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Context Engineering Is a Must-Learn Skill: Here's How Everyone Can Master It (Mon, 09 Feb 2026)
The Rise of Context Engineering In the rapidly evolving landscape of artificial intelligence, a new discipline has emerged that separates those who simply use AI tools from those who truly harness their power: context engineering. While prompt engineering has been the buzzword of the past few years, context engineering represents the next evolutionary step — a more sophisticated, systematic approach to working with large language models (LLMs) and AI systems. Context engineering is the art and science of designing, constructing, and optimizing the information environment in which an AI model operates. It goes far beyond crafting clever prompts; it encompasses the entire ecosystem of data, instructions, examples, and constraints that shape an AI’s understanding and outputs. As AI systems become more powerful and are integrated into critical business processes, mastering context engineering has become not just advantageous—it’s essential.
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Distributed Systems and Cloud Efficiency: A Deep Dive (Mon, 09 Feb 2026)
Cost Is a Distributed Systems Bug The first time you watch $18,000 evaporate overnight because someone left autoscaling unbounded on a Kubernetes cluster that decided to provision 400 nodes for a traffic spike that never materialized, you stop thinking about cloud bills as accounting theater. Cost becomes what it always was: a failure mode with teeth. Zoom’s FinOps team saw their AWS spend double from $20K to $40K daily — not gradually, not with warning klaxons, just a jump that would burn through $600K in thirty days if left unaddressed. The mechanics were mundane: a feature rollout triggered cascading retries in a microservice mesh, with each retry spawning EC2 Spot instances that didn’t terminate cleanly. The cost spike manifested before the performance degradation did. Traditional monitoring missed it entirely because nobody had instrumented the bill.
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Building a Self-Healing Observability System with AWS Bedrock AgentCore (Mon, 09 Feb 2026)
In today’s fast-paced cloud environments, keeping systems running smoothly isn’t just about monitoring them — it’s about making them smart enough to fix themselves. Enter the world of self-healing observability systems, where AI agents detect issues, analyze root causes, and take corrective actions without human intervention. With AWS Bedrock AgentCore, a powerful platform for building and deploying AI agents at scale, you can create a system that is reliable, secure, and efficient. In this article, we’ll dive deep into how to build such a system from scratch, complete with code examples, practical diagrams, and real-world insights. By the end, you’ll have a blueprint to implement your own self-healing setup.
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Agentic DataOps With Guardrails: MCP and MWAA for Pipeline Incident Response (Mon, 09 Feb 2026)
Failure of data pipelines increasingly feels a lot like a security incident. They occur at inconvenient times; dashboards become stale; delays in data availability impact business decisions; and the on-call engineer loses time navigating across various tools, including CloudWatch logs, tickets, chats, code, and the Airflow UI (MWAA), to identify root causes. Some of the questions you ask yourself during this process are: What broke, and why did it break? What are the logs actually saying? What is the safest option to recover? Is it repeating? In most teams, the real cost isn't clicking on retry. It is about finding context: the right DAG, the right task, the right logs, the right log lines, the downstream impact, and the safest next step to the recovery path. Most GenAI pilots in data teams don't help much since they are still passive. They can explain what to do, but can't reliably pull CloudWatch logs, correlate failure across runs, or propose a safe action that you can audit. 
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DevOps Cafe Podcast

DevOps Cafe Ep 79 - Guests: Joseph Jacks and Ben Kehoe (Mon, 13 Aug 2018)
Triggered by Google Next 2018, John and Damon chat with Joseph Jacks (stealth startup) and Ben Kehoe (iRobot) about their public disagreements — and agreements — about Kubernetes and Serverless. 
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DevOps Cafe Ep 78 - Guest: J. Paul Reed (Mon, 23 Jul 2018)
John and Damon chat with J.Paul Reed (Release Engineering Approaches) about the field of Systems Safety and Human Factors that studies why accidents happen and how to minimize the occurrence and impact. Show notes at http://devopscafe.org
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DevOps Cafe Ep. 77 - Damon interviews John (Wed, 20 Jun 2018)
A new season of DevOps Cafe is here. The topic of this episode is "DevSecOps." Damon interviews John about what this term means, why it matters now, and the overall state of security.  Show notes at http://devopscafe.org
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