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Introduction TL;DR OpenAI is revolutionizing AI accessibility through scalable systems like Codex and Sora, while also introducing innovations in GPT-5.3 for coding efficiency and social science applications. These developments address challenges in AI deployment, such as rate limits, security, and data analysis at scale. Context In recent years, AI adoption has faced hurdles like resource limitations, security threats, and scalability. OpenAI’s latest efforts, such as real-time access systems, advanced coding models, and social science-focused tools, aim to overcome these barriers. ...

Introduction TL;DR: India’s AI landscape is rapidly evolving, with significant advancements in infrastructure and innovation. From Neysa’s massive GPU deployment plans to the challenges of AI adoption in the Indian market, this article explores the opportunities and hurdles shaping India’s AI ecosystem. Context: With a booming tech industry and government-backed initiatives, India is emerging as a key player in the global AI race. However, challenges such as investor skepticism, power limits in AI data centers, and copyright concerns are testing the resilience of its AI ambitions. India’s AI Infrastructure Push Neysa’s $1.2 Billion Investment in GPU Deployment Neysa, backed by Blackstone, has announced a $1.2 billion financing initiative aimed at deploying over 20,000 GPUs. This effort underscores India’s commitment to building robust domestic AI compute infrastructure. As demand for AI applications accelerates, the need for scalable and localized computational resources becomes critical. ...

Introduction TL;DR: MCP servers offer AI agents the ability to interact with real-world systems using interactive terminal sessions. This breakthrough enhances automation and practical applications, bridging the gap between virtual intelligence and physical systems. Context: In the rapidly evolving landscape of AI and automation, one of the challenges has been enabling AI agents to effectively interact with real-world systems. MCP servers, as introduced in the latest development, address this issue by providing interactive terminal sessions that empower AI agents to perform tasks directly within real-world environments. This innovation opens up new possibilities for AI-driven automation across industries. What are MCP Servers? Definition and Key Features MCP servers are a framework designed to give AI agents access to interactive terminal sessions, enabling them to execute commands and interact with real-world systems in real-time. ...

Introduction AgenticMail provides an innovative solution for multi-agent coordination by integrating email and SMS into AI workflows. This tool is designed to streamline communication between AI agents, enabling seamless interaction and task execution across diverse environments. Whether you’re deploying AI agents in business operations or research, AgenticMail offers an intuitive platform to enhance efficiency and collaboration. TL;DR: AgenticMail leverages email and SMS to enable streamlined coordination among AI agents. This article explores its architecture, use cases, and practical implementation tips. ...

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. ...