airhacks.fm podcast with adam bien

An airhacks.fm conversation with Roni Dover (@doppleware) about: previously Roni on airhacks.fm "#252 BDD: Bug Driven Development vs. Continuous Observability", discussion about the Java community and its focus on innovation, Digma and Java, Digma's growth and user feedback, observability as a tool for early issue detection and better code design, the importance of continuous observability and reducing mental effort, Digma's elevator pitch and data science approach, the changing testing pyramid and the benefits of test containers, "#103 Unit Testing Considered Harmful", the cost and value of different types of tests, optimizing lambda costs and performance, linking System Tests to traces from quarkus JVMs, Digma's architecture and deployment options, recent features and improvements in Digma, the impact of observability on productivity and shorter feedback loops, AWS Lambda Power Tuning, the limitations and potential of large language models (LLMs) in generating tests and code, the importance of understanding the client and writing maintainable code, the challenges of complex code generated by LLMs, the potential of feeding runtime data to LLMs for code generation and optimization, the Java community's vibrant and innovative nature Roni Dover on twitter: @doppleware

An airhacks.fm conversation with Dmytro Liubarskyi (@langchain4j) about: Dmytro previously on "#285 How LangChain4j Happened", discussion about ONNX format and runtime for running neural network models in Java, using langchain4j library for seamless integration and data handling, embedding models for converting text into vector representations, strategies for handling longer text inputs by splitting and averaging embeddings, overview of the retrieval augmented generation (RAG) pipeline and its components, using embeddings for query transformation, routing, and data source selection in RAG, integrating Langchain4j with quarkus and CDI for building AI-powered applications, Langchain4j provides pre-packaged ONNX models as Maven dependencies, embedding models are faster and smaller compared to full language models, possibilities of using embeddings for query expansion, summarization, and data source selection, cross-checking model outputs using embeddings or another language model, decomposing complex AI services into smaller, specialized sub-modules, injecting the right tools and data based on query classification Dmytro Liubarskyi on twitter: @langchain4j

An airhacks.fm conversation with Jonathan Ellis (@spyced) about: Jonathan's first computer experiences with IBM PC 8086 and Thinkpad laptop with Red Hat Linux, becoming a key contributor to Apache Cassandra and founding datastax, starting DataStax to provide commercial support for Cassandra, early experiences with Java, C++, and python, discussion about the evolution of Java and its ecosystem, the importance of vector databases for semantic search and retrieval augmented generation, the development of JVector for high-performance vector search in Java, the potential of integrating JVector with LangChain for Java / langchain4j and quarkus for serverless deployment, the advantages of Java's productivity and performance for building concurrent data structures, the shift from locally installed software to cloud-based services, the challenges of being a manager and the benefits of taking a sabbatical to focus on creative pursuits, the importance of separating storage and compute in cloud databases, Cassandra's write-optimized architecture and improvements in read performance, DataStax's investment in Apache Pulsar for stream processing, the llama2java project for high-performance language models in Java Jonathan Ellis on twitter: @spyced

An airhacks.fm conversation with Alfonso Peterssen (@TheMukel) about: discussion about Alfonso's early programming experience and participation in the IOI competition, studying computer science and functional programming with Martin Odersky, internships at Google and Oracle Labs working on compilers and the Espresso project implementing a JVM in Java, espresso mentioned in "#208 GraalVM: Meta Circularity on Different Levels", "#194 GraalVM, Apple Silicon (M1) and Clouds", "#167 GraalVM and Java 17, Truffle, Espresso and Native Image" and "#157 The Ingredients of GraalVM", porting LLVM to pure Java in one class, integrating Large Language Models (LLMs) in Java by porting the LLAMA model from C to Java, GPU acceleration with tornadovm, TornadoVM appeared at "#282 TornadoVM, Paravox.ai: Java, AI, LLMs and Hardware Acceleration", performance of the Java port being within 10% of the C versions, potential huge opportunities for integrating AI and LLMs with enterprise Java systems for use cases like fraud detection, the Java port being a 1,000 line self-contained implementation with no external dependencies, the need for more resources and support to further develop the Java LLM integration, the llama2.java project Alfonso Peterssen on twitter: @TheMukel

