Practical systems.
Measured behaviour.
Outer Reaches draws on hands-on work in Linux infrastructure, AI inference, application development and deployment diagnostics.
Discuss a technical problem ↗Alexander Mazurovsky
I work across infrastructure and software: running inference locally, diagnosing performance, building applications and making systems easier to deploy and maintain.
Outer Reaches brings that experience to focused client projects. The starting point is the work you need to accomplish, followed by a design that makes its dependencies, limitations and operating requirements clear.
Work that informs the approach.
Moonshine / Local inference
Native C/HIP inference work involving ROCm, expert streaming and compressed expert storage. A foundation for measuring how memory, storage, model configuration and runtime performance interact.
Independent development · Inference systemsPermission-aware document applications
Prototyping ingestion, retrieval, source-grounded answers and audit trails for organizational information. Current design work focuses on permission boundaries and client-specific deployments.
Prototype and architecture work · Not a packaged productNexus / Agent harness
A self-hosted agent harness exploring persistent memory, scoped tool execution and traceable decisions. The Python implementation tracks source provenance and memory-use history, applies shared execution budgets, and separates model actions from operator authority.
Work on recovery, retrieval and audit trails informs how I approach reliable automation and human oversight in client applications.
Independent development · Agent runtime and memory systemsInference performance & reliability.
For teams already running models, a focused diagnostic engagement can investigate a specific bottleneck or deployment concern.
- Define a representative workload and record the environment.
- Measure latency, throughput, memory pressure and failure behaviour.
- Compare targeted changes against a reproducible baseline.
- Document findings, tradeoffs and recommended next steps.
Results depend on the model, hardware, runtime and workload. The deliverable is evidence and a recommendation, with implementation scoped as needed.
Bring the problem, not a prescribed stack.
A useful starting point is the behaviour you are seeing, what you expected, and the environment involved.
Contact us ↗