(I gave two talks at this event – the other one was on GitHub Copilot.)

Always great to engage with the OG Code Camp crew at Boston Code Camp. My talk description:

Haven’t heard about DORA yet? You will.

The annual DevOps Research and Assessment Report — affectionately known as the “DORA Report” — is a data-driven, research-backed set of practices and metrics that will make engineers happier and more productive while improving not just dev, ops, and security outcomes, but also business outcomes. DORA tends to also shine a light on practices that are common within teams that are measurably more effective than industry averages – examples will be drawn from cloud technologies, automation, and security outcomes.

In this talk we’ll explore the DORA report as a data-driven toolbox for helping you “get better at getting better” in the software delivery realm. We will give some historical context, then zoom in on findings from the recently published 2023 report. The goal is for you to leave this talk with an overall appreciation of the breadth of coverage and the impact of DORA as well as some specific metrics and capabilities you’ll want to focus on to level up your own teams.

The deck I used:

(I gave two talks at this event – the other one was on DORA.)

Always great to engage with the OG Code Camp crew at Boston Code Camp. My talk description:

According to legend, programmers back in the stone age would write code without IntelliSense and refactoring tools. The next generation of developers will wonder how our generation got anything done without AI-powered assistants. If you don’t know what GitHub Copilot is all about then come on by to get a glimpse of the future.

In this fast-paced demo-heavy talk we will see how you can go faster, stay in flow, and maybe even do more (unit tests anyone?) with GitHub Copilot, which became commercially available during 2023. Along the way we’ll learn to talk like an AI nerd by explaining and examining terms like “prompt engineering” (how to get Copilot to do what we really want), prompts vs. suggestions, what is a “conversational AI”, what do you mean by “non-deterministic”, and how does this relate to ChatGPT (and its underlying LLM). And hallucinations. All will be explained.

The deck I used: