Kim Harrison
Content and Developer Marketing, Analyst & Media Relations.
Skills
Tech Ethics & Responsible Engineering
Building with eyes open to unintended consequences. Covers the gap between engineering intent and real-world outcomes, responsible AI considerations, inclusive design, and the organizational practices that help teams anticipate harm before shipping. Drawn from practitioners at BBC Radio 4, Code for America, Thoughtworks, and others.
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Observability & Incident Response
The practice of operational reliability: instrumentation, monitoring, SLO setup, incident response, on-call design, and day-to-day reliability operations. Covers alerting strategies, error budgets, incident management processes, and blameless postmortems. Drawn from practitioners at Honeycomb, Datadog, PagerDuty, and others.
- +18
Content from the Library
Third Loop Ep. #13, Ten Times Faster at What? with Jennifer Riggins
On episode 13 of Third Loop, the Progressive Delivery team speaks with Jennifer Riggins about why faster code doesn’t automatically mean better software. They explore how AI exposes organizational bottlenecks, why psychological safety matters, and what Spotify’s growing feature set reveals about user value. Jennifer makes the case for smaller experiments, stronger feedback loops, and keeping people at the center of delivery.
Third Loop Ep. #12, Shipping at the Speed of Understanding with Steph Hippo
On episode 12 of Third Loop, the Progressive Delivery team speaks with Steph Hippo, Platform Engineering Director at Honeycomb, about what happens when AI accelerates development beyond the pace humans can absorb. They explore safe defaults, evolving code reviews, and the feedback loops that help teams understand what they ship. Steph makes the case for reinvesting saved time in better decisions, software quality, and human connection.
Third Loop Ep. #11, Durable Systems, Disposable Code with Chad Fowler
On episode 11 of Third Loop, the Progressive Delivery team sits down with Chad Fowler to explore regenerative software and what architecture looks like when AI makes code cheap enough to continually replace. They discuss Phoenix Architecture, Pace Layers, observability, architectural constraints, and why the system itself, rather than any individual implementation, may become the real asset in an AI-native future.
