{"date":"2025-11-14","schema_version":1,"captured_at":"2026-08-25T22:56:16.299Z","complete":true,"sources_captured":["attention","frontier","research","history"],"counts":{"attention":30,"frontier":25,"research":25,"history":15},"attention":[{"title":"Google Chrome","views":312372,"rank":3,"extract":"Google Chrome is a cross-platform web browser developed by Google. It was launched in September 2008 for Microsoft Windows and was built with free software components from Apple WebKit and Mozilla Firefox. Versions for Linux, macOS, iOS, iPadOS, and Android we","url":"https://en.wikipedia.org/wiki/Google_Chrome","thumb":"https://upload.wikimedia.org/wikipedia/commons/thumb/e/e1/Google_Chrome_icon_%28February_2022%29.svg/330px-Google_Chrome_icon_%28February_2022%29.svg.png?utm_source=en.wikipedia.org&utm_campaign=api&utm_content=thumbnail"},{"title":"James A. 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[pdf]","points":179,"comments":159,"url":"https://www.nber.org/system/files/working_papers/w34459/w34459.pdf","discussion":"https://news.ycombinator.com/item?id=45933317"},{"title":"Nvidia is gearing up to sell servers instead of just GPUs and components","points":178,"comments":77,"url":"https://www.tomshardware.com/tech-industry/artificial-intelligence/jp-morgan-says-nvidia-is-gearing-up-to-sell-entire-ai-servers-instead-of-just-ai-gpus-and-componentry-jensens-master-plan-of-vertical-integration-will-boost-profits-purportedly-starting-with-vera-rubin","discussion":"https://news.ycombinator.com/item?id=45926439"},{"title":"Backblaze Drive Stats for Q3 2025","points":175,"comments":22,"url":"https://www.backblaze.com/blog/backblaze-drive-stats-for-q3-2025/","discussion":"https://news.ycombinator.com/item?id=45926383"}],"research":[{"title":"Temporal Micro-Doppler Spectrogram-based ViT Multiclass Target Classification","abstract":"In this paper, we propose a new Temporal MDS-Vision Transformer (T-MDS-ViT) for multiclass target classification using millimeter-wave FMCW radar micro-Doppler spectrograms. Specifically, we design a transformer-based architecture that processes stacked range-velocity-angle (RVA) spatiotemporal tensors via patch embeddings and cross-axis attention mechanisms to explicitly model the sequential nature of MDS data acros","authors":["Nghia Thinh Nguyen","Tri Nhu Do"],"url":"http://arxiv.org/abs/2511.11951v1","arxiv_id":"2511.11951v1","published":"2025-11-14T23:53:33Z"},{"title":"AI-Open-RAN for Non-Terrestrial Networks","abstract":"In this paper, we propose the concept of AIO-RAN-NTN, a unified all-in-one Radio Access Network (RAN) for Non-Terrestrial Networks (NTNs), built on an open architecture that leverages open interfaces and artificial intelligence (AI)-based functionalities. This approach advances interoperability, flexibility, and intelligence in next-generation telecommunications. First, we provide a concise overview of the state-of-t","authors":["Tri Nhu Do"],"url":"http://arxiv.org/abs/2511.11947v1","arxiv_id":"2511.11947v1","published":"2025-11-14T23:41:52Z"},{"title":"Augmenting The Weather: A Hybrid Counterfactual-SMOTE Algorithm for Improving Crop Growth Prediction When Climate Changes","abstract":"In recent years, humanity has begun to experience the catastrophic effects of climate change as economic sectors (such as agriculture) struggle with unpredictable and extreme weather events. Artificial Intelligence (AI) should help us handle these climate challenges but its most promising solutions are not good at dealing with climate-disrupted data; specifically, machine learning methods that work from historical da","authors":["Mohammed Temraz","Mark T Keane"],"url":"http://arxiv.org/abs/2511.11945v1","arxiv_id":"2511.11945v1","published":"2025-11-14T23:35:21Z"},{"title":"Improving Neutrino Oscillation Measurements through Event Classification","abstract":"Precise neutrino energy reconstruction is essential for next-generation long-baseline oscillation experiments, yet current methods remain limited by large uncertainties in neutrino-nucleus interaction modeling. Even so, it is well established that different interaction channels produce systematically varying amounts of missing energy and therefore yield different reconstruction performance--information that standard ","authors":["Sebastian A. R. Ellis","Daniel C. Hackett","Shirley Weishi Li","Pedro A. N. Machado","Karla Tame-Narvaez"],"url":"http://arxiv.org/abs/2511.11938v2","arxiv_id":"2511.11938v2","published":"2025-11-14T23:26:51Z"},{"title":"A Deep Learning Framework for Thyroid Nodule Segmentation and Malignancy Classification from Ultrasound Images","abstract":"Ultrasound-based risk stratification of thyroid nodules is a critical clinical task, but it suffers from high inter-observer variability. While many deep learning (DL) models function as \"black boxes,\" we propose a fully automated, two-stage framework for interpretable malignancy prediction. Our method achieves interpretability by forcing the model to focus only on clinically relevant regions. First, a TransUNet mode","authors":["Omar Abdelrazik","Mohamed Elsayed","Noorul Wahab","Nasir Rajpoot","Adam Shephard"],"url":"http://arxiv.org/abs/2511.11937v1","arxiv_id":"2511.11937v1","published":"2025-11-14T23:23:24Z"},{"title":"A Neuromorphic Architecture for Scalable Event-Based Control","abstract":"This paper introduces the ``rebound Winner-Take-All (RWTA)\" motif as the basic element of a scalable neuromorphic control architecture. From the cellular level to the system level, the resulting architecture combines the reliability of discrete computation and the tunability of continuous regulation: it inherits the discrete computation capabilities of winner-take-all state machines and the continuous tuning capabili","authors":["Yongkang Huo","Fulvio Forni","Rodolphe Sepulchre"],"url":"http://arxiv.org/abs/2511.11924v2","arxiv_id":"2511.11924v2","published":"2025-11-14T23:08:56Z"},{"title":"Looking Forward: Challenges and Opportunities in Agentic AI Reliability","abstract":"This chapter presents perspectives for challenges and future development in building reliable AI systems, particularly, agentic AI systems. Several open research problems related to mitigating the risks of cascading failures are discussed. The chapter also sheds lights on research challenges and opportunities in aspects including dynamic environments, inconsistent task execution, unpredictable emergent behaviors, as ","authors":["Liudong Xing","Janet","Lin"],"url":"http://arxiv.org/abs/2511.11921v1","arxiv_id":"2511.11921v1","published":"2025-11-14T23:05:43Z"},{"title":"Constructing Political Coordinates: Aggregating Over the Opposition for Diverse News Recommendation","abstract":"In the past two decades, open access to news and information has increased rapidly, empowering educated political growth within democratic societies. News recommender systems (NRSs) have shown to be useful in this process, minimizing political disengagement and information overload by providing individuals with articles on topics that matter to them. Unfortunately, NRSs often conflate underlying user interest with th","authors":["Eamon Earl","Chen Ding","Richard Valenzano","Drai Paulen-Patterson"],"url":"http://arxiv.org/abs/2511.17574v1","arxiv_id":"2511.17574v1","published":"2025-11-14T23:04:04Z"},{"title":"Binary BPE: A Family of Cross-Platform Tokenizers for Binary Analysis","abstract":"Sequence models for binary analysis are bottlenecked by byte-level tokenization: raw bytes waste precious context window capacity for transformers and other neural network architectures, and many existing text-oriented tokenizers fail on arbitrary 0x00--0xFF sequences. To address this issue, we introduce the Binary BPE tokenizer family, a set of cross-platform Byte Pair Encoding (BPE) tokenizers for executables train","authors":["Michael J. Bommarito"],"url":"http://arxiv.org/abs/2511.17573v1","arxiv_id":"2511.17573v1","published":"2025-11-14T22:53:03Z"},{"title":"Batch Matrix-form Equations and Implementation of Multilayer Perceptrons","abstract":"Multilayer perceptrons (MLPs) remain fundamental to modern deep learning, yet their algorithmic details are rarely presented in complete, explicit \\emph{batch