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 analyzes Palantir’s shift from a profit‑centric, standardized software model to a responsibility‑driven, ontology‑based architecture that embeds engineers on‑site, leverages LLM orchestration, and prioritizes specificity and security, offering a new paradigm for enterprise software value creation.
The article examines Harness Engineering's ontology‑driven semantic foundation for AI agents, outlining the challenges of uncontrolled agents, multi‑dimensional safety requirements, architectural constraints, context engineering, feedback loops, and the Knora implementation that bridges technical control to business‑level governance.
China’s inability to produce a Palantir‑style company stems not from technical shortcomings but from a mismatched ecosystem—lack of In‑Q‑Tel‑like venture backing, restrictive capital structures, project‑based procurement, and divergent market rules—making the American model unfit for Chinese soil.
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.
