{"date":"2026-02-16","schema_version":1,"captured_at":"2026-08-25T23:28:33.149Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":16},"attention":[{"title":"Jeffrey Epstein","views":290203,"rank":4,"extract":"Jeffrey Edward Epstein was an American financier and child sex offender. He began his career as a math teacher at the Dalton School in New York City, before entering the banking and finance sector. 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Devs hate it","points":398,"comments":233,"url":"https://www.theregister.com/2026/02/16/anthropic_claude_ai_edits/","discussion":"https://news.ycombinator.com/item?id=47033622"},{"title":"UK Discord users were part of a Peter Thiel-linked data collection experiment","points":379,"comments":129,"url":"https://www.rockpapershotgun.com/good-news-uk-discord-users-were-part-of-a-peter-thiel-linked-data-collection-experiment","discussion":"https://news.ycombinator.com/item?id=47035679"},{"title":"Thanks a lot, AI: Hard drives are sold out for the year, says WD","points":377,"comments":317,"url":"https://mashable.com/article/ai-hard-drive-hdd-shortages-western-digital-sold-out","discussion":"https://news.ycombinator.com/item?id=47034192"},{"title":"SkillsBench: Benchmarking how well agent skills work across diverse tasks","points":364,"comments":171,"url":"https://arxiv.org/abs/2602.12670","discussion":"https://news.ycombinator.com/item?id=47040430"},{"title":"Privilege is bad grammar","points":337,"comments":279,"url":"https://tadaima.bearblog.dev/privilege-is-bad-grammar/","discussion":"https://news.ycombinator.com/item?id=47038125"},{"title":"Use protocols, not services","points":314,"comments":128,"url":"https://notnotp.com/notes/use-protocols-not-services/","discussion":"https://news.ycombinator.com/item?id=47038588"},{"title":"The Israeli spyware firm that accidentally just exposed itself","points":288,"comments":336,"url":"https://ahmedeldin.substack.com/p/the-israeli-spyware-firm-that-accidentally","discussion":"https://news.ycombinator.com/item?id=47033976"},{"title":"Show HN: Free alternative to Wispr Flow, Superwhisper, and Monologue","points":277,"comments":132,"url":"https://github.com/zachlatta/freeflow","discussion":"https://news.ycombinator.com/item?id=47040375"},{"title":"\"Token anxiety\", a slot machine by any other name","points":265,"comments":230,"url":"https://jkap.io/token-anxiety-or-a-slot-machine-by-any-other-name/","discussion":"https://news.ycombinator.com/item?id=47038318"},{"title":"A beginner's guide to split keyboards","points":263,"comments":254,"url":"https://www.justinmklam.com/posts/2026/02/beginners-guide-split-keyboards/","discussion":"https://news.ycombinator.com/item?id=47029368"},{"title":"Defer available in gcc and clang","points":263,"comments":251,"url":"https://gustedt.wordpress.com/2026/02/15/defer-available-in-gcc-and-clang/","discussion":"https://news.ycombinator.com/item?id=47031197"},{"title":"Running My Own XMPP Server","points":236,"comments":144,"url":"https://blog.dmcc.io/journal/xmpp-turn-stun-coturn-prosody/","discussion":"https://news.ycombinator.com/item?id=47034801"},{"title":"Evaluating AGENTS.md: are they helpful for coding agents?","points":232,"comments":161,"url":"https://arxiv.org/abs/2602.11988","discussion":"https://news.ycombinator.com/item?id=47034087"},{"title":"Don't Trust the Salt: AI Summarization, Multilingual Safety, and LLM Guardrails","points":225,"comments":88,"url":"https://royapakzad.substack.com/p/multilingual-llm-evaluation-to-guardrails","discussion":"https://news.ycombinator.com/item?id=47038032"},{"title":"H-1B Exposed: Banking sector visa sponsorship investigation","points":184,"comments":100,"url":"https://www.h1bexposed.tech/","discussion":"https://news.ycombinator.com/item?id=47041634"},{"title":"JavaScript-heavy approaches are not compatible with long-term performance goals","points":178,"comments":234,"url":"https://sgom.es/posts/2026-02-13-js-heavy-approaches-are-not-compatible-with-long-term-performance-goals/","discussion":"https://news.ycombinator.com/item?id=47029339"},{"title":"Show