Lovelace Studio uses AI to help players build survival craft sandbox worlds

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Lovelace Studio uses AI to help players build survival craft sandbox worlds

Lovelace Studio is using generative AI to build Nyric, a tool that can help bring gaming worlds to life for player builders who work in harmony. It’s a toolkit for building a community-driven multiverse, or metaverse, as you wish. Players can use AI tools to craft their own worlds, bringing realms to life in a survival crafting sandbox. The aim is to give agency to solo creators and social gamers and then connect them together, said Kayla Commali, CEO of Lovelace Studio, in an interview with GamesBeat. “We’re doing generative worlds with AI and gaming,” Commali. “We’ve been doing it for four years now since conceiving the idea. But lately it’s become something pretty tangible, pretty exciting. It’s a survival craft game for generative worlds, like Midjourney.” With Nyric, players can built generative AI worlds like Alice in Wonderland or Viking worlds, with any kind of style they want. It adds characters, themes and styles. Each realm is a hexagonal grid, interconnected with neighbors. In the gameplay loop, you can do pioneering and engage in regional diplomacy. Alex Engel (left) and Kayla Commali are cofounders of Lovelace Studio. They’re at the Dice Summit here. Companions agents, the faebots, are pioneering toolkits for player abilities in these hexgrid realms. Players are incentivized to create connections, such as forming trade routes with neighbors, leading community factions, and expanding their empires. The game worlds are persistent and built as procedurally generated hexagonal grids. Inside those worlds the players can craft the details and enjoy emergent gameplay. Wishlist Nyric today and keep a lookout for playtest updates on their Discord server. A resource economy drives community-centric gameplay on one side, while influence and diplomacy powers individual competition on the other. Network effects take hold in a persistent 3D multiverse, where early discoverability can catapult creator virality. There are a lot of AI game companies out there. But Commali said that Lovelace’s approach to enabling players to create their own virtual worlds is different. Players can use prompts to generate interactive 3D survival craft realms with multiple biomes. Lovelace Studio makes tools so you can create worlds. And inside the world is an AI companion that Commali calls a faebot. This faebot has personality traits and can remember things that have happened in the world. Players can modify the parameters of the world, including the sky, the ground, the environment or the weather. Soon enough, it looks like a unique place. The virtual worlds also have multiple users and they can be linked. Players can discover each other, and form a social network. Players can craft things together in the world like players do in the world of Valheim. After generating the world in a procedural way, players have to customize the world to make it unique. This means that it enhances human creativity, rather than doing all the work with AI and having the player take the credit for it. You can customize your assets, the stories, and the themes of the world. Lovelace is testing the game on Steam shortly. Commali and her Boston-based team have a background in robotics and computer vision. The team has raised $1.2 million from investors including Sequoia Capital Scout, HalfCourt Ventures, Blindspot Ventures, and Umami Capital. And it plans to raise more money. The company has been at it for four years, and it is contemplating a move to Web3. A faebot in Lovelace Studio’s virtual worlds. “There’s a lot of external pressure to go to Web3. We haven’t done it yet. Only if it makes sense. We’ve been really intentional about this,” Commali said. The team is planning to bring on an AI engineer and a tech artist to support development. Over time, the company expects to integrate with the Unity game engine. And it helps players easily share their worlds in an open platform. “We definitely didn’t want it to be like a walled garden. The word sounds like an isolated experience. Our structure is like a representation of social networking. You scale and grow and there’s no real ceiling,” she said. Origins A scene from a Nyric-created world. Commali has been on “self-taught journey” from biology to robotics and gaming. She learned computer vision. And cofounder Alex Engel has been in games for a couple of decades at places like Disruptor Beam and Turbine. Their team has grown to five people. “We were working with a lot of telemetry, a lot of runtime data and simulation systems, and also working with this extensions, starting five or six years ago,” Commali said. A jetpack built with Nyric tools. The focus was on runtime compute at the consumer level that powers gameplay, and allows emergent gameplay to percolate. Then generative AI came along and the team pivoted into procedural generation. “We believe a lot of people want to be content creators,” Commali said. “About 65% of social media platform users say they want to be a content creator. Only 3% are.” So the idea became to provide the tools to make it easy for players to become more like developers. Rather than build its own large language models, Lovelace Studio is building its platform as an API on top of OpenAI. “We have a pipeline that breaks down the world into like the biome, into the styles, the time periods, character and behaviors,” Commali said.

