February 2026

Welcome to HackerNoon’s Meet the Writer Interview series, where we learn a bit more about the contributors that have written some of our favorite stories. So let’s start! Tell us a bit about yourself. For example, name, profession, and personal interests. I am Yash, currently an undergrad at the Indian Institute of Technology Roorkee, studying Materials Engineering. Also, I am a part of the Blockchain Society of IIT Roorkee. I am more into developing on blockchain, but recently […]

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Imagine telling Claude: “Reply ‘Running 10 minutes late’ to Sarah and attach that funny cat meme I just uploaded.” Seconds later — done. No switching apps. No copy-pasting. Everything stays local, private, and scripted through natural language. That’s exactly what whatsapp-mcp-go enables. This lightweight, pure-Go project bridges your personal WhatsApp account to Claude Desktop (or Cursor) via the Model Context Protocol (MCP). Claude can read chats, search contacts, send messages (text, images, voice notes), and download media — […]

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Multi-Asset Momentum: Diversification Drives Activity on SIFX Global retail trading activity continues to evolve, with traders increasingly shifting from single-market exposure toward diversified, cross-asset strategies. In this environment, SIFX has reported measurable growth in multi-asset trading activity across its platform, reflecting broader market trends seen throughout 2026. According to platform data, users are engaging more actively across forex, commodities, cryptocurrencies, and equity-linked CFDs rather than concentrating on a single asset class. This diversification pattern suggests a maturing user […]

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AI in local government often fails because it’s used as a passive FAQ bot. Drawing on global methodologies from Dubai’s One Million Prompters initiative, this article outlines a ‘Civic AI Framework.’ By mastering prompt engineering, residents can use agentic AI like Readyly to audit local data, propose community solutions, and turn digital transparency into real-world action

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Despite the success of machine learning (ML) in surrogate modeling, its use in radiation dose assessment is limited by safety-critical constraints, scarce training-ready data, and challenges in selecting suitable architectures for physics-dominated systems. Within this context, rapid and accurate plume shine dose estimation serves as a practical test case, as it is critical for nuclear facility safety assessment and radiological emergency response, while conventional photon-transport-based calculations remain computationally expensive. In this work, an interpolation-assisted ML framework was developed […]

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Extensive research demonstrates that Deep Reinforcement Learning (DRL) models are susceptible to adversarially constructed inputs (i.e., adversarial examples), which can mislead the agent to take suboptimal or unsafe actions. Recent methods improve attack effectiveness by leveraging future rewards to guide adversarial perturbation generation over sequential time steps (i.e., reward-based attacks). However, these methods are unable to capture dependencies between different time steps in the perturbation generation process, resulting in a weak temporal correlation between the current perturbation and […]

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Imagine: You are standing in the checkout line with your credit card in hand after buying groceries and the terminal flashes red and says “Card declined”. You try again and still the same result. You are embarrassed at this point, so you pull out another card. You know you have got plenty of money in your account, but an AI agent somewhere thinks otherwise. Or worse, it thinks you are doing fraud, so it didn’t approve any of […]

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Active data acquisition is central to many learning and optimization tasks in deep neural networks, yet remains challenging because most approaches rely on predictive uncertainty estimates that are difficult to obtain reliably. To this end, we propose Goal-Oriented Influence- Maximizing Data Acquisition (GOIMDA), an active acquisition algorithm that avoids explicit posterior inference while remaining uncertainty-aware through inverse curvature. GOIMDA selects inputs by maximizing their expected influence on a user-specified goal functional, such as test loss, predictive entropy, or […]

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