HN: Glitchy camera – a circuit-bent camera simulator in the browser","points":173,"comments":22,"url":"https://glitchycam.com","discussion":"https://news.ycombinator.com/item?id=47033954"},{"title":"I guess I kinda get why people hate AI","points":164,"comments":259,"url":"https://anthony.noided.media/blog/ai/programming/2026/02/14/i-guess-i-kinda-get-why-people-hate-ai.html","discussion":"https://news.ycombinator.com/item?id=47037628"}],"research":[{"title":"DART: aDaptive Accept RejecT for non-linear top-K subset identification","abstract":"We consider the bandit problem of selecting $K$ out of $N$ arms at each time step. The reward can be a non-linear function of the rewards of the selected individual arms. The direct use of a multi-armed bandit algorithm requires choosing among $\\binom{N}{K}$ options, making the action space large. To simplify the problem, existing works on combinatorial bandits {typically} assume feedback as a linear function of indi","authors":["Agarwal, Mridul","Aggarwal, Vaneet","Quinn, Christopher J.","Umrawal, Abhishek"],"url":"https://arxiv.org/abs/2011.07687","arxiv_id":"2011.07687","published":"2026-02-16"},{"title":"AutoLL: Automatic Linear Layout of Graphs based on Deep Neural Network","abstract":"Linear layouts are a graph visualization method that can be used to capture an entry pattern in an adjacency matrix of a given graph. By reordering the node indices of the original adjacency matrix, linear layouts provide knowledge of latent graph structures. Conventional linear layout methods commonly aim to find an optimal reordering solution based on predefined features of a given matrix and loss function. However","authors":["Watanabe, Chihiro","Suzuki, Taiji"],"url":"https://arxiv.org/abs/2108.02431","arxiv_id":"2108.02431","published":"2026-02-16"},{"title":"Addressing Data Distribution Shifts in Online Machine Learning Powered Smart City Applications Using Augmented Test-Time Adaptation","abstract":"Data distribution shift is a common problem in machine learning-powered smart city applications where the test data differs from the training data. Augmenting smart city applications with online machine learning models can handle this issue at test time, albeit with high cost and unreliable performance. To overcome this limitation, we propose to endow test-time adaptation with a systematic active fine-tuning (SAF) la","authors":["Al-Maliki, Shawqi","Bouanani, Faissal El","Abdallah, Mohamed","Qadir, Junaid","Al-Fuqaha, Ala"],"url":"https://arxiv.org/abs/2211.01315","arxiv_id":"2211.01315","published":"2026-02-16"},{"title":"Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA) for Remote Land-use Change Detection","abstract":"Land-use monitoring is fundamental for spatial planning, particularly in view of compound impacts of growing global populations and climate change. Despite existing applications of deep learning in land use monitoring, standard convolutional kernels in deep neural networks limit the applications of these networks to the Euclidean domain only. Considering the geodesic nature of the measurement of the earth's surfa","authors":["Nazir, Usman","Islam, Wadood","Khalid, Sara","Taj, Murtaza"],"url":"https://arxiv.org/abs/2303.14322","arxiv_id":"2303.14322","published":"2026-02-16"},{"title":"The Hard-Constraint PINNs for Interface Optimal Control Problems","abstract":"We show that the physics-informed neural networks (PINNs), in combination with some recently developed discontinuity capturing neural networks, can be applied to solve optimal control problems subject to partial differential equations (PDEs) with interfaces and some control constraints. The resulting algorithm is mesh-free and scalable to different PDEs, and it ensures the control constraints rigorously. Since the bo","authors":["Lai, Ming-Chih","Song, Yongcun","Yuan, Xiaoming","Yue, Hangrui","Zeng, Tianyou"],"url":"https://arxiv.org/abs/2308.06709","arxiv_id":"2308.06709","published":"2026-02-16"},{"title":"Online