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DataFunSummit

Official account of the DataFun community, dedicated to sharing big data and AI industry summit news and speaker talks, with regular downloadable resource packs.

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Recent Articles

Latest from DataFunSummit

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DataFunSummit
DataFunSummit
May 1, 2026 · Artificial Intelligence

When to Use ChatGPT vs Codex: Exploring the New Era of AI Agents

This article explains how to choose between ChatGPT, Claude, Claude Code, and Codex, detailing Codex's seven core capabilities—including local file access, persistent memory, plugins, skills, image generation, computer control, automation, and the Chronicle screen‑monitoring feature—through concrete examples and step‑by‑step walkthroughs.

AI AgentsCodexOpenAI
0 likes · 14 min read
When to Use ChatGPT vs Codex: Exploring the New Era of AI Agents
DataFunSummit
DataFunSummit
May 1, 2026 · Artificial Intelligence

How Agentic Architectures Power the Next‑Gen Recommendation and Search Systems

This article summarizes a technical ebook that analyzes the evolution of recommendation and search systems—from deep‑learning models to large‑language‑model agents—detailing multi‑agent RAG architectures, Huawei’s KAR knowledge adapters, Baidu’s generative ranking (GRAB), Elasticsearch vector search, and performance results such as a 1.5% AUC lift and GPU‑accelerated throughput gains.

ElasticSearchRAGgenerative ranking
0 likes · 6 min read
How Agentic Architectures Power the Next‑Gen Recommendation and Search Systems
DataFunSummit
DataFunSummit
May 1, 2026 · Artificial Intelligence

From “Lobster” to Ontology: Unveiling the Next Wave of Self‑Evolving AI Agents and Data Governance

The DACon conference in Shanghai gathered over 8,000 developers, managers and experts, delivering 50 talks that explored self‑evolving AI agents, data‑centric ontology, Agent‑Ready big‑data infrastructure, AI‑AR ecosystem evolution, and the emerging challenges of Agentic data governance.

AI AgentsAI+ARAgentic Data Protocol
0 likes · 11 min read
From “Lobster” to Ontology: Unveiling the Next Wave of Self‑Evolving AI Agents and Data Governance
DataFunSummit
DataFunSummit
Apr 30, 2026 · Industry Insights

Why Palantir’s Edge Isn’t Unique – Chinese Enterprises Can Replicate Its Methodology

A panel of industry experts dissected Palantir’s rapid growth, revealing that its advantage lies in a systematic ontology‑driven methodology rather than exclusive technology, and argued that Chinese firms can adopt the same approach if they first resolve data governance, semantic consistency, and management challenges.

AI AgentsCapability vs CompetencyData Governance
0 likes · 26 min read
Why Palantir’s Edge Isn’t Unique – Chinese Enterprises Can Replicate Its Methodology
DataFunSummit
DataFunSummit
Apr 30, 2026 · Artificial Intelligence

Unpacking MemOS: How AI Agents Overcome the “Memory Pain” and Boost Cloud Calls by 200%

The article analyses why memory is the critical bottleneck for AI agents, compares model‑driven and application‑driven memory approaches, details MemOS’s five‑layer architecture and three‑layer coordination, and shows how its cloud service achieved 100‑200% monthly growth while reducing token usage and improving LLM response quality.

AI AgentEnterprise AIMemOS
0 likes · 16 min read
Unpacking MemOS: How AI Agents Overcome the “Memory Pain” and Boost Cloud Calls by 200%
DataFunSummit
DataFunSummit
Apr 29, 2026 · Industry Insights

Beyond the Data Rear‑view Mirror: Palantir’s Strategic Value and Real‑World Cases

Palantir leverages its Ontology‑driven data integration and AI platforms—Gotham, Foundry, and AIP—to transform fragmented data into actionable intelligence, delivering decision‑making advantages in government, aerospace, food, and energy sectors, while shifting from custom‑heavy services to an open, platform‑based ecosystem.

AI AgentsAI PlatformData Integration
0 likes · 11 min read
Beyond the Data Rear‑view Mirror: Palantir’s Strategic Value and Real‑World Cases
DataFunSummit
DataFunSummit
Apr 28, 2026 · Artificial Intelligence

How Knora’s Ontology‑Enhanced Large Model Solves Hallucination and Execution Gaps in Enterprise AI

The article explains how Knora 4.0 combines enterprise ontologies with large‑model AI to create a unified, autonomous execution loop, addressing six common AI‑deployment challenges, detailing the platform’s architecture, autonomous agents, real‑world case studies, roadmap, and expert round‑table insights.

AI ArchitectureAutonomous AgentsEnterprise AI
0 likes · 17 min read
How Knora’s Ontology‑Enhanced Large Model Solves Hallucination and Execution Gaps in Enterprise AI
DataFunSummit
DataFunSummit
Apr 28, 2026 · Big Data

Dynamic Table: A Next‑Generation Data Processing Architecture Powered by Incremental Computing

The article examines the limitations of traditional batch and stream processing, explains how Hologres Dynamic Table combines declarative freshness settings with stateful incremental computation to bridge the gap between low‑cost batch jobs and low‑latency streaming, and presents benchmark results and real‑world case studies.

Dynamic TableHologresbenchmark
0 likes · 13 min read
Dynamic Table: A Next‑Generation Data Processing Architecture Powered by Incremental Computing
DataFunSummit
DataFunSummit
Apr 27, 2026 · Artificial Intelligence

How Tencent Games Leverages AI to Turn Data Governance into a Service

Tencent Games’ data governance team details an AI‑driven, end‑to‑end semantic framework that shifts traditional rule‑based data management to a service‑oriented model, cutting storage waste by 30 %, halving development time, and boosting asset recommendation accuracy to 95 % across its global gaming platform.

AIBig DataData Governance
0 likes · 19 min read
How Tencent Games Leverages AI to Turn Data Governance into a Service
DataFunSummit
DataFunSummit
Apr 27, 2026 · Big Data

How MaxCompute Evolves Big Data Platforms for AI: Architecture, Core Capabilities, and Real‑World Cases

The article details MaxCompute's AI‑driven evolution, covering its multilayer architecture, multimodal storage management, SQL AI functions, the Python‑based MaxFrame framework, and several industry case studies that demonstrate performance gains and flexible resource scheduling for large‑scale AI workloads.

Data+AIDistributed ComputingMaxCompute
0 likes · 12 min read
How MaxCompute Evolves Big Data Platforms for AI: Architecture, Core Capabilities, and Real‑World Cases