January 2013 Blog Posts (5)

OSCON Call for Speakers

OSCON 2013
Call for Speakers Is Open for OSCON 2013

We're looking for speakers to be part of the program for the 15th edition of OSCON, happening July 22-26, 2013, in Portland, Oregon. If you have a new idea, a better way to do something, an interesting and instructive case study (battle scars optional), or the desire to pass on your hard-won knowledge, submit a proposal to lead sessions or tutorials.

Added by Michael Levin on January 25, 2013 at 6:45pm — No Comments

Nighthacking Tour with Java Evangelist Stephen Chin

From Jan 25th to Feb 7th Stephen Chin will be traveling across the Nordic countries and doing live video streaming of the journey. Along the way he will visit user groups, interview interesting folks, and hack on open source projects. The last stop will be at Jfokus 2013.…

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Added by Michael Levin on January 23, 2013 at 9:00am — No Comments

FLEX goes to Apache

Not familiar with FLEX but this article has some interesting info on it.  Another tool in the web developer's toolbox?

http://www.eweek.com/developer/apache-software-foundation-delivers-flex-as-top-level-project/?kc=EWKNLLIN01222013STR2

Added by Mike Bivins on January 22, 2013 at 12:02pm — No Comments

Is the next big thing already here?

Create or innovate. What does East know about West? A lot. We speak different languages, even use different alphabets. No problem, says Samsung. Samsung's ad dep't makes it clear, even funny. Hilarious!

Economic models are different, too. Like polar extremes. One gives you everything you need in exchange for your time. The other promises you everything you…

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Added by Michael Levin on January 22, 2013 at 7:30am — No Comments

Oracle Security Update (Java)

Oracle Security Update CVE-2013-0422
An Oracle Security Alert was issued today.  To learn more about the alert please refer to the following link.…
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Added by Michael Levin on January 14, 2013 at 1:30pm — No Comments

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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 Casals

How 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 Simone

Anthropic 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 Hoblitzell

Tracking 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

MIT'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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