Tensor Inference","abstract":"Contemporary applications, such as recommendation systems and mobile health monitoring, require real-time processing and analysis of sequentially arriving high-dimensional tensor data. Traditional offline learning, involving the storage and utilization of all data in each computational iteration, becomes impractical for these tasks. Furthermore, existing low-rank tensor methods lack the capability for online statisti","authors":["Wen, Xin","Sun, Will Wei","Zhang, Yichen"],"url":"https://arxiv.org/abs/2312.17111","arxiv_id":"2312.17111","published":"2026-02-16"},{"title":"Sample-Efficient "Clustering and Conquer" Procedures for Parallel Large-Scale Ranking and Selection","abstract":"This work aims to improve the sample efficiency of parallel large-scale ranking and selection (R&S) problems by leveraging correlation information. We modify the commonly used "divide and conquer" framework in parallel computing by adding a correlation-based clustering step, transforming it into "clustering and conquer". Analytical results under a symmetric benchmark scenario show that this seemin","authors":["Zhang, Zishi","Peng, Yijie"],"url":"https://arxiv.org/abs/2402.02196","arxiv_id":"2402.02196","published":"2026-02-16"},{"title":"Multi-Scale Texture Loss for CT denoising with GANs","abstract":"Generative Adversarial Networks (GANs) have proved as a powerful framework for denoising applications in medical imaging. However, GAN-based denoising algorithms still suffer from limitations in capturing complex relationships within the images. In this regard, the loss function plays a crucial role in guiding the image generation process, encompassing how much a synthetic image differs from a real image. To grasp hi","authors":["Di Feola, Francesco","Tronchin, Lorenzo","Guarrasi, Valerio","Soda, Paolo"],"url":"https://arxiv.org/abs/2403.16640","arxiv_id":"2403.16640","published":"2026-02-16"},{"title":"CATP: Cross-Attention Token Pruning for Accuracy Preserved Multimodal Model Inference","abstract":"In response to the rising interest in large multimodal models, we introduce Cross-Attention Token Pruning (CATP), a precision-focused token pruning method. Our approach leverages cross-attention layers in multimodal models, exemplified by BLIP-2, to extract valuable information for token importance determination. CATP employs a refined voting strategy across model heads and layers. In evaluations, CATP achieves up to","authors":["Liao, Ruqi","Zhao, Chuqing","Li, Jin","Feng, Weiqi","Lyu, Yi","Chen, Bingxian"],"url":"https://arxiv.org/abs/2404.08567","arxiv_id":"2404.08567","published":"2026-02-16"},{"title":"Low-Rank Online Dynamic Assortment with Dual Contextual Information","abstract":"As e-commerce expands, delivering real-time personalized recommendations from vast catalogs poses a critical challenge for retail platforms. Maximizing revenue requires careful consideration of both individual customer characteristics and available item features to continuously optimize assortments over time. In this paper, we consider the dynamic assortment problem with dual contexts -- user and item features. In hi","authors":["Lee, Seong Jin","Sun, Will Wei","Liu, Yufeng"],"url":"https://arxiv.org/abs/2404.17592","arxiv_id":"2404.17592","published":"2026-02-16"},{"title":"Multi-Dataset Multi-Task Learning for COVID-19 Prognosis","abstract":"In the fight against the COVID-19 pandemic, leveraging artificial intelligence to predict disease outcomes from chest radiographic images represents a significant scientific aim. The challenge, however, lies in the scarcity of large, labeled datasets with compatible tasks for training deep learning models without leading to overfitting. Addressing this issue, we introduce a novel multi-dataset multi-task training fra","authors":["Ruffini, Filippo","Tronchin, Lorenzo","Wu, Zhuoru","Chen, Wenting","Soda, Paolo","Shen, Linlin"],"url":"https://arxiv.org/abs/2405.13771","arxiv_id":"2405.13771","published":"2026-02-16"},{"title":"Compressible Dynamics in