Slow down to speed up
Slow down to speed up
One of the best ways to improve is slow down, observe, and make changes. It might happen in unexpected places, like looking at the night sky. Read: "Everything Starts Out Looking Like a Toy" #198
Hi, I’m Greg 👋! I write weekly product essays, including system “handshakes”, the expectations for workflow, and the jobs to be done for data. What is Data Operations? was the first post in the series.
This week’s toy: a browser-based homage to Hypercard that allows you to build cards in 1-bit glory. Try it out here. Limiting the “palette” of an app - in this case by making it have low-fi graphics - is a great way to make a compelling experience. Edition 198 of this newsletter is here - it’s May 13, 2024.
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The Big Idea
A short long-form essay about data things
⚙️ Slow down to speed up
This weekend, I saw something remarkable that I’ve never seen before: the Northern Lights.
We drove about 30 minutes to Blaine, WA where it’s pretty dark out, parked the car, and waited. At first, we didn’t see much. After a while, we started to see trails in the sky. Then, you could see some faint color in the sky.
Using our phones showed a different story. Using a long exposure (10s) and underexposing the photo let enough light in to show beautiful colors. It was breathtaking and amazing to see the colors change and shift over an hour or two.
The best pictures turned out to be ones that combined the beauty in the sky with details in the background. (In this photo, you can see some details from the Vancouver, BC skyline and some mountains.)
You’re not seeing the 40-50 shots that didn’t work out, and the 20-30 other “good ones” we found. This process is very similar to the learning we have when we’re building new tech for operations.
We didn’t know what to expect
As in many operations situations, we encountered a new situation and needed to wait, watch, and analyze to determine what was going on. Our first photos of the Northern Lights were pretty awful: blurry, overexposed, and poorly composed.
When you get initial results in your ops project that don’t meet expectations, what do you do? Change one variable, and keep doing the process until you get different results.
In this case, changing the default “Night Mode” 3 second exposure on the iPhone to a 10 second exposure made a huge difference. The longer exposure created beautiful colors that we weren’t seeing with our eyes only.
However, our fix also created more blurriness in the resulting image. We needed to adjust the way that we were holding the phone. Nope, we didn’t bring a tripod, so bracing the phone on the ground or with your knees and holding your breath made the best blur-free images.
We made adjustments and collaborated
It took some effort to get from “cool! Let’s take pictures” to “that’s a good picture.” A few different adjustments helped, including:
metering on the sky instead of the dark parts of the ground
changing the exposure (the basic iPhone camera app allows for bracketing without enabling specific ISO settings)
using the wide angle and “normal” lenses to see the difference
taking pictures in different directions and at different angles
We learned that sky pictures without any reference point are pretty dull, even if they include Aurora. Adding a silhouette of a person or a tree adds an interesting compositional choice and makes it easier to read the color.
What does taking night pictures teach you about operations?
When you’re in a unique situation (this is like an event or a conference x100), your typical approach might not be effective due to environmental or other factors. It’s important to adjust based on testing, to do it quickly and decisively, and to redirect when things are not working.
It also reminds your eyes give you different information than your other senses or tools. There are technical details that help you understand why aurora colors look more vibrant in a camera than to the eye, but they don’t compare to the overwhelming feeling of seeing something special and timebound.
Operations is all about rising to the moment, no matter what happens. Nature’s spectacle in a solar storm is a great place to practice those skills.
What’s the takeaway? Don’t discount the value of other skills you have that come into play during a typical operations task. By following a playbook and giving yourself space to improvise, you leave open the chance of real insight every time you encounter a new ops situation.
Links for Reading and Sharing
These are links that caught my 👀
1/ LLMs are not random - If you ask a GPT chatbot for information, you’re likely going to get an answer that someone has provided before. If the distribution of all of the answers is weighted, you’re not getting a random answer. This is great if you’re not looking for something random, but might have implications if you are.
2/ A notebook for LLM Chats - we use notebooks for code all of the time, so why not for Chatbot and LLM ideas? Grafychat is an interesting
3/ How do you measure productivity? - This engineering org uses a simple metric to track productivity: time spent doing value-added work. It sounds deceptively easy to track, but the basic idea sounds a lot easier to follow than an OKR. The idea: fixes that no one sees are important can’t be the majority of the work (and ought to be 20% time or thereabouts).
What to do next
Hit reply if you’ve got links to share, data stories, or want to say hello.
The next big thing always starts out being dismissed as a “toy.” - Chris Dixon