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'Find & kill them': China unveils AI-powered drone swarms that can hunt targets autonomously
A Chinese research team has unveiled a new artificial intelligence algorithm that it claims could significantly advance autonomous drone warfare, enabling swarms of fixed-wing drones to independently locate and eliminate enemy targets even in heavily contested environments where communications are jammed and visibility is limited. According to a peer-reviewed paper published on May 19 in China's aviation journal Acta Aeronautica et Astronautica Sinica, the algorithm — called HG-STR...
Human-in-the-Loop Contextual Bandits for Short-Term Rental Dynamic Pricing: Structural Equivalence of Historical Warm-Up and Approval-Gated Live Learning
Announce Type: new Abstract: Dynamic pricing in short-term rental (STR) markets presents a distinctive challenge for online learning algorithms: pricing decisions carry significant financial risk, operators require explainability, and market feedback is sparse (one booking outcome per listed night). We introduce the Human-in-the-Loop Gated Bandit (HITL-GB) framework, in which a contextual bandit algorithm generates price recommendations but a human agent retains authority to accept, modify,...
China Crude Buying Seen Languishing for Months as Demand Tumbles
An oil tanker is docked unloading crude oil at the port in Qingdao, in China's eastern Shandong province, on April 7, 2026. (Photo by CN-STR / AFP via Getty Images) / China OUT
Chinese scientists create ‘kill-them-all’ algorithm for drone warfare
A research team from northwestern China has released a new algorithm that could fundamentally change how drone swarms hunt and destroy enemy targets. The algorithm, known as HG-STR (Heterogeneous Graph Spatio-Temporal Reasoning), promises to allow a fleet of fixed-wing drones to autonomously search a vast battlefield and eliminate every single enemy, even when their communications are being jammed and their vision is blocked. It is the first known algorithm capable of achieving a 100 per cent...
Zeroserve: A zero-config web server you can script with eBPF
zeroserve is a small, fast, zero-config HTTPS server. You hand it a tarball of a website and it serves it - over HTTP/2 and TLS 1.3, with hot reload and a tiny resident footprint. The twist is that you can drop eBPF programs into the tarball and they run on every request, in userspace, as sandboxed middleware - rewriting, authenticating, and rate-limiting requests, or reverse-proxying them to a backend when you want it to act as a gateway in front of your app.
Restartable Sequences
May 31st, 2026 @ justine's web page The best kept secret at the frontier of system programming right now is the Linux 4.18+ (c. 2018) concept of restartable sequences or rseq for short. They allow you to create thread-safe data structures without locks or atomics which scale to microprocessors with many cores. It's currently only possible to use rseq on Linux using handwritten assembly code.
Semantic Triplet Restoration: A Novel Protocol for Hierarchical Table Understanding in Large Language Models
arXiv:2605.31550v1 Announce Type: new Abstract: Table question answering requires models to recover semantic relations encoded implicitly by two-dimensional layout, merged cells, and hierarchical headers. Current pipelines typically use HTML or Markdown as intermediate table representations, but these layout-oriented serializations introduce markup overhead and require large language models to infer header-cell alignments from row and column spans. We propose Semantic Triplet Restoration...
Zig ELF Linker Improvements Devlog
Devlog This page contains a curated list of recent changes to main branch Zig. Also available as an RSS feed. This page contains entries for the year 2026.
Zig: Build System Reworked
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A Lightweight Context-Driven Training-Free Network for Scene Text Segmentation and Recognition
arXiv:2503.15639v2 Announce Type: replace Abstract: Modern scene text recognition systems often depend on large end-to-end architectures that require extensive training and are prohibitively expensive for real-time scenarios. In such cases, the deployment of heavy models becomes impractical due to constraints on memory, computational resources, and latency. To address these challenges, we propose a novel, training-free plug-and-play framework that leverages the strengths of pre-trained text...