Building AI With MongoDB: Story Tools Studio Brings Gen AI To Gaming With Myth Maker AI

Mat Keep
February 27, 2024
#genAI

Story Tools Studio harnesses cutting-edge generative AI (gen AI) technologies to craft immersive, personalized, and infinite storytelling experiences.

Roy Altman, Founder and CEO at Story Tools Studio says, “Our flagship game Myth Maker AI leverages MUSE, an internally developed AI-powered, expert-guided story generator that blends a growing collection of advanced AI technology with creative artistry to weave real time narratives.”

The company’s founders have backgrounds in stage, film, and video production, and both share a deep love of gaming. When ChatGPT first launched, they immediately recognized an opportunity to redefine gaming experiences. And so Story Tools Studio and its MUSE engine were born.

MUSE (Modular User Story Engine) combines professionally crafted stories with user-empowered experiences. Players make intentional choices that guide the story with AI adapting to each decision in real time, providing a unique and personalized journey. MUSE separates the story from game mechanics, allowing the development of multiple game types. Its use of AI creates more agile teams with fewer dependencies.

Generating gameplay with AI

When a player starts a game in Myth Maker AI, they are presented with the option to choose their starting hero character. Under the covers, MUSE calls the GPT4 API, which takes the player’s selection and writes a fully customized adventure premise. From that initial personalized script, MUSE programmatically calls specialized AI models to collaboratively generate an immersive, multimodal gaming experience using images, animation, audio, and soon, video and 3D.

**Figure 1:** MUSE orchestrates multimodel genAI to create real time, unlimited stories

Altman says, “For story generation and text to voice, we run mainly in Azure’s OpenAI service. Visual assets are created via Leonardo AI, and we are constantly experimenting with new models to create richer modalities. Right now my team is working on generating enhanced 3D assets and video from text prompts. With the pace of AI advancement, the creativity of my team, and the input from our game testers, we are continuously deploying new features. And MongoDB with its dynamic and flexible document data model gives my developers the freedom to do that. This enables us to build a truly innovative, artistic platform, opening up a whole new world of experiences for both creators and audiences alike.”

By selecting MongoDB, we were able to create a prototype of our game in just 48 hours. It is only with MongoDB that we can release new features to production multiple times per day. We couldn’t achieve any of this with a relational database.
Roy Altman, Founder and CEO at Story Tools Studio

AI, transactions, and analytics with MongoDB

Altman’s engineering team has used MongoDB Atlas from the very start of the company. MongoDB stores all of the data used in the platform — user data, scripts, characters, worlds, coins, and prompts are all richly structured objects stored natively in MongoDB. The games are built in React and Javascript.

Beyond gameplay, the company’s developers are now exploring MongoDB’s ACID transactional integrity to support in-game monetization, alongside in-app intelligence to further improve the gaming experience through player analytics.

“By running MongoDB in Atlas, my engineering team is free to focus on AI-driven gaming experiences, and not on the grind of managing a database,” says Altman. “MongoDB has scaled seamlessly and automatically as we’ve graduated from our closed beta into public beta today. Every 24 hours we are organically adding dozens of new players with tens of gigabytes of new data streaming into the platform. We expect this to grow quickly when we kick off our marketing campaigns.”

What's next?

Myth Maker AI is just the start. Story Tools Studio has plans for multiple games in the next 12 to 18 months. At the same time MUSE is an extensible platform — the company is also looking to explore content generation outside of gaming in areas such as education and training.

Story Tools Studio is a member of the MongoDB AI Innovators program providing it with access to Atlas credits and technical best practices, allowing its engineers to freely explore what’s possible with gen AI and MongoDB. You can get started with your AI-powered apps by registering for MongoDB Atlas and exploring the tutorials available in our AI resources center.

You can also learn more about why the world’s largest games run on MongoDB.

