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Development

Featured in Development

Practical Benchmarking: How to Detect Performance Changes in Noisy Results

Matt Fleming provides tips for understanding noise, where it comes, and techniques for fighting it, plus some anecdotes and real-life examples from the world of open-source.

All in development

Architecture & Design

Featured in Architecture & Design

If Architectural Experimentation Is So Great, Why Aren’t You Doing It?

Architectural experimentation sounds like a great idea, yet it does not seem to be used very frequently. In this article, we will explore some of the reasons why teams don’t use this powerful tool more often, and what they can do about leveraging that tool for successful outcomes.

All in architecture-design

AI Infrastructure

Featured in AI, ML & Data Engineering

Understanding What Really Matters for Developer Productivity: A Conversation with Lizzie Matusov

In this podcast Michael Stiefel spoke with Lizzie Matusov about the dependency of effective, productive, and satisfied teams on good software architecture. Understanding this relationship requires understanding exactly what software productivity really is, how modern software engineering research has become more rigorous and practical, and how to apply that research to software development.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

The (Not So) Hidden Social Drivers behind the Highest Performing Engineering Teams

Lizzie Matusov delves into how trust and psychological safety serve as powerful signals of team success with practical methods to evaluate and measure these key social dimensions.

All in culture-methods

DevOps

Featured in DevOps

Checklist for Kubernetes in Production: Best Practices for SREs

This article provides SREs with a checklist for managing Kubernetes in production environments. It identifies common challenges including resource management, workload placement, high availability, health probes, storage, monitoring, and cost optimization. By implementing consistent GitOps automation across these areas, teams can significantly reduce complexity, and prevent downtime.

All in devops

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Docker + Apache Brooklyn = Clocker

Roopesh Shenoy
on Jun 20, 2014

Topics

Practical Benchmarking: How to Detect Performance Changes in Noisy Results

If Architectural Experimentation Is So Great, Why Aren’t You Doing It?

Understanding What Really Matters for Developer Productivity: A Conversation with Lizzie Matusov

The (Not So) Hidden Social Drivers behind the Highest Performing Engineering Teams

Checklist for Kubernetes in Production: Best Practices for SREs

Helpful links

Choose your language

Brooklyn

News about Brooklyn

Docker + Apache Brooklyn = Clocker

New Programming Language Vine Based on Interaction Nets

OpenSSF Publishes Security Baseline for Open-Source Projects

Practical Benchmarking: How to Detect Performance Changes in Noisy Results

If Architectural Experimentation Is So Great, Why Aren’t You Doing It?

How Monzo Bank Built a Cost-Effective, Unorthodox Backup System to Ensure Resilient Banking

Facilitating Software Architecture with Andrew Harmel-Law

The (Not So) Hidden Social Drivers behind the Highest Performing Engineering Teams

Investing in Open Source: The Open Source Pledge and Why it Matters

Using Artificial Intelligence for Analysis of Automated Testing Results

Understanding What Really Matters for Developer Productivity: A Conversation with Lizzie Matusov

OpenAI Introduces Software Engineering Benchmark

Google DeepMind Enhances AMIE for Long-Term Disease Management

GitLab Launches Support for Self-Hosted AI Platforms

Checklist for Kubernetes in Production: Best Practices for SREs

How Engineering Teams Are Tackling AI, Platform Engineering & DevEx: InfoQ Dev Summit Boston 2025

QCon London

InfoQ Dev Summit Boston

InfoQ Dev Summit Munich

QCon San Francisco

InfoQ Dev Summit New York

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