High-Performance Java, Or How JVector Happened

An airhacks.fm (airhacks.fm) conversation with Jonathan Ellis (@spyced ( / spyced ) ) about:
Jonathan's first computer experiences with IBM PC 8086 (en.wikipedia.org/wiki/Intel_8086) and Thinkpad laptop with Red Hat Linux,
becoming a key contributor to Apache Cassandra (cassandra.apache.org/_/index....) and founding datastax (www.datastax.com) ,
starting DataStax to provide commercial support for Cassandra (cassandra.apache.org) ,
early experiences with Java (www.java.com/en/) , C++ (en.wikipedia.org/wiki/C%2B%2B) , and python (www.python.org) ,
discussion about the evolution of Java and its ecosystem,
the importance of vector databases for semantic search (en.wikipedia.org/wiki/Semanti...) 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 (github.com/langchain4j/langch...) and quarkus (quarkus.io) for serverless (en.wikipedia.org/wiki/Serverl...) 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 (pulsar.apache.org) for stream processing,
the llama2java (github.com/mukel/llama2.java) project for high-performance language models in Java
Jonathan Ellis on twitter: @spyced ( / spyced )

[@karthikn5315]

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[@karthikn5315]

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