Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.
This article details Qichacha's technical journey of applying large language models to resolve field‑level data lineage challenges in a complex, multi‑source data environment, describing the motivation, architecture, practical implementation, engineering trade‑offs, and measurable outcomes.
The article details how 1688 transformed its platform from a traditional intent‑matching traffic hub into an Agentic AI system that understands business tasks, outlining a three‑step implementation of knowledge, trajectory and environment redesign, dual‑track evolution, novel evaluation methods, and the emerging role of product managers as evaluation engineers.
The article analyzes the critical role of memory for AI agents, compares model‑driven and application‑driven approaches, details the five‑layer MemOS architecture and its three‑layer memory coordination, and shows how MemOS‑powered cloud services achieved a 100‑200% month‑over‑month usage increase while cutting token consumption by up to 72%.
In a 90‑minute live technical session, three experts dissect ten core challenges of Agent engineering—sandbox vs permission boundaries, checkpoints, rollback, tool‑call safety, human‑in‑the‑loop, multi‑agent coordination, observability, and memory—showing that moving agents from "usable" to "trustworthy" requires fine‑grained execution controls rather than broader permissions.
The article analyzes how enterprise‑level AI agents, when deployed in production, struggle with factual data retrieval despite semantic capabilities, and argues that real‑time, low‑latency, multimodal analytics—exemplified by systems like Apache Doris and SelectDB—are the essential data entry points for successful Agent deployments.
The article analyzes the shortcomings of traditional data‑asset architectures, breaks down the three essential components of high‑quality AI datasets, and presents OpenClaw’s layered, operator‑based platform design that enables AI‑driven data governance, annotation, and model invocation at scale.
The article analyzes how the rapid commoditization of large‑model capabilities, illustrated by Palantir’s 85% Q1 2026 revenue growth, reshapes AI competition into three layers—model, wrapper, and infrastructure—highlighting ontology as the hard‑to‑copy moat for enterprise AI in high‑risk scenarios.
The article explains that the hidden infrastructure layer called Agent Harness—its OS‑like architecture, three‑layer abstraction, context‑rot problem, compounding error, and verification loops—determines whether impressive agent demos can survive in production, with concrete benchmarks showing harness improvements far outweigh model upgrades.
The article analyzes DataWorks Data Agent’s evolution from a helper‑style tool to an autonomous data‑centric AI agent, detailing its five‑stage roadmap, dual‑engine CLI/Claw architecture, unified runtime kernel, open skill ecosystem, and CPU‑GPU joint optimization for enterprise‑grade data automation.
This article walks through a complete multi‑modal product search solution that transforms textual and visual product data into embeddings, leverages dense, sparse and hybrid models, applies vector similarity and quantization techniques such as SQ and BBQ, and demonstrates how Elasticsearch Serverless provides a serverless, cost‑effective, auto‑scaling backbone for end‑to‑end retrieval.
