Ordinarily I use this retro process to come up with some goals for the month and spend the month working towards them.
I often course-correct or change things in the middle of the month, but by and large the goals form a roadmap
for the month that I spend most of my time working towards.
It’s all very…structured.
But last month I decided not to set any goals .
And it was great!
Rather than use a day of retrospecting to plan the month, I used much of the month itself to try and
plan a larger chunk of time—starting with the rest of the year, though with an eye towards the indefinite future.
Here’s my first draft:
Basically, there are three big buckets I’m planning to work on: ikigai—a code word I’m using for
exploratory do-gooding, Pegasus, and fun.
These buckets map closely to my current priorities—also borrowed in part from the concept of ikigai—of
making a difference (do-gooding), earning a living (Pegasus), and loving what I do (fun).
The fourth pillar of ikigai—being good at what you do—isn’t represented because it’s kind of a baseline assumption
around anything I’d choose to work on.
The do-gooding part is a topic of a future post, so I won’t say much about that yet—but I’ve decided
to start by teaching myself a bit of machine learning.
Preview of why I started looking into machine learning, though my thinking has changed a bit since I wrote this.
I chose Pegasus for earning, because it’s still the product I believe has the most opportunity for upside—and
because of the positive externalties of the Pegasus flywheel, depicted below.
The Pegasus Flywheel, from my 2019 year in review.
Basically, working on Pegasus also improves all my other past and future projects, and vice-versa.
And at the moment “fun” mostly means writing, though I suspect that will change in the future.
One final observation so far is that while I drew these three pillars as non-overlapping buckets,
many things I might work on hit two or even all three at the same time.
For example, building a web app that uses machine learning to say something novel about a humanitarian dataset
would likely be fun, improve Pegasus, and maybe do some good.
Not saying I’ll do that, but it’s fun to think of examples that check multiple axes of fulfillment!
Ok—that’s the plan. Onto the rest of the retro.
Last Month’s Output
Last Month’s Profit
Even though everything went down from last month, October was still my 3rd-most
profitable month of all-time, so I’m feeling fine about the above.
Pegasus had what I’d call an “average” month, and
Place Card Me dipped for the first time since Covid, but only barely.
Zoomed out, the trends remain up and to the right which is all I ever hope for.
Time breakdown for October 2020
I logged about 50 fewer hours than a usual month—mostly because I’ve been watching my new son during the day.
However, not having a day job to worry about for a few months means I still had more time than normal for my own projects.
A lot of time was logged to Pegasus last month, but that includes prepping and attending PyConZa as well as some
consulting for a Pegasus customer.
I’m expecting Pegasus to drop and my do-gooding (currently filed under “exploration”) to rise moving forwards.
OKR check in
My goal of $4500 monthly profit isn’t off to a great start,
but hopefully Place Card Me will pick up in November and December.
||Achieve financial independence through passive income
||$4,500 average monthly profit
This Month’s Goals
Now that I’ve got a bit more of a big-picture roadmap I’m returning to monthly goals:
- Continue improving Pegasus’s deployment and tools offering
- Publish the next article in my “ikigai” series
- Start a real data science and/or machine learning project
A few things I enjoyed this month:
- Essay: Patrick McKenzie’s “What Working At Stripe Has Been Like” was
a fascinating look inside the company, and doubles as the most epic job post/endorsement I’ve ever seen.
- Podcast: “Invest like the best” has great guests covering a wide
array of interesting topics in the tech and VC world. One of the more insightful things the host said about his
podcasting strategy was “assume your audience is brilliant”—a refreshing take on the typical regression-to-the-lowest-common-denominator
common in things like Buzzfeed. It also helps me think about creating content for my own little audience.
- Podcast: “How to save a planet” from Gimlet is like the “Planet Money”
of climate change. Interesting short takes on a variety of climate-adjacent topics.
- Podcast episode: “Joe Rogan and Kanye West”. This one is very hard to describe,
and not for everyone, but I found Kanye’s blend of genius and insanity strangely captivating. It is wild to imagine
working with/for him.
Happy November, and if you’re in the US and haven’t already, go vote!