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DataFunTalk
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DataFunTalk

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.

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Recent Articles

Latest from DataFunTalk

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DataFunTalk
DataFunTalk
May 16, 2026 · Artificial Intelligence

How to Turn AI into an S‑Level Employee: Practical Skill Training for Reliable Web Testing

The article explains why smart AI still fails at complex tasks, introduces the concept of engineering‑focused Skills that embed business SOPs, and shares four hard‑learned pitfalls plus a step‑by‑step, checklist‑driven training loop that turns a generic model into a dependable, self‑checking web‑testing assistant.

AI automationchecklistgate rules
0 likes · 19 min read
How to Turn AI into an S‑Level Employee: Practical Skill Training for Reliable Web Testing
DataFunTalk
DataFunTalk
May 16, 2026 · Artificial Intelligence

How Knora Combines Ontology and Large Models to Overcome AI Hallucinations and Execution Gaps in Enterprises

The article explains how YueDian Technology's Knora 4.0 platform fuses domain ontologies with large‑model AI to create a unified, trustworthy, and autonomous enterprise AI system that addresses hallucination, data integration, and execution challenges across complex business scenarios.

AI PlatformLarge Language Modelautonomous agents
0 likes · 14 min read
How Knora Combines Ontology and Large Models to Overcome AI Hallucinations and Execution Gaps in Enterprises
DataFunTalk
DataFunTalk
May 15, 2026 · Artificial Intelligence

Exploring Multimodal GraphRAG: Combining Document Intelligence, Knowledge Graphs, and Large Models

This article provides a comprehensive technical overview of multimodal GraphRAG, detailing document‑intelligence parsing pipelines, layout analysis, OCR‑pipeline vs OCR‑free approaches, knowledge‑graph integration for chunk relationships, multimodal indexing, retrieval‑generation workflows, and a comparative analysis of RAG, GraphRAG, and KG‑QA solutions.

Document IntelligenceGraphRAGLayout Analysis
0 likes · 23 min read
Exploring Multimodal GraphRAG: Combining Document Intelligence, Knowledge Graphs, and Large Models
DataFunTalk
DataFunTalk
May 15, 2026 · Industry Insights

How Liang Wenfeng’s DeepSeek Propelled Chinese AI Unicorns Past the Trillion‑Yuan Mark

In May 2024 China’s AI primary market exploded as DeepSeek secured its first external round, pushing its valuation to $45‑50 billion and sparking $30‑40 billion of financing across leading base‑model unicorns, while tying its V4 model to Huawei’s Ascend chips and reshaping valuation benchmarks for the sector.

AI financingChinese AI marketDeepSeek
0 likes · 17 min read
How Liang Wenfeng’s DeepSeek Propelled Chinese AI Unicorns Past the Trillion‑Yuan Mark
DataFunTalk
DataFunTalk
May 14, 2026 · Industry Insights

OpenAI’s Rapid Sprint: GPT‑5.6 Leaked and a $400 Subsidy to Oust Claude Code

Within three weeks of GPT‑5.5’s launch, internal code for GPT‑5.6 surfaced, prompting OpenAI to unveil a 2‑3× “ultrafast” mode and a two‑month free Codex offer worth $400 to lure Claude Code users, sparking a high‑speed AI competition with Anthropic’s Opus 4.7 Fast and highlighting a self‑reinforcing acceleration loop toward ASI.

AI accelerationClaude CodeCodex
0 likes · 7 min read
OpenAI’s Rapid Sprint: GPT‑5.6 Leaked and a $400 Subsidy to Oust Claude Code
DataFunTalk
DataFunTalk
May 14, 2026 · Artificial Intelligence

Where Is the Real Moat in the AI Era as Large Models Become Commoditized?

The article analyzes how the rapid commoditization of large‑model capabilities reshapes AI competition, arguing that the true moat lies not in the models themselves but in deep ontology‑driven infrastructure that can guarantee trustworthy outcomes in high‑risk enterprise scenarios, as illustrated by Palantir’s strategy.

AIPalantircompetitive landscape
0 likes · 12 min read
Where Is the Real Moat in the AI Era as Large Models Become Commoditized?
DataFunTalk
DataFunTalk
May 13, 2026 · Industry Insights

Why Palantir’s Value Is Rising: AI Commoditization, Ontology, and 85% Q1 Revenue Growth

As large‑model capabilities become commoditized, Palantir argues that the true moat lies in its ontology‑driven infrastructure, which integrates business semantics to ensure reliable AI in high‑risk contexts, a strategy reflected in its 85% Q1 revenue jump and a three‑layer AI competition model.

AI commoditizationAI competitionInfrastructure
0 likes · 11 min read
Why Palantir’s Value Is Rising: AI Commoditization, Ontology, and 85% Q1 Revenue Growth
DataFunTalk
DataFunTalk
May 13, 2026 · Artificial Intelligence

How a 0.5 MB AI Model Tackles Global Supply‑Chain Challenges: Li‑Net in Action

Li‑Net, a 0.5 MB multi‑channel time‑series model co‑developed by SF Technology and Chinese universities, achieves state‑of‑the‑art accuracy with linear‑complexity attention, runs on edge devices, and has been deployed across SF's global supply‑chain for demand forecasting, inventory optimization, and capacity planning, delivering measurable cost reductions.

AILi-NetSupply Chain
0 likes · 4 min read
How a 0.5 MB AI Model Tackles Global Supply‑Chain Challenges: Li‑Net in Action
DataFunTalk
DataFunTalk
May 13, 2026 · Industry Insights

Ilya Sutskever Testifies: 52‑Page Dossier Exposes Altman's Lies and $7 B Stake

In a dramatic courtroom appearance, OpenAI co‑founder Ilya Sutskever swore an oath, unveiled a 52‑page dossier accusing Sam Altman of habitual deception, detailed how Altman's tactics undermined executive information flow and AI safety, disclosed his roughly $7 billion ownership stake, and revealed board power struggles involving Microsoft, a near‑missed Anthropic merger, and the broader implications for OpenAI’s future.

AI governanceCorporate governanceIlya Sutskever
0 likes · 10 min read
Ilya Sutskever Testifies: 52‑Page Dossier Exposes Altman's Lies and $7 B Stake
DataFunTalk
DataFunTalk
May 12, 2026 · Artificial Intelligence

Deep Dive into Agent Harness: Unpacking the Architecture Behind AI Agents

The article dissects the concept of an Agent Harness—a comprehensive software infrastructure that wraps large language models to enable autonomous agents—detailing its three engineering layers, twelve production‑grade components, benchmark improvements, implementation patterns across Anthropic, OpenAI, LangChain, and design trade‑offs such as orchestration loops, tool integration, memory, context management, error handling, and safety.

AI AgentsAgent HarnessLLM
0 likes · 19 min read
Deep Dive into Agent Harness: Unpacking the Architecture Behind AI Agents