Tootfinder

from my link log —
A design space exploration of async/await.
https://cel.cs.brown.edu/blog/design-space-async-await/
saved 2026-09-15 https:/…

A Design Space Exploration of Async/Await
Every language pitches async/await as synchronous-looking code, and the same two keywords appear everywhere. But behind the shared syntax hide nine independent design decisions, and no two of seven modern runtimes agree on what even the simplest async programs should do! In this paper we map the design space of async/await so you can find out where your language stands.

Two Brunettes & A Gay
Indulge in an hour of witty banter and lively discussions with these cheeky 30-somethings, along with special guests from the entertainment industry and beyond...
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/two-br…

Generalization Error Estimation for Primal--Dual Algorithms in Non-Smooth Regression
Kai Tan, Pierre C Bellec
https://arxiv.org/abs/2608.13870 https://arxiv.org/pdf/2608.13870 https://arxiv.org/html/2608.13870
arXiv:2608.13870v1 Announce Type: new
Abstract: This paper studies trajectory-wise estimation of generalization error for primal--dual algorithms in non-smooth regression. Motivating examples include \(\ell_1\)-penalized least absolute deviations regression and square-root Lasso regression, where the data-fitting loss is non-differentiable and existing risk estimators for gradient-type optimization paths do not apply directly. We develop a general recursive framework that includes the Chambolle--Pock algorithm and related primal--dual splitting methods. We estimate risk by correcting each in-sample fitted value with a weighted combination of past dual iterates. The ideal weights are Stein derivative contractions and depend on the design covariance. We construct replacement weights from observable derivative contractions of the fitted-signal trajectory, yielding a covariance-free, data-driven correction. For high-dimensional Gaussian designs and fixed finite iteration horizon, we prove finite-sample guarantees for both estimators. For square-root ridge, we further establish a matched-Gaussian universality result beyond Gaussian designs. Numerical experiments show that the proposed estimators accurately track the out-of-sample risk along finite optimization paths.
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Wise

Since it was relevant to a discussion I just had on here and is something most people probably haven't thought about much (unless you've taken one of a handful of philosophy classes), I thought I'd try to lay out a key piece of Descartes' Meditations (#philosophy

@… You may already know about this. Just in case you don't, here!
Point-&-Click Showcase & Steam Event
https://

Point-&-Click Showcase & Steam Event - Application Form ➚
Point-&-Click Showcase is an upcoming [Trailer Showcase] and [Steam Event] organized by Rusty Lake and Amanita Design. We have one mission: Uplift and celebrate point-and-click games of all genres and the amazing teams behind them! As OG point-and-click Flash devs, we want to highlight the classics, the favorites, as well as promising upcoming titles (and help boost your sales and wishlists!). What is the Important Information? ➚ Form Submission Deadline: September 27, 2026 ➚ Event Dates:…

Träd, Gräs, och Stenar - "Rock för Kropp och Själ" (1972)
#NowPlaying #TradGrasOchStenar

This is the most detailed picture of a human cell ever made 🧪
https://www.instagram.com/reel/DX1CGIZMKJs/?igsh=NTc4MTIwNjQ2YQ

Sam Bousaba on Instagram: "This is a single human cell. The white filament-like structures are microtubules, and the small colored particles are ribosomes. Your body contains on the order of ~37 trillion cells. Each cell functions like a highly organized, self-regulating factory operating continuously. There is no central “supervisor”—yet thousands of processes run in parallel with remarkable coordination. Every ribosome is constantly building proteins by linking amino acids together. In eukaryotic (human) cells, a ribosome typically adds about 5–10 amino acids per second (not ~20). Since a single cell can contain millions of ribosomes, protein production is massive—resulting in millions to tens of millions of peptide bonds formed per second per cell. The microtubules act as an internal transport network. Motor proteins such as kinesin move along these tracks, carrying cellular cargo. They “walk” step by step using ATP, at speeds of roughly 500–1000 nanometers per second. Each cell contains a dense network of these filaments, with many motor proteins moving simultaneously. Just beneath the cell membrane lies the actin cortex, a dynamic meshwork that helps maintain cell shape and resist mechanical stress. This structure is continuously remodeled—assembled and disassembled—allowing the cell to adapt to its environment. Now scale this up. Across ~37 trillion cells, each containing millions of ribosomes and hundreds to thousands of mitochondria, your body is constantly producing energy in the form of ATP. Total ATP turnover is enormous—roughly equivalent to your body weight in ATP recycled every day. The DNA in a single human cell, if stretched out, measures about 2 meters. Across all your cells, this would span distances comparable to traveling from Earth to the Sun and back many times (the exact multiple depends on assumptions about cell count and DNA packing). None of these processes are under conscious control. They began when you were a single cell and have continued uninterrupted ever since."
62K likes, 2,387 comments - scienceoftheuniverse on May 2, 2026: "This is a single human cell. The white filament-like structures are microtubules, and the small colored particles are ribosomes. Your body contains on the order of ~37 trillion cells. Each cell functions like a highly organized, self-regulating factory operating continuously. There is no central “supervisor”—yet thousands of processes run in parallel with remarkable coordination. Every ribosome is constantly building prot…

SCALE-Sim EVA: Design Principles for an Extensible, Visualizable, and Adaptable Accelerator Simulation Framework
Jingtian Dang, Ritik Raj, Tushar Krishna
https://arxiv.org/abs/2608.12354 https://arxiv.org/pdf/2608.12354 https://arxiv.org/html/2608.12354
arXiv:2608.12354v1 Announce Type: new
Abstract: Modern AI accelerators increasingly combine heterogeneous compute units, hierarchical memories, local buffers, and specialized data movement paths. This diversity makes fixed accelerator simulators difficult to extend beyond their original execution model. We present SCALE-Sim EVA, an extensible, visualizable, and adaptable simulation framework for IR-aware accelerator modeling. EVA represents workloads as tensor-based commands, tracks runtime tensor placement and readiness, and executes commands on composable hardware components with user-defined functional units and memory behavior. Instead of replaying address-level cycle traces, EVA computes cycle timing from tensor readiness, hardware-unit availability, and modeled operation or transfer latency. Its command decomposition mechanism bridges compiler-level IR granularity and hardware-level execution granularity, allowing global tensor operations to be lowered and scheduled locally across a hardware hierarchy. EVA also emits open command and storage traces for visual analysis of execution timelines, memory occupancy, and resource contention.
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