matrix-form}. Rather, most references express gradients per sample or rely on automatic differentiation. Although automatic differentiation can achieve equally high computational efficiency, the usage of batch matrix-form makes the computational structure expli","authors":["Wieger Wesselink","Bram Grooten","Huub van de Wetering","Qiao Xiao","Decebal Constantin Mocanu"],"url":"http://arxiv.org/abs/2511.11918v1","arxiv_id":"2511.11918v1","published":"2025-11-14T22:52:27Z"},{"title":"An Analysis of Architectural Impact on LLM-based Abstract Visual Reasoning: A Systematic Benchmark on RAVEN-FAIR","abstract":"This study aims to systematically evaluate the performance of large language models (LLMs) in abstract visual reasoning problems. We examined four LLM models (GPT-4.1-Mini, Claude-3.5-Haiku, Gemini-1.5-Flash, Llama-3.3-70b) utilizing four different reasoning architectures (single-shot, embedding-controlled repetition, self-reflection, and multi-agent) on the RAVEN-FAIR dataset. Visual responses generated through a th","authors":["Sinan Urgun","Seçkin Arı"],"url":"http://arxiv.org/abs/2511.11916v1","arxiv_id":"2511.11916v1","published":"2025-11-14T22:50:22Z"},{"title":"Forgetting-MarI: LLM Unlearning via Marginal Information Regularization","abstract":"As AI models are trained on ever-expanding datasets, the ability to remove the influence of specific data from trained models has become essential for privacy protection and regulatory compliance. Unlearning addresses this challenge by selectively removing parametric knowledge from the trained models without retraining from scratch, which is critical for resource-intensive models such as Large Language Models (LLMs).","authors":["Shizhou Xu","Yuan Ni","Stefan Broecker","Thomas Strohmer"],"url":"http://arxiv.org/abs/2511.11914v3","arxiv_id":"2511.11914v3","published":"2025-11-14T22:48:39Z"},{"title":"PI-NAIM: Path-Integrated Neural Adaptive Imputation Model","abstract":"Medical imaging and multi-modal clinical settings often face the challange of missing modality in their diagnostic pipelines. Existing imputation methods either lack representational capacity or are computationally expensive. We propose PI-NAIM, a novel dual-path architecture that dynamically routes samples to optimized imputation approaches based on missingness complexity. Our framework integrates: (1) intelligent p","authors":["Afifa Khaled","Ebrahim Hamid Sumiea"],"url":"http://arxiv.org/abs/2511.11908v1","arxiv_id":"2511.11908v1","published":"2025-11-14T22:38:40Z"},{"title":"KVSwap: Disk-aware KV Cache Offloading for Long-Context On-device Inference","abstract":"Language models (LMs) underpin emerging mobile and embedded AI applications like meeting and video summarization and document analysis, which often require processing multiple long-context inputs. Running an LM locally on-device improves privacy, enables offline use, and reduces cost, but long-context inference quickly hits a \\emph{memory capacity wall} as the key-value (KV) cache grows linearly with context length a","authors":["Huawei Zhang","Chunwei Xia","Zheng Wang"],"url":"http://arxiv.org/abs/2511.11907v2","arxiv_id":"2511.11907v2","published":"2025-11-14T22:37:57Z"},{"title":"Robust Bidirectional Associative Memory via Regularization Inspired by the Subspace Rotation Algorithm","abstract":"Bidirectional Associative Memory (BAM) trained with Bidirectional Backpropagation (B-BP) often suffers from poor robustness and high sensitivity to noise and adversarial attacks. To address these issues, we propose a novel gradient-free training algorithm, the Bidirectional Subspace Rotation Algorithm (B-SRA), which significantly improves the robustness and convergence behavior of BAM. Through comprehensive experimen","authors":["Ci Lin","Tet Yeap","Iluju Kiringa"],"url":"http://arxiv.org/abs/2511.11902v2","arxiv_id":"2511.11902v2","published":"2025-11-14T22:15:07Z"},{"title":"End to End AI System for Surgical Gesture Sequence Recognition and Clinical Outcome Prediction","abstract":"Fine-grained analysis of intraoperative behavior and its impact on patient outcomes remain a longstanding