Field notes / 17 published
From the workbench.
I write here after making a hard product call, catching a bad assumption, or finding a better way to work.
The methodThe seven decisions that turn an idea into an order you can build in.
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Product scope, MVPs, AI-assisted development, and the habits that keep the work moving.
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A build plan written by an AI, scored against twelve questions published before the run. What it answered, what it did not, and why the split is not random.
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Measured usage from a small production site on Workers, D1, R2, Durable Objects and cron, against what the plan includes.
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What broke when I put a public MCP server on this site, and why every tool I used to check it was blind to the failure.
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Thirty days of crawler logs from a 26-page site, and what the AI crawler traffic actually turned out to be.
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What to do with the plan once v1 ships — closing open decisions, revisiting cuts with real evidence, and what to leave alone.
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How to derive the screens a product actually needs from its features, and write the flows that expose what is missing.
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Why agents follow instructions literally and break unstated rules, and how to write requirements as constraints instead.
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The difference between a one-off task brief and the durable product context an agent should read before every task.
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Why asking people if they would use it predicts nothing, and what evidence to look for instead before writing code.
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A complete product blueprint, section by section, filled in for a sample product — the format you can copy for your own.
………0reading13 min readRead field noteThe seven decisions that turn an idea into a build order, in the sequence that keeps each one from breaking the next.
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How to turn a growing inventory of ideas into one believable week of product progress.
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How a sensible feature list turns into a product nobody can explain, and what to cut first.
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A practical way to protect the one outcome your first release has to deliver.
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A short record of what happened is often more useful than a polished plan.
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Why a small request can break five other things, especially when an AI agent only sees the ticket.
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A practical boundary between useful autonomy and expensive guessing.
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If you are making the same calls right now — what belongs in v1, what a change quietly touches, what to build first — that is exactly what a Clarity Sprint produces, for your product rather than as a general principle.