March 2026

Off-road autonomous driving poses significant challenges such as navigating unmapped, variable terrain with uncertain and diverse dynamics. Addressing these challenges requires effective long-horizon planning and adaptable control. Reinforcement Learning (RL) offers a promising solution by learning control policies directly from interaction. However, because off-road driving is a long-horizon task with low-signal rewards, standard RL methods are challenging to apply in this setting. We introduce TADPO, a novel policy gradient formulation that extends Proximal Policy Optimization (PPO), leveraging off-policy […]

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The compliance industrial complex is selling a new product. Having peddled GDPR checklists and SOC2 reports for years, the same vendors are offering AI governance frameworks with the promises of making your neural networks ethical, safe, and compliant. While the policies sound reasonable, with impressive documentation and an expensive consultant, none of it is stopping breaches. As a cybersecurity ghostwriter who has collaborated with several Silicon Valley Startups and tech founders in the APAC region, I’ve watched hundreds […]

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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. ## 6 Ways to Use a Crypto Exchange Aggregator and Save on Swaps By @swapzone [ 7 Min read ] Maximize your crypto swaps! Learn 6 ways an exchange aggregator saves you money. Find the best rates on Bitcoin, Ethereum, & other cryptocurrencies on DEXs. […]

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Agentic Engineering Patterns > The defining characteristic of a coding agent is that it can execute the code that it writes. This is what makes coding agents so much more useful than LLMs that simply spit out code without any way to verify it. Never assume that code generated by an LLM works until that code has been executed. Coding agents have the ability to confirm that the code they have produced works as intended, or iterate further […]

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Modern neural network optimization relies heavily on architectural priorssuch as Batch Normalization and Residual connectionsto stabilize training dynamics. Without these, or in low-data regimes with aggressive augmentation, low-bias architectures like Vision Transformers (ViTs) often suffer from optimization collapse. This work adopts Sketched Isotropic Gaussian Regularization (SIGReg), recently introduced in the LeJEPA self-supervised framework, and repurposes it as a general optimization stabilizer for supervised learning. While the original formulation targets the full characteristic function, a computationally efficient variant is […]

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Congratulations. You’ve just walked into the only place on the internet where smart people finally get told the truth: Your brain is elite. Your life… isn’t matching it yet. Let’s fix that. 1. The Trap: You don’t think too much; you stimulate too much. High-IQ brains have a gift that becomes a curse: You can imagine 10,000 possibilities in 0.2 seconds. The problem is that your brain treats imagined danger like real danger. Enter the DMN: Default Mode Network: n This is your […]

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Search is shifting from link-based results to AI-generated answers. Modern AI search engines analyze intent, retrieve information through semantic retrieval, and generate responses using multiple sources. This article explains how AI search algorithms evaluate authority, credibility, and content depth-and why writers, developers, and marketers must rethink SEO strategies to remain visible in the age of generative search.

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Back in my student years, I had a book by a rather famous Dane about a rather famous programming language. And although I harbored genuine warm feelings for C++, I couldn’t get through even half of that book—the reading was that dreary. Well, it didn’t stop me from continuing to believe in the destiny fate had in store for me and C++. So, in the summer of 2011, I went to interview for a C++ developer. This is […]

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arXiv:2602.16537v2 Announce Type: replace-cross Abstract: We study online conformal prediction for non-stationary data streams subject to unknown distribution drift. While most prior work studied this problem under adversarial settings and/or assessed performance in terms of gaps of time-averaged marginal coverage, we instead evaluate performance through training-conditional cumulative regret. We specifically focus on independently generated data with two types of distribution shift: abrupt change points and smooth drift. When non-conformity score functions are pretrained on an independent dataset, we […]

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