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All Events in Sarasota (3)
October 15Wednesday
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Sarasota JUG - JRuby
October 15, 2008 at 6pm – Sarasota Can the Java and Ruby camps finally get together and feel the love? We may find out at the next meeting of the Sarasota Java Users group. Steve Goldsmith will discuss JRuby and using the Java Api.… Organized by Dave Moskowitz | Type: meeting
January 21Wednesday
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Sunjug: Building and Profiling an Application with Skyway, Netbeans, and JPA
January 21, 2009 at 6pm – Sarasota In this presentation, Joel Fradkin will describe useful several tools for building web applications. First, Joel will present a JPA backed application built using Netbeans. He will create an entity… Organized by David Moskowitz | Type: Meeting
December 11Wednesday
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OrlandoJUG
December 11, 2013 from 6pm to 9pm – Sarasota The SUNJug is having a brew night 12/11/. Consider driving out to Sarasota and meeting some like minded people. I hear Chris is a brew master. http://www.sunjug.org/2013/december Rideshare. Here a… Organized by Michael Levin | Type: meeting
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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