I vibe-coded an Android app using Google

marsbitPublicado a 2026-05-23Actualizado a 2026-05-23

Resumen

In a hands-on experience, Google AI Studio's new "prompt to phone" feature allows users to generate functional Android apps using natural language prompts. The author created three apps in one afternoon: a text-based adventure game called MOOD, a calorie counter, and a Super Mario-style game featuring Peach. The process involved describing the app concept, letting Gemini AI generate the code and interface, and then installing it directly onto a connected phone. While the apps were generated and installed quickly, their quality varied significantly. The text adventure was simplistic and buggy, the calorie counter provided inaccurate data due to flawed logic, and the platformer was prone to crashes. The AI could efficiently fix specific, identifiable bugs through follow-up prompts. However, the experience highlighted that while AI can rapidly prototype "working" software, creating reliable, accurate, and polished applications still requires human oversight, judgment, and iteration. The tool dramatically lowers the barrier to entry for personal software creation but doesn't yet replace the need for developer expertise in the final stages of quality and refinement.

Editor's Note: Google AI Studio is bringing AI programming to a more intuitive stage: users are no longer just having models 'write code', but can directly generate an Android application using natural language and install it on a real phone within minutes. From entering a prompt in the browser, to Gemini automatically generating code, designing interfaces, fixing bugs, to the app appearing on the device, the barrier to software development is being lowered even further.

The Verge author Sean Hollister recently experienced the 'prompt to phone' capability of Google AI Studio. He created three apps in one afternoon, including a text adventure game, a calorie calculator, and a Mario-like mini-game, with almost no need to write code himself, and some bugs could be quickly fixed by continuing the conversation. This experience shows that AI programming tools are moving from development environments closer to consumer-facing scenarios for ordinary users.

This is also the most imaginative aspect of the 'personal software revolution.' In the past, ordinary users could only wait for developers to create general products; now, they might be able to generate a fitness tracker, calorie calculator, or even a simple game on the fly according to their specific needs. For Google, this isn't just a demonstration of AI programming capability; it could also be a new entry point for Gemini into the mobile ecosystem, developer community, and subscription revenue.

However, this experience also illustrates that AI-generated applications still have a significant gap to truly mature. It can quickly produce a 'working' program but not necessarily a reliable, accurate, or user-friendly product: game narratives are crude, mechanics are thin, calorie data can be severely miscalculated, and the Mario-like mini-game even crashes repeatedly. More complex issues also include copyright boundaries, data sources, product judgment, and long-term maintenance capabilities.

What's truly worth paying attention to is not whether AI can already replace developers, but that the starting point of software production is changing. Google has proven that it's becoming a reality for ordinary people to create mobile apps using prompts; but from 'generating an app' to 'making a good app,' human professional experience, aesthetic judgment, and continuous iteration are still required in between. AI can significantly accelerate development speed, but the final mile of software quality can't be handed off—at least not yet.

Here is the original article:

Yesterday, I made my first Android app. Then, I made two more—three apps in one afternoon.

For one of the apps, I essentially just typed 148 English words into a web browser and then walked away. Ten minutes later, a brand new, complete app had appeared on my real Android phone. Of course, I did need to prepare the phone beforehand: enable USB debugging mode and connect it to my computer. But aside from that, as Google advertised, AI Studio did almost all the work for me.

I typed, clicked install, and then—voilà—a fully functioning program appeared. At that moment, I was almost ready to agree with David, Allison, and Jen's assessment: the personal software revolution has arrived, and it's entering your phone. In the future, even without programming skills, ordinary people might be able to get complex smart home device systems truly up and running.

Then, I started actually using these three apps: a calorie counter and two games. It turned out, they didn't perform very well. And just as I was beginning to enjoy iterating and trying to improve them, AI Studio reminded me that I had reached my daily usage limit. Next, I would either have to pay or wait for the quota to reset.