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Anunciando o GA do Atlas Device SDK para C++

A plataforma de dados para desenvolvedores do MongoDB foi projetada para oferecer flexibilidade e escalabilidade incomparáveis para desenvolvedores. Ao simplificar a integração de estruturas de dados complexas e Real-Time Analytics, e acelerar o desenvolvimento e a implantação de aplicações de missão crítica, sua adoção agregou um valor significativo às empresas de todos os setores. Hoje, continuamos nossa missão de oferecer a melhor experiência para os desenvolvedores e temos o prazer de anunciar a disponibilidade geral (GA) do Atlas Device SDK for C++ . As atualizações desta versão são resultado de várias iterações orientadas pelo feedback de nossos usuários de visualização e visam ao desempenho e à portabilidade. O Atlas Device SDK para C++ permite que os desenvolvedores armazenem dados sem esforço em dispositivos para acesso offline e, ao mesmo tempo, sincronizem perfeitamente os dados de e para o MongoDB Atlas cloud em suas aplicações C++. Ele serve como uma alternativa fácil de usar ao SQLite, oferecendo simplicidade devido à sua natureza de banco de dados orientado a objetos, eliminando a necessidade de uma camada de mapeamento separada ou ORM. Alinhado à missão da plataforma de dados para desenvolvedores do MongoDB de simplificar o processo de desenvolvimento, o SDK C++ incorpora a lógica de repetição de rede e a funcionalidade avançada de mesclagem de conflitos, eliminando a necessidade tradicional de escrever e manter códigos de sincronização extensos e complexos. Por que escolher o Atlas Device SDK para C++? O Atlas Device SDK para C++ é particularmente adequado para aplicações em dispositivos incorporados, IoT e cenários entre plataformas Ele serve como uma camada de persistência orientada a objeto abrangente para dispositivos de borda, móveis e incorporados, oferecendo suporte integrado para sincronização com o MongoDB Atlas como um back-end cloud. No cenário em evolução de dispositivos conectados e inteligentes, a demanda por mais dados, incluindo dados históricos para tomada de decisão automatizada, destaca a importância de camadas de persistência eficientes e tecnologias de sincronização em cloud em tempo real que sejam robustas em relação a mudanças nas conexões e interrupções de rede. O banco de dados incluído no Atlas Device SDK para C++ tem mais de uma década de história e é uma tecnologia madura, rica em recursos e pronta para empresas, integrada a dezenas de milhares de aplicativos no Google Play e na Apple App Store com bilhões de downloads. Seu design leve é otimizado para ambientes com recursos limitados. Ele considera fatores como computação, memória, largura de banda e uso da bateria em seu design. A incorporação do SDK diretamente no código da aplicação elimina a necessidade de tarefas adicionais de implantação e simplifica o processo de desenvolvimento. A natureza totalmente orientada a objetos do SDK orienta a modelagem de dados, fornecendo uma abordagem direta e idiomática. Isso contrasta com tecnologias alternativas, como o banco de dados SQLite, que exige uma biblioteca de mapeamento objeto-relacional, o que aumenta a complexidade e torna os futuros desenvolvimentos, manutenções e depurações mais desafiadores. Além disso, o armazenamento de dados subjacente do SDK permite integração perfeita com camadas reativas de UI em vários ambientes. No Atlas Device SDK para C++ damos exemplos de como integrar com o framework Qt , mas outras camadas de UI também podem ser adicionadas. Aprimoramentos na versão GA A nova API foi desenvolvida com base em medições de desempenho com foco e esforço coordenados para melhorar as operações de leitura/gravação da camada de dados. Houve grande interesse dos principais OEMs automotivos e de manufatura, e esse feedback foi inestimável para orientar nossa API final. Algumas das alterações adicionadas ao Atlas Device SDK para C++ incluem: Alinhar nossas APIs com outros Atlas Device SDKs, por exemplo, controle aprimorado do estado do banco de dados com monitoramento e compactação manual Tunelamento HTTP Melhor controle para as sessões do Atlas Device Sync Suporte ao Windows Compatibilidade com o OpenWRT entre outras distribuições Linux, com suporte a musl Suporte ao Android Automotive com arquivos de compilação Blueprint/Soong O que vem a seguir Olhando para o futuro, estamos trabalhando para o suporte geoespacial , bem como a capacidade de construir com uma variedade de gerenciadores de pacotes, como vcpkg e Conan. Agradecemos e valorizamos todos os comentários. Se você tiver comentários ou sugestões, compartilhe-os por meio do nosso projeto GitHub . Pronto para começar? Instale o Atlas Device SDK para C++ — comece sua jornada com nossos documentos ou vá direto para exemplos de projetos com código-fonte. Em seguida, registre-se no Atlas para se conectar ao Atlas Device Sync, um back-end móvel como serviço totalmente gerenciado. Aproveite a infraestrutura pronta para uso, os recursos de sincronização de dados, o manuseio de rede e muito mais para lançar rapidamente aplicativos móveis de nível empresarial. Por fim, diga para a gente o que você acha e participe de nossos fóruns . Nos vemos lá!

