January 9, 2022

2021: A retrospective

2021 has simultaneously been the longest and shortest year ever. For the most part it felt lacklustre but looking back there were great wins and lessons learned.


  • I gave my first ever tech talk! It was at the Rust London meetup on error handling in Rust
  • It gave me the confidence to do more talks. I ended up doing 6 this year 🚀
  • As a result of giving talks, I’ve met a bunch of people in the Rust community. It’s the first professional community I’ve felt a part of and it has been wonderful
  • Learning Rust continues to be one of the best economic decisions I’ve made. It has brought a number of amazing career opportunities my way
  • I transitioned into a software engineering role focusing on infrastructure and tooling


Here. We. Go.

Best practices

At the start of 2021, I had a dogmatic view to best practices. What do you mean you don’t have 90%+ unit test coverage, integration tests, automated deployments on merge to master, perfectly curated permissions, etc etc. This is a deeply flawed line of thinking. Implementing best practices within an organisation is a journey. Organisations, like products, are a permanently unfinished project. You cannot expect to have best practices across all spheres, right now. They take time and a massive amount of human effort to get right.

Even so, it is important to realise that there is no “true” set of best practices. Best practice is dependent entirely on the context you operate within i.e your organisation. For companies in highly regulated industries with highly sensitive data, security best practices will look different to companies with mostly non-sensitive data. Software engineers in the aerospace industry would likely laugh at the testing best practices present in most consumer SaaS companies. A 40 year old organisation with millions of lines of code that have passed through the hands of hundreds of engineers is going to have a different set of best practices to those of a 5 person, 2 year old startup with thousands of lines of code written to be cloud native. It is easy to look at an organisation (especially your own) and criticize. Oftentimes the criticism is invalid as it neglects the operating context of that organisation. The truth of the matter is that thousands of companies provide value to their customers with entirely different sets of best practices. A staff engineer I admire put it nicely

Best practice should be used extremely sparingly as an argument to anything. Best practice is such an overused term to the point where it’s become quite meaningless because it rarely talks about the organisational issues that stem beneath.

There is no such thing as best practice, only best practice within a context. This framing is important because in our field of perfectionists, it is easy to get swept up by all the talk from other tech companies about their best practices and believe you’re missing the mark. Sometimes, this is the case but frequently, its not applicable to you. As we always say, you are not Google

All of this is not to say that we should ignore the best practices shared in the public domain. Take inspiration from them and apply them where applicable. There are practices we can agree on: test your code, pay off technical debt, minimize downtime. However, the extent to and manner in which they are implemented depends on your context. Best practices come in different shapes and sizes.

Situational context

If there is one lesson that has rooted itself deep inside my mind it is this one. As a developer, time and time again, I’ve made the mistake of ignoring my context. And time and time again, I’ve seen other developers ignore their context. This is most obvious with new, shiny technology and paradigms. Microservices! Rust! NoSQL! Distributed tracing! Chaos engineering!

All these are fantastic when utilised in the right situation. That, once again, depends entirely on your context. Sometimes, a newly founded startup needs to use a microservice architecture from the get go, other times they don’t. Sometimes, you need to use Cassandra for your data store, most of the time Postgres is just fine.

There are plenty of factors to consider when adopting new technology

  • Do we have enough expertise to use it successfully? If we don’t have the expertise, are we willing to hire in or pay for training?
  • How large is the community? Will we have the necessary support?
  • Has it been around long enough that we can expect it to be maintained for the next 20 years? What happens if it is sunset?
  • How easy is it to change if we adopt the wrong technology?

I think I’ve made my point. Look at your own surroundings.


This was a particularly interesting lesson for me to learn. At the time, I was advocating for improving testing quality and coverage. In my naivety, I thought it would be an easy sell. We all know testing is good engineering practice, let’s do more of it. Makes sense? As I learned, testing for the sake of adhering to “best practice” is an extremely poor justification. What I was missing was a social/business reason to spend time improving our testing:

  • Our builds are passing but the application repeatedly fails in production and we’re losing X amount of time a week due to it
  • It takes me an extra X minutes to ensure my changes aren’t going to break the application
  • We’ve accidently shipped breaking changes X times due to lack of tests, impacting the productivity of every team

These are all compelling reasons to improve testing. There is a clear problem they are addressing: the ability of the team to deliver value to customers. Tech does not live in a vacuum removed from the social dynamics and complexity of an organisation. The aim of most software is not to be an artefact of great engineering. While we may have great technical reasons for addressing aspects of our code or development process, they must be married with a social/business reason. Everyone has different incentives, pressures and contexts and unfortunately for us engineers, very few of those people actually care about the quality of the code. However, they do care about how fast we can ship new features, how much revenue our products can generate, how customers feel about our products. Marry the business concerns with technical concerns and you’ve significantly increased your odds of seeing a green light on your proposed changes.

Psychological safety

To this day, I’ve never really shaken off that tinge of fear when deploying to production. That feeling, while entirely self-inflicted and mostly irrational, made me understand psychological safety from a technical perspective.

Anyone that knows me well enough knows that Rust is my favourite programming language. One pillar of its design is: fearless concurrency.

Fearless concurrency allows you to write code that is free of subtle bugs and is easy to refactor without introducing new bugs.

In the same way Rust empowers you to develop concurrent software without fear, so should our systems empower us to develop without fear. It is trivial to see that fearless development teams will have increased velocity. When you’re developing, you don’t want to have to double check and perform mental gymnastics to ensure you’re not writing to a production database. To what extent psychological safety improves velocity is debatable but it is definitely an ingredient in the elusive equation of developer velocity.

For teams to be fearless, production systems need safety rails. Developers need to feel enough psychological safety that they are happy to experiment and deploy to production, knowing that they won’t affect production. Of course, we cannot prevent everything but that is beside the point. Humans make mistakes and without safety rails, your production database will be dropped.

How exactly to improve psychological safety when developing is something I’m looking forward to exploring in the upcoming year. From my perspective, it can be anything from ensuring good unit test coverage to having robust production testing capabilities to ensuring the correct database privileges. The overarching goal is to build your systems to ensure your developers feel psychologically safe.

Developer productivity

In his essay on working at Stripe, Patrick McKenzie speaks to Stripe’s ability to move faster than the average startup.

A stupendous portion of that advantage is just consistently choosing to get more done. That sounds vacuous but hasn’t been in my experience. I have seen truly silly improvements occasioned by someone just consistently asking in meetings “Could we do that faster? What is the minimum increment required to ship? Could that be done faster?”

Developer productivity is similar in that development teams need to continuously ask, “how do we improve our own productivity?”. An effective way to do that is by baking in leverage into your development processes, often done through building platforms, tools, libraries and automating processes. Point 8 here

From my own experience, a significant portion of lost productivity is simply from having to repeatedly interact with messy manual processes (that are automatable) and incorrectly/undocumented systems. Yesterday it was spending an extra two hours getting a system working because the documentation was out of date. Today it is an extra 24 minutes configuring yet another EC2 instance. The “easy” fix is to find the areas where developers are losing the most time and work on incrementally improving them. This is the type of work that has compounding effects. On activities that are performed with high frequency (e.g testing services locally), even a 1% improvement on time spent can have an outsized impact on development velocity.

I don’t think Stripe is uniformly fast. I think teams at Stripe are just faster than most companies, blocked a bit less by peer teams, constrained a tiny bit less by internal tools, etc etc.

Sharing work publicly

The tweet says it all


I clearly tweet a lot. Or maybe not.


I still do not understand it.


Upwards and onwards
To infinity and beyond
Aluta continua
To an unforgettable year

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