I put together 5 of my favorite quotes and highlights from my notes. Original plan was to write them down everyday as I woke up, but Robie ended up always taking my notebook — and either way I memorized them after a couple of days.
This is the price I am willing to pay for retaining my composure.
The Google memo bothers me. It’s a smart-sounding piece of contrarian opinion that cherry picks facts to drive a point. It misappropriates real problems and assigns convenient explanations.
I don’t think it’s worth debunking, because it’s not even posing a question. The writer clearly assumes that he knows better than us. He’s mansplaining in the most ironic way: to other men and incorrectly.
First, my beliefs: women are equal to men in science, technology, engineering, and mathematics. You can generalize on the differences between men and women — it’s not fair, but it’s efficient. You shouldn’t be reductionist based on these generalizations — that’s unfair, and inefficient.
Second, my opinion: working on tech for women is not easy. The same situation where a male PM can get kudos from his team will result in shaken heads with a female PM — bossy vs. leader, bitchy vs. detailed, flirty vs friendly. Add the current vicious cycle of mostly male teams and it’s not easy to imagine what an unfriendly environment it can be.
I’ve worked with great female developers. I’ve worked with mediocre male developers. Anecdote is not evidence, so I shouldn’t say all female devs are great — but saying the opposite is just as incorrect.
In fact, its 56 per cent growth in North America in the quarter was far surpassed by its pace of expansion in Asia, South America and Africa.
This is interesting. While Amazon continues to become a 500 pound Gorilla in the US, its international expansion has been slower — and mostly limited to developed countries.
I believe that Amazon’s superpower is overcoming complexities of logistics at scale. However, when you move from large markets into smaller ones, you face restrictions that don’t scale at all.
Say you figure out logistics in Mexico, whatever expertise you acquired will do very little in figuring out Guatemala. You can repeat the example throughout South America — and I’m willing to bet in Asia and Africa too.
Shopping has morphed since the beginning of the web. Although most players are currently experimenting with mixed models, a simplified look at their strengths could look something like:
I’m very curious about the edge cases where Shopify and Postmates exists. While scale is more difficult to achieve, there’s a lot of flexibility that allows for more niche segments to crop up. Still, within large markets, the advantage doesn’t last long. As soon as product X had enough demand, the centralized infrastructure takes over with its lower costs.
But when the large market is actually a combination of smaller markets, there should be a lot more space for middle of the road logistics scale. Especially when there’s variations of tastes that don’t benefit exactly the same products in each of the markets.
Still need to work through this, but I believe (and hope) Amazon.com will not be the only online store in the future.
When shaving with a safety razor and brush, you usually fall down a rabbit hole of shaving creams and soaps. Last December I started to anxiously calculate when I should replace my favorite shaving cream — or maybe trying a new one? That’s when my frugal resolution for 2017 started.
Almost 8 months later, I still haven’t bought a new shaving cream or soap. Half-used and completely new ones keep appearing.
I miss having a new shaving thingy, but it feels great to finish up existing ones.
When dealing with large datasets1 remember to tell yourself the story of the resulting chart.
Most of us usually create charts with some sort of agenda. We kinda know what we want to show, and therefore aren’t surprised with the chart if it fits our expectations.
The problem is that good data organized incorrectly can still look right. The most painless way I’ve found to try to catch these issues is taking a step back and telling a story of what the data is showing without thinking about your slide title. Just really read the data calmly, and you will likely catch a surprise or two.
Thankfully I’ve avoided a few charts with volume numbers until December 2017 (US vs world date formate), 1000x sales numbers (coma vs period thousand’s separator), and my favorite: 70 weeks per year (careful when how you use the DATE() formula).
Anything that requires you to scroll down I’ll consider large. If all the data is viewable, it’s easier to keep a mental model of it.↩
There’s a moment at the end of a swim lap that you have to decide between stretching out and riding out your inertia — or doing one last stroke to reach the wall.
Of course there are different personalities: some prefer to hit the wall at full force, others do a final all out push just before the wall to glide into the finish.
A similar dynamic can also happen on projects. Some push their teams until a few hours (minutes?) before the deadline. I usually end up with a hard week and working weekend on the final stretch, but on the final days I let the team inertia set the pace.
Instead, we’ve seen subscriptions combined with price increases, customers balking, and insinuations that people just don’t want to pay for anything anymore. With more than one variable changing at once, I don’t think we can conclude that people hate subscriptions.
This ring true. It’s not as simple as saying I don’t like subscriptions.
Think about Medium this way. It’s a big public legal pad. In a perfect world, no one owns the pad. When you want to write something you tear off a sheet, write, when you’re done you tack it up to a global bulletin board where everyone can see it. […] Ghost is not such a place, and neither is WordPress.
There has to be space for a pinboard.in for blogging/writting. A one-person operation that can renders pretty static html and can survive with respectful display ads or non paid accounts.
[…] I’ve basically figured out all the traps to the point where I’ve actually written a program which for the past 6 months has been just doing the whole thing for me. So what used to take the last guy like a month, now takes maybe 10 minutes to clean the spreadsheet and run it through the program.
Although a particular case, this sort of question will become more common with AI, machine learning, and other deep learning applications.
And what happened was this accountant, he got a rush job from one of his clients. It was the kind of thing that in the old paper universe would’ve taken a couple days. This guy has this new electronic spreadsheet. So he plugs in the numbers, does the work in just a couple hours. Then, what he does - he just waits, let’s the thing sit on his desk for, like, two days, FedExs it back to the client. And the client was like, wow, you did it so fast.
There’s space for a lot of debate on ethical questions like this. But there’s opportunities created when industries