Codetown
Codetown ::: a software developer's community
Did you say JUG? Java User...What?
Isn’t the answer obvious ?
Well no ! It was the question I was asking myself three years ago, even though I had been working as a consultant and Java trainer for seven years. In 2007, my ex-colleague Éric Marcoux (Oracle ACE Director) suggested I join JUG Québec (Canada). I said JUG ? Java User…What ?
Several months later, I went to JavaOne 2008 and when I saw so many developers, architects, architects, engineers from so many countries and so many amazing projects...
... and heard Matt Thompson from SUN and nearly 40 JUG Leaders talk about importance of the Java community at «Think Globaly and Act Localy» session and met Micheal Levin the co-founder of the impressive SeneJUG in West Africa, the question wasn’t «What’s a JUG?» anymore but rather «How can a sense of community and belonging be fostered among Java developper in Africa ?». For me the need of JUG-AFRICA became evident.
After talking with Java developpers across Africa, I think JUG-AFRICA could help adress questions such as :
* How to take part in international and regional events ?
* How to increase the visibility of the communities accross the word ?
* How to create link with Java communities across the word ?
Comment
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.
Looking for Jobs or Staff?
Check out the Codetown Jobs group.
InfoQ Reading List
Presentation: How to Unlock Insights and Enable Discovery Within Petabytes of Autonomous Driving Data
Kyra Mozley discusses the evolution of autonomous vehicle perception, moving beyond expensive manual labeling to an embedding-first architecture. She explains how to leverage foundation models like CLIP and SAM for auto-labeling, RAG-inspired search, and few-shot adapters. This talk provides engineering leaders a blueprint for building modular, scalable vision systems that thrive on edge cases.
By Kyra MozleyArticle Series - AI Assisted Development: Real World Patterns, Pitfalls, and Production Readiness
In this series, we examine what happens after the proof of concept and how AI becomes part of the software delivery pipeline. As AI transitions from proof of concept to production, teams are discovering that the challenge extends beyond model performance to include architecture, process, and accountability. This transition is redefining what constitutes good software engineering.
By Arthur CasalsHow CyberArk Protects AI Agents with Instruction Detectors and History-Aware Validation
To prevent agents from obeying malicious instructions hidden in external data, all text entering an agent's context must be treated as untrusted, says Niv Rabin, principal software architect at AI-security firm CyberArk. His team developed an approach based on instruction detection and history-aware validation to protect against both malicious input data and context-history poisoning.
By Sergio De SimoneAnthropic announces Claude CoWork
Introducing Claude Cowork: Anthropic's groundbreaking AI agent revolutionizing file management on macOS. With advanced automation capabilities, it enhances document processing, organizes files, and executes multi-step workflows. Users must be cautious of backup needs due to recent issues. Explore its potential for efficient office solutions while ensuring data integrity.
By Andrew HoblitzellTracking and Controlling Data Flows at Scale in GenAI: Meta’s Privacy-Aware Infrastructure
Meta has revealed how it scales its Privacy-Aware Infrastructure (PAI) to support generative AI development while enforcing privacy across complex data flows. Using large-scale lineage tracking, PrivacyLib instrumentation, and runtime policy controls, the system enables consistent privacy enforcement for AI workloads like Meta AI glasses without introducing manual bottlenecks.
By Leela Kumili
© 2026 Created by Michael Levin.
Powered by
You need to be a member of Codetown to add comments!
Join Codetown