Astra Taylor - May 8, 2026 📍 It’s about #democracy. The Anti-Data Center Movement is Good, Actually
" - it represents a critical new front in the fight against tech-enabled authoritarianism. Where else can people push back on job-eating #algorithms, distorting deep fakes, and autonomous drone strikes?"
📌 https://substack.com/home/post/p-196929121
Also, The Guardian (May 8)
⬇️
The fight against AI datacenters isn’t just about tech – it’s about #democracy
Astra Taylor and Saul Levin
Sparse Cholesky Elimination Tree
https://www.reidatcheson.com/sparse/linear/cholesky/2026/04/09/etree.html
Here I derive the elimination tree for the (right-looking) sparse Cholesky algorithm for computing A = LL^T for lower triangular L and sparse matrices A. This tree forms the foundation for most sparse factorization software, even when the underlying assumptions of Cholesky (symmetric and definite) do not apply. Ultimately this tree tells us two things: 1. where nonzeros appear in the matrix L even if not present in the original A (i.e. “fill-in”) and 2. the task dependency graph of our resulting factorization. Traditionally this concept is usually presented in the context sparse triangular solves which is then used as a building-block to a Cholesky factorization. I wanted to instead work directly from a Cholesky factorization, which is what I do below.
Day 9/60: Sliding window patterns
Today's note was about using a stable invariant so sliding window patterns feels like a process instead of a trick. I kept coming back to the same checks: name the exact window, prefix, or pointer region each variable owns, reuse prior work instead of recomputing the same range each iteration, and test boundary sizes first because they expose weak invariants quickly.
The failure mode worth watching is moving boundaries before stating what region they actually represent. If that happens, the implementation usually looks busy while the invariant is already gone.
Day 9/60: Sliding window patterns
Today's note was about using a stable invariant so sliding window patterns feels like a process instead of a trick. I kept coming back to the same checks: name the exact window, prefix, or pointer region each variable owns, reuse prior work instead of recomputing the same range each iteration, and test boundary sizes first because they expose weak invariants quickly.
The failure mode worth watching is moving boundaries before stating what region they actually represent. If that happens, the implementation usually looks busy while the invariant is already gone.
Day 9/75: Sliding window - fixed size
Today's note was about using a stable invariant so sliding window - fixed size feels like a process instead of a trick. I kept coming back to the same checks: name the exact window, prefix, or pointer region each variable owns, reuse prior work instead of recomputing the same range each iteration, and test boundary sizes first because they expose weak invariants quickly.
The failure mode worth watching is moving boundaries before stating what region they actually represent. If that happens, the implementation usually looks busy while the invariant is already gone.
Day 9/75: Sliding window - fixed size
Today's note was about using a stable invariant so sliding window - fixed size feels like a process instead of a trick. I kept coming back to the same checks: name the exact window, prefix, or pointer region each variable owns, reuse prior work instead of recomputing the same range each iteration, and test boundary sizes first because they expose weak invariants quickly.
The failure mode worth watching is moving boundaries before stating what region they actually represent. If that happens, the implementation usually looks busy while the invariant is already gone.
Day 9/75: Sliding window - fixed size
Today's note was about using a stable invariant so sliding window - fixed size feels like a process instead of a trick. I kept coming back to the same checks: name the exact window, prefix, or pointer region each variable owns, reuse prior work instead of recomputing the same range each iteration, and test boundary sizes first because they expose weak invariants quickly.
The failure mode worth watching is moving boundaries before stating what region they actually represent. If that happens, the implementation usually looks busy while the invariant is already gone.
Day 9/75: Sliding window - fixed size
Today's note was about using a stable invariant so sliding window - fixed size feels like a process instead of a trick. I kept coming back to the same checks: name the exact window, prefix, or pointer region each variable owns, reuse prior work instead of recomputing the same range each iteration, and test boundary sizes first because they expose weak invariants quickly.
The failure mode worth watching is moving boundaries before stating what region they actually represent. If that happens, the implementation usually looks busy while the invariant is already gone.
Some fun with #algorithms and #boardgames
https://jmmonteiro.com/blog/2026-05-09/
RE: https://mastodon.com.pl/@antygon/116500749791347102
the caption:
"And that's how life goes.
"Welcome to the future."
the video:
"I'm 30. Not one of my friends has children. Zero. Not one. No one's having kids. Do you know how hard you need to abuse a mammal to make them not have children?
"Like, the kids that are like 18 today. There's a huge problem where they don't dance. Why is this? Because you get recorded. If you approach a girl and you mess it up, you get recorded."
#Video #Future #BigTech #Tech #SocialMedia #Algorithms #IAmDB
Artun Faliro
Hacker News
Think
João
Danny Boling ☮️