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Latest from DaTaobao Tech

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DaTaobao Tech

Oct 22, 2026 · Artificial Intelligence

Tmall's AI Digital Employee: Full-Chain Delivery Architecture Handling 100+ Monthly Requirements

Tmall's Digital Employee system uses a main-sub agent architecture with sandbox isolation and identity delegation to autonomously handle end-to-end software delivery from PRD to release via group chat, achieving 100+ monthly active deliveries across all business lines while measuring success through autonomous delivery rates and human intervention tracking.

AI AgentsMulti-Agent SystemsR&D management

0 likes · 47 min read

Tmall's AI Digital Employee: Full-Chain Delivery Architecture Handling 100+ Monthly Requirements

DaTaobao Tech

Oct 20, 2026 · R&D Management

Tmall's Digital Employee: How AI-Native Collaboration Turns R&D Dark Matter into Reusable Assets

Tmall's digital employee embeds an AI agent into DingTalk groups to share a single requirement context across product, dev, and test roles, cutting collaboration friction — information loss, wait times, search costs — and codifying tacit knowledge into a reusable context layer that enables three evolutionary stages from process sync to AI-led async collaboration.

AI-Native CollaborationAsync CollaborationContext Layer

0 likes · 40 min read

Tmall's Digital Employee: How AI-Native Collaboration Turns R&D Dark Matter into Reusable Assets

DaTaobao Tech

Oct 15, 2026 · Artificial Intelligence

On-Device Intent Detection at Taote: LoRA, ShortGPT Pruning & AWQ Quantization

Taote details its end-to-end on-device model pipeline for real-time user intent detection, using Qwen3-0.6B with LoRA fine-tuning, ShortGPT-based layer pruning, and AWQ quantization to achieve 80%+ on-device accuracy, enabling feed re-ranking and retention coupon scenarios with measurable GMV growth.

AWQ quantizationGMV growthLoRA fine-tuning

0 likes · 30 min read

On-Device Intent Detection at Taote: LoRA, ShortGPT Pruning & AWQ Quantization

DaTaobao Tech

Sep 23, 2026 · Artificial Intelligence

Self-Iterating AI Agent Pipeline Delivers 6 Mini-Games in 2 Weeks, 12 Iterations in 3 Days

The article details a multi-agent AI system that automates mini-game production and live operations, using LangGraph and Claude Agent SDK to orchestrate specialized agents for research, planning, art, coding, level design, and operations, achieving 6 game launches in 2 weeks and 12 game iterations in 3 days with 95% positive impact.

AI game developmentClaude Agent SDKLangGraph

0 likes · 31 min read

Self-Iterating AI Agent Pipeline Delivers 6 Mini-Games in 2 Weeks, 12 Iterations in 3 Days

DaTaobao Tech

Sep 21, 2026 · Artificial Intelligence

Loop Engineering: Building Reliable Automated Agent Workflows

This article explores loop engineering for coding agents, detailing six core components—automations, worktrees, skills, plugins, sub-agents, and memory—and emphasizes designing controlled, verifiable loops with explicit stop conditions, permission layers, and state persistence to shift engineers from operators to system designers.

AI workflowsAgent AutomationCoding Agents

0 likes · 26 min read

Loop Engineering: Building Reliable Automated Agent Workflows

DaTaobao Tech

Sep 16, 2026 · Artificial Intelligence

GEPA: Zero-Config Prompt Self-Evolution via Reflective Mutation & Pareto Optimization

This article details a production-ready GEPA (Genetic-Pareto Reflective Prompt Evolution) system that automates prompt optimization through a data-driven loop of inference, scoring, reflection, and Pareto-frontier selection, replacing manual trial-and-error with a task-agnostic, zero-configuration pipeline that supports classification, scoring, and clustering tasks while preserving explainability.

DSPyGEPALLM judge

0 likes · 31 min read

GEPA: Zero-Config Prompt Self-Evolution via Reflective Mutation & Pareto Optimization

DaTaobao Tech

Aug 21, 2026 · Artificial Intelligence

Best Practices for Developing AI Skills: A Case Study of the Baibu Detail Assistant

This article analyzes the end‑to‑end development of an AI Skill for the Baibu detail assistant, covering industry best practices, architecture with a bridge decoupling local and remote components, workflow design patterns, development efficiency tricks, and runtime optimizations that together illustrate how to build maintainable, high‑performance Skills.

AI Skill DevelopmentAgent ArchitectureControl Tuning

0 likes · 21 min read

Best Practices for Developing AI Skills: A Case Study of the Baibu Detail Assistant

DaTaobao Tech

Aug 19, 2026 · Big Data

Zero‑Code Real‑Time Data Pipelines Powered by an AI‑Driven Metric Engine

The article details how a live‑streaming data team built an AI‑assisted, metric‑driven system that lets users describe a real‑time data task in under 200 characters, automatically generates DSL and Flink SQL, and supports safe incremental adjustments via a Hook protocol, cutting development time from days to minutes.

AI-assisted data pipelineDSLFlink

0 likes · 17 min read

Zero‑Code Real‑Time Data Pipelines Powered by an AI‑Driven Metric Engine

DaTaobao Tech

Aug 14, 2026 · R&D Management

Rethinking Collaboration: Insights on Building AI‑Native Teams

The article argues that AI‑driven efficiency gains are limited by fragmented collaboration, proposes a three‑layer "knowledge base + Agent + human" model to replace humans as the sole orchestrators, and details the practical challenges of constructing such knowledge bases in legacy businesses.

AIAI-nativeAgent

0 likes · 20 min read

Rethinking Collaboration: Insights on Building AI‑Native Teams

DaTaobao Tech

Jul 15, 2026 · Artificial Intelligence

AI Native Model (AINMM): Five Levels to Measure Production AI Transformation

The article introduces the AI Native Maturity Model (AINMM), a five‑level framework inspired by CMMI that defines measurable criteria for assessing and guiding the evolution of existing production‑grade software projects toward AI‑driven development, offering concrete steps, tools, and case‑study insights to achieve ten‑fold efficiency gains.

AI IntegrationAI-nativeMaturity Model

0 likes · 39 min read

AI Native Model (AINMM): Five Levels to Measure Production AI Transformation