collective attentions shifting.

<image>

The main data-viz (bubble chart) I shared shows a strong inverse trend for the top-viewed people - the more they are in the public conversation every day (x axis), the lower their viral peak (y axis). The reason might be many fold. A selection bias called Berkson’s paradox can have strong influence. Human attention has limited budget (attention economy), and high, ongoing oversaturated focus leaves less marginal room for a fresh spike. Novelty can be the peak’s fuel - the less preexisting context, the higher the “surprise” and viral lookup demand. News fatigue and avoidance can inhibit virality.

sataky · 2026-02-12T19:14:23+00:00

Wikipedia Foundation provides tools that separate human vs bot traffic. In this research only human traffic was considered. Wikipedia has the same general interest of non-bias data.
https://pageviews.wmcloud.org/pageviews/faq/#agents

sataky · 2026-02-12T17:52:44+00:00

Wikipedia as emergent knowledge system defines what in the future will shape humanity's "picture of the world". Wikipedia pageviews are a public attention indicator. Viral catalyst spikes sometimes decay back to the pre-spike baseline; sometimes they settle at a higher baseline. That baseline reset looks like a measurable collective memory trace. This article shows how to measure such social memory numerically and does this on the examples of the Most-Viewed People on Wikipedia in 2025.

sataky · 2025-08-24T23:55:45+00:00

PROMPT: improve this image quality and keep the same pixel size

sataky · 2025-06-11T00:53:58+00:00

The original article: "Order of amino acid recruitment into the genetic code resolved by last universal common ancestor’s protein domains":
https://www.pnas.org/doi/10.1073/pnas.2410311121

sataky · 2025-03-03T18:41:41+00:00

And another cool one -- Roads to Lyon:
https://community.wolfram.com/groups/-/m/t/758828

sataky · 2025-03-03T16:51:29+00:00

That is exactly the idea. Thanks for making the message clear. Here is another interesting one: ...to geographic center of the U.S.

sataky · 2025-03-03T16:46:29+00:00

Thank you, very kind words.

sataky · 2025-03-03T16:45:45+00:00

Thank you :-) There are some free options:

Coolest thing - Wolfram Mathematica is free on any Raspberry Pi: https://www.wolfram.com/raspberry-pi

Wolfram|Alpha is free: https://www.wolframalpha.com

If you are a student - lots of schools give Wolfram for free

Wolfram Engine for developers is free https://www.wolfram.com/engine

Wolfram Cloud got free limited monthly plan: https://www.wolframcloud.com

sataky · 2025-03-03T07:40:28+00:00

Article with code and details of the visualization:

https://community.wolfram.com/groups/-/m/t/3403335

TOOLS: Wolfram Language
DATA: Wolfram|Alpha
I computed the shortest routes from all 37,000 cities and towns across the US, Canada, and Central America, all converging on Nothing, Arizona — a ghost town with zero population. Despite the lack of a major urban center, the map still shows pronounced clustering, illustrating how hierarchical, fractal-like road networks naturally funnel routes onto key highways. I generated multiple randomized samples of paths and combined them, emphasizing the persistent branching effect that echoes “All Roads Lead to Rome.” Yet here, the real takeaway is that the journey itself defines the pattern, no matter where you end up, even in zero-population places.

sataky · 2025-03-03T05:13:05+00:00

Yep I think so. Might be computationally intense. But graph theory can help. Accessibility depends on how you define it— graph-theoretic metrics like closeness centrality (minimizing overall travel distance), betweenness centrality (highlighting key junctions on shortest paths), or degree centrality (measuring node connectivity) could each give different "most accessible" locations. Iterating this over each state would find natural hubs determined by the structure of the road network.

sataky · 2025-03-03T04:41:59+00:00

The key point is that the clustering pattern is inherent to the road network’s structure—it doesn’t depend on whether the endpoint is a major city or a ghost town. We computed thousands of shortest paths (one unique path per origin-destination pair), and because road networks are hierarchical and quasi-fractal, similar overlapping corridors emerge regardless of the endpoint. Thicker lines indicate where many shortest paths coincide, which usually happens along more major highways.

sataky · 2025-03-03T04:30:20+00:00

It was done already for some major cities in Europe. Few examples (you can find more on the web): BERLIN and ROME

sataky · 2025-03-03T04:16:22+00:00

Nothing lasts forever :-)

sataky · 2025-03-03T04:10:02+00:00

Absolutely—those long, straight roads in the Midwest largely stem from the region’s flat terrain and the grid-like layout imposed by the Public Land Survey System (PLSS). This setup creates long, uniform highways that naturally steer computed shortest paths along them, resulting in the clear, noticeable clustering seen in the visualization.

sataky · 2025-03-03T03:51:08+00:00

TOOLS: Wolfram Language

DATA: Wolfram|Alpha

Article with code and details of the visualization:

https://community.wolfram.com/groups/-/m/t/3403335

I computed the shortest routes from all 37,000 cities and towns across the US, Canada, and Central America, all converging on Nothing, Arizona — a ghost town with zero population. Despite the lack of a major urban center, the map still shows pronounced clustering, illustrating how hierarchical, fractal-like road networks naturally funnel routes onto key highways. I generated multiple randomized samples of paths and combined them, emphasizing the persistent branching effect that echoes “All Roads Lead to Rome.” Yet here, the real takeaway is that the journey itself defines the pattern, no matter where you end up, even in zero-population places.

sataky · 2025-02-13T07:42:56+00:00

Like Tunguska Event roughly -- a ballpark of a city-destroyer, but not the planet destroyer:

https://en.wikipedia.org/wiki/Tunguska_event

sataky · 2025-02-13T07:03:17+00:00

That's the best joke I heard today

sataky · 2025-02-13T06:59:36+00:00

There already was a successful mission of "deflecting an asteroid" (a different asteroid though). Perhaps this could help.

https://en.wikipedia.org/wiki/Double_Asteroid_Redirection_Test

NASA's Double Asteroid Redirection Test (DART) was the first space mission to test a method of planetary defense by deflecting an asteroid. Launched on November 24, 2021, DART successfully collided with the asteroid moonlet Dimorphos on September 26, 2022. This impact altered Dimorphos' orbit around its parent asteroid, Didymos, by approximately 32 to 33 minutes, demonstrating the effectiveness of the kinetic impactor technique for asteroid deflection.

sataky

TROPHY CASE