Running Experiments in D

I don’t learn a language by reading about it—I learn it by putting it under pressure. With D now in my tool belt, the next step is straightforward: controlled experiments. Not throwaway code, not contrived examples, but small, focused systems that expose how the language actually behaves.

That’s the only way to separate capability from assumption.

Starting with the Fundamentals

The first set of experiments stays close to the core. Before building anything complex, I need to understand how D handles the basics under real conditions.

That means testing:

• Compilation speed and dependency handling
• Module structure and code organization
• Error handling patterns and failure behavior
• Interaction between stack, heap, and garbage collection

These aren’t exciting topics, but they’re the ones that determine whether a language holds up over time.

Memory and Control

One of the more interesting areas in D is its hybrid memory model. It offers garbage collection, but doesn’t force it. That opens the door to a range of experiments around control versus convenience.

I want to see:

• When the garbage collector becomes a liability
• How predictable manual memory management feels in practice
• Whether mixed approaches introduce complexity or flexibility
• How well performance holds under sustained workloads

This is where D either proves itself—or doesn’t.

Compile-Time Capabilities

D’s compile-time features are one of its defining traits. Templates, mixins, and compile-time function execution all promise a high degree of flexibility.

But flexibility without clarity is a problem.

So the focus here is disciplined use:

• Generating code at compile time without obscuring intent
• Keeping logic readable and auditable
• Avoiding “clever” constructs that reduce maintainability

If these features can be used without sacrificing clarity, they become valuable. If not, they’re just noise.

Building Small, Real Systems

Experiments aren’t limited to isolated features. I’m also building small, self-contained systems to see how everything fits together.

Things like:

• A minimal CLI tool with structured modules
• A data processing pipeline with controlled memory usage
• A simple runtime component that mimics parts of larger systems

These aren’t large projects, but they’re enough to reveal how the language behaves when pieces start interacting.

Measuring What Matters

I’m not interested in surface-level success. “It works” isn’t a meaningful result.

Each experiment is evaluated on:

• Predictability of behavior
• Clarity of implementation
• Performance under realistic conditions
• Ease of testing and debugging

If D can meet those standards consistently, it earns its place. If it falls short, that’s equally important to know.

Avoiding Experiment Drift

It’s easy for experiments to turn into unfocused exploration. I’m not letting that happen.

Each test has a purpose. Each result is documented. If something doesn’t provide insight, it gets cut. The goal is to build understanding, not accumulate code.

That discipline keeps the process useful.

Where This Leads

These experiments are groundwork. They’re not the end goal—they’re the validation phase.

If D proves itself through these tests, it moves into larger systems—AI work, tooling, and deeper integration into Fossil Logic projects. If it doesn’t, it stays limited.

That decision will be based on evidence, not expectation.

Closing Thoughts

Experimentation is where assumptions get tested and stripped away. D looks promising, but promise doesn’t mean much without results.

These experiments will determine whether it becomes a reliable part of the stack—or just another language that looked good on paper.

Either way, the process is worth it.