The portfolio
Every layer of LLM inference, covered by a purpose-built, open-source tool. From the metal to the mobile screen — each product has its own site, docs, and repository. They all speak GGUF, so models are portable across the stack.
Runtime + server
Local LLM runtime in Rust, built on llama.cpp, with native bindings for Rust, Python, Node.js, Go, PHP, and C/C++ — and drop-in Ollama CLI compatibility.
Who it's for: Polyglot teams and app developers who want one inference dependency across many languages.
Mobile
A Flutter FFI plugin, built on llama.cpp, for on-device GGUF model inference on Android and iOS — no cloud, no latency, complete privacy.
Who it's for: Mobile developers shipping private, offline AI features in Flutter apps.
Runtime / engine
A modular LLM inference runtime in Rust with a unified, type-safe interface across 47 architectures, built on three composable cores.
Who it's for: Rust engineers who want a composable, type-safe inference engine to build on.
Runtime (.NET)
A .NET-native runtime for local GGUF inference — managed-by-default, with optional Metal and Vulkan compute backends.
Who it's for: C# and .NET developers who want local inference with one package and no native setup.
Education
Learn how LLMs work by building one in Zig — an educational, book-shaped codebase implementing 18 transformer families across 6 progressive layers.
Who it's for: ML/systems-curious learners and Zig fans who learn by reading and writing code.
Bare metal
A Multiboot unikernel in C that boots on bare metal or in QEMU and serves an llama.cpp-compatible HTTP API — the kernel is the application.
Who it's for: Systems engineers exploring OS-free, boots-to-inference serving on bare metal.