Most recommendation products optimize for discovery: more options, longer lists, another feed. That works when the goal is shopping. It is less useful when the problem is deciding what to play tonight.
I kept reaching the same dead end with my own Steam backlog. A list of ten perfect matches was still ten decisions, and context mattered: two free hours is different from a whole weekend; wanting comfort is different from wanting something unfamiliar.
So PlusOnePlay takes the opposite approach:
starts with games you already own
Hey Product Hunt,
I built PlusOnePlay because I kept wasting the little gaming time I had on the hardest part: choosing.
During my computer engineering degree, I’d finally pick something from my backlog, play for hours, and realize it wasn’t clicking. That happened with The Witcher 3, Red Dead Redemption 2, Returnal, and other great games. They weren’t bad—just wrong for me or wrong for that night.
So I built the loop I wanted: connect Steam, choose a mood and game length, get one ranked pick with a clear reason, then launch it in Steam. It doesn’t try to sell you another game or hand you ten more choices. It starts with what you already own and gives you one answer.
GPT-5.6 Sol helped me audit, harden, and polish the product for OpenAI Day; the recommendation engine itself is deterministic and testable, not a chatbot.
The thing I care about most is whether the first pick actually feels right. If it misses, tell me what it chose and what you would have picked instead.