Personal Development
Archived at July 18th, 2020 and not updated thereafter.
Personal development
As a developer, it is important to always continue to learn and to make sure you keep up with the technological advancements. For this reason I spend at least one day a week on self-education.
What I am currently working on:
- Improvements within the infrastructure of Bytecode, applying Site Reliability Engineering (SRE) principles.
- Building a central API system (REST and gRPC) for Bytecode, consisting of several microservices.
- Creating code generation tools for project scaffolding (e.g. React and Redux) with the objective of more uniform code bases within Bytecode and less manual boilerplate code.
- Building a tool within Bytecode to define and test import boundaries with a domain specific language.
- Conduct research to create fixed “stacks” for development at Bytecode, and better definition of guidelines.
- Self-education towards the foundations of Computer Science and Computational Science and Engineering.
- Reading The Art of Computer Programming, part 1 to 4A (Donald Knuth)
What I’ve been working on lately (most notably):
- Delving deeper into Go/Golang development for enterprise applications
- Specialization towards software architecture/design and SRE
- Reading books related to productivity, management and soft-skills
- Reading books related to process/management with software development
- Reading Start With Why and similar books on startup branding and mission definition
- Delving deeper into compiler engineering and assembly language(s)
- Kubernetes in production
What I want to learn (most notably):
- Getting more acquainted with the purely functional programming languages Haskell and Elm
- Systems programming with Rust
- Embedded programming with C, C++ and/or Rust
- Become familiar with Erlang, OCaml, C++, Dart, F# and Smalltalk for research purposes
- Functional front-end web development with Elm, ReasonML and/or ClosureScript
- WebAssembly, icm. Rust, Go and/or C
- Artificial Intelligence and Neural Networks with Tensorflow