It Runs. Now What?
Flip the "considerate software" trait list and you get AI's default interaction flaw list. Click five inconsiderate spots in an app that runs fine, and see what you've been quietly putting up with
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTIt Runs. Now What?
Flip the "considerate software" trait list and you get AI's default interaction flaw list. Click five inconsiderate spots in an app that runs fine, and see what you've been quietly putting up with
Turn taste into a behavior the product can repeat. The useful outcome is not a nice opinion. It is a visible rule, a small example, and a way to tell when the experience falls below the bar.
Capture one before-and-after example that shows the quality bar without extra explanation.
Polish that improves the surface while leaving the user's uncertainty untouched.
Taste bugs show fast: open the page, colors clash, spacing is cramped, you frown in three seconds. Interaction bugs hide deep: the two-minute demo looks fine. They flare when a real user deletes the wrong row, drops the network, or opens an empty first screen.
That's the new pit in the Vibe Coding era. AI made「shipping it」easy. Its default interaction stops at「it runs」: blank empty states, alert popups for errors, deletes with no confirm, Save with no feedback. You click twice in review and feel fine. Users want to uninstall after a week.
| Dimension | Taste bugs | Interaction bugs |
|---|---|---|
| When they show | First three seconds after open | After use: wrong delete, offline, first open |
| Who finds them first | You can spot them at a glance | Users often find them for you—cost is churn |
| How AI gets them wrong | Rainbow colors, fighting type sizes—looks off | Demo looks fine; every edge case is a trap |
| How to review | Look: a screenshot is enough | Use: walk the flow, try every bad case |
Where does the list come from? Alan Cooper answered in About Face 4, Chapter 8,「Digital Product Etiquette」. Drawing on Nass and Reeves, he turned the research into one design principle:
Alan Cooper, About Face 4, Chapter 8
Cooper's observation cuts deep: interactive products anger us less from missing features than from being inconsiderate. He listed a dozen considerate traits. We pick the six best for checking AI output. Read each in reverse and you get a flaw standard.
| Considerate software… | Cooper's meaning | AI's default flip side |
|---|---|---|
| Is anticipatory | Predicts what you need next and preps it | First open is blank; next step is pure guesswork |
| Informs you in time | Tells you progress you care about—you shouldn't have to ask | You hit Save and nothing moves; a spinner with no word |
| Doesn't burden you with its problems | Digests tech failures itself; no code-flavored blame dump | Goes offline and throws Error: code 500 in your face |
| Is confident, but keeps a way back | Does what you say without nagging; if you err, helps recover | Either asks three times before delete, or delete is permanent |
| Doesn't ask needless questions | Remembers choices it can; sets defaults it can | Asks the same question every time you open |
| Helps you avoid dumb mistakes | Sees you about to err, quietly steadies you—no scolding | Watches you mis-delete, then pops「operation failed」 |
That right-hand column is the target for the spot-the-flaws lab next. Don't memorize—catch them once and they'll stick.
Quick division of labor across chapters: Taste Engineering owns looks, this chapter owns usability, Psychology owns how it feels. Each ships one acceptance checklist. Together they're the「three checklists before launch」.
One upside: AI can fix all six. It just doesn't by default. Name the bug and it can prescribe. That's why this chapter keeps drilling「say what you want clearly」.
Below is an AI-generated todo app,「Today's List」. Feature-complete. It runs. It hides five inconsiderate spots, all on this screen. Click whatever feels wrong. Hit one and a case card slides in on the right: which checklist rule it breaks, and how to fix it.
On the flaw list, two diseases get mixed up: no feedback is a bug, and over-feedback is a bug too. Cooper called this out in Chapter 8: interactive products love unnecessary notifications that show off.「Document saved!」modals waiting to be dismissed only interrupt you. Both editors below give Save feedback. Tap the one you find more considerate.
Wrapped the order-export feature this week. Integration passed; shipped Thursday.
Next week: expand export formats to Excel. About three workdays…
Wrapped the order-export feature this week. Integration passed; shipped Thursday.
Data side filled in a 30-day retention board; metrics aligned with product.
Next week: expand export formats to Excel. About three workdays…
You've seen the flaw list. Think of the software you use every day—including what you had AI build. Six kinds of inconsiderate. Pick the one that tortures you most, then see what others put up with.
