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Coffee Machines, Cloud Outages, and AI in the Workplace

Event artwork for Coffee Machines, Cloud Outages, and AI in the Workplace

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About this event

What to expect

When a simple AWS outage takes down not just your cloud services but also your office coffee machine, you know you've discovered something important about systemic risk and hidden dependencies. This meetup brings together two critical conversations shaping modern software development: learning from real-world infrastructure failures and navigating the rapid integration of AI into our daily workflows.

Understanding Cloud Resilience Through Real-World Failure

Alexander Weightman presents "What a Coffee Machine Revealed About the October 2025 AWS Outage: And What We Can Learn From It" — a fascinating exploration of how cascading failures ripple through interconnected systems. Using the coffee machine incident as an entry point, this talk unpacks the broader implications of cloud platform dependencies and what quality professionals need to understand about working with services like AWS and other cloud providers.

Key learning outcomes include:

  • Understanding how the 2025 AWS outage created cascading effects across seemingly unrelated systems
  • Practical strategies for quality roles to prepare for cloud provider failures
  • Systems-thinking approaches to resilience and risk management
  • Real-world insights into hidden dependencies in cloud-based architectures

This session is particularly valuable for QA engineers, testers, DevOps professionals, and anyone responsible for system reliability who wants to move beyond surface-level technical details to understand the deeper organisational and systemic implications of infrastructure failures.

AI in the Workplace: An Open Discussion

Following the structured talk, the meetup shifts into an interactive discussion about how AI is transforming daily work practices. This open forum encourages attendees to share real experiences — both successes and cautionary tales — about integrating AI-powered tools into testing, development, and quality processes.

Topics for discussion include:

  • Which AI tools have become daily essentials versus occasional experiments
  • Guardrails, governance, and quality checks for AI-generated work
  • Practical victories and lessons learned from AI integration failures
  • Strategies for extracting genuine value from AI without falling into hype traps

Whether you're using AI for test generation, documentation, code review, or exploring tools like ChatGPT and Claude for daily tasks, this conversation offers a chance to l

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