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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

Engineering Speed at Scale — Architectural Lessons from Sub-100-ms APIs

Sub‑100-ms APIs emerge from disciplined architecture using latency budgets, minimized hops, async fan‑out, layered caching, circuit breakers, and strong observability. But long‑term speed depends on culture, with teams owning p99, monitoring drift, managing thread pools, and treating performance as a shared, continuous responsibility.

All in architecture-design

AI Infrastructure

Featured in AI, ML & Data Engineering

Lessons Learned from Building LinkedIn’s First Agent: Hiring Assistant

Karthik Ramgopal and Daniel Hewlett discuss the evolution of AI at LinkedIn, from simple prompt chains to a sophisticated distributed agent platform. They explain the transition to a supervisor-sub-agent model that enables parallel development and modular quality evaluation and share a blueprint for building resilient, high-scale agentic systems.

All in ai-ml-data-eng

Culture & Methods

Featured in Culture & Methods

Changing Power Dynamics: What Senior Engineers Can Learn from Junior Engineers

Beth Anderson discusses the "power distance index" and its critical role in communication. Using the Korean Air Flight 801 tragedy as a case study, she explains the dangers of hierarchy-driven silence. She shares actionable frameworks for building the four stages of psychological safety, implementing reverse mentoring, and using PRs as tools for knowledge sharing rather than gatekeeping.

All in culture-methods

DevOps

Featured in DevOps

Fix SLO Breaches before They Repeat: an SRE AI Agent for Application Workloads

Bruno Borges discusses a paradigm shift in performance management: moving from manual tuning to automated SRE agents. He explains how to leverage the USE and jPDM methodologies alongside LLMs to reduce MTTR from hours to seconds. By utilizing MCP tools for real-time diagnostics and memory dump analysis, he shares how engineering leaders can scale systems while meeting strict objectives.