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TypeSafe Mario

An experimental controller that has Jev choose NES inputs to play Super Mario Bros. from structured emulator telemetry instead of screenshots, logging every decision with probabilities and latency.

Open github.comfhshaik/typesafe-mario
Category
Cookbooks & Demos
Published by
Community
Author
fhshaik
Added
2026-09-20
Tagscommunitypythongamesemulation

Highlights

  • Jev sees object-centric JSON: player motion, trajectory, up to three enemies, terrain, and reaction timing.
  • Each decision mixes a Choice for the next action, a Noul for jump usefulness, and a Score for danger.
  • Decisions are logged to artifacts/run-<timestamp>.jsonl with probabilities, latency, and outcome.
  • The dashboard shows the probability distribution, confidence, Jev latency, danger score, and reward.

Watch out

No ROM is included, so you supply a legally obtained copy, plus Python 3.13 or newer and a TypeSafe API key.

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From the community

Posts from builders shipping with Jev right now.

LLM-as-a-judge, sped up

Yuchen Jin
Yuchen Jin
Databricks
@Yuchenj_UW
·

Jev has spoken. It picked which model is AGI. 20–200x faster. 40–400x cheaper. This could make things like LLM-as-a-judge insanely fast and nearly free. (I tried a bunch of prompts and still didn’t burn through $0.10.)

Image
Diogo Almeida
Diogo Almeida
TypeSafe AI
@CompleteSkeptic

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x

Watch on X
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Instant compaction with Jev