airhacks.fm podcast with adam bien cover art

airhacks.fm podcast with adam bien

airhacks.fm podcast with adam bien

By: Adam Bien
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About this listen

 Java, Serverless, Clouds, Architecture and Web conversations with Adam BienAdam Bien, copyright 2022
Episodes
  • There Can Be Only One

    There Can Be Only One
    Jul 15 2025
    An airhacks.fm conversation with Maurice Naftalin (@mauricenaftalin) about: experiences with Visual Age for Java and its visual programming approach with arrows connecting components, working on British Department of Health and Social Security project using Visual Age for Java for benefits system navigation, comparison of various Java IDEs including Visual J++, Sun Java Workshop, JBuilder, Eclipse, NetBeans, IntelliJ IDEA, and Visual Studio Code, advantages of VS Code for polyglot programming and its growing ecosystem, visual programming experiences with state charts for reactive systems, IBM Rational tools and UML integration, successful visual programming with NetBeans Matisse GUI builder and AWS Step Functions, Model Driven Architecture and code generation from UML diagrams, writing Java Generics and Collections book with Philip Wadler for Java 5 and updating it for a second edition, changes in Java idioms over 15 years including deprecation of wrapper class constructors, sequence collections as major addition to Java collections framework, PECS (Producer Extends Consumer Super) principle for generics, underappreciated Java collections like NavigableMap, preference for method references and keeping lambdas concise in streams, using Class::method notation instead of Class.method, Scottish countryside and Edinburgh living experiences, early internet challenges with 300 baud acoustic couplers influencing views on network distribution versus CD-ROMs, transition from safety-critical systems to Java training and consulting, importance of understanding bounded wildcards in generics, future impact of Project Valhalla on generics and collections

    Maurice Naftalin on twitter: @mauricenaftalin
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    1 hr and 4 mins
  • TornadoVM: The Need for GPU Speed

    TornadoVM: The Need for GPU Speed
    Jul 6 2025
    An airhacks.fm conversation with Michalis Papadimitriou (@mikepapadim) about: starting with Java 8, first computer experiences with Pentium 2, doom 2 and Microsoft Paint, university introduction to Object-oriented programming using Objects First and bluej IDE, Monte Carlo simulations for financial portfolio optimization in Java, porting Java applications to OpenCL for GPU acceleration achieving 20x speedup, working at Huawei on GPU hardware, writing unit tests as introduction to TornadoVM, working on FPGA integration and Graal compiler optimizations, experience at OctoAI startup doing AI compiler optimizations for TensorFlow and PyTorch models, understanding model formats evolution from ONNX to GGUF, standardization of LLM inference through Llama models, implementing GPU-accelerated Llama 3 inference in pure Java using TornadoVM, achieving 3-6x speedup over CPU implementations, supporting multiple models including Mistral and working on qwen 3 and deepseek, differences between models mainly in normalization layers, GGUF becoming quasi-standard for LLM model distribution, TornadoVM's Consume and Persist API for optimizing GPU data transfers, challenges with OpenCL deprecation on macOS and plans for Metal backend, importance of developer experience and avoiding python dependencies for Java projects, runtime and compiler optimizations for GPU inference, kernel fusion techniques, upcoming integration with langchain4j, potential of Java ecosystem with Graal VM and Project Panama FFM for high-performance inference, advantages of Java's multi-threading capabilities for inference workloads

    Michalis Papadimitriou on twitter: @mikepapadim
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    1 hr
  • Building AI-Native Code Platform With Java for Java

    Building AI-Native Code Platform With Java for Java
    Jul 3 2025
    An airhacks.fm conversation with Jonathan Ellis (@spyced) about: brokk AI tool for code generation named after Norse god of the forge, AI as complement to experienced programmers' skillsets, age and productivity in programming, transition from JVector to working on Cassandra codebase, challenges with AI in large codebases with extensive context, building tools for historical Java codebases, comparison of productivity between younger and older programmers, brute force coding vs experienced approach, reading code quickly as a senior skill, AI generating nested if-else statements vs better structures, context sculpting in Brokk, open source nature of Brokk, no black boxes philosophy, surfacing AI context to users, automatic context pulling with manual override options, importing dependencies and decompiling JARs for context, syntax tree based summarization, Maven and Gradle dependency handling, unique Java-specific features, multiple AI model support simultaneously, Claude vs Gemini Pro performance differences, Git history as context source, capturing commits and diffs for regression analysis, migration analysis between commits, AI code review and technical debt cleanup, style for code style guidelines, using modern Java features like var and Streams, Error Prone and NullAway integration for code quality, comparison with Cursor's primitive features, branching conversation history, 80% time in Brokk vs 20% in IntelliJ workflow, sketching package structures for AI guidance, data structures guiding algorithms, Git browser by file and commit, unified diff as context, reflection moving away from due to tooling opacity, Jackson serialization refactoring with DTOs, enterprise features like session sync and sharing, unified API key management, rate limit advantages, parallel file processing with upgrade agent, LiteLLM integration for custom models, pricing model based on credits not requests, $20/month subscription with credits, free tier models like Grok 3 Mini and DeepSeek V3, architect mode for autonomous code generation, code button for smaller problems with compile-test loop, ask button for planning complex implementations, senior vs junior programmer AI effectiveness, self-editing capability achieved early in development, no vector search usage despite JVector background

    Jonathan Ellis on twitter: @spyced
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    1 hr and 2 mins

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