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Posted on June 25, 2012 at 9:30pm 0 Comments 1 Like
Gerrit Grunwald, aka @hansolo_ on twitter, has just ported his Swing based gauges and meters framework known as SteelSeries to JavaFX as part of the JFXtras-lab project. I can't tell you how many times since Java AWT first came out, that I have had to use meters…
ContinuePosted on June 3, 2012 at 8:11pm 0 Comments 0 Likes
One of the cool new features of the JavaFX 2.2 developer preview release is a new Canvas node that allows you to do free drawing within an area on the JavaFX scene similar to the HTML 5 Canvas. You can download this release for Windows, Mac, and Linux from JavaFX Developer Preview.
Being adventurous, I decided to take the JavaFX Canvas for a spin around the block. In doing…
ContinuePosted on September 27, 2011 at 4:51pm 0 Comments 0 Likes
Dean Iverson and I have been working on an open source project called GroovyFX that provides a Groovy binding that sits on the new JavaFX 2.0 platform. Dean has written a good blog on how to get started with GroovyFX here. It is already a little dated, but if you ignore the JavaFX build numbers and just download the…
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Created by Michael Levin Dec 18, 2008 at 6:56pm. Last updated by Michael Levin May 4, 2018.
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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 Casals
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 Simone
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 Hoblitzell
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
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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JIm,
Thanks for the great presentation last night. We all enjoyed it!
All the best,
Mike