digitado

I had a Mac Mini running OpenClaw as a personal automation server. It handled dozens of daily tasks without me lifting a finger. But when I needed the reasoning power of ChatGPT, specifically GPT-5.4 Pro, a model only available through the web interface, I was the middleware. I was the copy-paste layer between two systems that should have been talking to each other. That felt wrong. So I fixed it. Now, when I send a message in Telegram, […]

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The final moment of the global digital asset summit, Paris Blockchain Week (PBW), will culminate in a gala finale on April 17, at the Gustave Eiffel room on the first floor of the iconic Eiffel Tower in Paris. The official closing party, hosted by INDIGO Fund x $NRG x Rasa, will bring together a curated audience of founders, investors, partners, and industry leaders for a final moment of connection during the week, marking a symbolic close to the […]

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This paper presents a theoretical framework aimed at examining the Riemann Hypothesis (RH) through the lens of a differential interaction operator Φ(s,δ) acting on the Hilbert space l2(N). By mapping the Dirichlet η-function to a trace-class operator, we analyze the resulting phase torque J(δ,t), which is governed by a hyperbolic sine bias. We propose a product criterion wherein the operator trace vanishes if and only if a zero exists at mirrored coordinates across the critical line. Furthermore, we […]

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Reinforcement learning has become a powerful approach for enhancing large language model reasoning, but faces a fundamental dilemma: training on easy problems can cause overfitting and pass@k degradation, while training on hard problems often results in sparse rewards. Recent question augmentation methods address this by prepending partial solutions as hints. However, uniform hint provision may introduce redundant information while missing critical reasoning bottlenecks, and excessive hints can reduce reasoning diversity, causing pass@k degradation. We propose textbf{PieceHint}, a hint […]

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Docker is widely used to deploy applications in containerised environments. This study investigates whether the physical memory utilisation of databases deployed in Docker differs from that of equivalent non-Docker deployments during a Structured Query Language injection (SQLi) attack. A quantitative approach was adopted, using Glances to collect system data, JASP 0.18 for descriptive statistics and paired-samples t tests, and StatKey to examine mean differences. Two application stacks were evaluated: DVWA (PHP/MariaDB) and Acunetix (MySQL). Within the conditions examined […]

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Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic molecules across the entire periodic table, serve as foundational models for quantum-accurate physical simulations. However, uMLIP training requires second-order derivatives, which lack corresponding parallel training frameworks; moreover, scaling to the billion-parameter regime causes explosive growth in computation and communication overhead, making its training a tremendous challenge. We introduce MatRIS-MoE, a billion-parameter Mixture-of-Experts model built upon invariant architecture, and {Janus}, a pioneering high-dimensional […]

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Customer segmentation is a core application of data mining in the retail industry. Traditional K-means clustering is widely adopted here for its simple principle and high computational efficiency, yet it has notable drawbacks: random initial clustering centers easily lead to local optimal solutions, it is highly sensitive to abnormal data, and the cluster number K relies on manual experience, resulting in unstable clustering performance. This paper designs an improved K-means algorithm, which filters outliers through a two-layer mechanism […]

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Behavioral ransomware detection often achieves high accuracy in standard evaluations; however, these results frequently fail to generalize under distribution shifts or when encountering previously unseen families. This study evaluates detection performance on the MLRan dataset (4,880 samples across 64 families) using four rigorous evaluation protocols: stratified, temporal, family-disjoint, and open-set. To ensure a strict separation of learned features, the family-disjoint and open-set splits were executed at the family level. We propose the Hierarchical Sparse Neural Network (HSNN), a […]

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