How are you, hacker? 🪐 What’s happening in tech today, April 4, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Martin Luther King Assassination in 1968, Microsoft is founded in 1975, Apollo 6 launched in 1968, and we present you with these top quality stories. Want to Have Successful OpenTelemetry Projects? Implement This One Tip By @nfrankel [ 4 Min read ] In this post, I want to tackle a real-world […]

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Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]

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Massera and Schaffer [Ann. Math. (2), 1958] established a breakthrough upper bound for the Clarkson angle between two nonzero vectors in a normed linear space. Maligranda [Am. Math. Mon., 2006] improved Massera-Schaffer upper bound for the Clarkson angle. Pecaric and Rajic [Math. Inequal. Appl., 2007] extended Maligranda’s inequality to finitely many nonzero vectors. We derive a finite field version of Massera-Schaffer-Maligranda-Pecaric-Rajic inequality.

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arXiv:2512.06370v2 Announce Type: replace-cross Abstract: We lay the theoretical foundation for automating optimizer design in gradient-based learning. Based on the greedy principle, we formulate the problem of designing optimizers and their hyperparameters as maximizing the instantaneous decrease in loss. By treating an optimizer as a function that translates loss gradient signals into parameter motions, the problem reduces to a family of convex optimization problems over the space of optimizers. Solving these problems under various constraints not only recovers […]

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Systems don’t fail because they are poorly written; they fail because they cannot keep up with the complex and ever-changing needs of a business. When speaking with Ignacio Brasca, who is a staff software engineer with several years of experience building complex data systems, he emphasized that software behaves much like a biological system—and that the entropy involved in making it grow cannot be underestimated. As organizations and systems evolve together, developers must plan for long-term change from […]

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In a simple gridworld example, everything stays the same except the discount factor γ. Reward for boundary/forbidden: -1 Reward for target: +1 Only γ changes Case 1: γ = 0.9 The agent is long-term oriented. Future rewards are discounted slowly: γ⁵ ≈ 0.59 So even if the agent takes a -1 penalty now (entering a forbidden area), the future reward is still valuable enough to justify it. Result: The optimal policy is willing to take short-term losses to […]

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Deep learning networks excel at classification, yet identifying minimal architectures that reliably solve a task remains challenging. We present a computational methodology for systematically exploring and analyzing the relationships among convergence, pruning, and quantization. The workflow first performs a structured design sweep across a large set of architectures, then evaluates convergence behavior, pruning sensitivity, and quantization robustness on representative models. Focusing on well-known image classification of increasing complexity, and across Deep Neural Networks, Convolutional Neural Networks, and Vision […]

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arXiv:2601.18998v1 Announce Type: new Abstract: The rapid evolution of large language models (LLMs) has fuelled enthusiasm about their role in advancing scientific discovery, with studies exploring LLMs that autonomously generate and evaluate novel research ideas. However, little attention has been given to the possibility that such models could be exploited to produce harmful research by repurposing open science artefacts for malicious ends. We fill the gap by introducing an end-to-end pipeline that first bypasses LLM safeguards through persuasion-based […]

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