Welcome to Royfactory

Introduction Avoid Kkondae is a unique mobile puzzle game that cleverly integrates the strategic depth of classic Minesweeper with relatable workplace humor. The objective is to navigate a virtual office environment and safely escape by avoiding hidden ‘Kkondae’ (a Korean term for an old-fashioned, condescending person in a position of authority). The game utilizes the familiar grid and numbering system, where the number on each cell indicates the count of Kkondae in the surrounding eight cells. Success depends entirely on logical deduction and strategic planning, making it an engaging challenge for players looking for a quick yet profound mobile experience. ...

Introduction TL;DR: On November 13, 2025, AI-related stocks plunged on Wall Street, with Nvidia and Oracle experiencing significant losses. Investors are increasingly skeptical amid concerns about AI hype and potential interest rate hikes. The selloff has revived worries about an AI market bubble and valuation excess, with key mega-cap stocks posting sharp drops after record highs in recent weeks. 1. AI Stock Plunge: Key Data Major AI stocks dropped sharply, with the Nasdaq Composite losing over 2.3% led by declines in Nvidia (down 3.6%) and Oracle (down close to 4%). Nvidia’s market capitalization fell by roughly $150 billion in a single day, while Oracle retreated 25–30% from early September highs over the past month. ...

Introduction TL;DR: OpenAI’s Sora 2 model release triggered controversy over safety, privacy, and copyright. Advocacy groups call for suspension due to risks, while OpenAI insists on operating based on regulatory compliance and claimed benefits. Ongoing debates highlight urgent needs for robust AI governance. Sora 2 allows realistic AI-generated videos, but sparked criticism for its risks to privacy, copyright, and misuse. Public advocacy groups called for suspension, raising concerns over lack of safeguards and democratic threat. OpenAI is not stopping Sora 2, citing compliance and technical safeguards, including opt-in controls and watermarking. 1. Sora 2 Release and Safety Concerns The Sora 2 model enables high-fidelity video synthesis, significantly improving realism and character consistency over prior versions. Critics, however, warn of increased risks including deepfakes, digital harassment, and privacy abuse, especially impacting vulnerable populations. ...

Introduction TL;DR: Anthropic committed $50 billion to build new data centers across the US, creating 800 permanent jobs and 2,400 construction jobs starting from 2026. This marks a strategic shift to self-operated infrastructure for growing Claude AI demand and reflects intensifying global competition in AI investment and infrastructure. Anthropic’s Investment Rationale Anthropic announced on 2025-11-11 their $50 billion plan to construct dedicated AI data centers in Texas, New York, and additional US locations in cooperation with Fluidstack. This is part of a direct infrastructure strategy that reduces reliance on hyperscale cloud partners and meets surging computational demands from business clients. ...

Introduction TL;DR: As of 2025-11-12, Ant International has open-sourced its Falcon TST time-series AI forecasting model. The AI model achieves over 90% forecast accuracy and cuts FX costs by up to 60%. Antom, the MSME-focused app, is set to transform business operations and payments. Falcon TST Model Open Source: Explained Falcon TST is a proprietary AI model built with Mixture-of-Experts architecture and multi-patch tokenizers, comprising up to 2.5 billion parameters. It is deployed internally to optimize cashflow and FX management hourly, daily, and weekly, with demonstrated state-of-the-art results across long-range forecasting benchmarks.[6][7][3][1][2] ...

Introduction TL;DR The 2025 paper from Tsinghua University rigorously demonstrates that reinforcement learning (RL) with verifiable rewards (RLVR) increases the efficiency of sampling correct answers, but does not add new reasoning behaviors to language models. In pass@k evaluation (whether 1 of k samples is correct), RLVR-tuned models excel at low k but underperform base models at high k, indicating no expansion in reasoning capacity. All correct outputs from RL-trained models were already present in the base model’s distribution. The implication: Surpassing LLM reasoning limits likely requires a fundamentally new paradigm rather than more RL. RL and Reasoning: What the Study Says Tsinghua University researchers systematically evaluated RLVR in math, coding, and vision benchmarks across various LLM families. Their key assertion: RLVR “sharpens” the distribution, increasing efficiency without expanding the actual set of correct reasoning paths.[2][4][5][6][1][3] ...

