Mythogon as a performance-oriented decision and inference system.

Problem And Requirements

The goal was to explore AI-adjacent decision systems that separate high-level orchestration from performance-sensitive runtime work. The system needed a C++ core, clear module boundaries, room for server interfaces, and tooling that could support research without burying the engineering shape.

Architecture Chosen

Mythogon uses a CMake project layout with C++ headers under include/mythic/, executable examples, server modules, protobuf definitions, gRPC client/server examples, shader experiments, and Python scripts for configuration and research support.

• C++ core: engine, pipeline, state, selection, dynamics, vector space, factory, and constraint-system headers.
• Service surface: HTTP, WebSocket, auth, validation, queue, logging, rate-limit, error handling, and gRPC example modules.
• AI boundary: design notes treat llama.cpp-style inference as a stateless engine while orchestration, tools, and logging live outside it.
• GPU-adjacent work: compute shader experiments for projection, matrix operations, gating, and weighted fusion.

Tradeoffs

The system favors explicit boundaries over a single opaque AI runtime. C++ provides control over performance-sensitive components, while Python and TypeScript remain useful for tooling, visualization, orchestration, and web-facing workflows. That separation makes the system easier to inspect, test, and evolve without pretending every layer belongs in one language.

Testing And Quality

The project includes CMake presets, multiple build directories, C++ demo programs, server examples, generated protocol surfaces, and audit/design documents for architecture review. The quality signal is the emphasis on traceable boundaries: auth, tenancy, request flow, observability, and stateless inference are documented as separate concerns.

Deployment And Maintenance

Mythogon includes deployment and DNS scripts for the Mythic surface, Visual Studio/CMake setup artifacts, and server startup materials. The work demonstrates the maintenance side of systems engineering: keeping build tooling, local services, and integration documentation close to the code.

Outcome

Mythogon gives me a concrete C++ systems anchor for AI-focused engineering conversations. It shows that my work is not limited to web UI or prompt workflows: I can reason about runtime boundaries, native build systems, service interfaces, performance-oriented modules, and the operational scaffolding around them.