If you can’t build it you don’t understand it
Issue 122 · October 20, 2025
Welcome to your weekly download of memes and insights.
I am trying to organize observations about how I work with AI into somewhat of a framework. Two weeks ago I wrote about how I think with AI, last week about how I create with AI, and this week I’m focusing on how I build.
If creating turns ideas into form, building turns form into function.
The gap between a prototype and a working system is filled with decisions: how data moves, where logic lives, what breaks and why. Building with AI means treating architecture as conversation: describing intent until structure emerges.
What surprised me most about building with models is how much happens before code. When I explain what I want a system to do, the model shows me what I haven’t decided yet: the missing schema, the unclear dependency, the workflow I assumed was simple.
This literalness is the leverage. AI build for you by reflecting your specifications back with enough precision to reveal what you left implicit. The clearer the explanation, the closer the implementation lands to what you meant. Even when what you want isn’t what you need.
These field notes document how I am actually using AI, based on ChatGPT’s interpretation of our many conversations, coupled with a Cursor code repository with all my newsletter writing, both of which are used to document the behaviors I display according to the various AIs I interact with.
How I build with AI:
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Product design reasoning: Before touching an interface, I describe how a feature should behave – user intent, friction points, edge cases. AI helps expose assumptions and clarify purpose before design locks in decisions that are expensive to reverse.
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Interface sketching: I prototype flows entirely in language: what each screen does, what happens next, where feedback appears. Testing logic through conversation catches problems that wireframes miss and turns vague ideas into verifiable sequences.
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Architecture discussion: When deciding what lives where – server, client, local inference – I talk through the tradeoffs with AI. It doesn’t choose for me, but it makes structure tangible enough to evaluate stability, complexity, and constraint.
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Workflow automation design: I map full sequences before automating: triggers, data sources, transformations, outputs. The exchange turns scattered logic into a blueprint I can implement in steps, reducing confident mistakes from oversized prompts.
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Error explanation: Instead of jumping to fixes, I ask AI to explain what failed and why. This builds debugging intuition over time. I’m solving the current error by learning the pattern behind it.
Building becomes deliberate when you design through explanation. The model holds your intent steady while you refine it into working systems.
As trite as it sounds, AI doesn’t build for us – it builds with us, one augmented thought at a time.
Until next week,
MZ
MZ
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