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's Optimization Platform Ax 1.0 Streamlines LLM and System Optimization
Now stable, Ax is an open-source platform from Meta designed to help researchers and engineers apply machine learning to complex, resource-intensive experimentation. Over the past several years, Meta has used Ax to improve AI models, accelerate machine learning research, tune production infrastructure, and more.
By Sergio De SimoneLyft Rearchitects ML Platform with Hybrid AWS SageMaker-Kubernetes Approach
Lyft has rearchitected its machine learning platform LyftLearn into a hybrid system, moving offline workloads to AWS SageMaker while retaining Kubernetes for online model serving. Its decision to choose managed services where operational complexity was highest, while maintaining custom infrastructure where control mattered most, offers a pragmatic alternative to unified platform strategies.
By Eran StillerAWS Transform Custom Tackles Technical Debt
AWS Transform Custom revolutionizes code modernization with AI-driven, out-of-the-box transformations for Java, Node.js, and Python. This enterprise-focused tool accelerates application upgrades by up to 5x while learning from organizational nuances to deliver high-quality, repeatable transformations.
By Steef-Jan WiggersArticle: Trustworthy Productivity: Securing AI Accelerated Development
Autonomous AI agents amplify productivity but can cause severe damage without safeguards. Defend the ReAct loop—context, reasoning, and tools—through provenance gates, planner-critic separation, scoped credentials, sandboxed code, and STRIDE/MAESTRO threat modeling. With robust logging, bounded autonomy, and red-teaming, agents can deliver trustworthy productivity while minimizing risk.
By Sriram Madapusi VasudevanPresentation: Powering Enterprise AI Applications with Data and Open Source Software
Francisco Javier Arceo explored Feast, the open-source feature store designed to address common data challenges in the AI/ML lifecycle, such as feature redundancy, and low-latency serving at scale.
By Francisco Javier Arceo
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