Launching today
Jevtown
10,000 AI readers react to your post before you publish it
39 followers
10,000 AI readers react to your post before you publish it
39 followers
Write a post, a listing, a product or a headline, and 10,000 computed AI residents read it. The 600 it should matter to see it first, and it reaches the next 1,500 only if more readers were glad than annoyed, so a weak text dies for half a cent and a good one reaches everyone in 14 seconds. You get who stopped, liked, reposted or blocked, by interest, job, age, city and budget; the questions buyers would ask a listing first; and a demand curve over a product's price ladder. No sign-in.
Jevtown
Hi Product Hunt.
Most Jev demos ask the model for one decision: moderate this comment, route this email, score this ticket. I wanted to see what ten thousand decisions about the same text look like, so I built a town and put Jev in every house.
You write a post, a listing, a product or a headline. The 600 residents it should matter to read it first, and it travels further only while more of them are glad than annoyed. A weak text dies in the first wave for half a cent. A good one reaches all 10,000 in 14 seconds for about ten cents.
The test that convinced me: I wrote one iPhone listing two ways. The version that lets the buyer pay on inspection reached 2,100 residents and 142 wrote to the seller. The advance-payment-only rewrite reached 600 and stopped, with 204 of them suspecting a scam.
Three things I measured before building any of it, in case they help someone else building on Jev:
1. 200 personas in one request answer the same as one asked alone, so batching costs nothing in accuracy.
2. Reversing the order of the options shifts answers by 0.062, two and a half times the noise between two identical calls. So the order is fixed and never shuffled.
3. Asking "what is the highest price this buyer would pay" turns 90% of people into buyers. Writing the base rate into the question gives 48%, which matches what they actually do elsewhere in the same run. The calibration is real, but it calibrates the question you wrote.
No sign-in, no accounts. Ukrainian and English, and the language of your text picks the town.
Try it: https://jevtown.ivanhabor.com
The 30-second film of the two listings: https://youtu.be/Ktm2qwW7JAo
I would most like to hear about texts where the town got it wrong.
Do you also track how long they spent reading before they reacted.
Would love to see the questions buyers would ask part in action. Does it suggest rewrites too.
The idea of testing copy against actual simulated demand before spending on distribution is pretty interesting. The 14 second feedback loop sounds especially useful.
Jevtown
@aria_taylor Thanks! The 14 seconds is for a text good enough to reach all 10,000. A weak one stops in the first wave of 600, which takes 2 to 4 seconds and costs half a cent, so the quick answer is often "rewrite before you post". Try two versions of the same headline and compare the runs side by side. For a product, set a few prices and you get a demand curve and the price that earns the most. These are simulated readers, so it works best as a first filter before you pay for real distribution.