challenge. We present Frame-to-Outcome (F2O), an end-to-end system that translates tissue dissection videos into gesture sequences and uncovers patterns associated with postoperative outcomes. Leveraging transformer-based spatial and temporal modeling and frame-wise classification, F2O robustly detects consecutiv","authors":["Xi Li","Nicholas Matsumoto","Ujjwal Pasupulety","Atharva Deo","Cherine Yang","Jay Moran"],"url":"http://arxiv.org/abs/2511.11899v1","arxiv_id":"2511.11899v1","published":"2025-11-14T22:02:46Z"},{"title":"Prompt Triage: Structured Optimization Enhances Vision-Language Model Performance on Medical Imaging Benchmarks","abstract":"Vision-language foundation models (VLMs) show promise for diverse imaging tasks but often underperform on medical benchmarks. Prior efforts to improve performance include model finetuning, which requires large domain-specific datasets and significant compute, or manual prompt engineering, which is hard to generalize and often inaccessible to medical institutions seeking to deploy these tools. These challenges motivat","authors":["Arnav Singhvi","Vasiliki Bikia","Asad Aali","Akshay Chaudhari","Roxana Daneshjou"],"url":"http://arxiv.org/abs/2511.11898v1","arxiv_id":"2511.11898v1","published":"2025-11-14T22:01:08Z"},{"title":"VULPO: Context-Aware Vulnerability Detection via On-Policy LLM Optimization","abstract":"Large language models (LLMs) have recently shown strong potential in vulnerability detection (VD). However, accurately detecting vulnerabilities in real-world repositories requires reasoning over complex contextual interactions. Existing LLM-based VD approaches remain limited because current datasets lack complete contextual information and high-quality reasoning supervision, while existing optimization methods prima","authors":["Youpeng Li","Fuxun Yu","Weiliang Qi","Xinda Wang"],"url":"http://arxiv.org/abs/2511.11896v3","arxiv_id":"2511.11896v3","published":"2025-11-14T21:57:48Z"},{"title":"Chain-of-Generation: Progressive Latent Diffusion for Text-Guided Molecular Design","abstract":"Text-conditioned molecular generation aims to translate natural-language descriptions into chemical structures, enabling scientists to specify functional groups, scaffolds, and physicochemical constraints without handcrafted rules. Diffusion-based models, particularly latent diffusion models (LDMs), have recently shown promise by performing stochastic search in a continuous latent space that compactly captures molecu","authors":["Lingxiao Li","Haobo Zhang","Bin Chen","Jiayu Zhou"],"url":"http://arxiv.org/abs/2511.11894v1","arxiv_id":"2511.11894v1","published":"2025-11-14T21:54:10Z"},{"title":"FLEX: Feature Importance from Layered Counterfactual Explanations","abstract":"Machine learning models achieve state-of-the-art performance across domains, yet their lack of interpretability limits safe deployment in high-stakes settings. Counterfactual explanations are widely used to provide actionable \"what-if\" recourse, but they typically remain instance-specific and do not quantify which features systematically drive outcome changes within coherent regions of the feature space or across an ","authors":["Nawid Keshtmand","Roussel Desmond Nzoyem","Jeffrey Nicholas Clark"],"url":"http://arxiv.org/abs/2511.11891v1","arxiv_id":"2511.11891v1","published":"2025-11-14T21:48:24Z"},{"title":"Flash-Fusion: Enabling Expressive, Low-Latency Queries on IoT Sensor Streams with LLMs","abstract":"Smart cities and pervasive IoT deployments have generated interest in IoT data analysis across transportation and urban planning. At the same time, Large Language Models offer a new interface for exploring IoT data - particularly through natural language. Users today face two key challenges when working with IoT data using LLMs: (1) data collection infrastructure is expensive, producing terabytes of low-level sensor ","authors":["Kausar Patherya","Ashutosh Dhekne","Francisco Romero"],"url":"http://arxiv.org/abs/2511.11885v1","arxiv_id":"2511.11885v1","published":"2025-11-14T21:34:28Z"},{"title":"Better LLM Reasoning via Dual-Play","abstract":"Large