Welcome to Royfactory

Table of Contents Introducing the AI Defense Skill Scanner Multi-Engine Detection Methodology Enhancing Accuracy Through Meta-Analysis Integration and Workflow Readiness Critical Limitations and the Need for Human Oversight Introducing the AI Defense Skill Scanner The Cisco AI Defense Skill Scanner is a specialized security tool designed to assess the security posture of AI Agent Skills. It functions as a best-effort security scanner aimed at detecting known and probable risks within these agent skills, providing a critical layer in securing AI deployments. ...

Table of Contents Why Loops Are the Next Hype Cycle in AI Agentic Loops: Enabling Continuous AI Workflows Technical Foundations of AI Loops Loops and the Future of Compute Why Loops Are the Next Hype Cycle in AI The evolution of AI from single-step responses to complex, real-world task completion is fundamentally driven by iterative processes, positioning loops as the next major hype cycle in the field. This shift reflects the transition from traditional human coding methods to agent-based coding, where AI agents are responsible for executing continuous workflows rather than performing isolated, linear operations. ...

Table of Contents The Challenge of Real-Time 3D Mapping for Tiny Robots Introducing the Energy-Efficient System-on-a-Chip Optimizing Space with Gaussian Ellipsoids Real-World Applications and Future Potential The Challenge of Real-Time 3D Mapping for Tiny Robots Generating detailed, real-time three-dimensional maps for autonomous systems presents significant technical hurdles, primarily related to power consumption and memory storage. Traditional mapping methods struggle to meet the demands of small, battery-limited devices, severely restricting the ability of tiny robots (such as UAVs) to operate efficiently in complex environments. ...

Table of Contents How Conduit Enables AI Agent Payments Operator Control and Security Features Monetization Model and Platform Fees Technical Implementation and Deployment Implications for AI-Driven Bitcoin Ecosystem How Conduit Enables AI Agent Payments Conduit acts as a policy + accounting layer between Lightning Network Daemon (LND) nodes and AI agents, enabling secure, programmable payment workflows. By deploying Conduit as a self-hosted tool, operators establish a intermediary system that manages AI agent interactions with Bitcoin Lightning without exposing sensitive cryptographic keys. This architecture ensures that AI agents operate within defined financial boundaries while maintaining operator control over funds and transaction policies. ...

Introduction TL;DR: Constraints in AI are pivotal for steering innovation and ensuring practical application. From robotics laws to semantic containers, professionals can leverage constraints to solve operational and ethical dilemmas. This article provides insights into current AI developments and their implications for technology leaders. Constraints in AI, often seen as limitations, can foster creativity and innovation. By understanding their role, professionals can navigate challenges more effectively. Understanding AI Constraints What Are AI Constraints? AI constraints refer to technical, ethical, or operational limitations applied to artificial intelligence systems. These can include predefined rules, resource restrictions, or governance protocols that guide AI behavior. ...

Introduction TL;DR: Zhipu, a key player in China’s AI landscape, has experienced a remarkable 120% growth, underscoring the country’s push toward global AI leadership. This development highlights the rapid evolution of China’s AI market and its increasing influence on the global tech ecosystem. China’s burgeoning AI sector is drawing global attention as companies like Zhipu demonstrate exponential growth. With a staggering 120% surge, Zhipu has become a symbol of China’s ambition to dominate the AI industry. This article explores the implications of Zhipu’s recent growth and what it signals for the global AI landscape. ...

Introduction TL;DR: Price elasticity measures how demand responds to price changes, but naive models fail due to endogeneity. Causal and ML-based designs estimate more accurate effects, and scenario simulations help evaluate pricing decisions across demand, revenue, and inventory. (본문은 위 한국어 구조에 대응해 영문으로 동일하게 구성) References (Dynamic modeling and forecasting of price elasticity based on time series analysis and machine learning, 2025)[https://eurekamag.com/research/100/036/100036654.php] (Introduction to price elasticity of demand, 2026-02-14)[https://lilys.ai/notes/1075036] (Price elasticity definitions, accessed 2026-02-14)[https://contents.kocw.or.kr/KOCW/document/2015/korea_sejong/kimmyeongki/04.pdf] (Dynamic Pricing - Causal AI Solutions, accessed 2026-02-14)[https://economicai.com/en-PH/solutions/dynamic-pricing] (Adventures in Demand Analysis Using AI, accessed 2026-02-14)[https://arxiv.org/abs/2501.00382] (Machine learning and operation research based method for promotion optimization, accessed 2026-02-14)[https://www.sciencedirect.com/science/article/abs/pii/S1567422319300912]

Introduction TL;DR: Claude Cowork is a desktop agent mode that can access a user-approved local folder and tools, execute multi-step tasks, and produce real files (docs/spreadsheets/slides). As of 2026-02-11, it’s a research preview available on Claude Desktop (macOS + Windows x64) for paid plans (Pro/Max/Team/Enterprise); Windows arm64 isn’t supported. Why it matters: Agentic power means operational risk. Treat Cowork as a governed tool, not a chat upgrade. What Claude Cowork is (and isn’t) One-sentence definition Claude Cowork is an agentic desktop mode that turns prompts into planned, executed tasks with direct file outputs in a user-approved workspace. ...

Introduction Intermittent Demand Forecasting is a dedicated production track for SKUs with frequent zeros. You should start with Croston-family baselines (Croston/SBA/TSB), then expand to zero-inflated count time-series models only when the data-generating mechanism demands it. TL;DR: Define what “zero” means (true no-demand vs stockout/censoring vs missing), split the pipeline into an intermittent track, and validate with inventory KPIs (service level/cost), not just forecast scores. Why it matters: In intermittent SKUs, average accuracy can look fine while stockouts/overstock explode in a small subset of items. ...

Introduction AI Sales Forecasting often starts with feature-based ML (GBDT). This lesson shows when to move to deep learning and how to use foundation models as fast baselines. TL;DR: pick models based on covariate availability, rolling backtests, calibrated uncertainty, and cost/latency. Why it matters: Deep learning only pays off when it reduces decision risk (stockouts/overstock) at an acceptable operational cost. 1) Model landscape (train-from-scratch vs pretrained) Train-from-scratch: DeepAR, TFT, N-HiTS, TiDE, PatchTST Pretrained foundation models: TimesFM, Chronos, TimeGPT Why it matters: Pretrained models accelerate baselining; train-from-scratch can fit your domain more tightly. ...