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.
Free
Launch Team
Framer AI AgentsDesign and publish professional sites with AI
Promoted
Would love to see the questions buyers would ask part in action. Does it suggest rewrites too.
Jevtown
@shinyahayashi Here is one: https://jevtown.ivanhabor.com/p/171f5a1168
It's a Ukrainian iPhone listing written two ways. The first version gives the details and offers inspection on delivery. Of the 531 buyers who stopped at it, 24% would first ask "Is the price negotiable?" The second version says "prepayment only", and 43% of those who stopped would first ask for a safe deal or cash on delivery. It never got past the first wave, where 204 of 600 buyers smelled a scam.
It doesn't suggest rewrites, because Jev only answers questions and writes no text. You write the next version yourself, and the buyers' questions show what the listing is missing.
Jevtown
@shinyahayashi Jevtown still writes no text, but there is now an MCP server, so an agent like Claude can write the variants and have the town read them. compare_texts shows up to five variants to the first wave and ranks them. check_text follows one through the town and returns why people passed and what stopped them. It runs on your own Jev key. Setup is in the README: https://github.com/gaborishka/je...
Jevtown
What's new in Jevtown: why the town passed, your own audience, and an MCP server
https://youtu.be/cDokxlOpKac?si=CsCbkuLgkeFmoRMZ
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.
Try it: https://jevtown.ivanhabor.com
Code: https://github.com/gaborishka/jevtown
Wow this is a cool concept. Been playing with it for the last hour
Jevtown
@austinserb Thanks for spending an hour on it! What did you try? If you haven't yet, describe yourself as a resident at jevtown.ivanhabor.com/me and see how often Jev guesses your answers.
Indeed, usefull, for testing different versions of marketing posts.
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.