Machine Intelligence Engineer / Software Architect

LLM infrastructure for local runtime components, typed integration, and governed enterprise use.

Michael Kappel’s public .NET package ecosystem spans local GGUF/LLaMA-oriented runtime components, tokenization, sampling, tensors, execution backends, AI-memory tooling, and agent integration. The work applies software-architecture discipline around models; it does not claim foundation-model training or novel ML research.

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32verified public NuGet packages
21K+verified cumulative downloads
Local runtimeGGUF/LLaMA-oriented component lane
.NETtyped package and application foundation

Fast recruiter summary

What the package evidence demonstrates.

Counts and downloads were verified against the public profile during the August 2026 audit; NuGet remains the current registry authority.

Runtime decomposition

Model execution split into reviewable responsibilities.

Public packages separate abstractions, GGUF handling, LLaMA-oriented model components, tokenization, sampling, tensors, CPU kernels, acceleration, backends, and application-facing integration.

Application integration

Local models fit behind typed software boundaries.

Model-file validation, bounded loading, sessions, deterministic inputs, provider abstractions, and application facades make local inference a software integration problem that can be tested and governed.

Enterprise foundation

.NET, Azure, SQL, APIs, and delivery remain the base.

Runtime components can connect to established service architecture, data systems, deployment environments, observability, automated testing, and human review workflows.

Code-native architecture diagram

A layered local-runtime path from model artifact to reviewed output.

The layers describe package responsibilities; they do not assert benchmark leadership or complete support for every model and accelerator.

1. GGUF artifactLocal file intake begins with format, metadata, shard, and tensor-catalog validation.
2. Model layerLLaMA-oriented structures translate verified metadata into model-specific execution responsibilities.
3. Tensor and kernelsTensor primitives and CPU-kernel components define the lower-level computation boundary.
4. Backend abstractionManaged CPU and accelerator-specific packages keep execution choices behind explicit contracts.
5. Tokens and samplingTokenization, generation state, and sampling policies convert inputs and logits into bounded sequences.
6. Application facadeLocal endpoint and session components expose a typed integration surface for governed application use.

Package responsibilities

A reusable .NET ecosystem, organized by concern.

Package versions are intentionally left to NuGet so this page does not become stale.

Local application facade

UAIX.LmRuntime.LocalEndpoint provides the high-level local-only integration lane for verified intake, bounded loading, isolated sessions, and deterministic generation workflows.

Contracts and model intake

UAIX.LmRuntime.Abstractions, UAIX.LmRuntime.Gguf, and UAIX.LmRuntime.Models.Llama separate contracts, artifact inspection, and model-specific responsibilities.

Generation components

UAIX.LmRuntime.Tokenization and UAIX.LmRuntime.Sampling isolate text encoding and output-selection behavior from the application layer.

Computation layer

UAIX.LmRuntime.Tensors, UAIX.LmRuntime.Kernels.Cpu, and UAIX.LmRuntime.Acceleration define tensor, kernel, and acceleration seams.

Execution backends

The managed-CPU execution path is verified. GPU packages expose explicit registration, capability, and fail-closed diagnostic seams for CUDA, DirectML, Vulkan, Metal, and ROCm; they are not presented as proof of working GPU inference.

Memory and agent tooling

The broader public portfolio adds UAIX/.uai memory, build support, abstractions, and agent-client components around runtime and workflow integration.

Public proof paths

Verify package identity, architecture, and ecosystem context.

Use the registry for live metrics and the project sites for explanatory architecture.

NuGet registry

Michael.Kappel on NuGet is the authority for current package listings, versions, download counts, and package status.

Curated package map

NuGet Packages organizes public packages by engineering responsibility and links each package back to the registry.

Explicit non-claims

Infrastructure engineering, not model-training research.

The evidence supports public .NET packages, local-model/runtime components, a verified managed-CPU execution path, modular integration design, and enterprise AI workflows. It does not claim model training or fine-tuning of foundation models, inventing a new model architecture, state-of-the-art inference performance, universal hardware support, working GPU inference merely because GPU capability packages exist, or independently verified production-scale throughput. Download totals show registry use; they are not a performance benchmark.

FAQ

Common review questions.

Short answers are rendered in the HTML source and mirrored into JSON-LD where appropriate.

What public LLM infrastructure has Michael Kappel published?

The verified NuGet profile contains 32 public packages with 21K+ cumulative downloads across local LLM runtime components, AI memory and handoff, agent governance, interoperability, browser/P2P tooling, and observability.

Does this portfolio claim model training?

No. It demonstrates runtime, integration, memory, governance, and tooling work around models; it does not claim foundation-model training or research breakthroughs.

Which runtime components are represented?

Public packages cover contracts and abstractions for GGUF parsing, LLaMA-oriented execution, tokenization, sampling, tensors, managed CPU and accelerator backends, and related tooling.