エピソード

  • Quarkus and LangChain4J - A Match Made in Heaven

    Quarkus and LangChain4J - A Match Made in Heaven
    2024/10/20
    An airhacks.fm conversation with Georgios Andrianakis (@geoand86) about: discussion on integrating langchain4j with quarkus for enterprise AI applications, similarities between LLM integration and microservice architecture, benefits of using Java and MicroProfile for AI development, explanation of AI services, chat memory, and tools in LangChain4J, importance of session management and fault tolerance in LLM applications, vector databases and embeddings for efficient information retrieval, RAG (Retrieve Augmented Generation) implementation in enterprise settings, Quarkus dev mode features for LLM experimentation, native image support with GraalVM, local inference possibilities with Java 21's Vector API and quantized models, challenges in prompt engineering and model selection, upcoming features in LangChain4J including Ollama tool support and improved result streaming, future developments in Java for AI and GPU support with Project Babylon, importance of enterprise-grade features like CI/CD, testing, and cloud deployment for LLM applications

    Georgios Andrianakis on twitter: @geoand86
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    1 時間 3 分
  • Why JVector 3 Is The Most Advanced Embedded Vector Search Engine

    Why JVector 3 Is The Most Advanced Embedded Vector Search Engine
    2024/10/13
    An airhacks.fm conversation with Jonathan Ellis (@spyced) about: discussion of JVector 3 features and improvements, compression techniques for vector indexes, binary quantization vs product quantization, anisotropic product quantization for improved accuracy, indexing Wikipedia example, Cassandra integration, SIMD acceleration with Fused ADC, optimization with Chronicle Map, E5 embedding models, comparison of CPU vs GPU for vector search, implementation details and low-level optimizations in Java, use of Java Panama API and foreign function interface, JVector's performance advantages, memory footprint reduction, integration with Cassandra and Astra DB, challenges of vector search at scale, trade-offs between RAM usage and CPU performance, Eventual Consistency in distributed vector search, comparison of different embedding models and their accuracy, importance of re-ranking in vector search, advantages of JVector over other vector search implementations

    Jonathan Ellis on twitter: @spyced
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    54 分
  • The AI Revolution in Java Development and Devoxx Genie

    The AI Revolution in Java Development and Devoxx Genie
    2024/10/06
    An airhacks.fm conversation with Stephan Janssen (@Stephan007) about: Stephan previously appeared on "#254 How JavaPolis and Devoxx Happened", discussion on the AI revolution in programming, development of an AI-assisted photo sharing application, creation of the Devoxx Genie IntelliJ plugin for AI-augmented programming, advantages of Claude 3.5 from Anthropic, use of local AI models in development environments, integration of AI in Java development, langchain4j and its adoption by Red Hat, development of Java-based AI tools like Lama3.java, jlama and JVector, potential for specialized AI models in software development, challenges and opportunities for junior and senior developers in AI-augmented programming, importance of understanding cloud services and cost structures when using AI, potential future of prompt-based programming and code generation, discussion on maintaining and improving AI-generated code, exciting developments in Java for AI including project valhalla and tornadovm, potential for running AI models directly on Java without external dependencies, considerations for enterprise AI adoption and integration, the need for promoting Java's capabilities in AI development, potential for Visual Studio Code port of Devoxx Genie, the challenge of maintaining AI-generated code versus keeping prompts, the concept of "prompt ops" for software development, the use of AI for code review and improvement, the potential for AI to lower the barrier to entry for new developers, and the exciting future of AI in software development

    Stephan Janssen on twitter: @Stephan007
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    1 時間 9 分
  • From Apache Cassandra to Serverless: Exploring Cloud-Native Databases

    From Apache Cassandra to Serverless: Exploring Cloud-Native Databases
    2024/10/05
    An airhacks.fm conversation with Jake Luciani (@tjake) about: from Commodore 64 to cloud databases, early programming experiences with Basic and Excel macros, studying cognitive science and its influence on his career, transition to computer science, working at Bell Labs on R language, developing open-source projects like Night Rider MP3 player, creating a NoSQL database that led to involvement with Cassandra, building search API on top of Cassandra, joining datastax as an early employee, working on various aspects of Cassandra including compaction and streaming, challenges of byte buffer implementation, development of CQL (Cassandra Query Language), transition from NoSQL to SQL-like interfaces, separation of compute and storage in cloud databases, using S3 as the source of truth for Astra DB, implementing a Java file system abstraction for S3 integration, using etcd as a transactional cache for metadata, offering multiple APIs including REST and CQL drivers for astra DB, implementing JSON document storage and querying capabilities, cross-AZ cost considerations in cloud deployments, Java as a language for database development, future plans for jlama (Java-based LLM inference engine), the importance of open-source in cloud technologies, cost-driven architectures in cloud deployments, serverless vs. traditional deployments trade-offs, integration of AstraDB with cloud marketplaces and security considerations

