Putting RGAF to Work: Build and audit responsible AI with open source
As generative artificial intelligence shifts from experimental play to enterprise-grade deployment, the conversation has moved from what constitutes responsible AI to how we actually achieve it. This shift is being spearheaded by the LF AI & Data, an umbrella foundation under the Linux Foundation that supports and sustains open source… Read more.
Budgets as code with Flyte, OpenLineage, and Marquez
Authors: Ashok Prakash, Staff Engineer at Apple Isan Sahoo, Principal Engineer at Oracle In the world of multi-tenant GPU clusters, managing spiraling cloud costs is a persistent challenge. Traditional retrospective chargeback often fails to prevent budget violations, leaving teams struggling to control spend after the fact. To solve this, we're… Read more.
Demo to Production: An Open Source Architecture for Reliable AI Agents
Author: Ashok Prakash, Senior Principal Engineer at Oracle AI Summary The promise of AI agents is simple: automation. The reality, as many production teams have learned, is a story of unpredictable failures, security holes, and runaway costs. Moving an agent from a controlled demo to a reliable, cloud-native service is… Read more.
Turning Climate Risk Into Actionable City Plans with Trustworthy AI Agents
How do you turn climate risk data into clear next steps a city can actually act on? Wavestone set out to answer that at the IBM Watson TechXChange hackathon. They built Hive.ai, an open source platform that uses BeeAI — an LF AI & Data project — to generate city-specific… Read more.
Building Enterprise-Ready Multimodal RAG with Milvus
(Originally published on the Milvus blog by Lumina Wang) As multimodal AI continues to expand, from text-to-image generation to intelligent retrieval systems, developers are looking for ways to make these models not only creative but also production-ready. The open source vector database Milvus is showing how this can be done… Read more.
Leverage LLM for Next-Gen Recommender Systems: Design Patterns for Cost-Aware and Ethical Deployment
Author: Nishant Satya Lakshmikanth, Engineering Leader, LinkedIn Corporation Introduction In Part 1, we set the stage by examining how recommender systems evolved—from rule-based heuristics to deep learning and now toward LLM-powered, context-aware intelligence. Part 2 dove into the architectural and modeling layers that bring this transformation to life, covering embedding… Read more.
ACP Joins Forces with A2A
By: Kate Blair, Director of Incubation, IBM Research & Todd Segal, Principal Software Engineer, Google IBM Research launched the Agent Communication Protocol (ACP) in March 2025 to power its BeeAI Platform, an open-source platform exploring agent interpretability. Later that month, the BeeAI project—and with it, ACP—was donated to the Linux Foundation, solidifying our… Read more.
Leverage LLM for Next-Gen Recommender Systems: Technical Deep Dive into LLM-Enhanced Recommender Architectures
Author: Nishant Satya Lakshmikanth, Engineering Leader, LinkedIn Corporation Introduction In Part 1, we explored how Large Language Models (LLMs) are expanding the scope of recommender systems—from passive prediction tools to conversational, context-aware agents. But the real shift begins when these conceptual promises are translated into architecture and code. As organizations… Read more.
Leverage LLM for Next-Gen Recommender Systems: The Evolution of Recommender Systems and Rise of LLMs
Author: Nishant Satya Lakshmikanth, Engineering Leader, LinkedIn Corporation Introduction Recommender systems have come a long way—from basic rule-based approaches to sophisticated machine learning pipelines that serve billions of personalized suggestions daily. This part sets the stage by tracing that evolution and introducing how large language models (LLMs) are transforming the… Read more.