An airhacks.fm conversation with Anton Arhipov (@antonarhipov) about: Anton appeared previously on "#273 The Long Road to Java and Kotlin", discussion about Anton Arhipov's artwork using circles and a compass, attending the JVM Language Summit in 2011 where Kotlin was introduced by JetBrains, initial skepticism about the need for a new JVM language, JSR-305 Annotations for Software Defect Detection by William Pugh, Kotlin's null safety features and interoperability with Java, Kotlin's growth and adoption by Android developers, Kotlin's multiplatform capabilities for targeting native, JavaScript, and WebAssembly, Kotlin's potential beyond Android development, Kotlin's core libraries for date/time, serialization, and coroutines, the Kotlin compiler being self-hosted and written in Kotlin, benefits of Kotlin Native for serverless and IoT compared to GraalVM, Kotlin Multiplatform support in the upcoming JetBrains Fleet IDE, designers using similar UI principles across IDEs and applications Anton Arhipov on twitter: @antonarhipov

An airhacks.fm conversation with Gil Tene (@giltene) about: starting with hacking adventure games on a VAX-11/780 as a teenager, building computers and making money in high school, providing access to Usenet, early programming experiences with Pascal and C/C++, moving to Silicon Valley in 1994 and witnessing the rise of Java, working on fault-tolerant computer systems at Stratus Computer, co-founding Azul Systems and developing the Vega appliances to virtualize Java applications, the technical details of how Vega appliances worked by running JVMs on specialized hardware, the evolution of Azul to focus on pure software solutions such as Zing and supporting openJDK, Gil's continued involvement in coding and maintaining open-source libraries Gil Tene on twitter: @giltene

An airhacks.fm conversation with Dr. Zoran Sevarac (@zsevarac) about: Zoran previously on airhacks.fm: "#169 Deep Learning with Modern Java Code", discussion about the latest updates and features in DeepNetts, a full-stack Java AI platform, University of Minnesota's drug testing application using DeepNetts, Jefferson Lab's particle research using DeepNetts Community Edition, including GPU support for faster inference using jcuda, TensorFlow compatibility, and simplified AI integration with JSR-381, real-world applications of DeepNetts in drug testing and particle research, challenges and considerations for using GPUs in serverless environments, the potential of Apple's M-series chips for machine learning, exploring Project Babylon and Code Reflection in Java, using Panama and jextract for native library bindings, the importance of having developer tools and an IDE for building AI models, plans for integrating large language models into DeepNetts, the advantages of a pure Java solution for AI in enterprise applications, and the bright future of Java in the AI ecosystem, Deep Nets 3.1.0 release with GPU support Dr. Zoran Sevarac on twitter: @zsevarac

Jonathan Schneider, a Java refactoring entrepreneur with a background in self-taught C++ and a U.S. Army officer, discusses challenges in Java version migration and automated refactoring at Netflix. He shares insights on founding OpenRewrite and Moderne for enterprise refactoring, emphasizing the importance of maintaining code formatting and type attribution. The podcast also explores potential integrations with language models for code optimization.

An airhacks.fm conversation with Nicolai Parlog (@nipafx) about: Nicolai previously on "#206 Java 19: Millions of Threads in No Time", discussion about the underscore feature in Java 22 and its importance in pattern matching, using the underscore for unused lambda parameters and deconstruction of records, avoiding default branches when switching over sealed types, the deprecation and removal of underscore as a regular variable name, the foresight of the Java community in making underscore unusable, the simplicity of installing Java compared to other languages, the need for a minimalistic Java build tool for better developer experience, SdkMan, the bld tool as an example of a pure Java build tool, the process of contributing to OpenJDK and the importance of starting with a problem statement, the distinction between Java specifications and implementations, the concept of long-term support (LTS) in Java and its relation to vendors, the importance of using the right terminology to avoid misunderstandings in the Java ecosystem Nicolai Parlog on twitter: @nipafx

An airhacks.fm conversation with Dimitris Andreadis (@dandreadis) about: Dimitris appeared previously on "#64 Quarkus 1.0 and SpringBoot", discussion about integrating AI language models (LLMs) with Java applications using quarkus and langchain4j, OpenShift AI, the benefits of using Quarkus for AI integration, Drools and ML, the potential of using AI for rule engines and decision making, the challenges of handling state and context with LLMs, InfiniSpan and vector databases, the role of vector stores and embeddings for semantic search, the advantages of Java for enterprise applications and maintenance, the potential of using AI models natively with GraalVM, the importance of tools functionality for LLMs to call Java methods, the excitement around AI in the Java community, the future trajectory of tighter integration between Java and AI models, the potential of using AI for code generation and intelligent developer tooling Dimitris Andreadis on twitter: @dandreadis