Language Models (LLMs) have achieved remarkable progress through Reinforcement Learning with Verifiable Rewards (RLVR), yet still rely heavily on external supervision (e.g., curated labels). Adversarial learning, particularly through self-play, offers a promising alternative that enables models to iteratively learn from themselves - thus reducing reliance on external supervision. Dual-play extends adversarial l","authors":["Zhengxin Zhang","Chengyu Huang","Aochong Oliver Li","Claire Cardie"],"url":"http://arxiv.org/abs/2511.11881v3","arxiv_id":"2511.11881v3","published":"2025-11-14T21:19:07Z"},{"title":"Transformers vs. Recurrent Models for Estimating Forest Gross Primary Production","abstract":"Monitoring the spatiotemporal dynamics of forest CO$_2$ uptake (Gross Primary Production, GPP), remains a central challenge in terrestrial ecosystem research. While Eddy Covariance (EC) towers provide high-frequency estimates, their limited spatial coverage constrains large-scale assessments. Remote sensing offers a scalable alternative, yet most approaches rely on single-sensor spectral indices and statistical model","authors":["David Montero","Miguel D. Mahecha","Francesco Martinuzzi","César Aybar","Anne Klosterhalfen","Alexander Knohl"],"url":"http://arxiv.org/abs/2511.11880v1","arxiv_id":"2511.11880v1","published":"2025-11-14T21:18:01Z"},{"title":"Three Stage Narrative Analysis; Plot-Sentiment Breakdown, Structure Learning and Concept Detection","abstract":"Story understanding and analysis have long been challenging areas within Natural Language Understanding. Automated narrative analysis requires deep computational semantic representations along with syntactic processing. Moreover, the large volume of narrative data demands automated semantic analysis and computational learning rather than manual analytical approaches. In this paper, we propose a framework that analyze","authors":["Taimur Khan","Ramoza Ahsan","Mohib Hameed"],"url":"http://arxiv.org/abs/2511.11857v1","arxiv_id":"2511.11857v1","published":"2025-11-14T20:30:18Z"},{"title":"A Multimodal Manufacturing Safety Chatbot: Knowledge Base Design, Benchmark Development, and Evaluation of Multiple RAG Approaches","abstract":"Ensuring worker safety remains a critical challenge in modern manufacturing environments. Industry 5.0 reorients the prevailing manufacturing paradigm toward more human-centric operations. Using a design science research methodology, we identify three essential requirements for next-generation safety training systems: high accuracy, low latency, and low cost. We introduce a multimodal chatbot powered by large languag","authors":["Ryan Singh","Austin Hamilton","Amanda White","Michael Wise","Ibrahim Yousif","Arthur Carvalho"],"url":"http://arxiv.org/abs/2511.11847v2","arxiv_id":"2511.11847v2","published":"2025-11-14T20:10:23Z"}],"history":{"events":[{"year":2010,"text":"Red Bull Racing's Sebastian Vettel won the Drivers' Championship after winning the final race of the season, becoming the youngest Formula One champion.","url":"https://en.wikipedia.org/wiki/Red_Bull_Racing"},{"year":2003,"text":"Astronomers Michael E. Brown, Chad Trujillo, and David L. Rabinowitz discovered the trans-Neptunian object Sedna.","url":"https://en.wikipedia.org/wiki/Michael_E._Brown"},{"year":1995,"text":"As a result of budget conflicts between President Bill Clinton and the United States Congress led by Newt Gingrich, the federal government was forced to shut down non-essential services.","url":"https://en.wikipedia.org/wiki/Bill_Clinton"},{"year":1992,"text":"In poor conditions caused by Cyclone Forrest, Vietnam Airlines Flight 474 crashed near Nha Trang, killing 30 people.","url":"https://en.wikipedia.org/wiki/Cyclone_Forrest"},{"year":1991,"text":"Croatian War of Independence: Croatian naval commandos attacked the Yugoslav patrol boat Mukos, starting the Battle of the Dalmatian Channels.","url":"https://en.wikipedia.org/wiki/Croatian_War_of_Independence"},{"year":1990,"text":"Music producer Frank Farian admitted that the German R&B duo Milli Vanilli did not sing the vocals on their album Girl You