Deep Overparameterized Low-Rank Learning & Adaptation","abstract":"While overparameterization in machine learning models offers great benefits in terms of optimization and generalization, it also leads to increased computational requirements as model sizes grow. In this work, we show that by leveraging the inherent low-dimensional structures of data and compressible dynamics within the model parameters, we can reap the benefits of overparameterization without the computational burde","authors":["Yaras, Can","Wang, Peng","Balzano, Laura","Qu, Qing"],"url":"https://arxiv.org/abs/2406.04112","arxiv_id":"2406.04112","published":"2026-02-16"},{"title":"LTSM-Bundle: A Toolbox and Benchmark on Large Language Models for Time Series Forecasting","abstract":"Time Series Forecasting (TSF) has long been a challenge in time series analysis. Inspired by the success of Large Language Models (LLMs), researchers are now developing Large Time Series Models (LTSMs)-universal transformer-based models that use autoregressive prediction-to improve TSF. However, training LTSMs on heterogeneous time series data poses unique challenges, including diverse frequencies, dimensions, and pa","authors":["Chuang, Yu-Neng","Li, Songchen","Yuan, Jiayi","Wang, Guanchu","Lai, Kwei-Herng","Han, Joshua"],"url":"https://arxiv.org/abs/2406.14045","arxiv_id":"2406.14045","published":"2026-02-16"},{"title":"Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across Heads","abstract":"Vision Transformers and their variants have achieved remarkable success in diverse visual perception tasks. Despite their effectiveness, they suffer from two significant limitations. First, the quadratic computational complexity of multi-head self-attention (MHSA), which restricts scalability to large token counts, and second, a high dependency on large-scale training data to attain competitive performance. In this p","authors":["Rahimian, Ali K.","Govind, Manish K.","Maity, Subhajit","Reilly, Dominick","Kümmerle, Christian","Das, Srijan"],"url":"https://arxiv.org/abs/2406.19391","arxiv_id":"2406.19391","published":"2026-02-16"},{"title":"Continuous-time q-Learning for Jump-Diffusion Models under Tsallis Entropy","abstract":"This paper studies the continuous-time reinforcement learning in jump-diffusion models by featuring the q-learning (the continuous-time counterpart of Q-learning) under Tsallis entropy regularization. Contrary to the Shannon entropy, the general form of Tsallis entropy renders the optimal policy not necessarily a Gibbs measure. Herein, the Lagrange multiplier and KKT condition are needed to ensure that the learned po","authors":["Bo, Lijun","Huang, Yijie","Yu, Xiang","Zhang, Tingting"],"url":"https://arxiv.org/abs/2407.03888","arxiv_id":"2407.03888","published":"2026-02-16"},{"title":"MaskInversion: Localized Embeddings via Optimization of Explainability Maps","abstract":"Vision-language foundation models such as CLIP have achieved tremendous results in global vision-language alignment, but still show some limitations in creating representations for specific image regions. % To address this problem, we propose MaskInversion, a method that leverages the feature representations of pre-trained foundation models, such as CLIP, to generate a context-aware embedding for a query image region","authors":["Bousselham, Walid","Chaybouti, Sofian","Rupprecht, Christian","Ferrari, Vittorio","Kuehne, Hilde"],"url":"https://arxiv.org/abs/2407.20034","arxiv_id":"2407.20034","published":"2026-02-16"},{"title":"Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks","abstract":"Recent advancements in image synthesis, particularly with the advent of GAN and Diffusion models, have amplified public concerns regarding the dissemination of disinformation. To address such concerns, numerous AI-generated Image (AIGI) Detectors have been proposed and achieved promising performance in identifying fake images. However, there still lacks a systematic understanding of the adversarial robustness of