February 2026

Hierarchical Reinforcement Learning (HRL) frameworks like Option-Critic (OC) and Multi-updates Option Critic (MOC) have introduced significant advancements in learning reusable options. However, these methods underperform in multi-goal environments with sparse rewards, where actions must be linked to temporally distant outcomes. To address this limitation, we first propose MOC-HER, which integrates the Hindsight Experience Replay (HER) mechanism into the MOC framework. By relabeling goals from achieved outcomes, MOC-HER can solve sparse reward environments that are intractable for the original […]

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The release of Symfony 7.4 LTS marks a pivotal moment in the PHP ecosystem. While the framework itself introduces robust features like stricter type safety and native PHP serialization for containers, the real revolution is happening in how we serve these applications.Gone are the days when NGINX + PHP-FPM was the undisputed default. Application servers written in Go have matured, offering “Worker Mode” capabilities that boot your kernel once and keep it in memory, slashing latency and skyrocketing throughput. In […]

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Dear Engineer, Software should not evolve in the blink of an eye; it should be a careful, methodical beast that steadily improves and makes incremental progress, with fast shifts only when necessary to benefit the user. I say this not to justify bureaucratic procedure, nor to suggest that implementation should take longer than necessary, and certainly not to argue against adopting genuine advancements, but to argue for the importance of timelessness both in the solution and the problem […]

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Published on February 10, 2026 5:59 PM GMT Cross-posted from Telescopic Turnip Recommended soundtrack for this post As we all know, the march of technological progress is best summarized by this meme from Linkedin: Inventors constantly come up with exciting new inventions, each of them with the potential to change everything forever. But only a fraction of these ever establish themselves as a persistent part of civilization, and the rest vanish from collective consciousness. Before shutting down forever, […]

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For a long time, growth hacking felt like a cheat code. A clever funnel. n A loophole in an algorithm. n A viral trick no one else had noticed yet. If you were early enough, fast enough, or lucky enough, growth followed. But if you’re being honest, you already know the truth: Growth hacking no longer delivers repeatable results. Not because marketers got lazy. Not because audiences disappeared. n But because the environment fundamentally changed. Platforms matured. Algorithms […]

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How are you, hacker? 🪐 What’s happening in tech today, February 14, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you with these top quality stories. Courts Are Drowning in Cases. Can AI Save the Day Without Becoming a Liability? By @150sec [ 4 Min read ] As courts adopt AI to ease backlogs, experts warn of bias, opacity, and threats to judicial independence. Can efficiency coexist with due […]

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The Rust team has published a new point release of Rust, 1.77.1. Rust is a programming language that is empowering everyone to build reliable and efficient software. If you have a previous version of Rust installed via rustup, getting Rust 1.77.1 is as easy as: rustup update stable If you don’t have it already, you can get rustup from the appropriate page on our website. What’s in 1.77.1 Cargo enabled stripping of debuginfo in release builds by default […]

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Time series forecasting has long been dominated by model-centric approaches that formulate prediction as a single-pass mapping from historical observations to future values. Despite recent progress, such formulations often struggle in complex and evolving settings, largely because most forecasting models lack the ability to autonomously acquire informative evidence, reason about potential future changes, or revise predictions through iterative decision processes. In this work, we propose Cast-R1, a learned time series forecasting framework that reformulates forecasting as a sequential […]

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Large Language Models (LLMs) have demonstrated strong semantic reasoning across multimodal domains. However, their integration with graph-based models of brain connectivity remains limited. In addition, most existing fMRI analysis methods rely on static Functional Connectivity (FC) representations, which obscure transient neural dynamics critical for neurodevelopmental disorders such as autism. Recent state-space approaches, including Mamba, model temporal structure efficiently, but are typically used as standalone feature extractors without explicit high-level reasoning. We propose NeuroMambaLLM, an end-to-end framework that integrates […]

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