Sharing cutting‑edge big data and AI technologies, with occasional lifestyle insights.
This article presents a comprehensive collection of visual knowledge maps that outline data analysis steps, foundational concepts, technical tools, business processes, analyst skill frameworks, and related topics such as Python learning paths, Excel formulas, MySQL, and statistical methods.
The article explains how to construct a data metrics center by defining, categorizing, and governing indicators, illustrating the business‑data feedback loop, logical and business closure, and the roles of analysts, product managers, and engineers in turning data into actionable business insight.
Kimi K3, the world’s first open‑source 2.8‑trillion‑parameter model, showcases novel attention and MoE techniques, scores near‑top on AI benchmarks, triggers valuation shifts for Anthropic, sparks debate among OpenAI leaders, and signals a broader industry move toward open‑source AI as DeepSeek V4 looms.
This article explains a four‑step workflow that uses Codex to extract content, Image 2 to explore visual styles, high‑resolution visual drafts, and a mixed‑restoration strategy to produce professional, fully editable PPTX files, avoiding the common pitfall of AI‑only slide generation.
The LLM Architecture Gallery, created by Sebastian Raschka, consolidates metadata and standardized visual cards for major large‑language models—from GPT‑2 to trillion‑parameter systems—highlighting architecture trends such as sparse‑mixture‑of‑experts, evolving attention mechanisms, and lightweight alternatives, enabling researchers and developers to compare designs, parameters, licenses, and inference costs in one platform.
The article analyses how a policy‑driven boom of digital‑technology subsidiaries in Chinese central and local state‑owned enterprises exploded to over a thousand firms, then collapsed as most small, copy‑cat units faced fuzzy positioning, technical hollowing, institutional mismatch and internal waste, leaving only a few core players thriving.
This article systematically examines ten fundamental questions on digital twins—defining the concept, identifying stakeholders, comparing global research intensity, linking to smart manufacturing, exploring integration with New IT, outlining scientific challenges, standards, and commercial tool requirements—to guide researchers, decision‑makers, and practitioners.
This article systematically explains data warehouse modeling, covering ER, relational, and dimensional modeling, introduces star, snowflake, and constellation schemas, walks through a retail sales example, discusses fact attributes, slowly changing dimensions, and compares normalized, dimensional, and independent data‑mart approaches.
This article explains what data metrics are, why they matter, and provides a step‑by‑step methodology—including principles, design, implementation, and best‑practice models such as AARRR and MECE—to construct a robust data metric system that aligns with business goals and improves analysis efficiency.
The article explains what a data warehouse is, why it is built for decision support, compares ER and dimensional modeling, and details core concepts such as dimensions, facts, slowly changing dimensions, fact table types, and star versus snowflake schemas, highlighting practical trade‑offs.