AIGI","authors":["Diao, Yunfeng","Zhai, Naixin","Miao, Changtao","Yu, Zitong","Wei, Xingxing","Yang, Xun"],"url":"https://arxiv.org/abs/2407.20836","arxiv_id":"2407.20836","published":"2026-02-16"},{"title":"Generating Physical Dynamics under Priors","abstract":"Generating physically feasible dynamics in a data-driven context is challenging, especially when adhering to physical priors expressed in specific equations or formulas. Existing methodologies often overlook the integration of physical priors, resulting in violation of basic physical laws and suboptimal performance. In this paper, we introduce a novel framework that seamlessly incorporates physical priors into diffus","authors":["Zhou, Zihan","Wang, Xiaoxue","Yu, Tianshu"],"url":"https://arxiv.org/abs/2409.00730","arxiv_id":"2409.00730","published":"2026-02-16"},{"title":"Towards Representation Learning for Weighting Problems in Design-Based Causal Inference","abstract":"Reweighting a distribution to minimize a distance to a target distribution is a powerful and flexible strategy for estimating a wide range of causal effects, but can be challenging in practice because optimal weights typically depend on knowledge of the underlying data generating process. In this paper, we focus on design-based weights, which do not incorporate outcome information; prominent examples include prospect","authors":["Clivio, Oscar","Feller, Avi","Holmes, Chris"],"url":"https://arxiv.org/abs/2409.16407","arxiv_id":"2409.16407","published":"2026-02-16"},{"title":"Minmax Trend Filtering: Generalizations of Total Variation Denoising via a Local Minmax/Maxmin Formula","abstract":"Total Variation Denoising (TVD) is a fundamental denoising and smoothing method. In this article, we identify a new local minmax/maxmin formula producing two estimators which sandwich the univariate TVD estimator at every point. Operationally, this formula gives a local definition of TVD as a minmax/maxmin of a simple function of local averages. Moreover we find that this minmax/maxmin formula is generalizeable and c","authors":["Chatterjee, Sabyasachi"],"url":"https://arxiv.org/abs/2410.03041","arxiv_id":"2410.03041","published":"2026-02-16"},{"title":"Pixel-Based Similarities as an Alternative to Neural Data for Improving Convolutional Neural Network Adversarial Robustness","abstract":"Convolutional Neural Networks (CNNs) excel in many visual tasks but remain susceptible to adversarial attacks-imperceptible perturbations that degrade performance. Prior research reveals that brain-inspired regularizers, derived from neural recordings, can bolster CNN robustness; however, reliance on specialized data limits practical adoption. We revisit a regularizer proposed by Li et al. (2019) that aligns CNN repr","authors":["Attias, Elie","Pehlevan, Cengiz","Obeid, Dina"],"url":"https://arxiv.org/abs/2410.03952","arxiv_id":"2410.03952","published":"2026-02-16"},{"title":"Class Balancing Diversity Multimodal Ensemble for Alzheimer's Disease Diagnosis and Early Detection","abstract":"Alzheimer's disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical for delaying progression and improving patient outcomes. Traditional diagnostic methods and single-modality data often fall short in identifying early-stage AD and distinguishing it from Mild Cognitive Impairment (MCI). This study addr","authors":["Francesconi, Arianna","di Biase, Lazzaro","Cappetta, Donato","Rebecchi, Fabio","Soda, Paolo","Sicilia, Rosa"],"url":"https://arxiv.org/abs/2410.10374","arxiv_id":"2410.10374","published":"2026-02-16"},{"title":"SaVe-TAG: LLM-based Interpolation for Long-Tailed Text-Attributed Graphs","abstract":"Real-world graph data often follows long-tailed distributions, making it difficult for Graph Neural Networks (GNNs) to generalize well across both head and tail classes. Recent advances in Vicinal Risk Minimization (VRM) have shown promise in mitigating class imbalance with numeric interpolation; however, existing approaches largely rely on embedding-space arithmetic, which fails to capture the rich semantics