The most boring minute in your product could be its best feature, if you turn it into a game.
Think about the moments in your product when people just sit there: waiting for a file to upload, scrolling a catalog for the fifth minute, or clicking through the same sorting task they did yesterday.
Most teams treat those minutes as dead time. I wanted to see what happens if you turn one of them into a small game, and how fast you can build that with AI before committing to a full product build.
The experiment: picking something to watch
I started with the moment almost everyone recognises, when you open Netflix and spend twenty minutes scrolling without picking anything.
So we built Tonight’s Pick, a small game for choosing what to watch. Posters fly across the screen, you swipe through the ones you don’t care about to slice them, and you tap the ones you like to save them. Once three of them are saved, you pick one and you’re done.
The idea that made it work came during the iterations. The first version had random monsters as targets, and it was fun but pointless. When we swapped the monsters for real posters, every move in the game started building your shortlist, so playing and choosing became the same action.
It’s a browser prototype and a concept, not a Netflix feature, and Netflix is just the example everyone already knows.
Three places to try this
While people wait. Your product is generating a result, loading a file or building a report, and in the meantime the user gets a small scene to play with, maybe one that lets them try out the controls of whatever is being made or shows them a feature they haven’t found yet.
While people choose. Instead of a long catalog, a few quick actions build a list of what they like, and the same idea could be adapted for clothes, trips or design templates.
While people do routine work. Sorting files, clearing an inbox or practising vocabulary gets quick gestures, visible progress and satisfying feedback, and every move in the game is a piece of the real task getting done.
The pattern behind it
Each of these follows the same chain:
moment in the product → simple action → instant reaction → useful result
For our example, that’s choosing a film → slice or keep a poster → the poster splits or fills a slot → a short list of things to watch.
Four rules so the game doesn’t get in the way
The player can skip it at any time.
The result never waits for the game to finish.
Game effects never stand in for real loading progress.
The usual way of doing the task stays available.
How we built it
Grok found game references on X, ChatGPT matched the mechanic to a real user task and wrote the spec, and Claude built the browser prototype and the demo. After a few rounds we had the controls and visuals where we wanted them.
What you get from this is a working prototype you can put in front of users to see whether it helps them get through that step or just distracts them. We haven’t measured the effect on retention or conversion yet, and that’s the next thing to test.
The prompt
If you’ve thought of a boring minute in your own product while reading this, here’s a reusable prompt for finding the right mechanic and building the first version:
Help me find a spot for a short, useful interactive moment in my product.
Product: [description]
User: [who they are]
Current flow: [steps]
Problem moment: [waiting / choosing / routine]
What the user needs to get or do: [result]
Materials and style: [links, images, references]
Suggest three different mechanics using this pattern:
moment → action → feedback → useful result.
For each one, describe:
- what the user controls
- how the action connects to their actual task
- what happens if they skip it
- how the scene ends
- what signal would show it's helping
Pick the simplest one and build a browser prototype that works with
mouse and touch. The demo should fit in 15–25 seconds.
Use real user actions and give instant visual feedback. Never make
someone finish the game to reach the product. If the main result is
ready early, offer to open it right away.
Build the working mechanic first, then style it from the references.
Pick one moment in your product. Build the smallest playable version. Watch whether people finish the task, or just play with it.
Save the prompt for your next prototype.
@6vintik9 Giving the landing agent the actual ad helped. It had a specific promise to follow through on, instead of starting from scratch with “write a landing page.”
A full marketing team for a $20 product? Easy, and the result is better than most agencies send back.
I took a $20 skincare product from Naturium and handed the work a small marketing team would normally do to three AI agents: one did the research, one wrote the ad and one built the landing page.
I ended up with an ad built around a single reason to buy, plus a page that carries that same promise from the moment someone clicks.
Most brands lose people right at that click. The ad says one thing, the visitor lands on a homepage talking about something else, and now they have to figure out for themselves whether they’re in the right place.
The researcher
I gave the first agent the product page, the photos, the price and the customer reviews, and asked it to find the strongest reason people buy this product.
