Lucid recently launched!

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^{"Unbounded video games and fully differentiable reinforcement learning gyms."}

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TL;DR: Lucid is building generative simulations powered by fast world models. Instead of using traditional game engines with hard-coded physics, their models learn to simulate reality from pixels, enabling real-time interactive environments. With it they will train robots in their own imaginations and make unbounded gaming experiences. They trained the fastest world model ever seen to simulate minecraft end-to-end (20+fps on a gaming GPU).}

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‍Founded by Alberto Hojel & Rami Seid

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https://youtu.be/fnoyvrGOwIA

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The Problem: Game Worlds Are Static & Expensive to Build

Modern game development is slow, expensive, and constrained:

• GTA V took 3 years, 1,000 employees, and $100M+ to build—AAA game budgets are skyrocketing and they’re not getting any better.
• Despite the price tag, these games are inherently static, with predefined environments, objects, and interactions.
• Players can’t truly shape the world—every door, street, and event is pre-scripted.

Meanwhile, robotics faces its own bottleneck—AI models trained in simulators (MuJoCo, Isaac Sim, Gazebo) fail to generalize to the real world (Sim2Real gap) because today’s simulations are hand-coded approximations of physics rather than learned from real-world data.

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Their Solution: Generative World Models

Lucid replaces traditional game engines with a generative simulation engine that learns from data rather than being manually programmed.

• Every frame is generated in real-time, conditioned on player actions.
• Trained on video, not game scripts—their models learn the rules of physics directly from pixels rather than hardcoded logic.
• Infinite, dynamic game worlds—players can generate and explore entirely new environments just from a text prompt or sample concept art.

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A Neural Minecraft Simulator

They trained a neural network to simulate Minecraft end-to-end—every pixel is generated in real-time, learned from 200 hours of gameplay.

• Runs at 20+ FPS on an NVIDIA 4090—5× faster than existing world models (Decart’s Oasis <4 FPS).
• Aggressive latent compression—they utilize a VAE with 128x spatial compression allowing them to vastly reduce the amount of tokens needed to represent a single frame

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What’s Next? Training on the Real World

They are now training their models on real-world video data to build a general-purpose universe simulator for:

• Gaming: The last game engine humanity ever needs—generating unique environments dynamically from simple text or multimodal prompts.
• Robotics: Simulations that actually match reality—training embodied AI models in diverse, realistic environments. A fully differentiable, learned simulation framework for reinforcement learning.

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Learn More

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^{🌐 Visit lucidsim.co to learn more.}

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^{💥 Are you working on AI/robotics and need high-fidelity simulations? Lucid is selecting early partners to fine-tune LoRAs on domain-specific data. Want to explore the future of generative gaming? Sign up for early access to Lucid v2.}

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^{🤝 Interested and want to connect? Reach out to the founders here.}

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^{‍‍👣 Follow Lucid on X.}

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