Dedicated to sharing and discussing big data and AI technology applications, aiming to empower a million data scientists. Regularly hosts live tech talks and curates articles on big data, recommendation/search algorithms, advertising algorithms, NLP, intelligent risk control, autonomous driving, and machine learning/deep learning.
The author compares four Chinese large‑language models—GLM 5.2, Kimi 2.7, DeepSeek V4 and MiniMax M3—detailing their strengths, pricing, and ideal use‑cases for writing, coding, multimodal processing and high‑throughput batch tasks, and shares personal trust insights.
A technical livestream with experts from DeepSecurity, Ping An Life and China Mobile Jiutian reveals that the real bottleneck for AI agents in enterprises is not model capability but engineering, organizational processes and risk control, and outlines concrete strategies—code graphing, layered constraints, verification loops and metric‑driven adoption—to turn probabilistic AI output into reliable, organization‑wide productivity.
OpenAI and Anthropic are both rolling out facial‑recognition identity checks for ChatGPT and Claude, turning formerly open AI services into regulated tools by using age‑prediction models, organization verification, and strict ID policies to curb abuse and prepare for more capable AI agents.
The article walks through the challenges of large‑scale web crawling, explains Bright Data’s adaptive Crawler API and MCP protocol, discusses compliance and proxy strategies, and shows how to build a next‑generation AI search engine with LangGraph and Python tool integration.
The article explains how Alibaba Cloud's MaxCompute transforms a traditional data warehouse into a cloud‑native, multimodal Data+AI platform by introducing a four‑layer architecture, SQL‑based AI functions, the Python‑native MaxFrame framework, and a series of industry case studies that demonstrate performance gains and flexible resource scheduling.
The article examines Loop Engineering, a new AI‑driven workflow that replaces manual prompt writing with self‑sustaining loops, explains its six essential components, discusses costs, boundaries, and suitable use cases, and argues that the real benefit lies in shifting human effort from repetitive tasks to higher‑level supervision.
After the U.S. Commerce Department forced Anthropic to shut down Fable 5 and Mythos 5, Zhipu released GLM 5.2 as an open‑source coding model; the author evaluates its coding and agent capabilities, compares it with Claude and Opus, and highlights its strengths, limitations, and real‑world task performance.
This article examines the practical challenges of deploying Retrieval‑Augmented Generation (RAG) in enterprise settings, detailing the modular architecture, offline and online pipelines, hybrid retrieval, multi‑stage ranking, knowledge filtering, and two‑stage generation techniques that together improve search completeness, ranking quality, and answer accuracy.
Anthropic’s analysis of Claude‑powered Data Agents shows that reliable self‑service analytics depend on precise context resolution, rigorous verification, and strong data governance rather than simply generating SQL, with skills raising accuracy from under 21% to over 95% across most use cases.
The article explains how Knora 4.0 combines ontology with large‑model AI to move enterprise applications from isolated chat bots to autonomous, end‑to‑end systems, addressing six major challenges such as hallucinations, unstable outputs, weak planning, poor responsiveness, data integration difficulty, and long cold‑start cycles, and demonstrates the approach with real LED‑line use cases, architectural details, and a roadmap for future autonomous agents.
