I cannot count the number of times I’ve heard someone ask these two questions—
- What are other people doing?
- What is the best common practice?
While these questions have always bothered me, I could never really put my finger on why. I ran across a journal article recently that helped me understand a bit better. The root of the problem is this—what does best common mean, and how can following the best common produce a set of actions you can be confident will solve your problem?
Those who follow my work know I’ve been focused on building live webinars for the last year or two, but I am still creating pre-recorded material for Pearson. The latest is built from several live webinars which I no longer give; I’ve updated the material and turned them into a seven-hour course called How Networks Really Work. Although I begin here with the “four things,” the focus is on a problem/solution view of routed control planes.
I began writing this post just to remind readers this blog does have a number of RSS feeds—but then I thought … well, I probably need to explain why that piece of information is important.
The amount of writing, video, and audio being thrown at the average person today is astounding—so much so that, according to a lot of research, most people in the digital world have resorted to relying on social media as their primary source of news. Why do most people get their news from social media? I’m pretty convinced this is largely a matter of “it saves time.” The resulting feed might not be “perfect,” but it’s “close enough,” and no-one wants to spend time seeking out a wide variety of news sources so they will be better informed.
Decision making, especially in large organizations, fails in many interesting ways. Understanding these failure modes can help us cope with seemingly difficult situations, and learn how to make decisions better. On this episode of the Hedge, Frederico Lucifredi, Ethan Banks, and Russ White discuss Frederico’s thoughts on developing a taxonomy of indecision. You can find his presentation on this topic here.
Jack of all trades, master of none.
This singular saying—a misquote of Benjamin Franklin (more on this in a moment)—is the defining statement of our time. An alternative form might be the fox knows many small things, but the hedgehog knows one big thing.
When we think of automation—and more broadly tooling—we tend to think of automating the configuration, monitoring, and (possibly) the monitoring of a network. On the other hand, a friend once observed that when interviewing coders, the first thing he asked was about the tools they had developed and used for making themselves more efficient. This “self-tooling” process turns out to be important not just to be more efficient at work, but to use time more effectively in general. Join Nick Russo, Eyvonne Sharp, Tom Ammon, and Russ White as we discuss self-tooling.
The modern world craves our attention—but only in short bursts. To give your attention to any one thing for too long is failing, it seems, because you might miss out on something else of interest. We have entered the long tail of the attention economy, grounded in finding every smaller slices of time in which the user’s attention can be captured and used.
Innovation and disruption are part the air we breath in the information technology world. But what is innovation, and how do we become innovators? When you see someone who has invented a lot of things, either shown in patents or standards or software, you might wonder how you can become an innovator, too. In this episode of the Hedge, Tom Ammon, Eyvonne Sharp, and Russ White talk to Daniel Beveridge about the structure of innovation—how to position yourself in a place where you can innovate, and how to launch innovation.
The OSI model is perhaps the best-known—and perhaps the most-loved—model in the networking world. It’s taught in every basic networking course, and just about every blog (other than this one) has some article explaining the model someplace or another (for instance, here is one of the better examples).
This week is very busy for me, so rather than writing a single long, post, I’m throwing together some things that have been sitting in my pile to write about for a long while.
From Dalton Sweeny:
A physicist loses half the value of their physics knowledge in just four years whereas an English professor would take over 25 years to lose half the value of the knowledge they had at the beginning of their career. . . Software engineers with a traditional computer science background learn things that never expire with age: data structures, algorithms, compilers, distributed systems, etc. But most of us don’t work with these concepts directly. Abstractions and frameworks are built on top of these well studied ideas so we don’t have to get into the nitty-gritty details on the job (at least most of the time).