In Part 1, I wrote about the hard parts of moving from individual contributor to management — losing the IC identity, swimming in ambiguity, juggling stakeholders, learning to delegate, and becoming a coach. I promised tips. A few months into living this role for real, here is what actually helped me, not the polished advice from a slide deck.

None of this is universal. These are the habits that stopped me from either drowning in other people’s work or quietly sliding back into being the best engineer on the team again.

According to kubernetes.io:

Kubernetes is the defacto container orchestration platform today. It is a powerful tool that can help you manage your applications more efficiently and reliably. Kubernetes uses a declarative approach to configuration, which means that you define the desired state of your application and Kubernetes will work to make sure that the actual state matches the desired state. This makes it easy to deploy changes to your applications and to scale your applications up or down as needed.

However, managing kubernetes can be a nightmare. It has a steep learning curve and has a lot of features, which can take some time to master. While there is the official kubernetes dashboard, in my opinion Rancher by Suse, is the preferred tool for most administrators. The real power of Rancher is in its ability to manage multiple kubernetes clusters, while providing a single pane of glass for all administrative tasks.

A few weeks into Spring Boot land and I already have a new favourite kind of production pain: one slow downstream dependency quietly taking the whole request path with it. Not a crash. Not a clean 500. Just threads waiting, timeouts stacking, and suddenly your service looks unhealthy because someone else’s database is having a bad afternoon.

In the .NET world I reached for Polly when this showed up. In Java the answer that keeps coming up in code reviews is Resilience4j — the library that basically replaced Netflix Hystrix after Hystrix went into maintenance mode back in 2018. If you are wiring Spring Boot 3 services that talk to other services (and who isn’t), this is the pattern I am finally treating as non-optional.

One of my strengths at work is debugging/troubleshooting technical issues. I have been told that I have a knack for finding and fixing problems that others find hard to solve. Many people come to me for assistance when they encounter difficulties and I often am able to give them effective solutions or workarounds that allow them to continue their work. This is an ability that has come with a lot of practice and experience in dealing with various technical challenges. There are a few core ideas that underpin these abilities, some of which I try to enumerate below

In modern web architectures, a security token is used to grant access to a protected resource. It typically is a string of random characters that is generated by an identity/authorization server in response to a login/identification request. Bearer tokens are a type of security tokens - one that has gained a lot of popularity due to the introduction of the OAuth2.0 standard. The name “bearer token” comes from the fact that the token can be used by anyone who has it. This is in contrast to other types of tokens, such as proof of possession (PoP) tokens, which can only be used by the specific client that was issued the token. If the PoP token is stolen, it cannot be used by anyone else.

A transformer model is a neural network architecture that can automatically transform one type of input into another type of output. It was first introduced in the paper “Attention Is All You Need” by Vaswani et al. (2017). Transformer models are distinguished by their use of self-attention, a mechanism that allows them to learn long-range dependencies between input and output sequences. This makes them well-suited for tasks such as machine translation, text summarization, and question answering.

Transformer models are typically composed of two parts: an encoder and a decoder. The encoder takes the input sequence and produces a sequence of hidden representations. The decoder then takes these hidden representations and produces the output sequence. The encoder and decoder are both made up of a stack of self-attention layers. Each self-attention layer takes the hidden representations from the previous layer and produces new hidden representations that are weighted by their attention to each other. This allows the model to learn long-range dependencies between the input and output sequences.