Know It's True.","url":"https://en.wikipedia.org/wiki/Frank_Farian"},{"year":1970,"text":"Southern Airways Flight 932, chartered by the Marshall University football team, crashed into a hill near Ceredo, West Virginia, killing all 75 people on board.","url":"https://en.wikipedia.org/wiki/Southern_Airways_Flight_932"},{"year":1969,"text":"Apollo 12 (pictured) launched from the Kennedy Space Center, later becoming the second crewed flight to land on the Moon.","url":"https://en.wikipedia.org/wiki/Apollo_12"},{"year":1967,"text":"American physicist Theodore Maiman was given a patent for his ruby laser systems, the world's first laser.","url":"https://en.wikipedia.org/wiki/Theodore_Maiman"},{"year":1960,"text":"Ruby Bridges and the McDonogh Three became the first black children to attend an all-white elementary school in Louisiana as part of the New Orleans school desegregation crisis.","url":"https://en.wikipedia.org/wiki/Ruby_Bridges"},{"year":1941,"text":"World War II: German troops, aided by local auxiliaries, murdered nine thousand residents of the Słonim Ghetto in a single day.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1941,"text":"Second World War: After suffering torpedo damage the previous day, the British aircraft carrier HMS Ark Royal sank as she was being towed to Gibraltar for repairs.","url":"https://en.wikipedia.org/wiki/World_War_II"},{"year":1910,"text":"Aviator Eugene Burton Ely performed the first takeoff from a ship (pictured), flying from a makeshift deck on USS Birmingham in Hampton Roads, Virginia.","url":"https://en.wikipedia.org/wiki/Eugene_Burton_Ely"},{"year":1851,"text":"Moby-Dick, an epic novel by American writer Herman Melville, was first published in the United States.","url":"https://en.wikipedia.org/wiki/Moby-Dick"},{"year":1680,"text":"German astronomer Gottfried Kirch discovered the Great Comet of 1680, the first comet to be discovered by telescope.","url":"https://en.wikipedia.org/wiki/Gottfried_Kirch"}],"births":[{"year":2000,"text":"Xiyeon, South Korean singer and actress","url":"https://en.wikipedia.org/wiki/Xiyeon"},{"year":1998,"text":"Sofia Kenin, American tennis player","url":"https://en.wikipedia.org/wiki/Sofia_Kenin"},{"year":1998,"text":"DeVonta Smith, American football player","url":"https://en.wikipedia.org/wiki/DeVonta_Smith"},{"year":1997,"text":"Noussair Mazraoui, Moroccan footballer","url":"https://en.wikipedia.org/wiki/Noussair_Mazraoui"},{"year":1997,"text":"Christopher Nkunku, French footballer","url":"https://en.wikipedia.org/wiki/Christopher_Nkunku"},{"year":1997,"text":"Axel Tuanzebe, English-Congolese footballer","url":"https://en.wikipedia.org/wiki/Axel_Tuanzebe"},{"year":1996,"text":"Borna Ćorić, Croatian tennis player","url":"https://en.wikipedia.org/wiki/Borna_%C4%86ori%C4%87"},{"year":1996,"text":"Dawson Knox, American football player","url":"https://en.wikipedia.org/wiki/Dawson_Knox"}],"deaths":[{"year":2024,"text":"Vic Flick, English guitarist (born 1937)","url":"https://en.wikipedia.org/wiki/Vic_Flick"},{"year":2024,"text":"Peter Sinfield, English songwriter and producer (born 1943)","url":"https://en.wikipedia.org/wiki/Peter_Sinfield"},{"year":2020,"text":"Peter Florjancic, Slovene inventor and Olympic athlete (born 1919)","url":"https://en.wikipedia.org/wiki/Peter_Florjan%C4%8Di%C4%8D"},{"year":2020,"text":"Des O'Connor, English comedian, singer and television presenter (born 1932)","url":"https://en.wikipedia.org/wiki/Des_O'Connor"},{"year":2016,"text":"Gwen Ifill, American television journalist (born 1955)","url":"https://en.wikipedia.org/wiki/Gwen_Ifill"},{"year":2015,"text":"Nick Bockwinkel, American professional wrestler (born 1934)","url":"https://en.wikipedia.org/wiki/Nick_Bockwinkel"},{"year":2015,"text":"K. S. Gopalakrishnan, Indian director, producer, and screenwriter (born 1929)","url":"https://en.wikipedia.org/wiki/K._S._Gopalakrishnan"},{"year":2015,"text":"Warren Mitchell, English actor and screenwriter (born 1926)","url":"https://en.wikipedia.org/wiki/Warren_Mitchell"}]},"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. Every item links to its source."}