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I’ve been running a long-term experiment: can a single person, using neural networks, develop and maintain a massive codebase that matches banking solutions in reliability, while shipping features at the speed of a pet project? These are, of course, hardly compatible things, but my goal is to at least walk the path of a startup with 0 employees and tight deadlines. Currently, I’m working on a pet project, called Cynosure — an AI runtime in Golang aimed at […]

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Hi I’ll just leave this here for you guys to check out. It is llama.cpp with PrimeVHT2 integration which is like TurboQuant except it is working and better! reaching the maximum at 0.9987. One is pure llama.cpp with PrimeVHT2 and the other is llama-turbo with PrimeVHT2. PrimeVHT2 is the basis for the unrelease llama.cpp turbo algorithm https://github.com/nihilistau/llama-cpp-vht2 https://github.com/nihilistau/llama-PrimeVHT2 # PrimePE / Position_Is_Arithmetic — Session Context v3 ## Date: April 5, 2026 | Updated: VHT2 banded compression validated + […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## What If Your Perfect Partner Isn’t in Your Country? Inside Dating.com’s 2026 Emotional Intelligence By @socialdiscoverygroup [ 5 Min read ] Discover the top 10 emotionally intelligent countries where singles thrive, and relationships, built on empathy and self-awareness, flourish. Read More. The Kernel Is […]

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Multimodal representation learning is commonly built on a shared-private decomposition, treating latent information as either common to all modalities or specific to one. This binary view is often inadequate: many factors are shared by only subsets of modalities, and ignoring such partial sharing can over-align unrelated signals and obscure complementary information. We propose Hierarchical Contrastive Learning (HCL), a framework that learns globally shared, partially shared, and modality-specific representations within a unified model. HCL combines a hierarchical latent-variable formulation […]

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Obtaining high-quality labels is costly, whereas unlabeled covariates are often abundant, motivating semi-supervised inference methods with reliable uncertainty quantification. Prediction-powered inference (PPI) leverages a machine-learning predictor trained on a small labeled sample to improve efficiency, but it can lose efficiency under model misspecification and suffer from coverage distortions due to label reuse. We introduce Machine-Learning-Assisted Generalized Entropy Calibration (MEC), a cross-fitted, calibration-weighted variant of PPI. MEC improves efficiency by reweighting labeled samples to better align with the target […]

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The integration of real-time web technologies and Generative Artificial Intelligence (AI) into campus infrastructure is creating new paradigms for institutional resource management. In campus dining environments, traditional offline management leads to significant food waste, inefficient resource allocation, and delayed feedback loops. This survey reviews the transition from static Enterprise Resource Planning (ERP) systems to dynamic, AI-optimized ecosystems. We synthesize recent advancements in multiple core areas: (i) frictionless Role-Based Access Control (RBAC) using decentralized access codes, (ii) real-time data […]

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The rapid proliferation of large language model (LLM) powered multi-agent systems creates a non-trivial combinatorial optimization problem: routing heterogeneous tasks to the most cost-effective model tier while maintaining quality guarantees. Current production systems rely on static lookup tables, which over-provision expensive models and waste computational budget. We formalize the LLM Cascade Routing Problem (LCRP) as a Quadratic Unconstrained Binary Optimization (QUBO) problem and solve it using the Quantum Approximate Optimization Algorithm (QAOA). We benchmark QAOA against greedy heuristics […]

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arXiv:2403.11343v4 Announce Type: replace-cross Abstract: Federated learning has emerged as a powerful framework for analysing distributed data, yet two challenges remain pivotal: heterogeneity across sites and privacy of local data. In this paper, we address both challenges within a federated transfer learning framework, aiming to enhance learning on a target data set by leveraging information from multiple heterogeneous source data sets while adhering to privacy constraints. We rigorously formulate the notion of federated differential privacy, which offers privacy […]

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arXiv:2307.09366v2 Announce Type: replace-cross Abstract: We consider the problem of learning a sparse graph underlying an undirected Gaussian graphical model, a key problem in statistical machine learning. Given $n$ samples from a multivariate Gaussian distribution with $p$ variables, the goal is to estimate the $p times p$ inverse covariance matrix (aka precision matrix), assuming it is sparse (i.e., has a few nonzero entries). We propose GraphL0BnB, a new estimator based on an $ell_0$-penalized version of the pseudo-likelihood function, […]

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arXiv:2604.02504v1 Announce Type: new Abstract: Electric utilities must make massive capital investments in the coming years to respond to explosive growth in demand, aging assets and rising threats from extreme weather. Utilities today already have rigorous frameworks for capital planning, and there are opportunities to extend this capability to solve multi-objective optimization problems in the face of uncertainty. This work presents a four-part framework that 1) incorporates extreme weather as a source of uncertainty, 2) leverages a digital […]

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