airhacks.fm podcast with adam bien

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jclasslib--The 3k Stars Bytecode Editor
An airhacks.fm conversation with Ingo Kegel (@IngoKegel) about: jclasslib bytecode viewer development history starting in 2001, transition from CVS to Subversion to Git, SourceForge to GitHub migration, Swing UI development with FlatLaf look and feel, comparison between Swing and SWT APIs, Eclipse plugin development experiences, Visual Studio Code integration with jprofiler, Homebrew package management for Mac applications, Java desktop module and modularization, jlink for creating trimmed JDK distributions, security benefits of shipping only required modules, Java compatibility improvements since Java 17, Base64 encoder becoming public API, internal API access restrictions with module system, comparison of Java installation simplicity versus Node.js and python, potential JSON support in future JDK versions, NetBeans integration attempt and recognition issues, bytecode instrumentation for profiling, asm and ByteBuddy as standard bytecode manipulation libraries, class file format evolution and complexity, module system introducing new structures, stack map tables and verification challenges, using JClassLib for method signature extraction, dokka documentation system for Kotlin, package.md and package-info documentation patterns, potential revival of Swing for modern desktop applications, simplified application architectures compared to enterprise apps with 30-40 tabs, LLM and AI making applications simpler with chat interfaces, JClassLib use cases including learning JVM internals and editing class files, approximately 3000 GitHub stars indicating 30000+ users, IntelliJ IDEA plugin availability, physicist background influencing interest in Java internals, Java Language Specification and Class File Format books, experimental physics approach to understanding JVM Ingo Kegel on twitter: @IngoKegel
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JProfiler Visual Studio Code Integration -- The Kotlin Multiplatform Killer Use Case
An airhacks.fm conversation with Ingo Kegel (@IngoKegel) about: jprofiler Visual Studio Code integration using Kotlin Multiplatform, migrating Java code to Kotlin common code for cross-platform compatibility, transpiling to JavaScript for Node.js runtime, JClassLib bytecode viewer and manipulation library, Visual Studio Code's Language Server Protocol (LSP), profiling unit tests and performance regression testing, Java Flight Recorder (JFR) for production monitoring with custom business events, cost-driven development in cloud environments, serverless architecture with AWS Lambda and S3, performance optimization with parallelism in single-CPU environments, integrating profiling data with LLMs for automated optimization, MCP servers for AI agent integration, Gradle and Maven build system integration, cooperative window switching between JProfiler and VS Code, memory profiling and thread analysis, comparing streams vs for-loops performance, brokk AI's Swing-based LLM development tool, context-aware performance analysis, automated code optimization with AI agents, business event correlation with low-level JVM metrics, cost estimation based on cloud API calls, quarkus for fast startup times in serverless, performance assertions in System Tests, multi-monitor development workflow support Ingo Kegel on twitter: @IngoKegel
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1:11:19
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1:11:19
Vibe Coding and AttendeesOverflowException
An airhacks.fm conversation with Stephan Janssen (@Stephan007) about: Devoxx Belgium conference selling out in seconds with 7000+ page views, ticket coordination strategies by companies, VAT validation issues with European services, conference featuring largest Java AI content globally, AI and LLM focus with dedicated GenAI track, MCP and AI agents presentations, Java on GPU with tornadovm and Babylon project, quarkus AI agents for langchain4j, vibes coding experiences with React and Angular, importance of specifications for AI code generation, using multiple LLMs for review (Claude, GPT, DeepSeek), file system RAG replacing vector databases, MCP as evolution of RAG, Java as optimal language for LLM code generation due to type safety and standards, bce pattern and MicroProfile for consistent code structure, package-info files improving LLM context understanding, junior developers benefiting from AI as universal tutor, CSS generation without manual coding, dark/light theme support using CSS variables, enterprise challenges with dependency management, DevoxxGenie plugin popularity in enterprises with restricted AI access, open source contribution challenges, local LLMs becoming powerful for enterprise inference, TornadoVM Metal support enabling local inference, comparison with GraalVM inference performance, AI disrupting video production and creative industries, importance of prompt engineering skills, renaissance of development through AI empowerment Stephan Janssen on twitter: @Stephan007
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Java, LangChain4J and Enterprise LLMs
An airhacks.fm conversation with Antonio Goncalves (@agoncal) about: journey from Java Champion to Principal Software Engineer at Microsoft focusing on AI, the evolution from Java EE standards to modern AI development, writing technical books with LLM assistance, langchain4j as a Java SDK for LLMs providing abstraction over different AI providers, the importance of Java standards and patterns for LLM code generation, Boundary Control Entity (BCE / ECB) pattern recognition by LLMs, quarkus integration with LangChain4J enabling dependency injection and multi-tenancy, MCP (Model Context Protocol) as a new standard potentially replacing some RAG use cases, enterprise AI adoption using Azure AI Foundry and AWS Bedrock, model routers for optimal LLM selection based on prompt complexity, the future of small specialized models versus large general models, tornadovm enabling Java execution on GPUs with 6x performance improvements, GraalVM native compilation for LLM applications, the resurgence of Java EE patterns in the age of AI, using prompts as documentation in READMEs and JavaDocs, the advantage of type-safe languages like Java for LLM understanding, Microsoft's contribution to open source AI projects including LangChain4J, teaching new developers with AI assistance and the importance of curiosity, CERN's particle accelerator and its use of Java, the comparison between old "hallucinating architects" and modern LLM hallucinations, writing books about AI using AI tools for assistance, the structure of the Understanding LangChain4j book covering models RAG tools and MCP, enterprise requirements for data privacy and model training restrictions Antonio Goncalves on twitter: @agoncal
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1:16:27
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1:16:27
From SIMD to CUDA with TornadoVM
An airhacks.fm conversation with Michalis Papadimitriou (@mikepapadim) about: GPU acceleration for LLMs in Java using tornadovm, evolution from CPU-bound SIMD optimizations to GPU memory management, Alfonso's original Java port of llama.cpp using SIMD and Panama Vector API achieving 10 tokens per second, TornadoVM's initial hybrid approach combining CPU vector operations with GPU matrix multiplications, memory-bound nature of LLM inference versus compute-bound traditional workloads, introduction of persist and consume API to keep data on GPU between operations, reduction of host-GPU data transfers for improved performance, comparison with native CUDA implementations and optimization strategies, JIT compilation of kernels versus static optimization in frameworks like tensorrt, using LLMs like Claude to optimize GPU kernels, building MCP servers for automated kernel optimization, European Space Agency using TornadoVM in production for simulations, upcoming Metal backend support for Apple Silicon within 6-7 months, planned support for additional models including Mistral and gemma, potential for distributed inference across multiple GPUs, comparison with python and C++ implementations achieving near-native performance, modular architecture supporting OpenCL PTX and future hardware accelerators, challenges of new GPU hardware vendors like tenstorrent focusing on software ecosystem, planned quarkus and langchain4j integration demonstrations Michalis Papadimitriou on twitter: @mikepapadim
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