$1.9m teeth whitening ads don’t sell the pen first
they sell the smile she wants to see today
this creative works because it doesn’t start with features
not “easy to apply”
not “gentle on teeth”
not “use anywhere”
it starts with the result
visibly whiter teeth
before vs after proof
10-minute transformation
confidence she can picture immediately
then the product becomes the shortcut
bad ad = product first
good ad = outcome first
that’s why result-first ads beat clean product shots
rt + comment "smile" and i’ll send you the result-first ad breakdown
(follow for dm)
this OpenClaw bot finds local businesses with no branded gear, AI mocks up their logo on the right product, and runs the entire sale on autopilot.
here's how anyone can use this system to land local business clients:
- scrapes every indie business in a city from Google Maps in real time
- filters by review count, rating, and industry vertical
- maps each vertical to the right product
- pulls the logo + samples the palette from their actual visual identity
- AI-renders a photoreal mockup of the logo on the product
- writes a postcard with the owner's first name + a personalized buy link
- when they scan + pay, a print-on-demand API auto-prints + ships direct to the business
every step from discovery to fulfillment is automated.
reply "LOCAL" + RT and I'll send you a free guide so you can build this too
$26k+/day ads are using formats like this
boyfriend blind scent tests on camera
real reactions, no overthinking, pure curiosity
it feels like content, not marketing
claude writes the flow and dialogue
seedance turns it into realistic clips instantly
no creators to coordinate
no reshoots or production delays
just prompt → interaction → ready to run
same format reused across products
different couples, reactions, outcomes every time
one idea becomes dozens of videos fast
that’s why these scale so easily
rt + comment “scent” and i’ll send the setup
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AMD Senior AI Director confirms Claude has been nerfed. She analyzed Claude's session logs from Janurary to March:
> median thinking dropped from ~2,200 to ~600 chars
> API requests went up 80x from Feb to Mar. less thinking and failed attempts meaning more retries, burning more tokens, and spending more on tokens
> reads-per-edit dropped from 6.6x → 2.0x. model stops researching code before touching it.
> model tried to bail out or ask "should i continue" 173 times in 17 days (0 times before March 8).
> self-contradiction in reasoning ("oh wait, actually...") tripled.
> conventions like CLAUDE.md get ignored because there's less thinking budget to cross-check edits
> 5pm and 7pm PST are the worst hours, late night is significantly better. this means the thinking allocation is most likely GPU-load-sensitive.