January 2026

Our treasured language lets us leverage powerful tools and guard us from countless implementation details. Although exceptions have a bad name among many developers, a detailed analysis of how they work can greatly expand your understanding of how C++ really works. That’s what we’ll do! Before diving into the depths of exceptions let pick a right angle to how they could possibly work. In general, there are two options: the landing pad or via the frame handler. Sorry, […]

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“How can AI work to shape a future worth living around the world?” A 2025 – 2026 review through the lens of Knowledge Management, Graphs, Communities, Ontology, Connected Thinking and Pragmatic AI. When OpenAI CEO Sam Altman admitted we’re in an AI bubble in August 2025, I wasn’t surprised. I’d been watching the cracks form for months from my seat running graph technology events, teaching AI courses, and consulting on knowledge management across Europe and beyond. Some people […]

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Ransomware is evolving from “lock your files and demand payment” to “steal everything and threaten to leak it.” About 50% of attacks now skip encryption entirely. Payments are declining, but the damage is worse than ever. Welcome to 2026. The Old Playbook Is Dead Remember when ransomware was simple? Attackers encrypt your files, you pay up (or don’t), life moves on. Those days are over. In 2025, something shifted. Groups like Cl0p stopped bothering with encryption altogether. Why […]

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Prompt injection. The bane of every developer building with LLMs. It’s not a bug in your code. It’s a sophisticated attack that exploits the very nature of how LLMs interpret and prioritize natural language instructions. If you’re still relying on keyword blacklists and static filters, you’re already losing the battle. Attackers aren’t just trying to override instructions anymore. They’re subtly redirecting models, influencing multi-step interactions, and generally making a mess of your carefully crafted AI applications. This isn’t […]

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Note: This is a short edition. Based on the full paper published December 28, 2025: The Augmented Self: AI Scaffolds, Offloading, and the Drift Toward Dependency A subtle change is underway in how knowledge work begins. More and more, the first coherent version of a thought arrives already shaped—quickly, fluently, and with plausible next steps attached. This can feel like simple convenience. But when the starting point changes, the rest of the workflow changes with it: what gets practiced, what […]

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arXiv:2601.06296v2 Announce Type: replace-cross Abstract: A targeted learning (TL) framework is developed to estimate the difference in the restricted mean survival time (RMST) for a clinical trial with time-to-event outcomes. The approach starts by defining the target estimand as the RMST difference between investigational and control treatments. Next, an efficient estimation method is introduced: a targeted minimum loss estimator (TMLE) utilizing pseudo-observations. Moreover, a version of the copy reference (CR) approach is developed to perform a sensitivity analysis […]

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arXiv:2512.24145v2 Announce Type: replace-cross Abstract: Machine learning systems appear stochastic but are deterministically random, as seeded pseudorandom number generators produce identical realisations across repeated executions. Standard evaluation practice typically treats runs across alternatives as independent and does not exploit shared sources of randomness. This paper analyses the statistical structure of comparative evaluation under shared random seeds. Under this design, competing systems are evaluated using identical seeds, inducing matched stochastic realisations and yielding strict variance reduction whenever outcomes are […]

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arXiv:2512.13642v2 Announce Type: replace-cross Abstract: Model combination is a powerful approach for achieving superior performance compared to selecting a single model. We study both theoretically and empirically the effectiveness of ensembles of Multi-Frequency Echo State Networks (MFESNs), which have been shown to achieve state-of-the-art macroeconomic time series forecasting results (Ballarin et al., 2024a). The Hedge and Follow-the-Leader schemes are discussed, and their online learning guarantees are extended to settings with dependent data. In empirical applications, the proposed Ensemble […]

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