01 · MICRO JAM 063 · SHIPPED

DYNO
RUN.

A 3D prehistoric endless runner where a dinosaur runs through an infinite world, dodges obstacles and collects coins while trying to survive as long as possible.

ENGINEUNITY
LANGUAGEC#
PLATFORMWEBGL
FOCUSED TIME~10 HOURS
Dino Run gameplay
01

PLAYABLE BUILD

Play the game
on itch.io.

Dino Run
WEBGL · PLAY IN BROWSER

DINO RUN

The playable WebGL build is hosted on itch.io. Click below to launch the game.

PLAY DINO RUN
02

ABOUT THE GAME

Built to be
played immediately.

Dino Run was created for Micro Jam 063: Prehistoric. The goal was simple: take a small scope, build a complete playable loop and ship it before the deadline.

The final game combines lane movement, jumping, obstacle collisions, coin collection, score tracking, a persistent high score, pause and game-over flows, audio and a recycled endless world.

GAME JAMMICRO JAM 063
BUILD SIZE87 MB WEBGL
RELEASEITCH.IO + JAM
TEAMSOLO
03

WHAT I BUILT

Systems behind
the run.

01

PLAYER

Forward movement, A/D lane movement, ground checking and a looping running animation.

02

OBSTACLES

Player-relative random lane spawning, collision handling, cleanup and game-over flow.

03

COINS & SCORE

Coin spawning, collection, score tracking and persistent high scores using PlayerPrefs.

04

INFINITE WORLD

Reusable world segments are recycled behind the player to create an endless environment.

05

UI & MENUS

Lobby, Play, pause/resume, game over, retry, home and exit flows.

06

AUDIO

Coin, impact, game-over, button, running and forest ambience sounds.

04

GAME JAM TAKEAWAY

Build fast.
Ship complete.

“The biggest lesson from the jam was learning to prioritise the core loop, solve problems quickly and actually ship the game.”

— ARSHLAN KHAN

The biggest technical lesson was that finishing a game is different from making a prototype.

The project forced me to make scope decisions, debug under pressure, integrate multiple systems and get a working WebGL build out the door.

MICRO JAM 063 · SUBMITTED

Post-jam feedback also highlighted areas for future improvement: optimization, movement polish, obstacle variety and collision tuning.