Modern businesses run on digital trust. Customers expect their data to be safe, and partners expect operations to be reliable. To meet those expectations, security must be a daily practice, not a yearly project. This article walks through practical measures that reduce risk fast. Each section focuses on actions you can apply in most environments. The goal is simple – shrink the attack surface and strengthen your defences without adding needless complexity. Assessing Your Risk Landscape Start with […]

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Everyone believes that the digits of constants such as π or √2 cannot be distinguished from a sequence of random bits. The first few trillion successfully pass all tests of randomness. However, proving that they indeed behave perfectly randomly is arguably one of the oldest and most difficult unsolved math conjectures. So far, nobody succeeded in proving even the most basic facts for any of these constants, for instance: In the binary digits of π, is the proportion […]

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arXiv:2602.11164v1 Announce Type: new Abstract: Automated optimization modeling via Large Language Models (LLMs) has emerged as a promising approach to assist complex human decision-making. While post-training has become a pivotal technique to enhance LLMs’ capabilities in this domain, its effectiveness is severely constrained by the scarcity and underutilization of high-quality training data. However, through a detailed profiling of error patterns across various problem-response pairs drawn from post-training, we identify two fundamental limitations of existing automated optimization modeling approaches: […]

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We study the online resource allocation problem in which at each round, a budget $B$ must be allocated across $K$ arms under censored feedback. An arm yields a reward if and only if two conditions are satisfied: (i) the arm is activated according to an arm-specific Bernoulli random variable with unknown parameter, and (ii) the allocated budget exceeds a random threshold drawn from a parametric distribution with unknown parameter. Over $T$ rounds, the learner must jointly estimate the […]

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You may come across a programming task where you want to implement the following pattern: try this and, if it doesn’t work out, try the other thing. In other words, you want a computation composed of two (or more) alternatives which should be tried one after the other until one of them produces a useful result. As an example, let’s build a function that takes a String as input and returns Just String if that input is either […]

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arXiv:2602.16802v1 Announce Type: new Abstract: While Reinforcement Learning with Verifiable Rewards (RLVR) has shown strong effectiveness in reasoning tasks, it cannot be directly applied to non-verifiable domains lacking ground-truth verifiers, such as LLM alignment. In this work, we investigate whether reference-guided LLM-evaluators can bridge this gap by serving as soft “verifiers”. First, we design evaluation protocols that enhance LLM-based evaluators for LLM alignment using reference outputs. Through comprehensive experiments, we show that a reference-guided approach substantially improves the […]

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The Power BI Salesforce connector is often the first option teams use to bring Salesforce data into Power BI through native connectors such as Salesforce Objects and Salesforce Reports. This works for basic reporting, but limitations around data volume, refresh reliability, and scalability become more important as reporting grows. This guide explains how the native connectors work, where their limits become critical, and when Metrica Power BI Connector for Salesforce becomes the better fit. Which Native Power BI […]

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The rapid adoption of the Internet of Medical Things (IoMT) is transforming healthcare by enabling seamless connectivity among medical devices, systems, and services. However, it also introduces serious cybersecurity and patient safety concerns as attackers increasingly exploit new methods and emerging vulnerabilities to infiltrate IoMT networks. This paper proposes a novel Tsetlin Machine (TM)-based Intrusion Detection System (IDS) for detecting a wide range of cyberattacks targeting IoMT networks. The TM is a rule-based and interpretable machine learning (ML) […]

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Key Highlights: OpenAI entered into multiple partnerships last year, and now it has kicked off February 2026 with yet another one. OpenAI today announced that it has signed a multi-year deal worth $200 million with a leading cloud-native data platform provider called Snowflake. OpenAI models will integrate directly into Snowflake’s enterprise data platform As part of this deal, OpenAI’s frontier AI models now directly integrate into Snowflake’s enterprise data platform. OpenAI says that with this partnership, both companies […]

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