Game jam experiment
Orb Knight: how far can AI agents take a first-time game jam?
This is an experiment log: how far can I get in a large game jam, with almost no game development background, if I treat Codex and Claude Code as implementation agents and keep the feedback loop tight?
Result
Orb Knight placed 12th overall in Gamedev.js Jam 2026 and 6th in the Gameplay category. The public winners post lists the overall ranking, and the itch.io results page lists the Gameplay ranking. The game itself is a browser-only 3D action game about escaping a machine dungeon and pushing toward the castle road.
What was built
The final demo has third-person combat, shooting, melee, procedural foundry rooms, room transitions, pickups, shops, treasure rooms, boss encounters, loadout modules, audio, settings, Storybook playgrounds and persistent run progress.
Why include it here?
It is not a normal portfolio project and it is not trying to pretend I became a game developer overnight. The useful signal is different: I took an unfamiliar domain, used AI agents as a serious engineering tool, decomposed the work into systems, and shipped a playable result that ranked well in a competitive public setting.
Lessons
Agents are strongest with tight feedback loops
The useful pattern was not one giant prompt. It was short implementation loops, browser checks, screenshots, playable states and quick corrections.
Unknown domains force better decomposition
Without game development experience, the work had to be split into clear systems: movement, combat, rooms, loadout, physics, camera, UI, audio and progression.
Visual quality needs verification, not vibes
Screenshots, Storybook scenes and repeated playtests mattered because visual regressions and game feel problems are hard to catch from code alone.
The best result was learning velocity
The ranking was nice, but the real result was proving how far a small team can get in a new domain with strong agent workflows and fast taste-driven iteration.