Software infrastructure (by which I include everything ending with *aaS, or anything remotely similar to it) is an exciting field, in particular because (despite what the neo-luddites may say) it keeps getting better every year! I love working with something that moves so quickly.
It's a popular attitude among developers to rant about our tools and how broken things are. Maybe I'm an optimistic person, because my viewpoint is the complete opposite! I had my first job as a software engineer in 1999, and in the last two decades I've seen software engineering changing in ways that have made us orders of magnitude more productive.
I spent a ton of time looking at different software providers, both as a CTO, and as a nerd “advanced” consumer who builds stuff in my spare time. In the last 10 years, there has been an order of magnitude more products that cater directly to developers, through APIs, SDKs, and tooling.
Anyone who built software for a while knows that estimating how long something is going to take is hard. It's hard to come up with an unbiased estimate of how long something will take, when fundamentally the work in itself is about solving something.
A modern tech stack typically involves at least a frontend and backend but relatively quickly also grows to include a data platform. This typically grows out of the need for ad-hoc analysis and reporting but possibly evolves into a whole oil refinery of cronjobs, dashboards, bulk data copying, and much more.
It started with a tweet:
New years resolution: every plot I make during 2018 will contain uncertainty estimates
— Erik Bernhardsson (@fulhack) January 7, 2018 Why? Because I've been sitting in 100,000,000 meetings where people endlessly debate whether the monthly number of widgets is going up or down, or whether widget method X is more productive than widget method Y.
This is a bit of a rant but I really don't like software that invents its own query language. There's a trillion different ORMs out there. Another trillion databases with their own query language. Another trillion SaaS products where the only way to query is to learn some random query DSL they made up.
Ok, so I have to first preface this whole blog post by a few things:
I really struggle with the term microservices. I can't put my finger on exactly why. Maybe because the term is hopelessly ill-defined, maybe because it's gotten picked up by the hype train.
I've been reading up on operations research lately, including queueing theory. It started out as a way to understand the very complex mortgage process (I work at a mortgage startup) but it's turned into my little hammer and now I see nails everywhere.
I spent a few days during the holidays fixing up a bunch of semi-dormant open source projects and I have a couple of blog posts in the pipeline about various updates. First up, I made a number of fixes to Git of Theseus which is a tool (written in Python) that generates statistics about Git repositories.
Here's a dumb extremely accurate rule I'm postulating* for software engineering projects: *you need at least 3 examples before you solve the right problem*.
This is what I've noticed:
Don't factor out shared code between two classes.
I've written before about the importance of iterating quickly but I didn't necessarily talk about some concrete things you can do. When I've built up the tech team at Better, I've intentionally optimized for fast iteration speed above almost everything else.
I was reading yet another blog post titled “Why our team moved from <language X> to <language Y>” (I forgot which one) and I started wondering if you can generalize it a bit. Is it possible to generate a N * N contingency table of moving from language X to language Y?
As a project evolves, does the new code just add on top of the old code? Or does it replace the old code slowly over time? In order to understand this, I built a little thing to analyze Git projects, with help from the formidable GitPython project.
I generally haven't written much about software architecture. People make heuristics into religion. But here is something I thought about: how to build in self-correction into systems. This has been something just vaguely sitting in my head lacking a clear conceptual definition until a whole slew of things popped up today that all had the exact same issue at its core.
Here's a conclusion I've made building consumer products for many years: the speed at which a company innovates is limited by its iteration speed.
I don't even mean throughput here. I just mean the cycle time.
The easiest way to be a 10x engineer is to make 10 other engineers 2x more efficient. Someone can be a 10x engineer if they do nothing for 364 days then convinces the team to change programming language to a 2x more productive language.
Curious about Google's newly released TensorFlow? I don't have a beefy GPU machine, so I spent some time getting it to run on EC2. The steps on how to reproduce it are pretty brutal and I wouldn't recommend going through it unless you want to waste five hours of your live.
Every once in a while when talking to smart people the topic of automation comes up. Technology has made lots of occupations redundant, so what's next?
Switchboard operator, a long time ago
What about software engineers?
I just pinged a few million random IP addresses from my apartment in NYC. Here's the result:
What's going on with Sweden? Too much torrenting? Ireland is likewise super slow, but not Northern Ireland Eastern Ukraine is also super slow, maybe not surprising given current events.
Wow I guess it was more than a year ago that I tweeted this. Crazy how time flies by. Anyway, here's my rationale:
When I update one line of code I feel like I have to put in a long explanation about its side effects, why it's fully backwards compatible, and why it fixes some issue #xyz.