As a senior cloud engineer who has architected solutions across multiple cloud platforms, I’ve learned that choosing the right load balancer can make or break your application’s performance, scalability, and cost efficiency. This guide dives deep into the load balancing offerings from AWS, GCP, and Azure, explaining how they work internally and when to use each type. Understanding Load Balancer Fundamentals Before we dive into specific offerings, let’s understand what happens inside a load balancer. At its core, […]

Ver mais

arXiv:2506.15735v2 Announce Type: replace-cross Abstract: Identifying inputs that trigger specific behaviours or latent features in language models could have a wide range of safety use cases. We investigate a class of methods capable of generating targeted, linguistically fluent inputs that activate specific latent features or elicit model behaviours. We formalise this approach as context modification and present ContextBench — a benchmark with tasks assessing core method capabilities and potential safety applications. Our evaluation framework measures both elicitation strength […]

Ver mais

:::info Astounding Stories of Super-Science March, 1932, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Affair of the Brains – Chapter VIII:Dr. Ku Shows His Claws Astounding Stories of Super-Science March 1932: The Affair of the Brains – Chapter VIII Dr. Ku Shows His Claws By Anthony Gilmore ::: The scientist brushed back his thinning white hair with a trembling hand. He knew that voice. He walked […]

Ver mais

arXiv:2602.15252v1 Announce Type: new Abstract: In game theory, imperfect-recall decision problems model situations in which an agent forgets information it held before. They encompass games such as the “absentminded driver” and team games with limited communication. In this paper, we introduce the first benchmark suite for imperfect-recall decision problems. Our benchmarks capture a variety of problem types, including ones concerning privacy in AI systems that elicit sensitive information, and AI safety via testing of agents in simulation. Across […]

Ver mais

arXiv:2601.05304v1 Announce Type: cross Abstract: Neuro-symbolic reasoning systems face fundamental challenges in maintaining semantic coherence while satisfying physical and logical constraints. Building upon our previous work on Ontology Neural Networks, we present an enhanced framework that integrates topological conditioning with gradient stabilization mechanisms. The approach employs Forman-Ricci curvature to capture graph topology, Deep Delta Learning for stable rank-one perturbations during constraint projection, and Covariance Matrix Adaptation Evolution Strategy for parameter optimization. Experimental evaluation across multiple problem sizes demonstrates […]

Ver mais

arXiv:2601.10848v1 Announce Type: new Abstract: Machine Learning (ML) has emerged as a pivotal technology in the operation of large and complex systems, driving advancements in fields such as autonomous vehicles, healthcare diagnostics, and financial fraud detection. Despite its benefits, the deployment of ML models brings significant security challenges, such as adversarial attacks, which can compromise the integrity and reliability of these systems. To address these challenges, this paper builds upon the concept of Secure Machine Learning Operations (SecMLOps), […]

Ver mais