Articles

In response to the common pain point of persistent data issues in enterprise data architecture, storage, data warehouse design, metric definition, and data quality management, this article deeply analyzes the key challenges and solutions for building a reliable and efficient data system. The article elucidates the trade-offs between scale, real-time performance, cost, and complexity in data architecture, from the evolution of MPP, Lambda Architecture, Kappa Architecture to Lakehouse Architecture, and explains the exactly-once semantics and implementation of stream processing in detail. Next, it delves into the ACID principles of relational databases, the CAP theorem of NoSQL, and the innovation of big data storage (HDFS, table format), and compares LSM-Tree and B-Tree storage engines and distributed consistency protocols. The data warehouse design section emphasizes layering, standardized measurement system, and provides practical tips for massive data query optimization. In terms of metric definition, it introduces atomic metrics / derived metrics, OSM model, UJM model, AARRR model, and DuPont analysis, funnel analysis, dimension drill-down, trend analysis and other insight methods in detail. Finally, the article proposes the 'impossible triangle' and 'trust economics' concepts of data quality, and provides engineering solutions to improve data quality and efficiency through task service level agreement (SLA) optimization, multi-layered monitoring, alert grading, and automated attribution.

The article deeply analyzes the 'Empirical Study Based on 100 Trillion Tokens on OpenRouter' report jointly released by OpenRouter and a16z, and comprehensively summarizes the key development status and counter-intuitive views in the AI field in 2025. Based on massive real computing power consumption data, the report reveals that the usage of open-source models has steadily increased to 30% of the total traffic, among which Chinese open-source models perform particularly well. The traffic share of inference optimization models has soared to over 50%, marking the shift of AI from text generation to complex task execution. In terms of application scenarios, programming (50%+) and role-playing (52% of open-source traffic) have become the two major dominators. In addition, medium-sized models (15B-70B) have emerged as a new market mainstream. In terms of geopolitics, paid usage in Asia has doubled, and Chinese has become the second largest AI interaction language in the world, breaking the monopoly of Silicon Valley. The article introduces the concept of the 'Cinderella's Slipper Effect', emphasizing that user retention depends on whether the model can perfectly solve specific pain points first, rather than simply price advantages, and points out that the price elasticity of the AI model market is weak, but extremely low prices may trigger the 'Jevons Paradox'. Overall, the report depicts a new AI landscape of multi-model ecology, the normalization of Agent Reasoning, and intensified global competition.

This article presents a deep observation on entrepreneurship in the AI era by Yuan Ye, a partner at 5Y Capital. He proposes five core perspectives: First, the technological paradigm shifts from a focus on 'time share' during the mobile internet era to 'intelligence share' in the AI era, emphasizing that early-stage AI products should prioritize improving intelligent capabilities. Second, the expansion of the supply boundary in technological evolution is analyzed, that is, specifically, from physical goods and computing power to the sale of 'intelligence itself'. Third, it is pointed out that the interaction form of AI will surpass the current Chat mode, giving rise to more disruptive new entrances. Fourth, it is emphasized that the evaluation of AI product value should return to 'specific value creation' rather than just focusing on popularity or quantitative indicators. Finally, the fundamental shift of organizational capability from 'Product Manager-driven' to 'Researcher-driven' in the AI era is discussed. These views provide a profound strategic thinking framework for entrepreneurs in the AI era.

This article is an in-depth summary of Elon Musk's latest interview, covering his ambitious vision for the future world. The interview includes the construction of the X platform as a global collective consciousness and the technological integration of Tesla, SpaceX, and xAI to achieve deep space exploration. Musk predicts that AI will bring about a post-work society with 'Universal High Income' (UHI), and emphasizes that AI development must follow the three ethical principles of truth, beauty, and curiosity. He also expresses deep concern about the declining population and its threat to the expansion of civilization's consciousness. Finally, Musk advises young entrepreneurs to focus on becoming 'net contributors' by creating value for society rather than solely pursuing money or happiness. The article comprehensively presents Musk's multi-dimensional thinking on technology, economics, society, and philosophy.

The article introduces 'Adaptive Experience (AX)' as the evolution beyond traditional UI/UX, driven by AI, contextual intelligence, and automation. It explains how interfaces are shifting from static artifacts to dynamic, predictive, and self-evolving ecosystems, leveraging 'contextual engineering' via 'Signal Decision Platforms' to anticipate user needs. The author argues that designers' roles are transforming from 'pixel arrangers' to 'experience architects,' focusing on ethical boundaries, accessibility, and inclusivity within AI-powered systems. It envisions a future of 'Zero UI' and emotion-aware systems, emphasizing the balance between hyper-personalization and privacy-first design, advocating for human empathy as the guiding principle.

