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.
What's new in Jevtown: why the town passed, your own audience, and an MCP server https://youtu.be/cDokxlOpKac?si=x2XQNJ2fWgAksFxn Three things came out of the launch comments, and all three are live.
The town says why. When a check finishes, up to 100 of the people it reached get one more question. Those who scrolled past or got annoyed pick a reason, and those who liked it say what stopped them. For a post, those who stopped also say what they would comment. The page splits the reasons into ones about the text and ones about the audience, so you can tell a weak opening from the wrong readers. Jev also answers a few yes or no questions about the text itself:
Is there a concrete number, name or example?
Is it clear what the reader should do?
For a listing, does the first sentence say what is for sale?
Your own audience, in words. Describe whom the text is for, and only the residents who fit read it. "People who work in IT and are into startups" is 238 people, "people over 60" is 1,398. The town has no data on cities or company stages, so words about those do not count. The Audience tab shows how Jev read the description.
An MCP server. Jev writes no text, but an agent can. With the server, Claude or another MCP client writes variants, compares up to five on the first wave and follows the best one through the town. It runs on your own Jev key.
Before shipping, I tested every new question with paid runs and dropped three that did not hold up: how far people read, whether a text reads as written by AI, and whether a post puts its main point first. The numbers are in docs/measurements.md.
Three things came out of the launch comments, and all three are live.
The town says why. When a check finishes, up to 100 of the people it reached get one more question. Those who scrolled past or got annoyed pick a reason, and those who liked it say what stopped them. For a post, those who stopped also say what they would comment. The page splits the reasons into ones about the text and ones about the audience, so you can tell a weak opening from the wrong readers. Jev also answers a few yes or no questions about the text itself:
Is there a concrete number, name or example?
Is it clear what the reader should do?
For a listing, does the first sentence say what is for sale?
Your own audience, in words. Describe whom the text is for, and only the residents who fit read it. "People who work in IT and are into startups" is 238 people, "people over 60" is 1,398. The town has no data on cities or company stages, so words about those do not count. The Audience tab shows how Jev read the description.
An MCP server. Jev writes no text, but an agent can. With the server, Claude or another MCP client writes variants, compares up to five on the first wave and follows the best one through the town. It runs on your own Jev key.
Before shipping, I tested every new question with paid runs and dropped three that did not hold up: how far people read, whether a text reads as written by AI, and whether a post puts its main point first. The numbers are in docs/measurements.md.
I like it. Beautiful UI and cool Jev usecase. I'm sure many people are using Jev for AI detection but seems like a good use case here too
The idea of 10000 readers reacting before publishing is such a good pitch. Is the audience configurable for a niche like tech founders only
Have you compared your AI reactions to real human reactions to see how close they are.
Finally a tool that does not make me connect all my socials just to try it. How long does a typical test take to finish.
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.
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 88 upvotes and 24 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.
Jevtown was featured in Writing (59.4k followers), Marketing (469.7k followers) and Artificial Intelligence (481.3k followers) on Product Hunt. Together, these topics include over 227.5k products, making this a competitive space to launch in.
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.
Want to see how Jevtown stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
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.