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 article breaks down the newly named Agent Harness infrastructure, explaining how a three‑layer engineering abstraction—from Prompt to Context to Harness—addresses context rot, compounding errors, and verification loops, turning impressive demo agents into reliable production systems.
This guide walks you through installing Python, obtaining a PPIO API key, installing the PPHermes CLI, launching a Hermes Agent sandbox in the cloud, and managing its lifecycle, with optional integration to Feishu/Lark and AI‑agent skill usage.
This article details Xiaohongshu's data platform evolution from a simple ClickHouse‑based ad‑hoc system to a Lambda‑style architecture and finally a lakehouse solution, highlighting how the adoption of a new incremental computing model reduced architectural complexity, resource consumption and development effort each to roughly one‑third while delivering sub‑second query performance on petabyte‑scale data.
Anthropic suffered seven major Claude outages in half a month, exposing a severe compute shortage that forced the company to announce an early‑stage, $5 billion AI‑chip project, revamp its pricing and subscription model, and confront regulatory KYC hurdles while the broader AI industry pivots away from Nvidia toward custom silicon.
This article analyzes the challenges and practical solutions of deploying Retrieval‑Augmented Generation (RAG) in an enterprise office setting, covering background problems, modular architecture, offline and online pipelines, hybrid retrieval, multi‑stage ranking, knowledge filtering, prompt engineering, and model selection to achieve accurate, reliable answers.
A live discussion with data platform leaders reveals that the real challenge of AI‑driven data agents lies not in model strength but in building a stable, explainable semantic layer, managing prompt versus fine‑tuning trade‑offs, ensuring trustworthy multi‑turn conversations, and aligning cost with business value for production deployment.
This article examines three common misconceptions in enterprise intelligent data analysis, explains how a semantic metric layer can break data silos, and details Aloudata Agent’s dual‑path engine, multi‑agent collaboration, and product design that together deliver trustworthy, deep, and democratized analytics for modern businesses.
This article analyzes Xiaohongshu's data platform evolution—from a simple ClickHouse‑based analytics layer to a Lambda architecture and finally a lakehouse design—highlighting how adopting a new incremental computing model reduced architecture complexity, resource consumption, and development effort each to roughly one‑third while delivering sub‑second query performance on petabyte‑scale data.
Anthropic's Claude Mythos Preview outperforms GPT‑5.4, Gemini 3.1 Pro and Opus 4.6 across dozens of AI benchmarks, autonomously discovers thousands of software vulnerabilities, exploits them without human guidance, and raises serious alignment and security concerns for the industry.
In a four‑hour competition, algorithm engineer Zhang Zhen from a Chinese EV company detailed his end‑to‑end workflow for quantizing the massive Qwen3‑Next‑80B model, covering sensitive‑layer analysis, iterative smoothing, fallback strategies, and parallel "horse‑race" debugging that led his team to win the GeekDay challenge.
