Behind the work / The Atelier

Welcome to
the atelier.

A look inside my engineering workshop: the systems I build, the tools I use, and the experiments that shape how I work with AI.

VANCOUVER, BCSOFTWARE / INFRASTRUCTURE / AI01 — 04

$ show workbench

What’s on the whiteboard

Explore the decisions behind a selection of independent systems.

$ trace a memory

From data to agent context

19 adapters / local embeddings / pgvectorInteractive illustration

Explore how information can travel from a source through normalization and retrieval to an agent.

  1. Source
  2. Normalize
  3. Embed
  4. Store
  5. Agent
01 / Sample input

{ "type": "task", "status": "done" }

02 / Normalized

{ "entity": "task", "state": "completed" }

03 / Agent context

The agent can use a consistent record across sources.

Illustrative examples—not live personal data or a live model response.

$ inspect inference

The local inference rig

I run a self-hosted GPU inference bench for local models and embeddings, exploring quantization, model routing, and memory budgets.

COMPUTERTX 5070 Ti / 4070 SUPER
INFERENCEllama.cpp / CUDA
RETRIEVALPostgreSQL / pgvector

Architecture overview. The inference service is not exposed on this page.

INPUT→MODEL ROUTING→GPU INFERENCE→RESPONSE

$ review boundaries

A security-minded workshop

[scope] Role-specific tool permissions

[validate] Schemas and deterministic checks

[test] Prompt injection and escalation scenarios

[observe] Logs, boundaries, and failure analysis

Illustrative design principles from the security lab; not a live security feed.

$ query the résumé

Ask the workbench

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Project descriptions and experience are candidate-provided. This résumé's MCP tools are directly testable; other systems are available for an architecture walkthrough. Current and past employment positions and employers are private. Professional focus and target roles do not disclose a current job. Independent verification and references require direct contact.