The goal of science is to explain the most amount of observable phenomena with the fewest number of principles, confirm deeply held intuitions, and reveal surprising insights. This is known as the principle of parsimony.
In my new book, Wiring the Winning Organization, co-authored with Dr. Steve Spear, we present a very simple and parsimonious theory of performance, based on the three mechanisms of slowification, simplification, and amplification. I’ve been continually amazed at how every transformation can be described using these three mechanism:
- Slowify (i.e., "slow down to speed up") to make it easier and more forgiving to solve problems.
- Simplify (i.e., partition problems in time and space) to split apart large problems to make them easier to solve, most likely in parallel.
- Amplify (among other things, weak signals of failure) to make it obvious that problems need to be solved and that they were successfully resolved.
I've been outrageously delighted by the ability for GPT-4 and Claude 2 to interpret case studies and describe them through the lens these three mechanisms.
Over the next several months, I’ll be showing examples of these mechanism at work. I’ll be picking some of my favorite case studies from The DevOps Handbook and describing them through the lens of slowification, simplification, and amplification.
Links to some of these case studies:
- Interpretations of two Nordstrom case studies from the DevOps Handbook
- Interpretation of the Cloudflare outage post-incident review
- Interpretation of the Google leaked memo "We have no moat, and neither does OpenAI"
In this repo, I'll be sharing the prompts that I've been using.
- upload abridge case studies so you can replicate the results
- to avoid copyright issues, maybe I upload videos instead