OSCON is the O'Reilly Open Source Conference. And, it's right around the corner! The call for papers just occurred. It is an annual event that occurs in Portland, OR and this year it's July 20-24. 

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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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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 Mozley

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

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