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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
Jul 28, 2026 · Artificial Intelligence

Designing Next‑Gen Recommendation and Search with Multi‑Agent AI Architecture

The article reviews a series of technical case studies—including Alibaba Cloud AI Search's Agentic RAG, Baidu's GRAB generative ranking, Huawei Noah's LLM‑enhanced recommendation, and Elasticsearch vector RAG—showing how multi‑agent AI architectures address high‑concurrency, multimodal, and multi‑hop query challenges while delivering measurable performance gains.

AI AgentsAlibaba Cloud AI SearchBaidu GRAB
0 likes · 6 min read
Designing Next‑Gen Recommendation and Search with Multi‑Agent AI Architecture
DataFunSummit
DataFunSummit
Jul 28, 2026 · Artificial Intelligence

Why Enterprises Still Hesitate to Deploy Production‑Ready AI Agents Despite Palantir’s New Capabilities

Palantir’s Agent Stack introduces Orchestrator, observability, and Ontology layers to make AI agents durable, interruptible, and governed, but enterprises remain reluctant because trust, state management, permission control, and continuous evaluation are required before agents can operate on real business processes.

AI AgentsObservabilityOntology
0 likes · 14 min read
Why Enterprises Still Hesitate to Deploy Production‑Ready AI Agents Despite Palantir’s New Capabilities
DataFunSummit
DataFunSummit
Jul 27, 2026 · Industry Insights

Palantir’s True Moat: Enterprise AI Agents, Ontology, and Decision‑Layer Engineering

Palantir’s 2026 roadmap shows the company moving beyond stronger AI models toward a comprehensive engineering system that lets enterprise agents safely access business data, execute permission‑guarded actions, and integrate into decision‑making processes—a shift that reshapes AI budgets and offers a clear lens on the competitive landscape, especially for Chinese firms.

AI AgentsAI BudgetDecision Engineering
0 likes · 16 min read
Palantir’s True Moat: Enterprise AI Agents, Ontology, and Decision‑Layer Engineering
DataFunSummit
DataFunSummit
Jul 27, 2026 · Artificial Intelligence

Why Do Long‑Horizon AI Agents Still Use the Wrong Memories?

Adding memory to agents is now straightforward, but when agents run for weeks across many interactions, the real challenge shifts from merely retrieving past data to determining which past information remains valid, how to manage its lifecycle, and how to govern cost, updates, and deletion, as highlighted by Oracle's technical report and benchmark evaluations.

Agent MemoryLong-Horizon AIMemory Lifecycle
0 likes · 14 min read
Why Do Long‑Horizon AI Agents Still Use the Wrong Memories?
DataFunSummit
DataFunSummit
Jul 26, 2026 · Artificial Intelligence

How Ontology-Driven Agents Enable Controllable Execution in Harness Engineering

The article analyzes the limitations of current AI agents, proposes an ontology‑driven semantic foundation for Harness Engineering, and details three technical pillars—architectural constraints, context engineering, and feedback loops—illustrated with the Knora platform and concrete workflow examples.

AI AgentKnoraOntology
0 likes · 20 min read
How Ontology-Driven Agents Enable Controllable Execution in Harness Engineering
DataFunSummit
DataFunSummit
Jul 26, 2026 · Databases

Why Is PostgreSQL Gaining New Momentum as an AI‑Era Data Foundation?

PostgreSQL is attracting renewed interest not because it magically becomes an all‑purpose AI database, but because AI applications in production demand a unified, consistent platform that can manage business records, vector embeddings, agent state, permissions and tooling, and PostgreSQL’s extensible ecosystem—pgvector, serverless architectures, branching, and MCP—offers exactly that blend of relational reliability and AI‑native capabilities.

AIDatabase BranchingMCP
0 likes · 15 min read
Why Is PostgreSQL Gaining New Momentum as an AI‑Era Data Foundation?
DataFunSummit
DataFunSummit
Jul 25, 2026 · Cloud Native

Evolution of Agent Infrastructure: Engineering Insights from Tencent Cloud Agent Runtime

The article analyzes how agents transition from demo to production, revealing that beyond model capabilities, stability, elasticity, security, and governance become critical, and explains the engineering challenges and solutions—including session management, state persistence, scheduling mismatches, sandbox isolation, and open‑source strategies—that underpin Tencent Cloud's Agent Runtime.

Agent RuntimeCloud NativeKubernetes
0 likes · 26 min read
Evolution of Agent Infrastructure: Engineering Insights from Tencent Cloud Agent Runtime
DataFunSummit
DataFunSummit
Jul 25, 2026 · Artificial Intelligence

The Hidden Flaws of AI‑Driven “Lights‑Off” Software Factories

While AI‑powered coding agents promise a lights‑off software factory where developers never read code, this article reveals the growing maintainability nightmare, benchmark shortcomings, and why current large‑language models still fail to produce good design, urging a return to planning and human oversight.

AI codingagentic developmentbenchmark
0 likes · 13 min read
The Hidden Flaws of AI‑Driven “Lights‑Off” Software Factories
DataFunSummit
DataFunSummit
Jul 24, 2026 · Artificial Intelligence

Why Harness Engineering Fails: Hidden Defects of AI‑Powered Code Factories

The article analyzes the rise of “lights‑off” software factories that rely on AI agents to generate, review, and fix code, exposing their maintainability nightmare, the inability of current models to learn good design, the limits of existing benchmarks, and proposes a pragmatic four‑step workflow that re‑introduces human planning and oversight.

AI codingagentic developmentbenchmark
0 likes · 12 min read
Why Harness Engineering Fails: Hidden Defects of AI‑Powered Code Factories
DataFunSummit
DataFunSummit
Jul 24, 2026 · Industry Insights

Why High-Quality Data Is the New Bottleneck in Large Model Competition

In a four‑hour investor briefing, DeepSeek founder Liang Wenfeng explains that the real competitive edge for large language models now lies in the ability to continuously produce high‑quality training signals, a capability limited by time rather than capital.

AI IndustryData FlywheelDeepSeek
0 likes · 10 min read
Why High-Quality Data Is the New Bottleneck in Large Model Competition