January 2026

How are you, hacker? 🪐 What’s happening in tech today, January 17, 2026? The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Persian Gulf War began in 1991, Popeye the Sailor made his first appearance in 1929, Google Videos launched in 2006, and we present you with these top quality stories. 680 Hours, 4 Rebuilds, and Getting Fired: How I Built Software While Working Warehouse Shifts By @huckler [ 4 Min read ] […]

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Spatio-Temporal (ST) Foundation Models (STFMs) promise cross-dataset generalization, yet joint ST pretraining is computationally expensive and grapples with the heterogeneity of domain-specific spatial patterns. Substantially extending our preliminary conference version, we present FactoST-v2, an enhanced factorized framework redesigned for full weight transfer and arbitrary-length generalization. FactoST-v2 decouples universal temporal learning from domain-specific spatial adaptation. The first stage pretrains a minimalist encoder-only backbone using randomized sequence masking to capture invariant temporal dynamics, enabling probabilistic quantile prediction across variable horizons. […]

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Many companies rely on external services to keep their operations running smoothly. However, while third-party vendors help power systems and support day-to-day operations, each new integration adds a potential access point that attackers can target. In 2026, third-party risk influences the speed at which incidents spread, the effectiveness of compliance, and the rate at which teams can recover. To prepare for what lies ahead, it is helpful to understand the current risks and know the steps IT teams […]

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Ive been trying to train a quadruped bot using reinforcement learning, mostly tryna teach it to trot and stabilize by itself. Ive tried different policies like PPO, RecurrentPPO and SAC but the results have been disappointing. Im mainly having trouble creating a proper reward function which focuses on stability and trotting. Im fairly new to RL so im looking for some feedback here. submitted by /u/iz_bleep [link] [comments]

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People can use generative AI to create art, text, and music from datasets of previous art, which is significantly impacting the current creative economy. The debate of what makes an artist and the lack of clear compensation are growing concerns, prompting the evolving issue of royalty battles over AI-generated work. The Evolution of Royalty Battles AI is changing royalty battles to a debate about what makes art original. The traditional notions of authorship and ownership are being abandoned, […]

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Ensuring safety is a critical challenge in applying Reinforcement Learning (RL) to real-world scenarios. Constrained Reinforcement Learning (CRL) addresses this by maximizing returns under predefined constraints, typically formulated as the expected cumulative cost. However, expectation-based constraints overlook rare but high-impact extreme value events in the tail distribution, such as black swan incidents, which can lead to severe constraint violations. To address this issue, we propose the Extreme Value policy Optimization (EVO) algorithm, leveraging Extreme Value Theory (EVT) to […]

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The rapid integration of AI algorithms in safety-critical applications such as autonomous driving and healthcare is raising significant concerns about the ability to meet stringent safety standards. Traditional tools for formal safety verification struggle with the black-box nature of AI-driven systems and lack the flexibility needed to scale to the complexity of real-world applications. In this paper, we present a data-driven approach for safety verification and synthesis of black-box systems with discrete-time stochastic dynamics. We employ the concept […]

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Learning robust audio-visual embeddings requires bringing genuinely related audio and visual signals together while filtering out incidental co-occurrences – background noise, unrelated elements, or unannotated events. Most contrastive and triplet-loss methods use sparse annotated labels per clip and treat any co-occurrence as semantic similarity. For example, a video labeled "train" might also contain motorcycle audio and visual, because "motorcycle" is not the chosen annotation; standard methods treat these co-occurrences as negatives to true motorcycle anchors elsewhere, creating false […]

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