A lot of people seem convinced that the point of AI coding is to write low-quality code as fast as possible. Spew out barely-passable slop, open massive PRs, and merge them unvetted. Ship it! But t…

Welcome to GitHub's home for real-time and historical data on system performance.

Or how not to do a culture fit interview

BRUSSELS — The Dutch government is blocking a United States-based company’s attempts to acquire a key online identification IT supplier. Dutch firm Solvinity runs a platform for the country&#…

A blog about software craft and quality

A truly bizarre situation on Motorola phones has led to the software hijacking the Amazon app to inject an affiliate...

Dropbox CEO Drew Houston, who started the cloud storage company when he was 24, plans to step down and assume the role of executive chairman.

60-second updates. RFC 2136 TSIG. BYOD. DNSSEC. For homelabs, edge routers, and infrastructure teams.

Outsourcing plus LocalAI will soon become more economical vs Frontier labs

A sad look at the state of modern programming books.

There’s no clear connection between AI usage and productivity.

In this article, I try to understand why coding agents can be infuriating to
use. I think the problem is their conversational UX: they behave enough like
helpful colleagues to trigger our social instincts, but they don

How a software developer bypassed proprietary publishing silos using LibreOffice, Standard Ebooks, and LaTeX to build a sustainable, Git-tracked book production pipeline.

Does anybody actually like React? A cherry-picked collection of React (and React-tainted) criticism.

Why coding agents perform better in cohesive ecosystems like Go than fragmented ones like JavaScript or Python

A deterministic genomics engine with a compact memory footprint. Run whole-genome workloads in as little as 100 MB RAM. Built in Rust. - logannye/rosalind

Transformer-based large language models are increasingly used for long-horizon tasks; however, their attention mechanism scales poorly with context length. To handle this, we study a sleep-like consolidation mechanism in which a model periodically converts recent context into persistent fast weights before clearing its key-value cache. During sleep, the model performs $N$ offline recurrent passes over the accumulated context and updates the fast weights in its state-space model (SSM) blocks through a learned local rule. During inference, this shifts extra computation to sleep while preserving the latency of wake-time prediction. We test our method on controlled synthetic tasks, including cellular automata and multi-hop graph retrieval, as well as a realistic math reasoning task, on which a regular transformer as well as SSM-attention hybrid models fail. We then show that increasing sleep duration $N$ for our models improves performance, with the largest gains on examples that require deeper reasoning.

(Yes, but.)

Interesting article: [Wayback/Archive] Stack Overflow’s forum is dead thanks to AI, but the company’s still kicking… thanks to AI – Sherwood News. with this important quote: The complex…

This document describes the security content of macOS Tahoe 26.5.