January 2026

Federated Learning (FL) enables collaborative model training across decentralized devices while preserving data privacy. However, real-world FL deployments face critical challenges such as data imbalances, including label noise and non-IID distributions. RHFL+, a state-of-the-art method, was proposed to address these challenges in settings with heterogeneous client models. This work investigates the robustness of RHFL+ under class imbalances through three key contributions: (1) reproduction of RHFL+ along with all benchmark algorithms under a unified evaluation framework; (2) extension of […]

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Processing video is one of the heaviest tasks a web application can handle. If you are still running shell_exec(‘ffmpeg …’) inside a Controller, you are likely blocking your PHP-FPM threads, frustrating your users, and risking timeouts. In 2026, we don’t do that. We treat video processing as an asynchronous, distributed pipeline. With the release of Symfony 7.4, we have new tools that make this robust and native. We now have a dedicated #[Video] validation constraint, native support for shared directories, and the mature Messenger component to […]

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Introduction: The Illusion of Simplicity You probably type go build or go run dozens of times every week without thinking much about what happens under the hood. On the surface, these commands feel almost magical: you press Enter, and suddenly your code is compiled, linked, and – sometimes – executed. But beneath that simplicity lies a carefully orchestrated system, optimized to make your life as a developer easier while also being fast and predictable for machines. Understanding how […]

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Introduction Writing with AI assistance is not the same as writing by AI. When you write directly with AI, you’re prompting it to generate content from scratch. When you write with AI assistance, you remain the author—AI simply helps refine your work rather than doing most of it for you. The Problem with Full AI Generation Yes, you can create a story from scratch using AI prompts, but the result will likely be short and won’t capture exactly […]

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We propose DeePM (Deep Portfolio Manager), a structured deep-learning macro portfolio manager trained end-to-end to maximize a robust, risk-adjusted utility. DeePM addresses three fundamental challenges in financial learning: (1) it resolves the asynchronous "ragged filtration" problem via a Directed Delay (Causal Sieve) mechanism that prioritizes causal impulse-response learning over information freshness; (2) it combats low signal-to-noise ratios via a Macroeconomic Graph Prior, regularizing cross-asset dependence according to economic first principles; and (3) it optimizes a distributionally robust objective […]

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