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Topics

Development

Featured in Development

How to Use Apache Spark to Craft a Multi-Year Data Regression Testing and Simulations Framework

Vivek Yadav, an engineering manager from Stripe, shares his experience in building a testing system based on multi-year worth of data. He shares insights into why Apache Spark was the choice for creating such a system and how it fits in the "traditional" engineering practices.

All in development

Architecture & Design

Featured in Architecture & Design

Architecture in a Flow of AI-Augmented Change

While AI adoption is surging, most organizations fail to scale past pilots. The solution lies in organizational structure, not just technology. This article details how architects can enable "fast flow" by defining clear domains and guardrails. Learn how to shift from controlling outcomes to curating context, allowing AI to drive continuous, valuable business change.

All in architecture-design

AI Infrastructure

Featured in AI, ML & Data Engineering

NextGen Search - Where AI Meets OpenSearch Through MCP

In this article, authors Srikanth Daggumalli and Arun Lakshmanan discuss next-generation context-aware conversational search using OpenSearch and AI agents powered by Large Language Models (LLMs) and Model Context Protocol (MCP).

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

Empowering Teams: Decentralizing Architectural Decision-Making

Peter Hunter & Elena Stojmilova share Open GI's journey from a slow, legacy monolith to a cloud-native SaaS platform. They detail how adopting Team Topologies and a decentralized architectural approach empowered teams. Key practices discussed include utilizing Domain-Driven Design to create a Context Map, implementing the Advice Process with Architectural Principles, and more.

All in culture-methods

DevOps

Featured in DevOps

Overload Protection: the Missing Pillar of Platform Engineering

Overload protection is often overlooked in platform engineering, leaving teams to create inconsistent, fragile fixes. Centralized rate limits, quotas, adaptive controls, and clear visibility give services predictable ways to handle traffic spikes, reduce reliability debt, and prevent cascading failures across systems.

All in devops

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QCon AI New York 2025

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QCon London 2026

Learn what works in AI, architecture, data, security & FinTech.

Early Bird ends Dec 20.

Learn how leading engineering teams run AI in production—reliably, securely, and at scale.

Early Bird ends Jan 13.

InfoQ Homepage Interviews John Lam on IronRuby, Microsoft and Open Source

John Lam on IronRuby, Microsoft and Open Source

29:14

Bio

John Lam is Program Manager on the Dynamic Language Runtime team at Microsoft. He created RubyCLR, and is now working on the team that is creating IronRuby, Microsoft's implementation of Ruby.

About the conference

Oct 30, 2008

Interview with

John Lam

by

Rob Bazinet

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