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Flex Loft
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The Flex Loft is a place Flex coders can come to share ideas, ask questions and share tips about Flex, a powerful user interface programming environment. Join us and explore this rich tool.
Members: 13
Latest Activity: Aug 2, 2010
Discussion Forum
Adobe Users Group of Greater Orlando 5 Replies
OK, reposting this to a more permanent home...If you're interested in Flex and live near Orlando, there's an Adobe Users Group here that meets the first Monday of each month at DeVry. The meetings…Continue
Tags: Orlando, group, users, flex, adobe
Started by Greg Groves. Last reply by Matthew Zimmer Oct 7, 2009.
Developing Rich Internet Applications (RIA’s) by combining Flex with Turbogears.
Fred Sells did this presentation for the OrlandoJUG. Here's the writeup and I'll attach the source code to this discussion so we can work through it. Stay tuned for the code!"Flex is a (mostly) open…Continue
Tags: Orlandojug, Fred Sells, turbogears, python, flex
Started by Michael Levin Feb 4, 2009.
Deploying a Flex app
I'm working my way through "First Steps in Flex", by Bruce Eckel and James Ward. So far, I'm on the "Item Renderers" chapter. Of course, at this point I am already thinking about how I'm going to…Continue
Tags: tutorial, flex, deployment, webservers, programming
Started by Michael Levin Feb 1, 2009.
First Steps in Flex
"First Steps in Flex", by Bruce Eckel and James Ward is an intro to Flex. The nice thing about this book is that you can be…Continue
Tags: webdev, tutorial, programming, eckel, books
Started by Michael Levin Feb 1, 2009.
Flex Reading List
Comment Wall
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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
Article 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 KumiliMIT's Recursive Language Models Improve Performance on Long-Context Tasks
Researchers at MIT's CSAIL published a design for Recursive Language Models (RLM), a technique for improving LLM performance on long-context tasks. RLMs use a programming environment to recursively decompose and process inputs, and can handle prompts up to 100x longer than base LLMs.
By Anthony Alford
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