Recently AWS made a new feature available in Cloudwatch: AI Operations. What does it do? In it’s own words: Amazon Q Developer now includes a new generative-AI investigations experience that helps you troubleshoot operational issues by automating information gathering, analyzing observability data, and providing tailored recommendations. With this tool one is able to create a trail or notebook of observations and have AWS automatically find new hopefully relevant observations and even suggest causes and actions.

Much has been written about the problems with story points. What do they mean? Why do we use them? My intention is not convince you of their value, but here is how I use them, and what they mean to me. Maybe this will be of use to you if you find yourself in a pointy environment, with no hope of rescue. I use them in two ways: a trick to trigger discussion and refinement of stories a way to prevent overloading a team in the long haul In a sense, points are an artefact of the way work is fed into the team: batched vs streamed; owned by the team vs thrown at the team.

A quick and dirty guide to making an EPUB book from a set of Markdown files using pandoc. In this example I have strarted a book with chapters and a cover image. Prequisites 🔗 some markdown files pandoc Markdown File Structure 🔗I have given the chapters numbered prefixes and added a cover image: -rw-rw-r-- 1 ubuntu ubuntu 160 May 28 15:15 01-title.md -rw-rw-r-- 1 ubuntu ubuntu 123 May 28 14:53 02-introduction.