Harder and better (slides)
At Scenic City Summit in Chattanooga last week, I gave a closing keynote about 3 ways our jobs are harder than they used to be, and how each of these makes our jobs better. Annotated slides are on Dropbox.
complexity
Tradeoffs in Coordination Among Teams
The other day in Budapest, Jez Humble and I wondered, what is the CAP theorem for teams? In distributed database systems, the CAP theorem says: choose two of Consistency, Availability, and Partitioning — and you must choose Partitioning. Consider a system for building software together. Unless the software is built by exactly one person, we …
Post-agile: microservices and heads-up development
Notes from Craft Conference 2015, Budapest. Craft conference was all about microservices this year.[1] Yet, it was about so much more at the same time — even when it was talking about microservices. lobby of the venue. Very cool, and always packed Dan and I went on about microservices in our opening keynote,[2] about how it’s not …
Charting team course with a Seamap
Biggest visible distinguisher of great teams: there is no roadmap – Marty Cagan #CraftConf — Jessica Kerr (@jessitron) April 24, 2015 Great teams are focused on results, not on projects. But how many of us really see the results of our efforts? Can we track the impact of what we’re doing today all the way …
Accidental vs Deliberate Context
In all decisions, we bring our context with us. Layers of context, from what we read about that morning to who our heroes were growing up. We don’t realize how much context we assume in our communications, and in our code. One time I taught someone how to make the Baby Vampire face. It involves …
REST as a debugging strategy
In REST there’s this rule: don’t save low-level links. Instead, start from the top and navigate the returned hyperlinks, as they may have changed. Detailed knowledge is transitory.This same philosophy helps in daily programming work. Say a bug report comes in: “Data is missing from this report.” My pair is more familiar with the reporting …
Causality: tougher than it looks, but we can take it on
We like to take a hunk of data, graph one factor against another, demonstrate correlation, and infer causality. This naive form of analysis is appealing in its simplicity, but it doesn’t cut it in the real world. With Big Data, we can identify correlation out the wazoo, but it’s time to get way more sophisticated …
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