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Wonderhop

A daily game: guess what most people say, or what none do

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Hello HN!Wonderhop is a small browser game with no account needed to play, everyday there are 4 rounds of 7 quesitons like "Name a food that a lot of people hate" The goal depends on the mode: Either gives the answer most people would give, the one no one would give, the oldest answer or the most recent one and there's a leaderboard for the day.The most interesting part of the project was understanding answers that are typed freely and in 10 languages, there was some typos, plurals, nickname, or even duplicate way of sayign the same thing. We needed AI judge that checks it really existes conencted to some API like wikipedia to judge the veracity of each answer really quickly (less than 1s on average)The percentage starts from an estimate but move really fast with the answers of real players. If the list missed a good answer, it gets added as soon as a few different players give it.(There are also a lot of secrets and pop culture references added in the game :) )A large part of the code was written with AI tools, but the artistic direction the concept and every choice design are human-madeWe'd love to know if the goal is clear from the very first question while playing and were any good answers rejected unfairlyWe are a couple of developers from Montreal that like guessing games so we built this in about two month.Thanks a lot!

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HN · launched Oct 13, 2026 · 104 upvotes (top 10%)

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Could you build this?

Partial The rounds, timers, and scoring UI are simple to vibe-code. But the 'what most people say' mode needs a growing corpus of real player answers and clustering to match free-text responses, and that data only exists once players have generated it.

What it would actually take: Use a Postgres backend, with answers normalized and clustered via embeddings to group synonyms. The core difficulty is the answer dataset: a meaningful 'most common' ranking requires a large body of responses, which means distribution and a cold-start strategy, plus some NLP tuning for fuzzy matching.

Discussion

10 comments analyzed.

Concerns raised: LLM is too lenient and easily manipulated into awarding points, Unclear mode changes between rounds

Feature requests: Display mode instructions above the prompt box

Competitors

Other products that read as similar to this one : 331 launches clear the similarity bar, closest 8 shown.

Attention rank: #13 of 332 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).

Launched 349 days after the earliest competitor.

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