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Hazelcast Platform Architecture Guide

Hazelcast Platform 5.x has emerged as the definitive solution for distributed computing architectures, combining in-memory data grid capabilities with stream processing, microservices support, and enterprise-grade security in a unified platform. This comprehensive analysis reveals how organizations across industries are leveraging Hazelcast's capabilities to build resilient, high-performance systems that scale from startup applications to mission-critical enterprise deployments processing billions of events per second.

The platform's evolution to a unified architecture in version 5.x eliminates the complexity of managing separate systems for caching, stream processing, and messaging, while introducing breakthrough capabilities like Thread-Per-Core (TPC) architecture that delivers linear scaling and vector search for AI workloads. Real-world implementations demonstrate performance improvements of up to 200x over traditional approaches, with organizations like BNP Parib

Enterprise RAG Framework Evaluation: Strategic Technical Analysis 2024-2025

The RAG development landscape has matured significantly, with enterprise-grade platforms emerging alongside innovative specialized frameworks. This comprehensive evaluation reveals LangChain and Microsoft's dual strategy (Semantic Kernel + AutoGen) as enterprise leaders, while Dify and CrewAI represent the strongest emerging competitors in visual workflow and multi-agent categories respectively.

Enterprise Readiness & Architecture

Tier 1: Production-Ready Enterprise Platforms

LangChain dominates enterprise readiness with the most comprehensive offering. The platform supports millions of users through modular, microservices-based architecture with LangGraph Platform enabling sophisticated orchestration. Enterprise features include SOC2-compliant LangSmith observability, native workspace multi-tenancy, and hybrid deployment models supporting both SaaS control planes with self-hosted data planes. Prod

SWE-bench LLM Evaluation: Comprehensive Technical Analysis

The evaluation landscape for Large Language Models in software development has undergone dramatic transformation since SWE-bench's introduction in 2023. Performance metrics have exploded from 1.96% initial resolution rates to over 70% on certain variants, representing one of the most rapid capability progressions in AI benchmarking history. However, recent critical analysis reveals fundamental methodological flaws that significantly inflate these performance claims, while new evaluation paradigms emerge to address these limitations.

This technical analysis examines the current state of LLM coding evaluation through six critical dimensions: core methodologies, extended standards, alternative frameworks, performance benchmarks, technical limitations, and emerging trends. The findings illuminate both remarkable progress and substantial evaluation challenges that impact enterprise deployment strategies.

SWE-bench core methodology and technical

Git Worktree 企業級深度實戰指南

Git worktree 已從簡單的特性發展為現代軟體開發中的關鍵工具，特別適用於管理大型代碼庫、遺留系統維護，以及複雜的開發工作流程。本指南基於企業級實戰案例和效能分析，為技術架構師提供全面的實施策略。

完整命令語法與核心架構

詳細命令語法參考

核心命令集合：

Debezium WAL 容量問題完整解決方案

1. WAL 檔案增長的根本原因分析

1.1 Debezium CDC 抄寫過程中 WAL 檔案累積的機制

核心機制分析： PostgreSQL 的 Write-Ahead Log (WAL) 在 Debezium CDC 場景中扮演關鍵角色。當資料變更時，變更首先寫入 WAL，然後才更新資料檔案。Debezium 使用 PostgreSQL 的邏輯複製功能，透過 pgoutput 或 wal2json 外掛程式從 WAL 中讀取變更事件。

WAL 累積的具體流程：

SuperClaude 企業級使用手冊

SuperClaude v3.0.0.1 是一個革命性的 Claude Code 配置框架，將通用 AI 助手轉變為專業的企業級開發夥伴。該框架通過 16 個專業化命令、9 個認知角色和 70% 的 Token 優化，已為 6,500+ 開發者提供了結構化、高效的 AI 輔助開發解決方案。

從技術架構師的角度分析，SuperClaude 解決了企業級開發中的三大核心挑戰：AI 助手缺乏專業化、開發工作流程標準化困難，以及 Token 成本過高。通過證據驅動的方法論和模塊化設計，它為現代軟體開發提供了可擴展、可維護的 AI 輔助框架。

⚠️ 從 v2 升級？重要提示！

如果您是從 SuperClaude v2 升級，請注意以下關鍵變化：

徹底清理舊檔案：請手動刪除以下可能存在的文件和目錄，以避免衝突：

SuperClaude/ (舊的專案根目錄)

BPMN Process Engine Landscape Analysis

The BPMN process engine market spans mature commercial platforms, vibrant open source projects, and innovative emerging solutions. This analysis examines 25+ engines across technical capabilities, enterprise readiness, and architectural approaches, revealing significant innovation in cloud-native and microservices-oriented designs while highlighting persistent challenges in full BPMN 2.0 specification compliance.

Commercial BPMN engine powerhouses

Market leaders with distinct positioning

Camunda Platform leads the cloud-native transformation with its Zeebe engine, achieving horizontal scalability through distributed architecture without central database dependencies. The platform processes hundreds of thousands of workflow instances per second while maintaining enterprise-grade reliability. Version 8.6 (October 2024) introduced significant enterprise licensing changes, requiring commercial licenses for production use.

CloudNativePG 雙中心高可用性架構與無資料遺失同步機制

CloudNativePG 透過主備架構實現企業級 PostgreSQL 高可用性部署，雖然不支援真正的雙活架構，但提供快速故障轉移的主備模式，可達成 RPO ≤ 5 分鐘的災難復原能力。作為 Kubernetes 原生的 PostgreSQL 運算子，CloudNativePG 利用 PostgreSQL 內建的同步複寫機制，實現無資料遺失的資料同步，同時提供自動化的生命週期管理和故障恢復能力。

CloudNativePG 核心架構與基本概念

CloudNativePG 是專為 Kubernetes 環境設計的 PostgreSQL 運算子，採用完全雲原生的設計理念。該系統使用主備（primary/standby）架構，結合 PostgreSQL 原生的串流複寫技術，而非依賴外部工具如 Patroni 或 repmgr。核心組件包括 CloudNativePG 控制器管理器、實例管理器（Instance Manager）和多個自定義資源定義（CRDs）。

控制器管理器部署在 cnpg-system 命名空間中，負責整體集群的生命週期管理。實例管理器作為每個 PostgreSQL Pod 的 PID 1 執行程序，直接管理 PostgreSQL 程序的啟動、監控和故障恢復。系統透過 Cluster、Backup、ScheduledBackup、Pooler 等 CRDs 提供聲明式配置介面。

如何在 Node.js 的 `package.json` 中使用本地套件

在 Node.js 專案開發中，有時您會需要使用本地開發中的套件版本，而不是從 npm 註冊表安裝的公開版本。這在以下情況特別有用：

您正在開發一個新的套件，並希望在另一個專案中測試它。
您正在修復一個套件中的錯誤，並希望在您的專案中驗證修復。
您需要對現有套件進行客製化修改，並在您的專案中使用該修改版本。

以下是幾種將 package.json 中的套件指向本地版本的方法：

	{
	// Use IntelliSense to learn about possible attributes.
	// Hover to view descriptions of existing attributes.
	// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
	"version": "0.2.0",
	"configurations": [
	{
	"name": "Debug with MCP Inspector",
	"type": "node",
	"request": "launch",