JevMade

Sign in
← Back to experiments

Playable demos & bots

Human vs JEV · Table Tennis

A table-tennis game where Jev's answer is the paddle's target pixel and a tiny local servo simply moves there.

Source screenshot of Human vs JEV · Table Tennis
SOURCE SCREENSHOTFull screenshot ↗

What it does

Pure mode has no trajectory predictor. The maker reports an 88% hit rate after adding time-to-arrival context.

How you can use it

Sketch your game court by dividing the paddle movement area into numbered landing zones. Your developer can write a script that sends the ball speed, position, and travel time to the model. The model then chooses the best zone, and basic game code slides the paddle there.

Maker-reported (not independently measured by JevMade)

88% hit rate at a 1.9 s median latency (author-reported) · About 700 input tokens per call at $0.042/M tokens, roughly $0.00003 per decision (author-reported) · Adding a time-to-arrival sentence to the state raised the hit rate from 50% to 88% (author-reported)

Primitives
choice
Platform
Python
Added
Project created
GitHub stars
1 (snapshot, not live)

Keep this for later

Sign in to bookmark experiments, guides and videos, and keep notes only you can see.

Continue to sign in

We’ll bring you back to this listing.