Exploring developer platforms and infrastructure where powerful systems remain explicit, composable, and predictable without increasing cognitive overhead.

Ongoing exploration into explicit system design across web infrastructure and AI ecosystems.

Modern engineering ecosystems increasingly introduce complexity through implicit abstractions and convenience-driven design.

Recurring tensions in modern systems:

• Simplicity and control
• Ergonomics and predictability
• Accessibility and performance

Exploring approaches that avoid these tradeoffs.

Systems designed to

• Reduce cognitive overhead without hiding behaviour
• Remain explicit and composable
• Scale from learning environments to production-grade infrastructure

An exploration of explicit HTTP abstractions and composable middleware with predictable performance characteristics.

Rust web development often forces tradeoffs between ergonomics and power.
Volga investigates an alternative model where explicit APIs preserve reasoning clarity while lowering adoption barriers.

Part of a broader exploration into explicit infrastructure design.

Model Context Protocol SDK exploring production-grade AI agent infrastructure.

Modern AI tooling frequently optimises for experimentation rather than system design.
Neva explores structured agent architectures with strong typing, explicit runtime behaviour and extremely low overhead.

Extends exploration into explicit system design into AI infrastructure.

• Distributed system architecture
• Async runtimes and network systems

• Developer platforms & DX engineering
• Cloud-native infrastructure

• AI infrastructure & agent frameworks
• Identity-driven system design

• Explicit architecture > implicit magic
• Composability > framework abstraction
• Performance as architecture
• Systems thinking over tool-driven development
• Solving real constraints over building abstractions for their own sake

Active explorations:

• How explicit system design can reduce cognitive overhead without limiting flexibility
• Designing middleware and runtime abstractions that remain predictable under scale
• Bridging high-performance infrastructure principles into AI agent ecosystems
• Balancing developer ergonomics with runtime transparency

These explorations evolve continuously through engineering-notes.

LinkedIn: https://www.linkedin.com/in/romanemreis