Writing samples

The sentences behind the systems.

Case studies show how I think; these show how I write. Two representative excerpts from developer documentation I authored at Intel: a task-oriented get-started guide and a REST API reference. The original pages have since been retired from intel.com as the product lines evolved, so the excerpts are reproduced here with identifying keys and credentials removed. Full samples available on request.

Get-started guideEdge AIAutomated Self-Checkout · Intel reference implementation

Get Started Guide for Automated Self-Checkout

Why this one: a multi-step install and evaluation flow compressed to a 30-minute path, with prerequisites stated up front, every command runnable, and verification built into each stage.

Time to complete: 30 minutes · Languages: Python, Bash

This guide helps you get started with the automated self-checkout system. You will learn how to install the required software, run pre-configured pipelines, and monitor the system using Grafana.

Prerequisites

Intel® Core™ processor, 16 GB RAM or higher, 64 GB available disk
Docker installed; Docker Compose v2 (optional); Git installed
Ubuntu LTS boot device and an internet connection

Install the package

Download the reference implementation package.
Unzip the package: unzip automated-self-checkout.zip
Navigate to the directory: cd automated-self-checkout
Change permissions for the executable: chmod 755 edgesoftware
Install: ./edgesoftware install

You will be prompted for a product key during installation. Upon success, you will see “Installation of package complete” and the status of each module.

Run and evaluate the pipeline

The solution processes video streams from multiple cameras to detect and classify products. Start the pipeline server, then launch the Grafana dashboard to visualize detection and tracking, including bounding boxes and average FPS per stream.

make run-pipeline-server
docker ps                          # confirm containers are up
./src/pipeline-server/status.sh   # average FPS and pipeline latency

Stop the services after evaluation with make down-pipeline-server.

API referenceRESTEdge Video Analytics Microservice (EVAM) · Intel

EVAM REST API Reference

Why this one: reference writing is a different discipline from guides. Every endpoint carries its method, path, parameters, request and response examples, and error behavior, in a shape a developer can consume without reading top to bottom.

The Edge Video Analytics Microservice (EVAM) REST API allows you to manage and control video analytics pipelines: configure, start, stop, and monitor them through the endpoints below.

Authentication

Include your API key in the X-API-Key header of each request. A missing or invalid key returns 401 Unauthorized.

Deploy a pipeline

POST /pipelines/{name}/{version}

Parameter	Type	Required	Description
`name`	String	Yes	The pipeline to deploy. Alphanumeric characters only.
`version`	String	Yes	The version of the pipeline.

Responses: 200 OK means the pipeline instance started. 400 Bad Request means an invalid name or version.

Retrieve pipeline status

GET /pipelines/status returns every instance with its state and performance:

[
  { "id": 1, "state": "COMPLETED", "avg_fps": 8.93, "avg_pipeline_latency": 0.45 },
  { "id": 2, "state": "RUNNING",   "avg_fps": 6.36, "avg_pipeline_latency": 0.65 }
]

Key	Description
`state`	Current state of the instance (COMPLETED, RUNNING, and so on).
`avg_fps`	Average frames per second processed by the pipeline.
`avg_pipeline_latency`	Average latency, in seconds, experienced by the pipeline.

Stop a pipeline

DELETE /pipelines/{instance_id} stops a running or queued instance. 404 Not Found if the instance does not exist.

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