inheren","authors":["Wang, Leyao","Wang, Yu","Ni, Bo","Zhao, Yuying","Wang, Hanyu","Ma, Yao"],"url":"https://arxiv.org/abs/2410.16882","arxiv_id":"2410.16882","published":"2026-02-16"},{"title":"Efficient Feature Aggregation and Scale-Aware Regression for Monocular 3D Object Detection","abstract":"Monocular 3D object detection has attracted great attention due to simplicity and low cost. Existing methods typically follow conventional 2D detection paradigms, first locating object centers and then predicting 3D attributes via neighboring features. However, these methods predominantly rely on progressive cross-scale feature aggregation and focus solely on local information, which may result in a lack of global aw","authors":["Wang, Yifan","Yang, Xiaochen","Pu, Fanqi","Liao, Qingmin","Yang, Wenming"],"url":"https://arxiv.org/abs/2411.02747","arxiv_id":"2411.02747","published":"2026-02-16"},{"title":"Plastic Arbor: a modern simulation framework for synaptic plasticity -- from single synapses to networks of morphological neurons","abstract":"Arbor is a software library designed for efficient simulation of large-scale networks of biological neurons with detailed morphological structures. It combines customizable neuronal and synaptic mechanisms with high-performance computing, supporting multi-core CPU and GPU systems. In humans and other animals, synaptic plasticity processes play a vital role in cognitive functions, including learning and memory. Recent","authors":["Luboeinski, Jannik","Schmitt, Sebastian","Shafiee, Shirin","Hater, Thorsten","Bösch, Fabian","Tetzlaff, Christian"],"url":"https://arxiv.org/abs/2411.16445","arxiv_id":"2411.16445","published":"2026-02-16"}],"history":{"events":[{"year":2013,"text":"At least 91 people were killed and 190 others injured after a bomb hidden in a water tank exploded at a market in Hazara Town, Pakistan.","url":"https://en.wikipedia.org/wiki/February_2013_Quetta_bombing"},{"year":1998,"text":"While on approach to Chiang Kai-shek International Airport, China Airlines Flight 676 crashed due to an over-reliance of the autopilot, which resulted in the deaths of 202 people.","url":"https://en.wikipedia.org/wiki/Taoyuan_International_Airport"},{"year":1996,"text":"Eleven people died in a train collision in Silver Spring, Maryland, leading to the creation of comprehensive U.S. federal rules for the design of passenger cars.","url":"https://en.wikipedia.org/wiki/1996_Maryland_train_collision"},{"year":1985,"text":"The Lebanese Shia political and paramilitary organization Hezbollah released a manifesto describing its ideology and goals.","url":"https://en.wikipedia.org/wiki/Shia_Islam"},{"year":1983,"text":"The Ash Wednesday bushfires burned over half a million acres (over 2,000 km2) each in both South Australia and Victoria, killing 75 people and injuring 2,676 others.","url":"https://en.wikipedia.org/wiki/Ash_Wednesday_bushfires"},{"year":1961,"text":"The DuSable Museum, the first museum dedicated to the study and conservation of African American history, culture, and art, was chartered.","url":"https://en.wikipedia.org/wiki/DuSable_Black_History_Museum"},{"year":1959,"text":"Fidel Castro was sworn in as Prime Minister of Cuba, beginning his decades-long rule over the country.","url":"https://en.wikipedia.org/wiki/Fidel_Castro"},{"year":1936,"text":"The Popular Front, a coalition of left-wing parties, came to power in the Spanish general election, a factor in the outbreak of the Spanish Civil War five months later.","url":"https://en.wikipedia.org/wiki/Popular_Front_(Spain)"},{"year":1922,"text":"A landslide in Byblos revealed a sarcophagus in an underground tomb that was later discovered to be part of a large Bronze Age necropolis.","url":"https://en.wikipedia.org/wiki/Byblos"},{"year":1918,"text":"The Council of Lithuania signed the Act of Independence (pictured), proclaiming the restoration of an independent Lithuania.","url":"https://en.wikipedia.org/wiki/Council_of_Lithuania"},{"year":1900,"text":"The Southern Cross Expedition led by Carsten Borchgrevink (pictured) achieved