    Jake Luciani on twitter: @tjake
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    1 時間 16 分
  • Revolutionizing AI with Java: From LLMs to Vector APIs

    Revolutionizing AI with Java: From LLMs to Vector APIs
    2024/09/28
    An airhacks.fm conversation with Alfonso Peterssen (@TheMukel) about: Alfonso previously appeared on "#294 LLama2.java: LLM integration with A 100% Pure Java file", discussion of llama2.java and llama3.java projects for running LLMs in Java, performance comparison between Java and C implementations, use of Vector API in Java for matrix multiplication, challenges and potential improvements in Vector API implementation, integration of various LLM models like Mistral, phi, qwen or gemma, differences in model sizes and capabilities, tokenization and chat format challenges across different models, potential for Java Community Process (JCP) standardization of gguf parsing, quantization techniques and their impact on performance, plans for integrating with langchain4j, advantages of pure Java implementations for AI models, potential for GraalVM and native image optimizations, discussion on the future of specialized AI models for specific tasks, challenges in training models with language capabilities but limited world knowledge, importance of SIMD instructions and vector operations for performance optimization, potential improvements in Java's handling of different float formats like float16 and bfloat16, discussion on the role of smaller, specialized AI models in enterprise applications and development tools

    Alfonso Peterssen on twitter: @TheMukel
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    1 時間 9 分
  • JAX-RS With- and Without Reactive Programming in Quarkus

    JAX-RS With- and Without Reactive Programming in Quarkus
    2024/09/22
    An airhacks.fm conversation with Georgios Andrianakis (@geoand86) about: discussion on JAX-RS and reactive programming in quarkus, comparison of blocking vs non-blocking approaches, performance considerations for different use cases, Quarkus underlying architecture using Vert.x, handling of HTTP requests and responses, thread management in Quarkus, reactive vs traditional programming models, integration with databases using Hibernate and Hibernate Reactive, JSON serialization options (Jackson, JSON-B), balancing act between supporting standards and providing modern features, documentation challenges for a large project like Quarkus, detecting blocked event loop threads, CPU-intensive tasks in reactive programming, non-blocking database drivers for reactive programming, historical perspective on messaging systems and their challenges, use cases for reactive programming, performance characteristics of blocking vs non-blocking systems under high load, brief mention of LangChain for Java and its similarity to JPA for LLMs

    Georgios Andrianakis on twitter: @geoand86
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    1 時間 7 分
  • Developer and Database Administrator

    Developer and Database Administrator
    2024/09/10
    An airhacks.fm conversation with Gerald Venzl (@GeraldVenzl) about: from a 386 computer with SimCity to Oracle's database evangelist, early interest in computer hardware and software, apprenticeship as a programmer in Austria, work experience with Oracle database and PLSQL, Steven Feuerstein, PLSQL expert, career moves to New York, London, and San Francisco, role as product manager and team leader at Oracle, efforts to attract developers to Oracle technologies, involvement in Oracle ACE Program, work on docker files for Oracle Database, challenges with ARM port for Mac, popular JavaOne talk on optimizing Java code for database performance, discussion of Oracle's various database technologies including NoSQL and TimesTen, importance of educating developers on database best practices, evolution of database performance techniques, future topics for discussion including Oracle architecture, Java integration, and business logic in databases, Gerald's team of evangelists across Europe, ways to contact Gerald and his team for speaking engagements or information

    Gerald Venzl on twitter: @GeraldVenzl
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    1 時間 2 分
  • Java 22 and 23 Features

    Java 22 and 23 Features
    2024/09/03
    An airhacks.fm conversation with Nicolai Parlog (@nipafx) about: Java 22 and 23 new features overview, including unnamed variables with underscore, multi-source file launching, G1 region pinning, Foreign Function & Memory API finalization, Markdown Javadoc support, ZGC generational collector by default, discussion on Java installation and beginner-friendliness, debate on proper use of LTS terminology for Java releases, potential for Java in AI/ML space with new vector APIs and native performance, comparison of Java to python for AI workloads, challenges and opportunities for Java adoption in data science and machine learning domains, importance of specialized AI models vs general models for enterprise use cases, trade-offs between developer experience and operational efficiency for different languages and runtimes, potential future directions for Java in high-performance computing and AI acceleration, previously, Nicolai appeared on "#300 Object-Oriented Programming (OOP) vs. Data-Oriented Programming (DOP) in Java"

    Nicolai Parlog on twitter: @nipafx
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    1 時間 31 分