A Day in the Life of a Developer

How a Developer Spends One Day When the Hard Parts of Computer Vision Are Already Solved

Imagine a world where building and scaling computer vision pipelines across hundreds of thousands of cameras is something a single developer can do in a day.

At Plainsight, that’s the future we’re working backwards from.

To understand what this future looks like, it helps to remember why computer vision is usually slow, expensive, and fragile. Most CV systems struggle under three kinds of overhead:

1. The physical environment: cameras, lighting, bandwidth, reliability
2. The compute infrastructure: scaling video processing across sites
3. The machine learning workflow: training, validation, rollout, and rollback

Plainsight exists to absorb that complexity so developers can focus on delivering value, not fighting infrastructure and models.

Plainsight is a computer vision platform built to remove the operational complexity of deploying CV at scale. It abstracts away the hardest problems: managing cameras, scaling video infrastructure, and operating the machine learning lifecycle across diverse environments. By doing so, Plainsight enables developers to build, deploy, and improve computer vision pipelines as easily as modern software systems.

To make that concrete, here’s a hypothetical, but realistic, day in the life of a developer once those hard parts are already solved.

A Day in the Life of a Developer

I’m a developer at Nebula, a company that designs and operates next-generation global data centers optimized for intensive AI workloads. Our customers are the largest organizations in the world, training large language models and running agentic workflows at massive scale.

Even our smallest data center spans more than five acres and operates over 150,000 cameras for continuous monitoring. Across our global footprint, we store and process live video streams in the cloud at enormous scale.

Our computer vision needs range from the simple, verifying employee ID badges, to the highly specialized, like analyzing the color and behavior of exhaust fumes from a rooftop. Most importantly, we need to:

• Add new video pipelines within a day
• Scale every pipeline across every new data center globally
• Improve performance continuously without growing headcount

Today, my Jira board has three tasks:

1. Create a new computer vision pipeline: Build a pipeline to monitor whether warehouse workers comply with required safety standards.
2. Deploy pipelines at scale: Roll out all existing pipelines to a newly launched data center in Nevada.
3. Upgrade models globally: Update every deployed pipeline to use the latest computer vision model from OpenAI.

My goal is straightforward: make our computer vision better than it was yesterday.

Morning: Building a New Safety Monitoring Computer Vision Pipeline 

I start by building a pipeline to monitor whether warehouse workers comply with safety standards.

From the HuggingFace Discord channels, I’ve learned that the fastest way to develop computer vision applications is using OpenFilter pipelines. I head to OpenFilter Hub and, within minutes, find three open-source filters:

• Workers wearing safety helmets
• Workers wearing safety boots
• Worker ID card visibility

In my IDE, I ask GitHub Copilot to create an OpenFilter pipeline using those filters, with our live camera streams as the source and our subject-data store as the destination. Copilot follows the OpenFilter MCP Server instructions and sets up the workspace in minutes.

Next, I deploy the pipeline in shadow mode to one of our existing data centers. In this mode, the pipeline processes real video without affecting live operations, collecting data to tune its model.

To validate accuracy quickly, I use the OpenFilter Synthetic Video Framework. Starting from a handful of real videos, I generate synthetic footage containing known safety violations. Because the ground truth is known, I can measure exactly how many violations the pipeline detects.

I configure a quality gate, similar to unit test coverage, requiring each build to catch at least 9 out of 10 violations. Once the pipeline meets that bar, I promote it to enforcing mode.

Task one is done before lunch.

Midday: Deploying at Massive Scale Without Touching Infrastructure

My next task is deploying all existing pipelines from our Alaska data center to a brand-new facility in Nevada.

The pipelines themselves stay the same, but the environment does not. Models tuned for snow, overcast skies, and cold light won’t perform well in a hot, dry desert climate. Each deployment needs its own optimized model.

Using the OpenFilter deployment system, I replicate every pipeline to Nevada. Each instance starts in shadow mode, adapting to local conditions while synthetic video accelerates training. Over time, the models converge, and I promote them to enforcement.

In a single step, I add another 200,000 cameras to our global deployment.

While this is running, my pager goes off. One pipeline instance in Alaska has started producing inaccurate subject data. Instead of debugging hardware or retraining models manually, I open the Plainsight portal and trigger a rollback. The issue resolves within five minutes.

I grab a coffee, knowing the Nevada deployment will continue optimizing automatically.

Afternoon: Upgrading the Latest OpenAI Computer Vision Model

The final task of the day is upgrading hundreds of OpenFilter pipelines across thousands of deployments worldwide to the latest OpenAI computer vision model.

Each data center has its own optimized model, so a global, forced upgrade would risk degrading performance. Fortunately, OpenFilter supports Blue/Green deployments.

I ask Copilot in my IDE to implement the model update, then run playback tests against recorded video. The new version performs better, so I push the changes to GitHub and begin a controlled rollout.

Only 1% of camera feeds per deployment switch to the new Green version. I monitor globally aggregated dashboards comparing the new and old pipelines.

Most regions improve immediately. One region in the Pacific Northwest, where heavy rain is common, does not. The system automatically rolls back the change in that region only, protecting performance everywhere else.

I log a task to tune the model for that environment tomorrow.

Ending the Day With a Different Kind of Productivity

By the end of the day, I’ve:

• Built and validated a new computer vision pipeline
• Scaled all pipelines to a new data center with hundreds of thousands of cameras
• Started a global model upgrade with automatic safety rails

In a traditional computer vision organization, this would take months, dozens of engineers, and massive operational overhead.

In this future Plainsight scenario, one developer does it in a day because the platform absorbs the hardest problems: physical environments, infrastructure scale, and the machine learning lifecycle. The story is fictional, but the goal is very real.

This is the developer experience Plainsight is building toward: fast iteration, instant scale, and continuous improvement, without the burdens that usually comes with computer vision.