Score breakdown
Popularity is tracked separately. Support, ads, sponsorships, and tips never affect these signals.
Why it matters
Useful for AI researchers, ML researchers, agent developers, simulation engineers, robotics teams, autonomous-systems engineers, game-AI developers, esports researchers, AI-curious readers tracking open-world-model releases, and any developer wiring an AI coding agent to real-time latent-diffusion world models -- and who can pair mira-wm/mira with pixi (creates the environment and installs MIRA) +
Who should use it
Who should skip it
Skip mira-wm/mira if the source link, documentation, or setup requirements do not align with your current workflow or stack.
About this signal
mira-wm/mira is tracked by RepoRadar as a apache-2.0 5b-parameter latent-d in the Real-Time 2v2 Rocket League World Model (Latent section. It was first seen on 2026-07-07 and last updated on 2026-07-07. The current verdict is 'try now' with a Gold tier and moderate setup difficulty. mira-wm/mira leads on workflow potential (9.4) and novelty (9.0); its lowest signal is setup ease (6.4), so factor that in before investing setup time. This page summarizes the evidence RepoRadar has captured from captured source metadata. The score, tier, risk label, and verdict on this page are never influenced by sponsorship, ads, or tips — they reflect only the usefulness, popularity, novelty, momentum, maturity, and evidence signals described in the RepoRadar methodology.
How this item is evaluated
RepoRadar assigned mira-wm/mira a composite score of 8.3 out of 10, placing it in the Gold tier. This score combines weighted sub-signals: usefulness (35%), novelty (18%), momentum (14%), maturity (10%), open-source/build quality (7%), evidence quality (6%), workflow potential (6%), and setup ease (4%). Popularity is tracked separately at 1.0 and never affects the composite score or tier. The risk label of 'low' reflects inherent user-impacting hazards, not generic novelty. Items with no risk flag may still require normal code review before production use.
Putting this into practice? Read How to evaluate an AI tool before you adopt it for the checklist behind this score.
Risk explanation
The 198* / 9-fork / 4-subscriber repo is widely used (General Intuition + Kyutai + Epic Games collaboration) but the model and dataset are scoped to Rocket League 2v2 (a competitive game with a small action space + dense visual feedback + clear game-state ground truth); the consumer SHOULD verify the model on the consumer's task before generalizing to other games; the single-GPU 20 FPS inference is the canonical inference surface and the consumer SHOULD benchmark the consumer's inference host (the README requires an NVIDIA GPU) before relying on the model in production; the pixi install requires NVIDIA GPU + torch >= 2.8 (installed by pixi).