Codetown
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Java SE Town
Information
This group focuses on the Java SE environment. It's the core Java group. Questions? Answers? Musings? This is the place for you!
Members: 14
Latest Activity: Jun 26, 2019
Discussion Forum
Java 8 Resources to help you learn (thanks to Mattias Karlsson) 2 Replies
Guess you know about all the resources and video cast Oracle providedat the Java 8 Launch site:…Continue
Started by Michael Levin. Last reply by Michael Levin Mar 29, 2014.
Josh Bloch on Java and Programming
In this interview, Google’s Josh Bloch shares his views on the…Continue
Started by Michael Levin Dec 21, 2010.
JCertif is on! Rockin' in the Congo...
JCertif is one of the first Java related conferences in the Republic of Congo, Brazzaville. Sponsored by…Continue
Tags: Max Bonbhel, Michael Levin, swampcast, africajug, congojug
Started by Michael Levin Aug 25, 2010.
JSR Watch: Here’s to Progress
Here's an update from the Chair of the JCP and director of the JCP Program office: Patrick Curranhttp://java.ulitzer.com/node/965152JSR Watch: Here’s…Continue
Started by Michael Levin May 19, 2009.
Concurrent Programming
Are you doing much concurrent programming? I attended some of the concurrency talks at the NFJS conference and I'm using a mix of the old synchronized and the new(ish) java.util.concurrent classes.…Continue
Tags: Concurrency, J2SE
Started by Matt Weber Aug 31, 2008.
Comment Wall
Comment
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Comment by Matt Weber on August 31, 2008 at 1:48pm
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Are you doing much concurrent programming? I attended some of the concurrency talks at the NFJS conference and I'm using a mix of the old synchronized and the new(ish) java.util.concurrent classes. I'm interested in a discussion about the usage and decisions about when one is better than the other.
Happy 10th year, JCertif!
Notes
Welcome to Codetown!
Codetown is a social network. It's got blogs, forums, groups, personal pages and more! You might think of Codetown as a funky camper van with lots of compartments for your stuff and a great multimedia system, too! Best of all, Codetown has room for all of your friends.
Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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InfoQ Reading List
Meta AI Releases Llama 4: Early Impressions and Community Feedback
Meta has officially released the first models in its new Llama 4 family—Scout and Maverick—marking a step forward in its open-weight large language model ecosystem. Designed with a native multimodal architecture and a mixture-of-experts (MoE) framework, these models aim to support a broader range of applications, from image understanding to long-context reasoning.
By Robert KrzaczyńskiQCon London: Monzo's Recipe for Developer Experience: Assemble, Build, Communicate
Fabien Deshayes spoke on how Monzo has created and optimised their developer experience teams in a talk at QCon London 2025. Deshayes outlined some techniques for building an effective Developer Experience platform, focusing on three key aspects: assembling effective teams, building impactful products, and communicating value across the organisation.
By Matt SaundersHow Meta is Using a New Metric for Developers: Diff Authoring Time
Diff Authoring Time (DAT). DAT is a new metric developed by engineers at Meta to measure the duration required for developers to submit changes, known as "diffs," to the codebase. By tracking the time from the initiation of a code change to its submission, DAT offers insights into the efficiency of the development process and helps identify areas for improvement.
By Craig RisiPresentation: Unleashing Llama's Potential: CPU-based Fine-tuning
Anil Rajput and Rema Hariharan discuss the crucial role of CPU architecture in optimizing Large Language Model (LLM), specifically Llama, performance. They explain hardware-software synchronization for TCO reduction and latency improvements. Learn about core utilization, cache impact, memory bandwidth considerations, and the benefits of chiplet architecture for LLM deployments on CPUs.
By Anil Rajput, Rema HariharanArticle: Bridging Modalities: Multimodal RAG for Advanced Information Retrieval
In this article, authors discuss how multi-model retrieval augmented generation (RAG) techniques can enhance AI by integrating multiple modalities like text, images, and audio for deeper contextual understanding, with help of a practical example of a healthcare application.
By Suruchi Shah, Suraj Dharmapuram
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