Erik Sturcke
@erik
- 21 Followers
- 181 Following
- 138 Posts
Ben Tsai
Graham Sutherland / Polynomialsecurity advice, 1996: writing your passwords down in a notebook is a very bad idea and nobody should do it
security advice, 2026: writing your passwords down in a notebook is one of the most secure storage methods for most users
(fun how threat models change over time, eh?)
I think we need a new framework to understand the software development life cycle. We have correlated writing code "manually" with some base level of design, architecture, debugging, and understanding of the system you are building. But when the whole system gets spit out from an LLM, those things didn't happen. At least not nearly in the same way. So we risk shipping code that is provides value at an unprecedented cost of quality, maintainability, security.
Wendy NatherRE: https://mastodon.online/@ferrix/115866040924740382
One of my favorite German words: Sollbruchstelle. And as my spouse used to say, “Sollbruchstelle heißt nicht unbedingt Wollbruchstelle.”
Tom VII 7thI uploaded this surprise video late on Christmas Eve (per the recommendation of my social media team) and then also forgot to even mention it on Mastodon, as though I don't love MEGAFAUNA??
Mathematically extra-complicated Secretest Santa 2025
John Carlos BaezI've decided to spend some time on a quest that's likely to fail. I'm trying to start with the Standard Model of particle physics, ponder its patterns, and figure out *why* it's that way.
That's *not* what most particle physicists have been doing for the last 40 years. In many ways the Standard Model looks complicated and arbitrary, so they often try to embed it in some larger, more symmetrical theory. That hasn't worked too well, so it's worth trying something else - even though it's likely to fail.
This is my report on some patterns that pop out if you stare at the Standard Model long enough. These are mostly not my own discoveries, but I'm trying to package them a bit more neatly.
This is the first of two parts!
https://www.youtube.com/watch?v=6zrp5HVK-tE
Note: this talk is not for beginners. If you're just getting started, try my course on the Standard Model:
Can We Understand the Standard Model?
Holy crap! The Gaia space telescope is expected to find 120,000 ± 22,000 planets orbiting other stars! Most will be super-Jupiters, because those are the easiest to find. But we'll know much more about other worlds than we do now.
The mission ran from 2014 to March 2025, orbiting at the Earth-Moon Lagrange point L2. It collected huge amounts of data. They repeatedly measured the positions of over a billion stars - so accurately that they could see a star moving by an amount equal to the size of a pinhead on the Moon, as seen from Earth!
They won't be able to process and release all this data until 2030. But some is already out, and they predict 7,500 ± 2,100 planet discoveries from this first release.
They've also found lots of other great stuff. Like 20 stars moving faster than the Galactic escape velocity: 7 leaving the Milky Way, and 13 approaching the Milky Way, which may have come from other galaxies. Like 4 stars orbiting each other. And like the Gaia-Enceladus population, the remains of a dwarf galaxy that collided with the Milky Way 10 billion years ago.
Despite our many other problems, we're in the golden age of astronomy.
On the Exoplanet Yield of Gaia Astrometry
We re-examine the expected yield of Gaia astrometric planet detections using updated models for giant-planet occurrence, the local stellar population, and Gaia's demonstrated astrometric precision. Our analysis combines a semi-analytic model that clarifies key scaling relations with more realistic Monte Carlo simulations. We predict $7{,}500 \pm 2{,}100$ planet discoveries in the 5-year dataset (DR4) and $120{,}000 \pm 22{,}000$ over the full 10-year mission (DR5), with the dominant error arising from uncertainties in giant-planet occurrence. We evaluate the sensitivity of these forecasts to the detection threshold and the desired precision for measurements of planet masses and orbital parameters. Roughly $1{,}900 \pm 540$ planets in DR4 and $38{,}000 \pm 7{,}300$ planets in DR5 should have masses and orbital periods determined to better than $20$%. Most detections will be super-Jupiters ($3$ - $13 M_{\rm J}$) on $2$ - $5$AU orbits around GKM-type stars ($0.4$ - $1.3 M_\odot$) within $500$ pc. Unresolved binary stars will lead to spurious planet detections, but we estimate that genuine planets will outnumber them by a factor of $5$ or more. An exception is planets around M-dwarfs with $a < 1$AU, for which the false-positive rate is expected to be about $50$%. To support community preparation for upcoming data releases, we provide mock catalogs of Gaia exoplanets and planet-impostor binaries.
It's our best theory of elementary particles and forces. It's absolutely amazing: it took centuries of genius to discover that the world is like this, and it's absolutely shocking. But nobody believes it's the last word, so we simply call it The Standard Model.
But what does this theory say? I'll try to explain *part* of it in this series of videos. I begin by introducing the cast of characters - the particles.
If you have questions, please ask - either here or on YouTube! Intelligent questions keep me motivated. Without them, I get bored.
By the way, these videos will contain mistakes. For example, this time I forgot to mention one key particle before saying "So I've introduced all the actors in the drama." When I get better at editing videos, I will correct slips like this. But I will always try to point out errors in a "pinned" comment right below the video. So look down there!
(I don't plan to explain the details of quantum field theory. So even if you watch all my videos, you'll get just a *taste* of the Standard Model. But I will get into some of the math, so it will be much more than just chat. It will be substantial.)
Standard Model - Part 1: Particles
I finished the thing: https://bentsai.org/posts/text-is-best
https://social.lol/@bentsai/115340278012480755
https://social.lol/@bentsai/115340278012480755
Best of MLTSHP