It came back with who the buyer is, what makes them buy, the facts that back it up and the photos that show it. I asked it to source every claim or mark it as an assumption, so I could see which parts were facts and which were guesses.
It settled on [BUYING MOTIVE], and the ad and the page were built on that.
The creative director
The second agent got the research and was allowed to sell exactly one motive, so it couldn’t fall back on listing every benefit the product has.
It wrote one ad with the copy, the image placement and the button text: [AD HEADLINE].
The landing builder
The third agent got both the research and the ad. A page written from the product alone tends to wander off from whatever the ad promised.
I asked for a page where someone who clicked recognises what they came for straight away, so it opens on the same promise as the ad and keeps it all the way down to the button.
Claude then put all three outputs together into the assets you see above.
Why I didn’t do it in one chat
When I run everything through a single chat, I keep re-explaining the product at every step, and by the time I get to the landing page the original idea has gotten lost in everything said along the way.
With three chats, each one only works on its own piece. I decide what moves forward, so the motive from the research makes it into the ad and the page without getting watered down.
Setting it up
Make one project and put the product brief in it, so every chat works from the same facts.
Open a separate chat for each role. Once you’re happy with a result, paste it into the next chat yourself rather than expecting it to remember.
To test another angle, keep the product facts, swap in a different buying motive and run the ad and the page again. You’ll have a second ad and page pair to test against the first.
@VARnaldinho The handoff was the part I wanted to test: could the same buying motive survive all three steps? We got the ad and page built. Whether anyone buys is still an open question.
@6vintik9 "AI traders do an 18x in 36 trades" sounds cool until you realize $3.6k is basically just one lucky meme coin gamble that the bot happened to catch on a trend. Still, the pipeline with separated agent roles is pretty slick, can't deny that.
PROJECT BRIEF
Product: [name, URL]
Price: [price]
Photos: [attach]
Customer notes: [reviews, DMs, support questions]
CHAT 1: RESEARCHER
Find the strongest reason someone buys this product.
Return: the buyer, the buying motive, supporting facts, usable images.
Rule: every claim needs a source or is labelled as an assumption.
CHAT 2: CREATIVE DIRECTOR
Input: the approved research.
Return: one ad with exact copy, image placement and CTA.
Rule: sell one buying motive only.
CHAT 3: LANDING BUILDER
Input: the approved research and the approved ad.
Return: a landing page that continues the ad's promise.
Rule: the visitor should recognise what they clicked for straight away.
NEW ANGLE
Keep the product facts, replace the buying motive, rerun chats 2 and 3.
Paste this before your next landing page (or run it on the one you have):
Act as a blunt performance marketer who has reviewed thousands of landing pages. Audit this page for default AI patterns, in design and copy.
Flag every default: tiny all-caps badge above the headline, gradient text or one colored word in the headline, three identical cards with an icon in a rounded square, emoji as icons, glowing blobs, purple-to-blue everything, “Trusted by 50,000+” or logos I didn’t give you, words like supercharge, seamless, unlock, effortless, elevate, and any headline that could sit on any product.
For each one, one line on what a real visitor feels when they see it.
Rewrite it so only this product could use it:
– headline that names the customer, the result and the timeframe, in words a customer would say out loud
– one-sentence subheadline that says how it works, no adjectives
– show the product doing its job (a real table, chart, schedule, menu) instead of feature cards
– proof I can back up, or [ADD REAL PROOF]. Never invent numbers, logos or testimonials.
– CTA that says what happens after the click
Name the one change that makes this page look least like the other 50 in my category.
Short sentences. No buzzwords.
99% of AI landing pages are the same page. Here’s how to build the 1%.
Your landing page looks like the other 99%.
The 1% banned 6 things:
No caps badge over the headline.
No gradient text.
No “Trusted by 50,000+”.
No 3 identical icon cards.
No emoji icons.
No “supercharge”.
Same Claude, but totally different sites.
The full prompt ↓
@ryangrowthh thanks, really simple and useful guide. I'm now trying to make big things with AI in marketing and start my journey soon. If you wanna see how AI real works inside ads - coming soon