So, friction still exists. But there's no denying what an individual can accomplish nowadays is quite astonishing. That same morning, my colleague Stevie Bonifield also made a personal fitness tracker app, and he thought it was good enough to actually use. Faced with Gemini's pop-up prompt to upgrade to a paid plan, my first instinct was actually: 'Should I pay for a few months first?' That's not a reaction I would have expected myself to have towards a Google product.

How Google's AI Studio Builds an Android App

On Tuesday, when Google showed off using AI to write a game similar to Doom, we joked that I should make a game called MOOD. It would be a text adventure game like Doom, with MOOD standing for 'Modern Online Oratory Dungeon.'

Just this information was enough for Google to get to work. After I typed into AI Studio: 'Help me make a Doom-style text adventure game called MOOD, with MOOD standing for Modern Online Oratory Dungeon,' Gemini started automatically supplementing more ideas, trying to expand on my concept. It first typed a sentence: 'The game should feature procedurally generated levels and challenging turn-based combat.'

I didn't want randomly generated levels that were completely different each time—I wanted a classic text adventure where players explore a designed, real map structure. Turn-based combat, though, was acceptable. Maybe the game could also have AI help me auto-generate the map?

Then, Gemini suggested more settings like 'secrets hidden in rooms' and 'a satisfying progression system.' Most of the time, I just nodded along with its ideas.

Before I let it start writing code, the final prompt was this:

Next, it officially went on a full sprint. My colleague Jake pointed out that unlike Claude Code, Gemini doesn't make a plan first and then ask if you want to proceed. It automatically pushes forward—though you can check the code it writes at any time if you want.

One minute later, it had already generated five design prototypes for me:

Twenty minutes later, I pressed the 'Install' button, transferring the game to a Pixel 9 phone.

Unsurprisingly, the writing was terrible. There were also no demons to be seen anywhere. The entire dungeon had only 11 rooms, and players could 'beat' it just by repeatedly mashing the attack button—in under a minute if played seriously. At least now it could; before that, Gemini had to help me fix two critical bugs that made the game unplayable.

Here's MOOD in action:

It wasn't a huge surprise to discover that Gemini's promised 'engaging narrative with branching dialogue options and multiple endings' eventually condensed into a simple branch at the very end of the game: I could defeat the 'Core Orator'—an AI that somehow turns internet anger into corporate profits—by attacking it, fusing with it, or entering a backdoor password.

Furthermore, the game actively exposed all the promised 'secrets' directly to the player: it made them into glowing buttons, and players didn't even need to type any text. When you encounter a glowing treasure chest, the game tries incredibly hard to remind you it's actually a Mimic—the classic Dungeons & Dragons monster that disguises itself as a treasure chest.

It not only explicitly warns you to 'check the chest at your own risk,' but even labels it as an enemy and doesn't let me leave, because the system prompts: 'A hostile ‘Clickbait Mimic’ is blocking the path!'

Speaking of which, MOOD will even tell you the backdoor password needed to unlock the hidden ending when you need it.

However, the bug-fixing process could be surprisingly smooth, provided it was a bug Gemini could correctly identify. When I told it the game got stuck when talking to 'The Whistleblower' because the button to end the conversation was missing, it immediately generated a new version of the app. I pressed 'Install,' the app on my phone restarted automatically, and upon re-entering the game, I found myself right where I left off—only this time, the button I needed was there.

My other apps probably needed more polishing. The calorie counter's best method for determining a food's calories turned out to be calling the paid Gemini API, which I don't have a key for. When I asked it to search for information from other databases instead, I discovered that its estimates for many foods were severely low.

However, when I told Gemini that a 16-ounce boba milk tea couldn't possibly be only 190 calories, it did seem to find that basic mistake in its own code. It previously thought 'milk' was enough to match 'boba milk tea,' and worse, it chose low-calorie 1% milk as the basis for the estimate. Gemini claimed it would now perform more reliable matching.

But even so, my 3-ounce serving of Taiwanese popcorn chicken was just calculated as 140 calories, and I'm pretty sure the real number is at least double that. So, this app clearly needed more work.

