February 2026

Federated Learning (FL) enables distributed model training on edge devices while preserving data privacy. However, clients tend to have non-Independent and Identically Distributed (non-IID) data, which often leads to client-drift, and therefore diminishing convergence speed and model performance. While adaptive optimizers have been proposed to mitigate these effects, they frequently introduce computational complexity or communication overhead unsuitable for resource-constrained IoT environments. This paper introduces Federated Zero Mean Gradients (FedZMG), a novel, parameter-free, client-side optimization algorithm designed to tackle […]

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There’s a lot of focus right now on whether AI can write “perfect” code and what this will mean. As models will get better and context windows get bigger, will code quality improve? Will we soon reach a point where AI produces production-ready software on the first try? If the answer is “Yes, AI can get it right first time”, we should be focused on giving AI the perfect context, all our rules and standards, doing upfront planning on requirements, specifications […]

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Quantum reservoir computers (QRCs) have emerged as a promising approach to quantum machine learning, since they utilize the natural dynamics of quantum systems for data processing and are simple to train. Here, we consider n-qubit quantum extreme learning machines (QELMs) with continuous-time reservoir dynamics. QELMs are memoryless QRCs capable of various ML tasks, including image classification and time series forecasting. We apply the Pauli transfer matrix (PTM) formalism to theoretically analyze the influence of encoding, reservoir dynamics, and […]

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The Invisible Waste of the Centralized Cloud As we head into 2026, a staggering and largely ignored crisis is unfolding in the world of technology: the massive waste inherent in centralized cloud infrastructure. Enterprises are currently wasting upwards of $44 billion on cloud spending due to a legacy model of over-provisioning, where organizations pay for peak capacity that they rarely use. This waste isn’t just a financial burden; it’s a resource crisis. Centralized data centers consume vast amounts […]

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ggml.ai joins Hugging Face to ensure the long-term progress of Local AI I don’t normally cover acquisition news like this, but I have some thoughts. It’s hard to overstate the impact Georgi Gerganov has had on the local model space. Back in March 2023 his release of llama.cpp made it possible to run a local LLM on consumer hardware. The original README said: The main goal is to run the model using 4-bit quantization on a MacBook. […] […]

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