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.
A data‑driven comparison of OpenAI's GPT‑5.4, Anthropic's Claude Opus 4.6, and Google Gemini shows how each model performs on desktop‑agent, coding, and multimodal benchmarks, reveals pricing differences, and offers concrete recommendations for developers, startups, and enterprise users.
The article reviews GPT‑5.4’s release, comparing its code ability, world knowledge, and multimodal understanding to Claude Opus 4.6 and GPT‑5.3‑Codex, presents benchmark scores (GDPval 83%, SWE‑Bench 57.7%, OSWorld 75%, ToolAthon 54.6%), and highlights new features such as a 1‑million‑token context window, native computer usage, and tool‑search optimization, while discussing pricing and practical usage in OpenClaw.
Healthpeak, a large healthcare‑real‑estate REIT, replaced fragmented spreadsheets with Palantir’s AI‑driven operating system, using an ontology‑based data model and intelligent workflow automation to cut billing cycles from days to hours, eliminate manual errors, and free managers to focus on tenant relationships.
Healthpeak tackled fragmented data, manual meter‑reading, and scaling bottlenecks by deploying Palantir's AI Platform, building a four‑layer ontology‑driven system that automates billing, detects anomalies, orchestrates workflows, and frees property managers to focus on tenant relationships, delivering near‑zero marginal cost and dramatic efficiency gains.
This article presents a series of seven technical case studies—including Tencent Cloud’s Iceberg‑based batch‑stream integration, AI‑driven data governance with Apache Gravitino, Xiaohongshu’s lakehouse evolution, and a multimodal data‑lake solution—detailing challenges, architectural designs, implementation steps, performance results, and future directions.
This article explains the challenges of applying large language models in real‑world office scenarios and presents a detailed, step‑by‑step RAG (Retrieval‑Augmented Generation) solution—including architecture, offline document processing, query rewriting, hybrid retrieval, multi‑stage ranking, knowledge filtering, and prompt‑driven generation—backed by practical lessons from a Chinese mobile operator.
This article reviews three advanced AI-driven solutions—Alibaba Cloud's Agentic RAG for high‑concurrency multimodal search, Huawei Noah's LLM‑augmented recommendation architecture, and Baidu's generative ranking model GRAB—detailing their challenges, designs, performance gains, and practical deployment insights.
Google’s Nano Banana 2 (Gemini 3.1 Flash Image) upgrades image generation with real‑time web knowledge, clearer text rendering, consistent character/object handling, and broad product integration, positioning the model as a fast, configurable rendering engine rather than a niche creative tool.
This article explains the challenges large language models face in real‑world applications, introduces Retrieval‑Augmented Generation (RAG) as a solution, and details a modular RAG architecture, its components, and practical techniques for document parsing, query rewriting, hybrid retrieval, ranking, and answer generation in an enterprise setting.
OpenClaw is an open‑source, headless AI agent that runs locally, interacts through WhatsApp, Telegram or Discord, stores persistent memory, and can automate tasks ranging from car‑price negotiation to autonomous phone calls, while raising significant security, privacy, and operational challenges.
