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Latest from JD Tech

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JD Tech

Dec 18, 2025 · Backend Development

Can AI Prompts Supercharge Your Backend, Frontend, and Big Data Projects?

This article showcases a series of real‑world development cases—from implementing a guided inventory task in a Java backend and generating Vue rule code, to writing unit tests, analyzing report data, converting SQL to Hive, debugging startup errors, publishing Maven APIs, optimizing slow SQL queries, and resolving MySQL deadlocks—demonstrating how AI‑driven prompts can accelerate coding, testing, and troubleshooting across multiple domains.

SQLbackendbigdata

0 likes · 31 min read

Can AI Prompts Supercharge Your Backend, Frontend, and Big Data Projects?

JD Tech

Dec 16, 2025 · Databases

How DongSQL’s New Kernel Boosts E‑commerce DB Performance by Up to 215%

The article provides an in‑depth technical analysis of DongSQL V1.1.0, detailing its syntax extensions, concurrency control mechanisms, query‑execution optimizations, thread‑pool redesign, and extensive benchmark results that demonstrate significant performance gains for e‑commerce workloads.

Concurrency ControlPerformance Benchmarke-commerce

0 likes · 13 min read

How DongSQL’s New Kernel Boosts E‑commerce DB Performance by Up to 215%

JD Tech

Nov 28, 2025 · Artificial Intelligence

How JD Ads Uses Large Language Models to Transform Advertising

This article details JD Advertising's shift from generic to domain‑specific large models, the design of AI‑driven ad agents, the end‑to‑end GRAM retrieval‑alignment system, CTR‑guided AIGC for creatives, ultra‑low‑latency inference techniques, and ARM‑based optimizations that together reshape modern ad marketing.

CTR optimizationIntelligent Agentsadvertising AI

0 likes · 19 min read

How JD Ads Uses Large Language Models to Transform Advertising

JD Tech

Nov 11, 2025 · Databases

How JIMDB’s Big‑Hot Key Strategy Boosts Cache Performance by 80%

JIMDB, a Redis‑based distributed cache, introduces the Big‑Hot Key concept and a multi‑layer proactive governance framework that dynamically identifies resource‑intensive keys, automatically mitigates them, and delivers up to an 80% performance gain while dramatically improving system stability.

Big-Hot KeyJimdbRedis

0 likes · 39 min read

How JIMDB’s Big‑Hot Key Strategy Boosts Cache Performance by 80%

JD Tech

Nov 6, 2025 · Artificial Intelligence

LLMs Revolutionize Recommendation Systems: From Generative Models to Production

This article surveys the evolution of generative recommendation systems powered by large language models, detailing their technical foundations, engineering challenges, recent breakthroughs, and future research directions, while highlighting why the paradigm shift is occurring now.

AI engineeringGenerative RecommendationLLM

0 likes · 30 min read

LLMs Revolutionize Recommendation Systems: From Generative Models to Production

JD Tech

Nov 5, 2025 · Artificial Intelligence

How JoyCode Agent Achieved 74.6% Pass@1 on SWE‑bench Verified and Ranked Top‑3 Globally

JoyCode Agent, an AI‑driven multi‑agent system, secured a 74.6% pass@1 rate on the SWE‑bench Verified benchmark, placing it in the global Top‑3 while cutting computational resource usage by 30‑50% through a novel patch‑test co‑generation and iterative verification pipeline.

AIAgent architecturePatch Generation

0 likes · 34 min read

How JoyCode Agent Achieved 74.6% Pass@1 on SWE‑bench Verified and Ranked Top‑3 Globally

JD Tech

Oct 24, 2025 · Frontend Development

How JD Finance Boosted Chart Rendering Speed with ROMA and ECharts

This article details how JD Finance tackled intermittent chart rendering failures in its mobile app by integrating Apache ECharts via ROMA, redesigning the WebView caching strategy, optimizing data flow between Native and WebView, and adding auto‑retry mechanisms to achieve near‑100% success and 200 ms faster startup rendering.

0 likes · 7 min read

How JD Finance Boosted Chart Rendering Speed with ROMA and ECharts

JD Tech

Oct 9, 2025 · Artificial Intelligence

What Is Retrieval‑Augmented Generation (RAG) and How Does It Boost AI Accuracy?

This article explains Retrieval‑Augmented Generation (RAG), an AI framework that combines external knowledge retrieval with large language models, covering its motivations, data preparation, chunking strategies, vectorization, storage, query processing, retrieval, reranking, prompt engineering, and LLM generation, plus practical optimization tips.

ChunkingLLMRAG

0 likes · 14 min read

What Is Retrieval‑Augmented Generation (RAG) and How Does It Boost AI Accuracy?

JD Tech

Sep 26, 2025 · Operations

Avoiding High‑Availability Pitfalls: Real‑World JD Lessons and Solutions

This article examines common high‑availability challenges across applications, databases, caches, message queues, containers, and GC, presenting real JD engineering cases, root‑cause analyses, and practical mitigation strategies to help engineers design more resilient systems.

Message QueueRedisdatabase

0 likes · 37 min read

Avoiding High‑Availability Pitfalls: Real‑World JD Lessons and Solutions

JD Tech

Sep 18, 2025 · Artificial Intelligence

How I Turned a General LLM into a Precise E‑commerce Risk Detector

The article recounts how a risk‑control algorithm engineer progressively refined a generic large language model through four stages of prompt engineering—role‑playing, business knowledge injection, deeper analysis, and a double‑hypothesis decision framework—to transform it into a precise e‑commerce fraud detection expert.

AILLMRisk Detection

0 likes · 12 min read

How I Turned a General LLM into a Precise E‑commerce Risk Detector