a new Farthest South of 78° 50'S, making the first landing at the Great Ice Barrier.","url":"https://en.wikipedia.org/wiki/Southern_Cross_Expedition"},{"year":1862,"text":"American Civil War: A Union victory at the Battle of Fort Donelson gave General Ulysses S. Grant the nickname \"Unconditional Surrender\".","url":"https://en.wikipedia.org/wiki/American_Civil_War"},{"year":1859,"text":"The French government passed a law setting the musical note A4 to a frequency of 435 hertz, in the first attempt to standardize concert pitch.","url":"https://en.wikipedia.org/wiki/Musical_note"},{"year":1804,"text":"First Barbary War: Lieutenant Stephen Decatur led a U.S. Navy raid to destroy the captured USS Philadelphia in the harbor of Tripoli (depicted), denying her use to the Barbary States.","url":"https://en.wikipedia.org/wiki/First_Barbary_War"},{"year":1270,"text":"Livonian Crusade: In the Battle of Karuse, the Grand Duchy of Lithuania achieved a decisive victory over the Livonian Order on the frozen surface of the Baltic Sea.","url":"https://en.wikipedia.org/wiki/Livonian_Crusade"},{"year":1249,"text":"King Louis IX dispatched André de Longjumeau as the French ambassador to the Mongol Empire.","url":"https://en.wikipedia.org/wiki/Louis_IX_of_France"}],"births":[{"year":2001,"text":"Yuki Naito, Japanese tennis player","url":"https://en.wikipedia.org/wiki/Yuki_Naito"},{"year":2000,"text":"Koffee, Jamaican singer, songwriter and rapper","url":"https://en.wikipedia.org/wiki/Original_Koffee"},{"year":2000,"text":"Coby White, American basketball player","url":"https://en.wikipedia.org/wiki/Coby_White"},{"year":2000,"text":"Carlos Yulo, Filipino artistic gymnast","url":"https://en.wikipedia.org/wiki/Carlos_Yulo"},{"year":1999,"text":"Ignatius Ganago, Cameroonian footballer","url":"https://en.wikipedia.org/wiki/Ignatius_Ganago"},{"year":1999,"text":"Marie Ulven Ringheim, Norwegian singer, songwriter and music producer","url":"https://en.wikipedia.org/wiki/Girl_in_Red"},{"year":1998,"text":"An Hyejin, South Korean volleyball player","url":"https://en.wikipedia.org/wiki/An_Hye-jin"},{"year":1998,"text":"Kim Suji, South Korean diver","url":"https://en.wikipedia.org/wiki/Kim_Su-ji_(diver)"}],"deaths":[{"year":2026,"text":"Billy Steinberg, American songwriter (born 1950)","url":"https://en.wikipedia.org/wiki/Billy_Steinberg"},{"year":2026,"text":"Frederick Wiseman, American filmmaker (born 1930)","url":"https://en.wikipedia.org/wiki/Frederick_Wiseman"},{"year":2025,"text":"Viktor Antonov, Bulgarian artist (born 1972)","url":"https://en.wikipedia.org/wiki/Viktor_Antonov_(artist)"},{"year":2024,"text":"Alexei Navalny, Russian activist (born 1976)","url":"https://en.wikipedia.org/wiki/Alexei_Navalny"},{"year":2021,"text":"Gustavo Noboa, Ecuadorian politician, 42nd President of Ecuador (born 1937)","url":"https://en.wikipedia.org/wiki/Gustavo_Noboa"},{"year":2019,"text":"Bruno Ganz, Swiss actor (born 1941)","url":"https://en.wikipedia.org/wiki/Bruno_Ganz"},{"year":2016,"text":"Boutros Boutros-Ghali, Egyptian politician and diplomat, 6th Secretary-General of the United Nations (born 1922)","url":"https://en.wikipedia.org/wiki/Boutros_Boutros-Ghali"},{"year":2015,"text":"Lasse Braun, Algerian-Italian director, producer, and screenwriter (born 1936)","url":"https://en.wikipedia.org/wiki/Lasse_Braun"}]},"census":null,"sources":[{"id":"attention","name":"Wikipedia most-read","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"},{"id":"frontier","name":"Hacker News","url":"https://news.ycombinator.com","licence":"Public API — titles and links, credited","attribution":"Hacker News / Y Combinator"},{"id":"research","name":"arXiv cs.AI","url":"https://arxiv.org","licence":"Open metadata, per arXiv API terms","attribution":"arXiv.org"},{"id":"history","name":"Wikipedia On this day","url":"https://api.wikimedia.org","licence":"CC BY-SA 4.0","attribution":"Wikipedia contributors"}],"licence":"Records are CC BY-SA 4.0, inheriting from Wikipedia. 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