Lovelace Studio is using generative AI to build Nyric, a tool that can help bring gaming worlds to life for player builders who work in harmony.

It’s a toolkit for building a community-driven multiverse, or metaverse, as you wish. Players can use AI tools to craft their own worlds, bringing realms to life in a survival crafting sandbox. The aim is to give agency to solo creators and social gamers and then connect them together, said Kayla Commali, CEO of Lovelace Studio, in an interview with GamesBeat.

“We’re doing generative worlds with AI and gaming,” Commali. “We’ve been doing it for four years now since conceiving the idea. But lately it’s become something pretty tangible, pretty exciting. It’s a survival craft game for generative worlds, like Midjourney.”

With Nyric, players can built generative AI worlds like Alice in Wonderland or Viking worlds, with any kind of style they want. It adds characters, themes and styles. Each realm is a hexagonal grid, interconnected with neighbors. In the gameplay loop, you can do pioneering and engage in regional diplomacy.

Alex Engel (left) and Kayla Commali are cofounders of Lovelace Studio. They’re at the Dice Summit here.

Companions agents, the faebots, are pioneering toolkits for player abilities in these hexgrid realms. Players are incentivized to create connections, such as forming trade routes with neighbors, leading community factions, and expanding their empires. The game worlds are persistent and built as procedurally generated hexagonal grids. Inside those worlds the players can craft the details and enjoy emergent gameplay.

Wishlist Nyric today and keep a lookout for playtest updates on their Discord server.

A resource economy drives community-centric gameplay on one side, while influence and diplomacy powers individual competition on the other. Network effects take hold in a persistent 3D multiverse, where early discoverability can catapult creator virality.

There are a lot of AI game companies out there. But Commali said that Lovelace’s approach to enabling players to create their own virtual worlds is different. Players can use prompts to generate interactive 3D survival craft realms with multiple biomes.

Lovelace Studio makes tools so you can create worlds.

And inside the world is an AI companion that Commali calls a faebot. This faebot has personality traits and can remember things that have happened in the world. Players can modify the parameters of the world, including the sky, the ground, the environment or the weather. Soon enough, it looks like a unique place.

The virtual worlds also have multiple users and they can be linked. Players can discover each other, and form a social network. Players can craft things together in the world like players do in the world of Valheim.

After generating the world in a procedural way, players have to customize the world to make it unique. This means that it enhances human creativity, rather than doing all the work with AI and having the player take the credit for it.

You can customize your assets, the stories, and the themes of the world. Lovelace is testing the game on Steam shortly.

Commali and her Boston-based team have a background in robotics and computer vision. The team has raised $1.2 million from investors including Sequoia Capital Scout, HalfCourt Ventures, Blindspot Ventures, and Umami Capital. And it plans to raise more money.

The company has been at it for four years, and it is contemplating a move to Web3.

A faebot in Lovelace Studio's virtual worlds. — A faebot in Lovelace Studio’s virtual worlds.

“There’s a lot of external pressure to go to Web3. We haven’t done it yet. Only if it makes sense. We’ve been really intentional about this,” Commali said.

The team is planning to bring on an AI engineer and a tech artist to support development.

Over time, the company expects to integrate with the Unity game engine. And it helps players easily share their worlds in an open platform.

“We definitely didn’t want it to be like a walled garden. The word sounds like an isolated experience. Our structure is like a representation of social networking. You scale and grow and there’s no real ceiling,” she said.

Origins

Commali has been on “self-taught journey” from biology to robotics and gaming. She learned computer vision. And cofounder Alex Engel has been in games for a couple of decades at places like Disruptor Beam and Turbine. Their team has grown to five people.

“We were working with a lot of telemetry, a lot of runtime data and simulation systems, and also working with this extensions, starting five or six years ago,” Commali said.

The focus was on runtime compute at the consumer level that powers gameplay, and allows emergent gameplay to percolate. Then generative AI came along and the team pivoted into procedural generation.

“We believe a lot of people want to be content creators,” Commali said. “About 65% of social media platform users say they want to be a content creator. Only 3% are.”

So the idea became to provide the tools to make it easy for players to become more like developers. Rather than build its own large language models, Lovelace Studio is building its platform as an API on top of OpenAI.

“We have a pipeline that breaks down the world into like the biome, into the styles, the time periods, character and behaviors,” Commali said.

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Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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