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Development

Featured in Development

Expanding Swift from Apps to Services

Cory Benfield discusses the evolution of Swift from an app language to a critical tool for secure, high-scale services. He explains how Swift’s lack of a garbage collector eliminates tail latency and shares how its "zero-cost abstractions" rival C performance. He shares Apple’s roadmap for incremental adoption and demonstrates groundbreaking new interoperability for C++ and Java ecosystems.

All in development

Architecture & Design

Featured in Architecture & Design

[Video Podcast] Improving Valkey with Madelyn Olson

In this episode, Thomas Betts chats with Madelyn Olson, a maintainer of the Valkey project and a Principal Software Development Engineer at Amazon ElastiCache and Amazon MemoryDB. The conversation covers how Valkey started as an open source fork of Redis and how the maintainers optimized the memory usage and improved throughput.

All in architecture-design

AI Infrastructure

Featured in AI, ML & Data Engineering

Building LLMs in Resource-Constrained Environments: A Hands-On Perspective

In this article, the author argues that infrastructure and compute limitations can drive innovation. It demonstrates how smaller, efficient models, synthetic data generation, and disciplined engineering enable the creation of impactful LLM-based AI systems despite severe resource constraints.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

Scaling to 100+ as a Director: Lessons from Growing Engineering Organizations

Thiago Ghisi discusses the strategic evolution required to lead 100+ engineers without breaking the organization. He explains his "Three Levels of Impact" framework and shares practical lessons on speeding up decision-making, cultivating leadership teams, and building organizational resilience. Learn why he views reorgs as a continuous deployment feature rather than a one-time traumatic event.

All in culture-methods

DevOps

Featured in DevOps

From Alert Fatigue to Agent-Assisted Intelligent Observability

As systems grow, observability becomes harder to maintain and incidents harder to diagnose. Agentic observability layers AI on existing tools, starting in read-only mode to detect anomalies and summarize issues. Over time, agents add context, correlate signals, and automate low-risk tasks. This approach frees engineers to focus on analysis and judgment.

All in devops

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Martin Fowler on Enterprise Rails

Martin Fowler likens DHH to Kent Beck and draws conclusions about the future of Ruby and Rails in enterprisey settings.

Obie Fernandez
on Jul 11, 2006

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Topics

Expanding Swift from Apps to Services

[Video Podcast] Improving Valkey with Madelyn Olson

Building LLMs in Resource-Constrained Environments: A Hands-On Perspective

Scaling to 100+ as a Director: Lessons from Growing Engineering Organizations

From Alert Fatigue to Agent-Assisted Intelligent Observability

Helpful links

Choose your language

News

Martin Fowler on Enterprise Rails

How CNAME Ordering in RFC Specs Caused Cloudflare 1.1.1.1 Outage

Expanding Swift from Apps to Services

Google Pushes for gRPC Support in Model Context Protocol

Uber Moves In-House Search Indexing to Pull-Based Ingestion in OpenSearch

[Video Podcast] Improving Valkey with Madelyn Olson

LinkedIn Leverages GitHub Actions, CodeQL, and Semgrep for Code Scanning

Getting Feedback from Test-Driven Development and Testing in Production

Scaling to 100+ as a Director: Lessons from Growing Engineering Organizations

The Technical Founder's Path: Code, Leadership, and Balance

Building LLMs in Resource-Constrained Environments: A Hands-On Perspective

Next Moca Releases Agent Definition Language as an Open Source Specification

Cloudflare Demonstrates Moltworker, Bringing Self-Hosted AI Agents to the Edge

Datadog Integrates Google Agent Development Kit into LLM Observability Tools

From Alert Fatigue to Agent-Assisted Intelligent Observability

Etleap Launches Iceberg Pipeline Platform to Simplify Enterprise Adoption of Apache Iceberg

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