You have the flaw list. You still need a how-to. Taste review can use screenshots. Interaction review means walking the flow—three times, each as a different person.
| Pass | Who you play | How you walk | What bugs to catch |
|---|---|---|---|
| First | Power user | Follow the happy path through the main flow | Daily friction: Saved-no-feedback, Asks-forever |
| Second | Unlucky user | Err on purpose: delete a row, drop the net, mistype a format, double-submit | Blame-dump errors, Gone-for-good, Crash-and-lose-all |
| Third | New user | Clear data and start from zero. Pause on every first screen | Blank empty state, next step is guesswork |
Passes two and three get skipped most often—and that's exactly where AI's bugs pile up. Demos always show pass one: smooth network, data ready, path straight. Walk the other two for users and you catch the bugs before launch.
Keep the considerate checklist nearby. On every screen ask: Is this anticipatory? Does it inform? Is it dumping blame? Is there a way back? Asking needless questions? Helping me avoid dumb mistakes? Six questions done—that screen passes.
Interaction bugs hide deeper: Taste issues show in three seconds. Interaction issues wait for wrong deletes, offline, or first open—and users often find them for you.
「It runs」is only a pass: AI's default interaction stops at「it runs」. What angers users is usually inconsiderate behavior; missing features rank lower (Cooper, About Face 4, Chapter 8).
Flip the considerate list into a flaw list: Anticipatory, informs in time, no blame dump, confident with a way back, no needless questions, helps avoid dumb mistakes. Run all six against AI output.
Review interaction by using it, not only looking: Walk the flow. Try the bad cases: delete a row, drop the net, open a fresh account. The next four lessons unpack each handle—starting with the three states.
Source: Original to Xiaoshan Academy's「Interaction Engineering」series; some interaction principles adapted from About Face 4: The Essentials of Interaction Design.
Turn the feeling in “A glance vs a walkthrough” into a judgment
“Taste bugs show fast: open the page, colors clash, spacing is cramped, you frown in three seconds.” points out that AI has lowered the bar for making something usable. The skill readers need is noticing what is wrong and turning that feeling into an actionable requirement.
Watch the user's next action, not just the surface
Turn “That's the new pit in the Vibe Coding era.” into observable questions: does the user know what happened, what to do next, and how to recover from an empty or failed state? Does the hierarchy make the important information visible first?
Pretty is not the same as usable
Apply “Review interaction by using it, not only looking: Walk the flow.” to a second screen or flow. Record one moment of hesitation and the user action after the change; observable behavior is stronger evidence than polish alone.
From “A glance vs a walkthrough” to “Considerate is a checklist. Flip it and it becomes a flaw list”
“A glance vs a walkthrough” grounds the problem in “Taste bugs show fast: open the page, colors clash, spacing is cramped, you frown in three seconds. Interaction bugs hide deep: the two-minute demo looks fine. They flare when a real user deletes the wrong row…”. “Considerate is a checklist. Flip it and it becomes a flaw list” then moves it toward “Where does the list come from? Alan Cooper answered in About Face 4 , Chapter 8,「Digital Product Etiquette」. Drawing on Nass and Reeves, he turned the research into one design principle”. Together, they show that the lesson is not just a conclusion to remember, but a claim with conditions.
Carry the judgment into the next situation
For experience work, turn abstract impressions into user actions: did the person understand the state, find the next step, recover from an error, and want to continue?
- “A glance vs a walkthrough”: Taste bugs show fast: open the page, colors clash, spacing is cramped, you frown in three seconds. Interaction bugs hide deep: the two-minute demo looks fine. They flare when a real user deletes the wrong row…
- “Considerate is a checklist. Flip it and it becomes a flaw list”: Where does the list come from? Alan Cooper answered in About Face 4 , Chapter 8,「Digital Product Etiquette」. Drawing on Nass and Reeves, he turned the research into one design principle
- “The closing point”: Below is an AI-generated todo app,「Today's List」. Feature-complete. It runs. It hides five inconsiderate spots , all on this screen. Click whatever feels wrong. Hit one and a case card slides in on the right: w…
The final “The closing point” brings the discussion to “Below is an AI-generated todo app,「Today's List」. Feature-complete. It runs. It hides five inconsiderate spots , all on this screen. Click whatever feels wrong. Hit one and a case card slides in on the right: w…”. The useful thing to carry forward is knowing which judgments must be revisited when input, scale, or risk changes.
I turned one judgment from this article into a small experiment I could run today. Knowing what to observe next is more useful than simply remembering the conclusion.
After reading this, I first looked for the conditions behind the idea instead of copying the method into a project. That order made the later trade-offs much clearer.
When this judgment reaches real work, which constraint should be added first? I am curious which step matters most between reading and the first practical attempt.
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