3h • 💬 General discussion
Why Simple Pipelines Outperform “Smart” AI Systems
Every few months, a new AI orchestration framework drops. More dashboards. More abstractions. More complexity.
You wire up a simple workflow… and spend hours debugging it.
Here’s the truth: most AI workflows don’t need “smart” orchestration. They need structure.
A simpler approach already exists: Jake's folder architecture.
Inspired by Doug McIlroy and Unix pipelines:
Do one thing well
Use plain text
Make steps work together
The idea: Folder = Pipeline
Each step is a folder:
instructions.md → what to do
output.md → result
Flow: AI runs → human reviews → move to next step
That’s it. No frameworks. No hidden state.
Example: /01-research → /02-draft → /03-review → /04-publish
Why it works:
Clear input/output at every step
Human becomes the control layer
Easy to debug, edit, and stop
Works with any AI tool
Upgrade it with one small addition: Add status.md
RESULT: SUCCESS | WARN | FAIL
Now every step is measurable, not guesswork.
Rules that make it powerful: • One folder, one task
• Plain text only
• Always include a stop instruction
• Review before moving forward
• Version your pipeline like code
When to use it:
When accuracy matters more than speed
When human review adds value
When you want clarity, not abstraction
The Unix pipeline is 50+ years old and still runs the internet.
Your AI workflow doesn’t need more tools.
It needs better structure.
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Why Simple Pipelines Outperform “Smart” AI Systems
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Jake Van Clief, giving you the Cliff notes on the new AI age.
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