10,000 AI readers react to your post before you publish it
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.
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.
I would most like to hear about texts where the town got it wrong.
About Jevtown on Product Hunt
“10,000 AI readers react to your post before you publish it”
Jevtown launched on Product Hunt on September 21st, 2026 and earned 87 upvotes and 20 comments, placing #16 on the daily leaderboard. 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.
On the analytics side, Jevtown competes within Writing, Marketing and Artificial Intelligence — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how Jevtown performed against the three products that launched closest to it on the same day.
Who hunted Jevtown?
Jevtown was hunted by Ivan Gabor. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Ivan Gabor
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.