Replit Agent builds a dog-catching game from kennel data
Danny Prevoznik used Replit Agent to build a Pokémon-style dog-catching game that uses American Kennel Club regional population data to set each dog’s rarity.
x.com
Suggested prompt
Suggested prompt written by Agents In Use, not the source author. Replace bracketed inputs and review tool permissions first.
Build a small dog-collecting game. Use licensed regional breed population data to set catch rarity, show how each value is calculated, and keep the first playable loop simple.
Reach people exploring AI agents
See placements →Evidence at a glance
Sourced account- Source poster / publisher
- Danny Prevoznik (@DanielPrevoznik)
- Original account
- Read the source ↗
- Published
- Source link checked
- Reported result
- Prevoznik reports building the game with Replit Agent and shared a demo video. No public release, player feedback or measured result is reported.
- Human involvement
- The author selected the concept and data source. The source does not describe testing, code review, data licensing checks or publication approval.
A linked account supports attribution. It does not mean we independently reproduced its result.
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How the agent did it
- Prevoznik chose a dog-themed collect-and-catch game as the project.
- He used regional dog-population data from the American Kennel Club as an input to the rarity mechanic.
- He built the app with Replit Agent and shared a short video of the result.
Danny Prevoznik built a dog-themed “catch ’em all” game with Replit Agent. The unusual design choice is that a dog’s rarity comes from American Kennel Club data about regional dog populations, connecting the game mechanic to real-world breed prevalence rather than assigning rarity at random. Prevoznik says he scraped the data and built the whole app with Replit Agent; the X post includes a short video of the result.
The source gives a clear concept and a concrete artifact, but it does not name the breeds, describe the data fields, explain how regional populations were normalized, or say whether the game was published to an app store. It also does not report player numbers or a test of whether the rarity system felt balanced. Those details should not be inferred from the demo.
The idea is a useful pattern for a personal game project: find a dataset that can shape a game rule, transform the data into a simple mechanic, then build a small playable loop around it. The suggested prompt below is ours, not the original prompt. Anyone copying the approach should check the source data’s licensing and explain how real population figures translate into in-game rarity.
Prompt
Suggested prompt (written by Agents In Use, not the author's)
Source: X · Danny Prevoznik (@DanielPrevoznik)
Frequently asked questions
What did Replit Agent help build?
Prevoznik says it built a dog-themed Pokémon-style “catch ’em all” app.
How does the game decide which dogs are rare?
The author says American Kennel Club regional dog-population data sets the rarity of each catch; the post does not explain the calculation.
Did the source report a public launch or player results?
No. It says the app was built and shows a video, but does not report an app-store launch or player metrics.
More details
About this account
Prevoznik says Replit Agent built the app; the post does not describe which implementation decisions were delegated or how he reviewed the generated work.
Reviewed archived direct X capture from 2026-10-15 (HTTP 200), including the post, author and displayed date; current X reader returned 403. The post includes a demo video but no prompt or launch claim.
Credit: Danny Prevoznik (@DanielPrevoznik) — X
Source published
Added to Agents In Use
Source checked
Tools and topics
Replit Agent · American Kennel Club data
game design · dogs · public data
Editorial completeness: 100/100. Measures documented fields, not agent performance or verified outcomes.