Our investigation reveals how covert surveillance actors exploit global telecom networks to conduct long-term location tracking campaigns. By analyzing mobile operator signalling infrastructure, we uncover persistent vulnerabilities that enable sophisticated tracking, raising critical questions about accountability and security in telecommunications.

Kepler has developed a groundbreaking AI platform for the financial services sector that ensures data accuracy and verifiability. By integrating complex data from millions of sources, their system allows analysts to perform comprehensive financial analysis with confidence, reducing dependency on traditional, error-prone methods.

A broken sync issue with a Wahoo ELEMNT Bolt v3 cycling computer led the author to reverse engineer its Bluetooth protocol, uncovering a hidden developer mode. Through meticulous investigation, they unlocked various diagnostic features and tools, only to discover that the original problem was with their phone, not the device.

This article explores the experience of using Haskell in a large, fast-growing fintech company, Mercury. It highlights how operational knowledge, system adaptability, and engineering practices contribute to the reliability of a massive Haskell codebase, despite its complexity and the inexperience of many team members.

A recent Harvard study reveals that AI outperforms human doctors in emergency medicine triage, diagnosing accurately in critical moments. While AI shows promise as a second opinion tool for clinicians, experts stress that doctors will remain essential, especially in complex patient interactions and ethical decision-making.

Dell, Intel, and David Heinemeier Hansson team up to deliver seamless Day One support for Omarchy on XPS laptops. This collaboration aims to eliminate the traditional delays in Linux hardware compatibility, promising users a smooth, out-of-the-box experience with cutting-edge technology and developer-centric features.

An open-weights Chinese model, Kimi K2.6, won a recent AI programming contest, outperforming major competitors like GPT-5.5 and Claude. The contest involved a challenging sliding-tile letter puzzle, showcasing different strategies among participant models and highlighting notable strengths and weaknesses in their approaches.

After years of using off-the-shelf software, an enthusiast shares their journey of creating a personalized computing environment from scratch. This article explores the joy and practicality of tailoring tools like editors and file managers to fit individual workflows, highlighting how easier it is to build custom software today.

In an intriguing link between blindness and schizophrenia, researchers found that congenital cortical blindness seems to protect against the mental illness. This protective factor highlights the role of the brain's visual cortex in shaping perception and could lead to new treatment approaches for schizophrenia by understanding early brain development.

The rise of AI-driven coding tools raises concerns about cognitive atrophy among developers. As coding responsibilities shift away from hands-on programming to oversight, a lack of direct engagement with code may undermine critical thinking and problem-solving skills, especially for junior developers. This trend could impact overall software quality and innovation.

The Senate Judiciary Committee has unanimously approved the GUARD Act, aimed at requiring AI chatbot users to verify their age via stringent measures. Critics warn this could lead to extensive data collection and surveillance, raising concerns about privacy and the implications for all users, not just minors.

Many developers mistakenly believe that terminal applications are inherently accessible, assuming text-only interfaces are easier for screen readers. However, modern Text User Interfaces (TUIs) can create significant accessibility challenges, often hampering user experience, especially for the visually impaired. This article explores these pitfalls and contrasts them with traditional command-line tools.

This article explores social bias in code generated by Large Language Models (LLMs). Through a study evaluating bias across various demographic dimensions, it proposes the Fairness Monitor Agent (FMA) as an effective solution to mitigate bias while improving code accuracy. The findings highlight the complexities of achieving fairness in automated coding.

This paper presents a new approach to reinforcement learning that addresses computational challenges in large environments. By developing an algorithm that minimizes regret efficiently, it opens pathways for handling infinite state and action spaces, significantly advancing the capabilities of offline oracle-efficient methods in policy optimization.

BWLA introduces a novel post-training quantization framework for large language models, achieving 1-bit weight and low-bit activations while maintaining accuracy. By utilizing advanced techniques, it significantly enhances inference speed and model performance, paving the way for practical deployment in real-world applications.