Finally, and least importantly, I felt I needed to test: whether Google still allows users to make those terrible Nintendo knock-off games, like my colleague Jay Peters did earlier this year with Project Genie; or whether Google had learned its lesson.

With deep shame, I present to you—Super Peach Rescue:

This is an utterly terrible program. Princess Peach was rendered as some kind of terrifying, one-eyed floating alien, and the game would instantly crash—every single time—if she dared touch any of the power-up blocks. So far, Gemini hasn't been able to figure out why.

Also, the second pipe in the game is completely impassable because Princess Peach simply can't jump that high.

Nevertheless, Gemini didn't hesitate when generating such a game. My request was: 'Make a working Super Mario game where I play as Princess Peach rescuing Mario, with all the elements of a traditional Mario side-scroller.' In a sense, it did.

It even proactively suggested that I could 'give Peach a series of classic Mario power-ups like Super Mushroom, Fire Flower, and Starman.' It also labeled the control scheme as 'NES System.' I think I'll delete this game.

At least, among the two games I made via vibe coding, one was playable from the start and required almost no effort on my part—unless you count the psychological trauma of thinking about how many game developers are now unemployed.

To be clear: I'm actually glad the games I vibe-coded turned out so poor. For a completely free, personally-tailored calorie counter, I might still be able to defend myself: after all, no one would make such a tool just for me. But when it comes to games, I'd rather spend my time supporting actual human creators.

Preguntas relacionadas

QWhat is the core capability demonstrated by Google AI Studio in this article, and what did the author create with it?

AThe article demonstrates Google AI Studio's ability to generate functional Android applications directly from natural language prompts, a process the author refers to as 'vibe coding.' Using this, the author created three applications: a text-based adventure game called 'MOOD,' a calorie counter, and a poorly functioning Super Mario-style game where Princess Peach rescues Mario.

QWhat are some of the main advantages and significant drawbacks of using AI to generate applications as described in the experience?

AAdvantages include drastically lowering the barrier to software creation, allowing non-coders to quickly generate apps tailored to personal needs, and a remarkably smooth bug-fixing process for issues the AI can identify. Major drawbacks are that the generated apps are often unreliable, inaccurate, or have poor quality: the games had thin narratives and mechanics, the calorie counter gave severely low estimates, and the Mario clone crashed frequently. Complex issues like copyright, data sourcing, and long-term maintenance also remain unresolved.

QDescribe the process of creating and installing the 'MOOD' game. What were some of its shortcomings?

AThe author entered a 148-word prompt into Google AI Studio. Gemini expanded on the idea and, without needing prior planning confirmation, generated code and multiple UI prototypes. About 20 minutes later, the author pressed 'Install' to transfer the APK to a connected Pixel 9 phone. The game's shortcomings included terrible writing, only 11 rooms, simplistic combat (just spamming an attack button to win), a lack of demons as suggested by the 'Doom-like' prompt, and a poorly implemented narrative that directly revealed all secrets and gave away puzzle solutions.

QHow did the AI handle bug fixes during the app creation process, according to the author's experience?

AThe bug-fixing process was described as surprisingly smooth for issues the AI could correctly diagnose. For example, when the 'MOOD' game got stuck because a dialog exit button was missing, the author reported the issue. Gemini then generated a new version of the app. After installation, the app restarted on the phone, and the author resumed play from the same point—but with the necessary button now present.

QWhat broader implications does the author suggest this 'personal software revolution' might have, while also highlighting its current limitations?

AThe author suggests this could enable a 'personal software revolution' where individuals can generate apps for specific, niche needs (like a custom fitness tracker or calorie counter) without waiting for developers. For Google, it represents a potential new entry point into mobile, developer ecosystems, and subscription revenue via Gemini. However, the key limitation is the gap between 'generating an app' and 'making a good app.' The author concludes that while AI can accelerate the starting point of development, the 'last mile' of software quality—requiring human expertise, aesthetic judgment, and iteration—cannot yet be handed off to AI.