Jensen Huang reveals that a deep 'fear of failure,' rather than the pursuit of success, has driven him for 33 years, creating a constant sense of urgency. He points out that AI development is gradual, with no sudden AI singularity, and emphasizes AI's role in collaborative defense against cybersecurity threats. Huang predicts AI energy needs will be minimal in a decade, suggesting Small Modular Reactors as the solution. He also recounts NVIDIA's journey from near bankruptcy to leading the AI revolution through GPU and CUDA innovation. Huang argues AI will enhance jobs, not replace them, and could bridge the global technology gap.

The article details Hugging Face Skills, a new tool that empowers coding agents such as Claude, Codex, and Gemini CLI to perform end-to-end fine-tuning of large language models. It explains how agents can interpret natural language commands to validate datasets, select appropriate cloud GPU hardware (Hugging Face Jobs), generate and update training scripts, submit jobs, monitor progress via Trackio, and push the final models to the Hugging Face Hub. The skill supports various production-grade training methods including Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO), accommodating models from 0.5B to 70B parameters. It also covers practical aspects like cost estimation, dataset validation, real-time monitoring, and converting models to GGUF for local deployment. This innovation significantly simplifies complex MLOps tasks, making LLM fine-tuning more accessible and efficient for developers.

This article, produced by Tencent Technology Engineering and authored by huaxing, aims to provide readers with an accessible introduction to the basic knowledge of LLMs, especially the principles of its mainstream architecture, the Transformer. The article first reviews the evolution of LLMs and introduces the importance of the Transformer as the cornerstone of modern Deep Learning models. Then, through an example of translating 'Transformer is powerful.' into 'Transformer很强大。', it gradually breaks down the various core components of the Transformer, including Tokenization, Word Embedding, Positional Encoding, Neural Network fundamentals, and the working principles and interactions of the most important Attention Mechanism (Self-Attention, Causal Attention, Multi-Head Attention). The article explains in detail the roles of Q, K, and V vectors in the Attention Mechanism and demonstrates the attention weight distribution between words during the translation process through a visual heat map. Finally, the article briefly introduces the cutting-edge exploration of LLM architectures in 2025, such as the innovative applications of hybrid architectures like MoE, MLA, GQA, and the architectural features of flagship open-source models like DeepSeek, OLMo, and Gemma. Overall, the article is detailed, logically clear, and accompanied by rich illustrations and vivid analogies, greatly reducing the barrier to understanding the complex underlying principles of LLMs and offering valuable insights for technology professionals seeking a deeper understanding of large model operation.

This interview offers a deep dive into the insights of Sequoia Capital partner Jess Lee on product, leadership, and investment. Drawing from her own experiences, she highlights the central role of people in overcoming challenges and introduces her framework of Emotional Quotient (EQ), Intelligence Quotient (IQ), Perseverance Quotient (PQ), and Joy Quotient (JQ). At Google, she learned the value of engineering excellence and ambitious goals, while also acknowledging the need for better user communication and stressing that vision and conviction should lead user understanding. Jess Lee recounts her transition from product manager to CEO at Polyvore, emphasizing the importance of taking initiative, building complementary teams, and embracing uncertainty. She frankly discusses Polyvore's missteps in market and business model selection, contrasting it with Figma's success to illustrate the fundamental differences in business 'physical laws'. As an investor, she underscores the significance of execution and storytelling, while looking ahead to the vast potential of AI in consumer applications and the micro-drama market. The article also explores her unique journey of adaptation and growth within the Venture Capital (VC) industry and Sequoia's support for her role as a 'builder'.

This article systematically traces the evolution of the DeepSeek Large Language Model (LLM) from V3 to V3.2, elaborating on the technical innovations and considerations behind each major version. First, the article introduces the MoE (Mixture of Experts) architecture and MLA (Multi-head Latent Attention) mechanism in the V3 version, which respectively solve the problems of efficiency and memory consumption for long sequences of large models. Next, the RLVR (Reinforcement Learning with Verifiable Rewards) training paradigm introduced in the R1 version significantly improved the accuracy and reliability of the model in reasoning tasks like mathematics and code through automated program verification, and elaborated on its difference from traditional RLHF. V3.1 marked DeepSeek's transition to a hybrid model. Subsequently, the core innovation of the V3.2-Exp version, the DSA (DeepSeek Sparse Attention) mechanism, reduces the computational complexity of attention from O(L²) to O(L×k) by allowing the model to autonomously learn to focus on key tokens, greatly improving the efficiency of long text processing. Finally, the article focuses on analyzing how the Self-Verification and Self-Refinement technologies verified in DeepSeekMath V2 ensure rigorous reasoning and accurate answers through multi-model collaboration and iterative optimization. Ultimately, these technologies are integrated into V3.2, making it a product-level model with powerful performance, efficient reasoning, and rigorous logic.