airhacks.fm podcast with adam bien Adam Bien

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

An airhacks.fm conversation with Jonathan Schneider (@jon_k_schneider) about:
from Pentium 2 machine and a rural high school to becoming a Java refactoring entrepreneur,
self-taught C++ in high school,
officer in the U.S. Army and deployed to Iraq and Afghanistan,
worked on Java projects at an insurance company between deployments,
joined Netflix to work on engineering tools,
challenges of migrating Java versions and libraries in a freedom and responsibility culture,
started the OpenRewrite project at Netflix for automated refactoring and code migration,
founded the micrometer metrics instrumentation project at Pivotal,
challenges of introducing automated pull requests in enterprise environments,
rebooted OpenRewrite while working with Gradle's Hans Dockter,
founded Moderne to commercialize OpenRewrite for large-scale enterprise refactoring,
the origin of the Moderne name and its Art Deco roots,
OpenRewrite's Maven and Gradle plugins for refactoring and styling using a visitor pattern on an enriched AST,
Moderne's enterprise offering for large-scale refactoring and impact analysis,
potential integration with large language models and retrieval-augmented generation for code optimization
Jonathan Schneider on twitter: @jon_k_schneider

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