Tag: Systems Thinking

The Backyard Quarry, Part 8: From Rocks to Reality

At the beginning of this series, the problem seemed simple.

There were a lot of rocks in the yard.

Some were small.

Some were large.

A few were firmly in what I’ve been calling Engine Block Class.

The original idea was straightforward: catalog them, maybe sell a few, and build a small system around the process.

Along the way, the project grew.

What We Built

Across the previous posts, the Backyard Quarry gradually evolved into something more structured.

We explored:

  • designing a schema for physical objects
  • capturing images and measurements
  • building ingestion pipelines
  • indexing and searching the dataset
  • representing objects as digital twins
  • scaling the system as the dataset grows

None of these ideas are particularly new on their own.

But when combined, they form a recognizable structure.

The Pattern Behind the Project

What the Quarry experiment revealed is that many modern systems share the same underlying architecture.

It doesn’t matter whether the input is:

  • rocks in a backyard
  • industrial machine parts
  • museum artifacts
  • scanned environments
  • sensor data
  • documents or images

The pattern remains surprisingly consistent.

We start with the physical world.

We capture information from it.

We transform that information into structured data.

Then we build systems on top of that structure.

The Signature Architecture

At a high level, the pattern looks like this:

Diagram showing a system architecture where physical world inputs flow through capture, ingestion, processing, storage, indexing, and application layers.
A common architecture pattern for systems that transform real-world inputs into usable digital platforms.

Each layer has a role:

Capture Layer

The interface between the real world and the system.

Examples:

  • cameras
  • sensors
  • manual input
  • scanning systems

Ingestion Pipeline

Raw inputs enter the system.

Queues and ingestion services buffer incoming data.

This stage provides resilience and scalability.

Processing & Transformation

Raw inputs are converted into usable forms.

Examples:

  • metadata extraction
  • photogrammetry
  • feature generation
  • classification

Structured Data + Assets

The system stores both:

  • structured records
  • unstructured assets

This is where digital twins live.

Indexing & Search

Data becomes usable.

Indexes, embeddings, and search systems allow retrieval and exploration.

Applications

Finally, systems are built on top of the data:

  • dashboards
  • analytics
  • automation
  • AI systems

Recognizing Systems

One of the more interesting outcomes of the Quarry project is how quickly the pattern became recognizable.

Once you see it, it’s hard to miss.

Manufacturing systems follow this structure.

Archival systems follow this structure.

Many modern AI systems follow this structure.

Even systems designed to analyze motion or sensor data follow this structure.

Different inputs.

Same architecture.

Systems Thinking

The biggest shift in perspective comes when you stop thinking about individual objects and start thinking about the system as a whole.

Instead of asking:

  • How do we catalog this rock?

You start asking:

  • How does the system handle many objects over time?

This change in perspective leads to different kinds of decisions:

  • how pipelines are structured
  • how data flows through the system
  • how failures are handled
  • how the system evolves

At that point, the problem is no longer about objects.

It’s about systems.

A Small Experiment

The Backyard Quarry began as a small experiment.

A dataset that happened to be available.

A problem that seemed simple.

But small experiments are often useful.

They allow ideas to emerge in a manageable setting.

The same architectural questions that appear in large organizations also appear here — just at a smaller scale.

The Real Takeaway

The real lesson from the Quarry isn’t about rocks.

It’s about recognizing patterns.

Modern systems often share common structures.

Once you understand those structures, it becomes easier to design new systems.

You start to see the same ideas appearing in different places.

And that recognition becomes a powerful tool.

One Last Observation

Some engineering lessons come from large projects.

Others come from experiments.

Occasionally, they come from a pile of rocks in the backyard.

And if you happen to need a carefully documented specimen from the Backyard Quarry, inventory may still be available.

Shipping, however, remains an unsolved optimization problem.

The Rock Quarry Series

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What I’ve Been Building: Systems, AI, and Real-World Data

Over the past several weeks, I’ve been spending a lot of time thinking about systems.

Some of that thinking has taken the form of writing.

If you’ve come across any of my recent posts, they might seem like they cover very different topics:

  • cataloging rocks in a backyard
  • building AI systems using MCP
  • working with documents, images, and real-world data

At first glance, they don’t appear to have much in common.

But they’re all exploring the same underlying idea.

The Common Thread

Across all of these posts, the focus has been on a specific kind of problem:

How do we turn messy, real-world inputs into structured, usable systems?

That problem shows up in many different forms.

Sometimes the input is physical:

  • objects
  • artifacts
  • environments

Sometimes it’s digital:

  • documents
  • images
  • logs

Sometimes it’s dynamic:

  • motion
  • behavior
  • sensor data

But the challenge is the same.

The input is unstructured.

The system needs structure.

The Backyard Quarry

One way I explored this idea was through a small project I called the Backyard Quarry.

It started with a simple observation:

There are a lot of rocks in the yard.

From there, the problem evolved into something more interesting:

  • how to represent physical objects as data
  • how to capture images and measurements
  • how to build pipelines around that data
  • how to search and organize it
  • how to think about digital twins

What began as a small experiment became a way to explore system design in a constrained, tangible setting.

MCP and AI Systems

In parallel, I’ve been writing about building AI systems using MCP.

On the surface, this looks very different.

Instead of rocks, the inputs are:

  • documents
  • APIs
  • models
  • agent workflows

But the structure is familiar.

  • inputs are ingested
  • processed
  • transformed
  • routed
  • used by applications

The system still needs to handle:

  • variability
  • scale
  • imperfect data
  • orchestration

Different inputs.

Same patterns.

From Objects to Systems

One of the more useful realizations in working through these ideas is this:

The problem is rarely about the individual object.
It’s about the system that handles many objects over time.

Whether the object is:

  • a rock
  • a document
  • a sensor reading

The questions become:

  • how is it represented?
  • how does it enter the system?
  • how is it transformed?
  • how is it stored?
  • how is it retrieved?

These are system-level questions.

A Shared Architecture

Across these different domains, a common architecture begins to emerge.

Diagram showing how raw inputs are captured, processed, structured, indexed, and used by applications in a data system.
A common pattern for transforming real-world inputs into usable systems.

The labels change depending on the domain.

But the structure remains consistent.

Why This Matters

Understanding this pattern makes it easier to approach new problems.

Instead of starting from scratch each time, you can ask:

  • Where does the data come from?
  • How does it enter the system?
  • What transformations are required?
  • How will it be used?

This reduces complexity.

It also makes systems more predictable.

What I’m Interested In

Going forward, I’m particularly interested in systems that sit at the boundary between:

  • the physical world and digital systems
  • unstructured inputs and structured data
  • human workflows and automated processes

That includes areas like:

  • digital archiving
  • photogrammetry and 3D capture
  • AI-assisted analysis
  • systems that track objects or behavior over time

These problems are messy.

Which is part of what makes them interesting.

A Continuing Exploration

The posts I’ve been writing are not meant to be definitive.

They’re part of an ongoing exploration.

A way to think through problems in public.

And occasionally, a way to use a slightly unusual example — like a pile of rocks — to make broader ideas easier to see.

If You’re Interested

If any of this resonates, you might find these useful:

The Backyard Quarry Series

A systems-focused look at modeling and working with physical objects starting with Turning Rocks Into Data.

MCP and AI Systems

A technical exploration of building agent-based systems and data pipelines. I’d suggest starting with The End of Glue Code: Why MCP is the USB-C Moment for AI Systems.

More to come.

And if nothing else, it turns out that even a backyard can be a good place to think about system design.

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