February 2026

Author(s): Gaurav Shrivastav Originally published on Towards AI. A practical guide to understanding and implementing model specialization for real-world applications Last month, I helped a startup replace their GPT-4-powered customer service system with a fine-tuned 7B parameter model. The results were surprising: 15% better accuracy, 95% lower costs, and zero API dependencies. More importantly, the smaller model understood their specific business context in ways GPT-4 never could. Photo by Hitesh Choudhary on UnsplashThis article discusses the advantages of […]

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Every technology that changed the world did so by disappearing. Nobody thinks about the electrical grid when they turn on a light. Nobody thinks about SMTP when they send an email. The technology became so reliable, so embedded in the products built on top of it, that it stopped being something people noticed. That’s where crypto is heading. And the projects that understand this will define the next decade of this industry. Crypto’s infrastructure has gotten remarkably good, […]

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As institutional blockchain adoption hits a $933M market cap, discover why Ethereum, Hedera, and RYT have moved from pilot projects to the core of national digital identity and CBDC ecosystems. The pivot from speculative madness to sovereign infrastructure is no longer a projection; it is a line item in national budgets. While retail traders spent years chasing animal-themed tokens, the public sector quietly began rebuilding the foundations of civic trust. By 2026, the global blockchain government market is […]

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Parameter sharing is a key strategy in multi-agent reinforcement learning (MARL) for improving scalability, yet conventional fully shared architectures often collapse into homogeneous behaviors. Recent methods introduce diversity through clustering, pruning, or masking, but typically compromise resource efficiency. We propose Prism, a parameter sharing framework that induces inter-agent diversity by representing shared networks in the spectral domain via singular value decomposition (SVD). All agents share the singular vector directions while learning distinct spectral masks on singular values. This […]

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By embedding Agentic AI across billing and payment domains and integrating it with CRM platforms, telecom operators can shift call centers from investigation-heavy support to AI-assisted, first-call resolution—reducing AHT by up to 50% while improving customer trust and revenue accuracy. As our telecom services become more real-time, digital, and partner-driven, our traditional billing and revenue systems are struggling to keep up. Agentic AI allows us to move away from reactive, manual revenue operations toward intelligent, self-optimizing revenue platforms. […]

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With experience I can say that all the AI usage in Test Automation relies mostly on the Machine Learning techniques and prediction based analytics to identify bugs or security risks, experience from the previous test runs to improve the problem detection and even generate test scenarios automatically. Here I will try to simplify it further so that maximum Testers can understand the enhancements AI can offer in Automation and implement it in real time scenarios. AI implementation in […]

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For ethical and safe AI, machine unlearning rises as a critical topic aiming to protect sensitive, private, and copyrighted knowledge from misuse. To achieve this goal, it is common to conduct gradient ascent (GA) to reverse the training on undesired data. However, such a reversal is prone to catastrophic collapse, which leads to serious performance degradation in general tasks. As a solution, we propose model extrapolation as an alternative to GA, which reaches the counterpart direction in the […]

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How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## The Hidden Problem With Group Rewards in Multi-Agent AI By @praveenmyakala [ 3 Min read ] Group rewards are breaking your multi-agent RL training. Decoupled normalization keeps coordination intact while stopping gradient collapse. Read More. Architecting Trustworthy Healthcare Data Platforms Using Declarative Pipelines By […]

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