I didn’t get into programming through computer science.

I got into it through HyperCard.
Stacks. Cards. Scripts. Buttons.
You could actually see how software worked, which apparently we decided was too simple and replaced with 47 layers of frameworks and build systems.

My first app?

A fly-fishing tackle box database for my dad, uploaded to GEnie.

New post:
https://spatiallyadjusted.com/hypercard-taught-me-software-isnt-magic

If you’ve never seen HyperCard, this video is worth your time:
https://www.youtube.com/watch?v=hxHkNToXga8

AI is incredible at generating output.
What it’s not great at (yet) is generating outcomes.

In spatial systems, the hard part isn’t the rendering. It’s the workflow — the metadata, the contracts, the integrations, the discipline that makes something buildable.

AI isn’t the product. Workflow is.

https://spatiallyadjusted.com/ai-isnt-the-product-the-workflow-is

#AI #GIS #SpatialComputing #ProductStrategy

Cloud-native didn’t fail. We finished:

• storage
• compute
• deployment

We skipped:
• workflows
• failure modes
• observability
• accountability

Then we blamed “complexity.” Cloud-native didn’t create it. It just made it visible.

https://spatiallyadjusted.com/cloud-native-didnt-fail

#CloudNative #GIS #Architecture #Scale #Systems

Every modern GIS platform claims to be “self-service.” What they really did was move complexity out of sight and onto users.

Invisible workflows, optional metadata, silent failure — and one poor human who “just knows how it works.”

https://spatiallyadjusted.com/the-lie-of-self-service-gis

I really enjoyed Bill Dollins’ recent Post-GIS Revisited post — not because it settles the question, but because it refuses to.

It got me thinking (again) about how GIS didn’t disappear so much as dissolve into workflows, metadata, and systems that don’t need heroics anymore.

I wrote up a few thoughts as a continuation of the conversation:
https://spatiallyadjusted.com/post-gis-revisited-again

Curious how others are experiencing this shift in practice.

At scale, systems don’t fail sometimes. They fail constantly.

The real problem isn’t failure — it’s pretending failure is exceptional.

When failure semantics aren’t explicit, humans appear to interpret partial success, retries, and blast radius.

Lessons from Scale #10:

Failure Is a First-Class API

https://spatiallyadjusted.com/lessons-from-scale-10-failure-is-a-first-class-api

#systems #reliability #failure #workflows #cloudnative

Observability is often treated as debugging exhaust.

At scale, it becomes something else entirely: the way systems communicate reality to users. When systems aren’t observable, humans reappear as historians and state lookups.

Lessons from Scale #9:

Observability Is a User Feature

https://spatiallyadjusted.com/lessons-from-scale-9-observability-is-a-user-feature

#observability #systems #workflows #reliability #cloudnative

APIs are necessary.

They are also wildly insufficient.
Integration failures don’t happen at endpoints — they happen at state, retries, and ownership of “what happens next.”

When workflows are missing, humans quietly reappear as the integration layer.

Lessons from Scale #8: APIs Don’t Integrate Systems. Workflows Do.

https://spatiallyadjusted.com/lessons-from-scale-8-apis-dont-integrate-systems-workflows-do

#systems #workflows #integration #cloudnative

GIS has worked for a long time because people quietly absorbed the complexity.

- They reran jobs.
- They fixed projections.
- They explained caveats no system ever documented.

That looks like flexibility. It’s actually fragility.

https://spatiallyadjusted.com/humans-are-not-a-scalable-integration-pattern

At scale, standards don’t break — they calcify.

What starts as a useful interface slowly turns into doctrine.

Validation replaces understanding.

Humans absorb the mismatch.

This post evolved because of the discussion around it, which is kind of the point.

https://spatiallyadjusted.com/lessons-from-scale-6-standards-dont-fail

#systems #architecture #gis #metadata #standards