Codetown
Codetown ::: a software developer's community
All group Events (4)
December 15Wednesday
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GatorLUG - Linux Users Group
December 15, 2010 from 6pm to 9pm – The Lab Greetings All, You are invited to join us at the next GatorLUG meeting in beautiful (but way too cold ) Gainesville, Florida. We'll end a long year with a relaxed social meeting at The Laboratory.… Organized by Clint Collins | Type: user, group, meeting
February 23Thursday
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Running Java, Play! and Scala Apps on the Cloud
February 23, 2012 from 6pm to 8:30pm – Orlando James Ward from Heroku will be presenting at the Orlando Java User Group. Session Description: Heroku is a Polyglot Cloud Application Platform that makes it easy to deploy Java, Play! and Scala… Organized by James Ward | Type: orlando, java, user, group
May 14Wednesday
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GatorJUG - Hello Java 8 with Adam Davis
May 14, 2014 from 6pm to 9pm – Santa Fe College Calling all Java lovers! Our May, 2014 meeting is coming up. Please RSVP, spread the word and invite a friend. Join us and keep your skills sharp. Network and find new projects and people for your ex… Organized by Dan Lackey | Type: java, user, group, meeting, gatorjug, codetown
August 22Wednesday
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Introduction to TornadoFX Workshop
August 22, 2018 from 6pm to 7pm – Chicago 1871 In a world where everyone and their mother uses web development, a quiet grassroots movement is bringing back native development. Whether you're brand-new to coding, or you just have framework fatigu… Organized by Chicago GDG | Type: google, developer, group, meetup
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
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
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