After six years in software development, I began noticing a pattern: most problems were just variations of ones I’d already solved. That raised a deeper question: should these problems even exist?
At its core, software is a flowchart—compute nodes for logic, decision nodes for branching. It boils down to bitwise operations and system calls. But over time, we’ve layered on protocols, libraries, tools, and frameworks—all in the name of abstraction. To what end?
That question sent me down a path to uncover the core principles of software—what I call a “theory of everything” for building robust, scalable, maintainable systems. Along the way, I identified six persistent pain points:
Readability – Enables faster learning and iteration
Correctness – Software should do what it claims
Scalability – Covers monitoring, alerting, and resource limits
Distributivity / Dark Matter – Includes client libraries and emulation
Reproducibility – Debugging should be deterministic
Security – Internal soundness and external threat modeling
To ground this, let’s simulate the journey of building a SaaS product.
The Journey Begins
Early on, speed was my top priority. JavaScript and Python allowed quick iteration but sacrificed correctness. Go and Rust provided safety but slowed feedback. Choosing a language meant picking between speed and reliability.
I launched an MVP and brought on two engineers. Initially, things went well. But soon, bug reports piled up. While debugging a small codebase was easy, the growing surface area made issues harder to trace. We realized correctness had to be built-in, so we enforced test coverage.
Scaling the Product
As usage grew globally, we had to go distributed. This meant thinking beyond individual services—we needed system-wide stability.
We shifted from a reactive approach to a proactive one: we added monitoring, alerting, and centralized logging to catch issues early. Backward compatibility also became critical—new deployments couldn’t break old clients.
Debugging and Security
To reduce debugging time, we captured client-side data and added tracing. At the same time, we hardened the system against DDoS attacks and vulnerabilities.
But with every new tool, library, or third-party service, we lost control and added complexity. Security remained fragile—often treated as an afterthought.
A Different Vision All of this led to a new idea: what if a compiler could handle this out-of-the-box?
Languages like C, Java, Go, and Rust are great for writing programs. But software is more than code. It needs built-in correctness, observability, scalability, debuggability, and security. Today, these are bolted on manually—through CI, test suites, dashboards, infra tools, and cloud APIs.
What if they were built in from day one?
I’m working on a new kind of compiler—not just to generate binaries, but to help you build complete software systems. The goal: eliminate the need for manual integrations and third-party tools.
No config. No boilerplate.
You won’t need a database, a stream processor, or a deployment manager. The compiler is the platform. Run the binary, expose a port, and you’re live.
Software, as it should be—correct by construction, observable by design, secure by default.