I stopped building AI tools and started doing the tasks manually instead

Dev.to / 4/3/2026

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Key Points

The author shifted from building AI tools to manually completing customer-requested repetitive tasks after realizing automation assumptions were often wrong.
They launched a minimal service using a simple, non-AI stack (Django, Stripe, SQLite, static HTML) where users pay a small fee to have tasks executed and results emailed.
Customer submissions are systematically categorized (input/output types, difficulty, whether AI would fail, and whether tasks recur) to discover repeatable patterns.
The manually executed work functions as product discovery with real revenue, and those observed patterns later inform the first automated tools.
The author invites others to share alternative approaches for identifying what to build, arguing that the “product” is the dataset of task submissions rather than the software itself.

I've been building AI products for a while now. Every time I thought I knew what to automate, I was wrong.

So I tried something different. Instead of building another tool, I put up a simple page: describe a repetitive task, pay $5, I do it and email you the result.

The tech stack is intentionally boring. Django backend, Stripe checkout, SQLite, static HTML frontend. No React. No AI. No accounts. The whole thing is under 500 lines of code.

Why? Because the product isn't the software. The product is the dataset of tasks people submit. Every submission gets categorized: input type, output type, difficulty, whether AI failed at it, whether it's recurring.

After enough submissions, patterns emerge. Those patterns become the first automated tools. The manual execution phase is just product discovery with real revenue.

The site is euphorie.com if you want to see how minimal it is.

Has anyone else tried using manual execution as a product discovery method? Curious what other approaches people have used to figure out what to build.