February 22, 2024

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Staff Engineering at MongoDB: Your Path to Making Broad Impact

Andrew Whitaker is a Senior Staff Engineer at MongoDB. His previous experience spans tiny startups to enormous organizations like AWS, where he held several different roles focusing on databases. Before joining MongoDB, he worked at a startup building optimized machine learning models in the cloud. Read on to learn more about why Andrew decided to join MongoDB in a senior-level engineering role and how his work is driving improvement within our engineering organization. Why MongoDB I have long been a fan of MongoDB’s products and services. MongoDB the database has always been a pleasure to work with – the system “brings joy” to quote a phrase. As a Python developer, I appreciate how the Python driver feels “Pythonic” in a completely natural way. The programmer interacts with the database using Python constructs: dictionaries, lists, and primitive types. By contrast, SQL databases force me to change my mental model, and the query language feels like an add-on that does not blend with the core language. As an engineer, I am always looking to expand my knowledge and grow my skills. The scope of challenges engineers face at MongoDB is what triggered my interest in the company. We obviously have people working on core databases and distributed systems. But, we also have teams dedicated to machine learning, streaming data, analytics, networking, developer tooling, drivers, and many more areas. It is very hard to get bored working at MongoDB. Finally, I would be remiss if I did not mention the people. Overall, MongoDB’s engineering culture prioritizes intelligence, low ego, and an ability to get stuff done. CL/CI (Continuous Learning, Continuous Improvement) Working at MongoDB has provided me with opportunities for continued learning and growth. Though I do not program as much as I did earlier in my career, I have recently been exploring the Rust language. I’m excited by Rust because it avoids the tradeoffs between predictable performance and safety. My work in the search space has given me exposure to the fast moving world of AI: vector embeddings, RAG, etc. For various reasons, I think MongoDB is uniquely positioned to do well in this area. On top of this, I’m working on some initiatives that are not fully public. I can say that one focus area is improving the sharding experience for our customers. We believe MongoDB sharding is best-in-breed. Still, the process requires more manual configuration than we think is ideal: customers select the shard key, cluster type, shard count, etc. We give guidance here, but I think we can raise the bar in terms of offering a seamless experience with less “futz”. I’m also working with the search team. We believe there is a natural affinity between MongoDB’s document model and AI/ML workloads. We have some features in the works that extend this integration in new and interesting ways. I also spend a fair bit of time driving quality improvements across our suite of products. Our CTO Jim Scharf frequently refers to our “ big 4 ” goals: security, durability, availability, and performance. These goals are more important than any feature we build. I’ve been working across the company to help teams define their availability SLO/SLAs. It turns out that measuring availability is a subtle topic. For example, a naive approach of counting the percentage of failed requests can underestimate downtime because customers make fewer requests when a service is unavailable. So, the first step is to clarify the definition of availability. Finally, as a lapsed academic (in a distant life, I was a graduate student at the University of Washington Department of Computer Science and Engineering), I’m always interested in finding ways to bridge theory and practice. I’ve been collaborating with some folks in our research team to drive improvements to our replication protocols. There are theoretical results that suggest it is impossible to simultaneously achieve low latency and strong consistency (“linearizability” in the technical jargon). However, we believe there are intermediate points in the consistency/latency spectrum that have not been fully explored. This work hasn't been made into a product yet, but stay tuned. Flexible working MongoDB is a hybrid company. Like many of our engineers, I work outside the company headquarters in New York City (I live in Seattle). I appreciate MongoDB’s approach to hybrid working and that company leadership, starting with Dev , cares about the well-being of their employees. It seems there are companies that don’t seem to trust their employees to make decisions, such as which days to come into the office, so I’m thankful for the autonomy I receive at MongoDB to work in a way that’s best for me. Remote work has its challenges, but I would say that the benefit for my work/life balance has been transformative. Final thoughts I have found MongoDB engineers demonstrate a strong mix of technical depth, pragmatism, and empathy. I have yet to find the “smart jerk” prototype that seems to exist throughout the tech industry. Overall, I have found MongoDB is open to change and growth at both the team level and the individual level. There is a willingness to evolve and improve that aligns with the company’s values and leadership principles and enables the success of our technology and people. Find out more about MongoDB culture and career opportunities by joining our talent community .

November 20, 2024