Author

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.

2.7k

Articles

Likes

15.2k

Views

Comments

Latest from DataFunTalk

100 recent articles max

DataFunTalk

Aug 15, 2026 · Artificial Intelligence

Why Real-Time Agents Need More Than One Loop: Google’s AMIE Splits Talk, Think, and See

Real‑time agents face a three‑way conflict—low‑latency interaction, slow reasoning, and continuous perception—so Google’s AMIE (Video) replaces a single loop with three asynchronous agents (Talker, Planner, Perception), cutting average latency from 21.4 s to 2.6 s while preserving task performance.

AMIEAgent Architectureasynchronous orchestration

0 likes · 13 min read

Why Real-Time Agents Need More Than One Loop: Google’s AMIE Splits Talk, Think, and See

DataFunTalk

Aug 15, 2026 · Artificial Intelligence

What AI Maturity Level Have Financial Institutions Actually Achieved?

The article presents a six‑layer maturity framework for large‑model AI in finance, explains how institutions can internalise generic models into knowledge, data, skills and decision systems, and shows how to evaluate technical depth, business value and risk for each layer.

AIFinancial Servicesbusiness value

0 likes · 22 min read

What AI Maturity Level Have Financial Institutions Actually Achieved?

DataFunTalk

Aug 14, 2026 · Artificial Intelligence

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

The article explains that an Agent Harness— the full software infrastructure surrounding an LLM— is essential for production‑grade AI agents, detailing its definition, three engineering layers, twelve concrete components, execution loops, framework implementations, and key design decisions that separate harness failures from model shortcomings.

AI AgentsContext ManagementLLM

0 likes · 20 min read

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

DataFunTalk

Aug 14, 2026 · Artificial Intelligence

Why DeepSeek V4 Pro’s 87.9 Score Signals Agent Benchmarks Moving from Model to System

DeepSeek V4 Pro scored 87.9 on Terminal‑Bench 2.1 using the Harness Minimal Mode with max reasoning effort, temperature 1.0 and top_p 0.95, while Vals AI reported 54.68 under a different harness, illustrating that modern Agent benchmarks evaluate the whole system rather than just the underlying model.

AI evaluationAgent BenchmarkDeepSeek

0 likes · 10 min read

Why DeepSeek V4 Pro’s 87.9 Score Signals Agent Benchmarks Moving from Model to System

DataFunTalk

Aug 14, 2026 · Artificial Intelligence

Gemini 3.7 Flash: A Three‑Week Agent‑Focused Update Over 3.6

Google released Gemini 3.7 Flash only 23 days after 3.6, keeping the same 1M‑token context but delivering algorithmic tweaks that boost coding, terminal, tool‑calling and multi‑step agent workflows, with benchmark gains in software‑engineering tasks while retaining the same pricing model.

AgentCodingGemini

0 likes · 12 min read

Gemini 3.7 Flash: A Three‑Week Agent‑Focused Update Over 3.6

DataFunTalk

Aug 13, 2026 · Industry Insights

Why Palantir’s Real Moat Lies in Decision‑Making Agents, Not Just AI Models

The article analyzes Palantir’s 2026 product roadmap—AIP Analyst, Ontology MCP, Global Branching and Pro‑code Agent—to show how the company is shifting from selling model capabilities to building an engineered decision‑system platform that lets enterprise agents act safely, a trend that reshapes AI budgets and competition, especially in China’s market.

AgentDecision SystemsEnterprise AI

0 likes · 16 min read

Why Palantir’s Real Moat Lies in Decision‑Making Agents, Not Just AI Models

DataFunTalk

Aug 13, 2026 · Artificial Intelligence

Overnight DeepSeek V4 Pro Test Reveals Disappointing Performance – Not Fit for Codex

After integrating the newly released DeepSeek V4 Pro into Codex and running 41 million tokens, the author finds the model’s silent operation, excessive context copying, weak frontend and writing abilities, and higher latency make it unsuitable as a primary model despite solid throughput and low cost.

DeepSeek V4 ProLLM evaluationcontext reuse

0 likes · 7 min read

Overnight DeepSeek V4 Pro Test Reveals Disappointing Performance – Not Fit for Codex

DataFunTalk

Aug 12, 2026 · Databases

Why Teams That Shun Database Agents May Still Be Digging Their Own Pits

The article warns that while coding agents accelerate development, they erase accumulated experience, causing repeated mistakes across team members—especially in large teams—so rejecting database agentization means refusing to turn knowledge into a lasting asset.

agent pitfallscoding agentsdatabase agents

0 likes · 2 min read

Why Teams That Shun Database Agents May Still Be Digging Their Own Pits

DataFunTalk

Aug 12, 2026 · Artificial Intelligence

MemoHarness: The Next Evolution of Agents Happens Outside the Model

MemoHarness proposes shifting agent evolution from model parameters to the external control system, breaking the task execution into six editable dimensions, recording trajectories as experience, and demonstrating performance gains on terminal, code generation, and finance tasks while acknowledging limited experimental scale and transferability.

AIExperience LearningExternal Control

0 likes · 16 min read

MemoHarness: The Next Evolution of Agents Happens Outside the Model

DataFunTalk

Aug 12, 2026 · Artificial Intelligence

Why Real‑Time Agents Need Multiple Loops: Google’s AMIE Splits Talk, Think, See

Real‑time agents face a three‑way conflict among low‑latency interaction, deep reasoning, and continuous perception, making a single‑loop design a bottleneck; Google’s AMIE solves this by decomposing the system into three asynchronous agents—Talker, Planner, and Perception—demonstrating dramatic latency reduction and higher task scores, and revealing a broader architectural shift toward time‑scale‑aware agent runtimes.

Agent RuntimeGoogle AMIEMulti-Agent Systems

0 likes · 12 min read

Why Real‑Time Agents Need Multiple Loops: Google’s AMIE Splits Talk, Think, See