GameReverie is an open-source Codex Skill for taking a game from idea to first playable and through ongoing iteration. It separates design, implementation, and independent review, preserves project decisions across sessions, and turns playtest feedback into follow-up work. Guide development yourself or delegate a bounded goal. Developed with GPT-6 Astra, with a playable Snake demo and development records. Free and MIT-licensed.
Hi everyone — I’m the maker of GameReverie, an open-source Codex Skill for game development. I built and released it over three days with GPT-6 Astra for the Astra Challenge.
I wanted a workflow that wouldn’t stop at the first playable. I still need to play the game, figure out what feels off, discuss changes, and get them implemented and reviewed. GameReverie gives design, coordination, implementation, and review separate roles. Decisions and progress stay in project documents, so I can pick up the work in another session.
To try it out, I used GameReverie to build a Godot Snake game with one-way ramps, then added dash and whole-body jumps. Astra handled design, coordination, and technical decisions; Luna implemented tasks, and Sol independently reviewed the changes. That’s the model setup I used for the demo, not a requirement.
The reviewer caught a timing bug in the landing animation, which was fixed and reviewed again. My own playtesting led to changes in movement and ramp interactions. Those corrections are part of the demo too. The video is condensed from real sessions and gameplay.
GameReverie is free and MIT-licensed. The repo includes the Skill, a playable demo, and development records.
Where does AI tend to get stuck when you’re working on a game?
About GameReverie on Product Hunt
“A Codex Skill for building and iterating games”
GameReverie launched on Product Hunt on September 18th, 2026 and earned 59 upvotes and 2 comments, placing #60 on the daily leaderboard. GameReverie is an open-source Codex Skill for taking a game from idea to first playable and through ongoing iteration. It separates design, implementation, and independent review, preserves project decisions across sessions, and turns playtest feedback into follow-up work. Guide development yourself or delegate a bounded goal. Developed with GPT-6 Astra, with a playable Snake demo and development records. Free and MIT-licensed.
On the analytics side, GameReverie competes within Open Source, Developer Tools, Artificial Intelligence, GitHub and OpenAI Day — topics that collectively have 1.1M followers on Product Hunt. The dashboard above tracks how GameReverie performed against the three products that launched closest to it on the same day.
Who hunted GameReverie?
GameReverie was hunted by Li Zenghui. 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.
For a complete overview of GameReverie including community comment highlights and product details, visit the product overview.
Li Zenghui
Hi everyone — I’m the maker of GameReverie, an open-source Codex Skill for game development. I built and released it over three days with GPT-6 Astra for the Astra Challenge.
I wanted a workflow that wouldn’t stop at the first playable. I still need to play the game, figure out what feels off, discuss changes, and get them implemented and reviewed. GameReverie gives design, coordination, implementation, and review separate roles. Decisions and progress stay in project documents, so I can pick up the work in another session.
To try it out, I used GameReverie to build a Godot Snake game with one-way ramps, then added dash and whole-body jumps. Astra handled design, coordination, and technical decisions; Luna implemented tasks, and Sol independently reviewed the changes. That’s the model setup I used for the demo, not a requirement.
The reviewer caught a timing bug in the landing animation, which was fixed and reviewed again. My own playtesting led to changes in movement and ramp interactions. Those corrections are part of the demo too. The video is condensed from real sessions and gameplay.
GameReverie is free and MIT-licensed. The repo includes the Skill, a playable demo, and development records.
Where does AI tend to get stuck when you’re working on a game?