From Code to Cognitive Systems: Solving the Dark Matter of Software Engineering

Modern software systems are bloated. To build even a basic service today, we end up stitching together dozens of dependencies — for building, testing, deploying, monitoring, and securing. But if software is just a bunch of CPU instructions and syscalls, why is building it so complicated?

Here is the list of language specific libraries and tools available to choose from:

JavaScript

Python

Golang

Java

Rust

C/C++

This talk is about the “dark matter” of software engineering — the invisible effort that makes systems work but rarely gets visibility. Think of all the internal tools, version compatibility layers, reproducible builds, CLI wrappers, and CVE patching we do just to keep things running. We’ll explore where this complexity comes from, why it’s unavoidable in current systems, and how we might do better.

We’ll walk through a realistic (and slightly exaggerated) story of building a SaaS startup from scratch — and look at how complexity creeps in over time. From there, we’ll cover five core sections:

1. Why is modern software bloated? Meeting requirements like readability, correctness, scalability, and security ends up pushing us toward toolchains and third-party libraries — which multiply over time.

2. What exactly is dark matter in software? It’s the glue work: internal libraries, integration scripts, legacy support, and the countless tasks engineers do that don’t show up in the main codebase — but are essential.

3. Can we reduce the bloat? Yes, by shifting how we design systems. We’ll talk about ideas like the actor model, composition over configuration, and domain-driven design to keep complexity in check.

4. Do popular languages even recognize this problem? Most don’t. While they offer syntactic sugar and performance, they often ignore system-level reasoning, recovery, and long-term simplicity.

5. Introducing ASLang — a new experimental language I’m building to tackle these challenges directly by focusing on pure functions, predictable behavior, and cognitive clarity.

1. Modern Software Development Standards

2. The Paradox of Choice

3. Systems Architecture

4. Domain Driven Design and Actor Model