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create-ai-chat-context

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🚀 BREAKTHROUGH: Detection-Hourglass-System (DHS) - Revolutionary auto-detection of AI conversation chunks with zero-cost processing. Universal AI memory that actually works!

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# Java Project Overview ## Project Context **Technology Stack:** Java projects, Spring Boot, Maven/Gradle - **Language:** Java 17+ (LTS) - **Build Tool:** Maven or Gradle - **Framework:** Spring Boot, Quarkus - **Database:** JPA/Hibernate, MyBatis - **Testing:** JUnit 5, Mockito ## Architecture & Patterns - **Layered Architecture:** Controller/Service/Repository - **Dependency Injection:** Spring IoC container - **Data Access:** JPA entities and repositories - **Configuration:** Application properties, profiles ## Development Workflow - **Development:** IDE with hot reload - **Build:** mvn compile, gradle build - **Testing:** Unit tests, integration tests - **Packaging:** JAR/WAR deployment artifacts ## Key Dependencies & Tools - Framework: spring-boot-starter-web - Database: spring-boot-starter-data-jpa - Testing: spring-boot-starter-test - Utilities: apache-commons, guava ## Performance Considerations - **JVM Tuning:** Heap size, GC algorithms - **Profiling:** JProfiler, VisualVM - **Caching:** Spring Cache, Redis - **Connection Pooling:** HikariCP ## Security & Best Practices - **Spring Security:** Authentication, authorization - **OWASP:** Security vulnerability scanning - **Dependencies:** SNYK, OWASP dependency check - **Secrets:** Vault, encrypted properties ## Deployment Strategy - **Application Server:** Embedded Tomcat - **Containerization:** Docker with JDK base image - **Cloud:** AWS Elastic Beanstalk, Azure - **Monitoring:** Micrometer, Prometheus --- **For AI Assistants:** This project uses Java patterns and conventions. Consider the technology stack and architecture when making suggestions.