Official JD Tech public account delivering best practices and technology innovation.
This guide explains why Playwright has become essential for front‑end automation testing, walks through installing Node.js and Playwright, shows how to configure a project, provides runnable demo code, and illustrates common API calls such as GET and POST requests with detailed examples.
JoyCode Agent leverages a patch‑test co‑generation and iterative validation framework to achieve a 74.6% Pass@1 score on the SWE‑bench Verified benchmark, reducing resource consumption by 30‑50% and introducing a closed‑loop multi‑agent pipeline that integrates testing, patch generation, trajectory compression, similarity retrieval, and decision arbitration.
This technical report details JoyCode Agent’s end‑to‑end pipeline that couples patch generation with fail‑to‑pass and pass‑to‑pass test creation, uses trajectory compression, CSR similarity retrieval, and multi‑agent iterative retries to achieve a 74.6% Pass@1 score on the SWE‑bench Verified benchmark while cutting compute costs by 30‑50%.
This article explains Apache Hudi’s core concepts, including its timeline architecture, file layout, indexing mechanisms, and the two primary table types—Copy on Write and Merge on Read—along with their trade‑offs and the various query modes such as snapshot, time‑travel, and incremental queries.
This article provides an in‑depth technical analysis of DongSQL’s kernel redesign—including RETURNING clause, Inventory Hint, CCL concurrency control, Statement Outline, and thread‑pool optimizations—showing how each feature dramatically improves throughput and latency for high‑traffic e‑commerce workloads.
JoyAgent, JD's open‑source intelligent‑agent platform, now adds multimodal Retrieval‑Augmented Generation (RAG) capabilities, combining graph‑based knowledge, hierarchical chunking, and vision‑language models to handle text, images, tables, and API data for enterprise knowledge processing and evaluation.
This article explains why effective chunking is crucial for RAG performance, compares seven major chunking strategies—including fixed‑size, semantic, recursive, document‑structure, agent‑driven, sentence, and paragraph methods—and offers practical guidance on selecting and optimizing chunks for real‑world AI applications.
This article explains experimental heterogeneity (HTE), clarifies key concepts such as CATE and ITE, discusses why analyzing treatment‑effect variation matters for business, compares statistical and machine‑learning methods, and introduces an open‑source Python tool that automates CATE discovery and reporting.
The article explains how Easy Code Reader MCP Server integrates with Joycode and large language models to automatically retrieve and analyze Java source code across multiple projects or JAR files, improving code‑agent accuracy and developer productivity.
This article outlines the motivation, design, architecture, engineering implementation, large‑model selection, and future improvement plans for an AI‑driven experiment analysis agent that integrates data aggregation, modular workflow orchestration, and interactive frontend features to streamline AB‑test insights.
