Runnable tutorials, small implementation exercises, and practical follow-ups to the longer engineering articles. These posts are kept separate from the main Technology stream so readers can go directly to the code and operator exercises.

  • Hands-On: Check What the Deployed Site Serves

    Serve an invented miniature site on loopback and see the difference between a missing asset, a successful HTTP response, and a redirect to the right page.

  • Hands-On: Build a Small Prepublication Check Runner

    Run three small gates against an invented article, break one input at a time, and watch the runner stop without touching a real Jekyll checkout.

  • Hands-On: Render and Review a Diagram Before Jekyll Builds

    Take the actual DOT and Mermaid sources from Part 6, render them locally, change a label, and see where the Jekyll build begins.

  • Hands-On: Follow a Project Through the Catalog

    Run an offline copy of the project generator on three invented repositories and inspect the data and scaffolding it produces.

  • Hands-On: Keep a Short Link Through a Source Move

    Run the site’s short-link calculation on invented posts and watch what stays fixed when a source path changes.

  • Hands-On: Find the Limits of a Jekyll Metadata Check

    Run the site’s taxonomy plugin against invented posts and see both the bad metadata it rejects and the mistake a successful build still lets through.

  • Hands-On: Build a Jekyll Preview Switch

    Build one small Jekyll fixture in current and review modes, then prove which posts each mode includes without touching your site server.

  • Hands-On: Build a Fail-Closed Release Evidence Ledger

    Run a standard-library Python lab that binds release evidence to one artifact identity and rejects missing gates, stale observations, mixed identities, and unsupported claims.

  • Hands-On: Audit an Isolated Agent Testbed Before First Run

    Run a model-free Python preflight that rejects shared roots, port collisions, contradictory knowledge ownership, duplicate declarations, and reused inference canaries before provisioning begins.

  • Hands-On: Package a Read-Only Component Triage Skill

    Run a model-free Python lab that validates invented operational evidence, correlates canonical plans with observed state, and renders a review packet without executing a lifecycle command.

  • Hands-On: Tune One llama.cpp Variable at a Time

    Run a model-free Python lab that checks one controlled intervention, active-path evidence, and correctness before comparing invented inference results.

  • Hands-On: Build a Fail-Closed Inference Benchmark Comparator

    Build a standard-library benchmark comparator that rejects incompatible runs and correctness drift before calculating median, direction-aware performance deltas.

  • Hands-On: Put a TTS Bridge Through a Model-Free Test Bench

    Build a model-free TTS test bench with bounded capability and registry metadata, opaque registered references, a legacy compatibility path, layered health, redacted events, and deterministic cleanup.

  • Hands-On: Put Multimodal Context Hygiene Through a Test Bench

    Run sanitized conversations through stock and derived Qwen templates, verify exactly what changes, exercise the routing boundary, and inspect the passive model-proxy relationship.

  • Hands-On: Build and Test a Passive Proxy Lab

    Run a passive proxy, deterministic fake model endpoint, and six privacy and byte-preservation canaries using only the Python standard library.

  • Hands-On: Build a Fail-Closed Memory Router

    Authorization has to choose the candidate set before relevance ranking begins. This runnable Python router and its seven canaries make that ordering visible and testable.

  • Hands-On: Build an Idempotent Intake Manifest

    A first import is easy. I built and tested a bounded Python and SQLite manifest that keeps source identity and history intact across reruns, changes, disappearance, and restoration.

  • Hands-On: Isolated Remote Development with SSH

    A step-by-step tutorial for using SSH keys, isolated accounts, remote editor workspaces, and tunneled Python debugging on the same Mac, another machine, or a VPS.