30 秒でわかる
Colin Matthews builds a habit-tracker prototype and explains how to plan, inspect generated code and test product ideas with customers.
- 1Start with a sketchDefine the screens, behavior and data the prototype needs.
- 2Plan before codingAsk for an implementation plan without writing code, then review assumptions.
- 3Choose storage deliberatelyBrowser local storage persists locally but is not a shared database or backup.
- 4Debug in small stepsProvide specific errors and make focused changes.
ポイント
- Describe the problem and data model before generating code.
- Ask for a plan and inspect it before implementation.
- Make small changes and roll back when necessary.
- Understand the limits of browser storage and public links.
- Show prototypes to customers to test assumptions.
チャプターごとに整えた全文を読む
Choose a tool and define the goal
Understand the workflow and sketch a habit tracker.
Product educator Colin Matthews demonstrates a habit tracker and a simple technique: describe the goal, ask the AI for an implementation plan and explicitly tell it not to write code yet.
He groups tools into chatbots, cloud development environments and local coding assistants. His 2025 comparison is based on how easily a user can run, store and deploy the resulting code, not on what code a model can theoretically write.
Matthews describes personal tools for resizing images and converting GIFs, and suggests keeping a reusable prototype of an existing product to explore new features with designers and customers.
The demonstration begins with a low-fidelity mock: a list of habits, completion dates and a detail view. A sketch helps clarify what the application should do before prompting.
Plan and implement
Review the data model and build incrementally.
His prompt specifies the interface and a data model for habits and daily completions, including future compatibility with a calendar view. He asks for a plan starting with the data model, without code.
Reviewing the plan gives the user a chance to correct assumptions and learn how the system will work. Matthews says the technique can improve results, but it does not guarantee correctness.
After approving the plan, he implements the prototype in small steps. He demonstrates inspecting generated code, adding features and using rollback when an iteration goes wrong.
Storage, styling and deployment
Persist data locally and refine the prototype.
When the prototype loses entries on refresh, Matthews requests browser local storage. It can persist data in that browser, but it is not a shared database or a backup and can be cleared.
He asks the AI to propose styling changes before applying them. The same plan-first technique helps separate desired visual changes from unwanted layout or behavior changes.
The discussion covers code diffs, deployment and optional database integrations as they existed in early 2025. Public prototype links can expose content, so check access settings before sharing sensitive material.
Learning and customer feedback
Debug systematically and test ideas with users.
To avoid getting stuck, Matthews recommends defining requirements and data early, working in small batches, describing the exact issue and using files or error details for context.
He emphasizes system concepts such as client, server, storage and data models. Hands-on prototyping can build intuition, but production systems may need deeper engineering, security and operational review.
For product managers, start with a simple prototype of an existing experience, collaborate with designers and show a proposed change to customers. The purpose is to learn whether the idea solves a real problem.
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