1. New Orleans's Car-Crash Conspiracy (www.newyorker.com)

High-speed accidents, crooked lawyers, and poor people desperate for cash—it was the kind of scheme that could have been cooked up only in the Big Easy.

2. Optimization of 32-bit Unsigned Division by Constants on 64-bit Targets (arxiv.org)

Granlund and Montgomery proposed an optimization method for unsigned integer division by constants [3]. Their method (called the GM method in this paper) was further improved in part by works such as [1] and [7], and is now adopted by major compilers including GCC, Clang, Microsoft Compiler, and Apple Clang. However, for example, for x/7, the generated code is designed for 32-bit CPUs and therefore does not fully exploit 64-bit capabilities. This paper proposes an optimization method for 32-bit unsigned division by constants targeting 64-bit CPUs. We implemented patches for LLVM/GCC and achieved speedups of 1.67x on Intel Xeon w9-3495X (Sapphire Rapids) and 1.98x on Apple M4 (Apple M-series SoC) in the microbenchmark described later. The LLVM patch has already been merged into llvm:main [6], demonstrating the practical applicability of the proposed method.

4. Who's Been Impersonating This ProPublica Reporter? (www.propublica.org)

A mysterious impostor who claimed to be ProPublica reporter Robert Faturechi reached out to a Canadian official and a Latvian businessman working with Ukraine. So, the real Robert did some reporting of his own.

5. B-trees and database indexes (2024) (planetscale.com)

B-trees are used by many modern DBMSs. Learn how they work, how databases use them, and how your choice of primary key can affect index performance.

18. What We Learned Building a Rust Runtime for TypeScript (encore.dev)

Encore built a custom Rust runtime to power TypeScript backend applications. 67K lines of Rust handling the full HTTP request lifecycle, database querying, pub/sub, tracing, and connecting to Node.js via NAPI. Here's what we learned.

27. The AI Layoff Trap (arxiv.org)

If AI displaces human workers faster than the economy can reabsorb them, it risks eroding the very consumer demand firms depend on. We show that knowing this is not enough for firms to stop it. In a competitive task-based model, demand externalities trap rational firms in an automation arms race, displacing workers well beyond what is collectively optimal. The resulting loss harms both workers and firm owners. More competition and "better" AI amplify the excess; wage adjustments and free entry cannot eliminate it. Neither can capital income taxes, worker equity participation, universal basic income, upskilling, or Coasian bargaining. Only a Pigouvian automation tax can. The results suggest that policy should address not only the aftermath of AI labor displacement but also the competitive incentives that drive it.

Article Details

Browse by Date

Type to search HN and engineering blog articles
Press Escape to close  |  ↑↓ to navigate  |  Enter to open