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Cyber-Breach: The Jev Protocol - A tactical cyberpunk arena combat game powered by TypeSafe AI Jev System One decision model
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CommunityJev Demos & Experiments
Cyber Breach Jev
A Three.js infinite-grid Tron arena shooter where Jev drives three systems: enemy combat choices and threat scores, a director picking encounters and pacing, and a P-key autopilot running 5-10 decisions per second. Ships with a local engine.
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- 0
- Language
- JavaScript
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- Author
- rchovatiya88
- Last verified
- Oct 2026
About this project
An infinite 3D arcade cyberpunk arena shooter built with Three.js and powered by Jev—the sub-100ms "System One" decision model developed by TypeSafe AI (released September 2026). Boundless Infinite Movement: The operative is no longer confined to a box. Move limitlessly across a seamless scrolling 3D Tron grid in all directions with dynamic tracking camera.
Quoted from the project's README. Read the full README.
Works forJev for Classification
Used inJev for Software & DevTools
- Languages
- JavaScript
Highlights
- enemy_ai.py picks a combat_action Choice, a threat_assessment Score and Nouls for vulnerability and berserk.
- game_director.py picks a director_event Choice, a pacing_tension Score and a grant_emergency_aid Noul.
- Press P for the Doom-bot autopilot at 5-10 decisions per second; jev_hud.js draws the live decision stream.
- python3 -m testing.verify_app drives the app through a Cua browser and feeds observations into Jev.
Watch out
Out of the box nothing calls TypeSafe. The README states the game runs with a built-in local calibrated decision engine at zero latency and zero cost, so a no-key run exercises the substitute, not the model - set TYPESAFE_API_KEY before ./run.sh or enter one through the JEV CONFIG dialog to hit the real API. Against the live path the README publishes no request rate, no token count, no price and no measured latency, and the sub-100ms figure is stated without a measurement. The autopilot is specified at 5-10 decisions per second with no budget cap or rate limiter described, which is a lot of calls to leave running. This README copy is cut off inside the Cua verification diagram, so the emitted artifact formats and the remaining pipeline steps were not visible. No license is stated in the portion reviewed, and the assets are not discussed. Copy reviewed against the repository README on 2026-10-04, not from running it.
Questions about this entry
Short answers drawn from the linked project or page.
By default it is local. The README says the game runs out of the box with a built-in calibrated local decision engine at zero latency and zero API cost, and the architecture diagram labels it TypeSafe AI Jev Model or local RLCD engine. To hit the real model you export TYPESAFE_API_KEY before ./run.sh, or click JEV CONFIG in the top bar and enter it at runtime.
The README does not say. It gives the rate for the Doom-bot mode and nothing about price, tokens or throttling for the live path, and there is no budget cap, rate limiter or call counter described anywhere in the quickstart, HUD or controls sections. You would be estimating before switching P on and walking away.
The game is the infinite scrolling wireframe grid, radar, trails and de-rezzing particles; the Jev part is three Python modules. enemy_ai.py supplies combat_action, threat_assessment, is_player_vulnerable and trigger_berserk; game_director.py supplies director_event, pacing_tension and grant_emergency_aid; bot_pilot.py handles the P-key autopilot; jev_hud.js renders candidate probability bars, confidence, rubric steps and latency gauges.
It drives the running app through a Cua browser with browser_prepare and browser_navigate, reads semantic state and dom refs, and evaluates deterministic verify_state predicates on window bounds and element states, while recording full interaction trajectories. Those observations then go into a Jev decision layer (Choice for action selection and failure categorisation, Score for build health and UX quality, Noul as a pass-probability gate) and out to a triage reporter emitting Markdown and JSON verification artifacts. Plain unit tests run with python3 -m unittest discover -s tests.
WASD or arrows move, mouse and left click fire, Space is the blink dash, V toggles the chase and isometric cameras, P toggles the Jev autopilot, M toggles music, N toggles sound effects, and C toggles CRT scanlines.
Launch with ./run.sh, or python3 -m backend.app, and open http://localhost:8000; the unit tests run with python3 -m unittest discover -s tests -p 'test_*.py'.
python3 -m testing.verify_app --url http://localhost:8000 --cdp-port 9225 --headed drives the app through Cua Driver v0.28.2 over Chrome CDP, headed or headless.
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