Introduction TL;DR: AI psychology now investigates human-AI trust, ethics, and clinical efficacy in mental health, with recent trials confirming chatbot therapy’s value. Key risks, privacy, and regulation take precedence in global and Korean contexts. In 2025, core research focuses on cognitive modeling with AI, overcoming shortages in mental health care, and implementing robust ethical and legal safeguards. AI Psychology: Core Concepts Human-AI Interaction, Trust, and Cognitive Modeling Modern AI models can predict diverse human behaviors, simulate psychological experiments, and offer insights on trust and error propagation effects. AI “virtual labs” now empower researchers to expand experimental scope and precision across decision-making, memory, and problem-solving tasks. ...

Introduction TL;DR: Meta has open sourced Omnilingual ASR, a multilingual speech recognition system supporting over 1,600 spoken languages, including more than 500 previously unserved low-resource languages, as of 2025-11-10. Featuring in-context learning and a public dataset, it sets a new industry benchmark for accessible, high-accuracy ASR across the globe. The system leverages up to 7B parameter wav2vec 2.0 models, supports rapid user-driven language extension, and provides free, permissively licensed models and corpus for research and development. Key Takeaways Over 1,600 languages covered, including 500+ low-resource, via open-source release on 2025-11-10 In-context learning enables rapid expansion to new languages with only a few audio-text samples Models range from lightweight (300M) to high-performance (7B parameters), freely licensed Industry-best accuracy: char error rate <10% for 78% of supported languages Large-scale corpus (Omnilingual ASR Corpus) and model suite open for research and deployment Core Features 1,600+ languages (500+ low-resource) supported, overcoming prior ASR limitations Architecture: 7B parameter Omnilingual wav2vec 2.0 encoder with both CTC and transformer decoders In-context learning: add new languages with just a few user-provided samples Omnilingual ASR Corpus includes 350+ minority languages, all open sourced Apache 2.0 and CC-BY licensing, full model and dataset access for all Why it matters: Expands AI speech recognition to digitally marginalized communities and drives global language inclusion. ...

Introduction TL;DR: Google unveiled its 7th-generation AI chip Ironwood in November 2025, achieving 4x to 10x performance improvements over previous chips. Ironwood boasts 4,614 FP8 TFLOPS, 192GB HBM3E memory, 9,216-scale pod architecture, and market-leading efficiency. Anthropic secured access to up to one million Google TPUs in a multi-billion dollar deal, intensifying competition and AI infrastructure scale. The announcement marks Google’s ambitious attempt to outpace Nvidia and Microsoft/OpenAI partnerships in next-gen AI computing. Ironwood, the latest Google custom silicon for AI, is engineered for next-gen LLMs, multimodal AI, and massive-scale inference, representing a major leap in hardware for cloud and enterprise AI. This strategic move positions Google as a formidable competitor in the AI infrastructure market, directly challenging the dominance of traditional GPU manufacturers. 1. Ironwood: Core Features and Advancements 1.1. Specs & Architecture Ironwood (TPU v7) delivers 4,614 FP8 TFLOPS per chip, 192GB of HBM3E memory, 7.37TB/s bandwidth, and scales up to 9,216 chips per superpod via 9.6Tb/s ICI, enabling parallel training and ultra-low-latency inference of large-scale models. Its perf/watt is doubled versus Trillium (v6e) and up to 30x more efficient than the original 2018 TPU. Ironwood’s chief target: LLMs (like Claude), complex RL, and AI serving at massive scale. ...

Introduction TL;DR: Hyperparameter tuning refers to systematically adjusting external settings like learning rate, batch size, and regularization in machine/deep learning models prior to training. Optimal hyperparameters directly impact performance, training efficiency, and generalization. The main keywords here are “hyperparameter tuning,” “optimization,” and “AI model performance,” which are critical in any serious data science or engineering project. This process distinguishes successful production models from experimental prototypes, enabling teams to extract maximum value from their data and computational resources. 1. Difference Between Hyperparameters and Parameters 1.1. What Defines a Hyperparameter? 1.1. What Defines a Hyperparameter? Hyperparameters are user-specified model settings such as learning rate, batch size, number of epochs, regularization coefficients, dropout rate, and more. Parameters are learned (weights, biases) during model training. ...