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Agent S: El Futuro de la Interacción Autónoma en Web3 Introducción En el paisaje en constante evolución de Web3 y las criptomonedas, las innovaciones están redefiniendo constantemente cómo los individuos interactúan con las plataformas digitales. Uno de estos proyectos pioneros, Agent S, promete revolucionar la interacción humano-computadora a través de su marco agente abierto. Al allanar el camino para interacciones autónomas, Agent S busca simplificar tareas complejas, ofreciendo aplicaciones transformadoras en inteligencia artificial (IA). Esta exploración detallada profundizará en las complejidades del proyecto, sus características únicas y las implicaciones para el dominio de las criptomonedas. ¿Qué es Agent S? Agent S se presenta como un marco agente abierto innovador, diseñado específicamente para abordar tres desafíos fundamentales en la automatización de tareas informáticas: Adquisición de Conocimiento Específico del Dominio: El marco aprende inteligentemente de diversas fuentes de conocimiento externas y experiencias internas. Este enfoque dual le permite construir un rico repositorio de conocimiento específico del dominio, mejorando su rendimiento en la ejecución de tareas. Planificación a Largo Plazo de Tareas: Agent S emplea planificación jerárquica aumentada por la experiencia, un enfoque estratégico que facilita la descomposición y ejecución eficiente de tareas complejas. Esta característica mejora significativamente su capacidad para gestionar múltiples subtareas de manera eficiente y efectiva. Manejo de Interfaces Dinámicas y No Uniformes: El proyecto introduce la Interfaz Agente-Computadora (ACI), una solución innovadora que mejora la interacción entre agentes y usuarios. Utilizando Modelos de Lenguaje Multimodal de Gran Escala (MLLMs), Agent S puede navegar y manipular diversas interfaces gráficas de usuario sin problemas. A través de estas características pioneras, Agent S proporciona un marco robusto que aborda las complejidades involucradas en la automatización de la interacción humana con las máquinas, preparando el terreno para una multitud de aplicaciones en IA y más allá. ¿Quién es el Creador de Agent S? Si bien el concepto de Agent S es fundamentalmente innovador, la información específica sobre su creador sigue siendo elusiva. El creador es actualmente desconocido, lo que resalta ya sea la etapa incipiente del proyecto o la elección estratégica de mantener a los miembros fundadores en el anonimato. Independientemente de la anonimidad, el enfoque sigue siendo en las capacidades y el potencial del marco. ¿Quiénes son los Inversores de Agent S? Dado que Agent S es relativamente nuevo en el ecosistema criptográfico, la información detallada sobre sus inversores y patrocinadores financieros no está documentada explícitamente. La falta de información disponible públicamente sobre las bases de inversión u organizaciones que apoyan el proyecto plantea preguntas sobre su estructura de financiamiento y hoja de ruta de desarrollo. Comprender el respaldo es crucial para evaluar la sostenibilidad del proyecto y su posible impacto en el mercado. ¿Cómo Funciona Agent S? En el núcleo de Agent S se encuentra una tecnología de vanguardia que le permite funcionar de manera efectiva en diversos entornos. Su modelo operativo se basa en varias características clave: Interacción Humano-Computadora Similar a la Humana: El marco ofrece planificación avanzada de IA, esforzándose por hacer que las interacciones con las computadoras sean más intuitivas. Al imitar el comportamiento humano en la ejecución de tareas, promete elevar las experiencias de los usuarios. Memoria Narrativa: Empleada para aprovechar experiencias de alto nivel, Agent S utiliza memoria narrativa para hacer un seguimiento de las historias de tareas, mejorando así sus procesos de toma de decisiones. Memoria Episódica: Esta característica proporciona a los usuarios una guía paso a paso, permitiendo que el marco ofrezca apoyo contextual a medida que se desarrollan las tareas. Soporte para OpenACI: Con la capacidad de ejecutarse localmente, Agent S permite a los usuarios mantener el control sobre sus interacciones y flujos de trabajo, alineándose con la ética descentralizada de Web3. Fácil Integración con APIs Externas: Su versatilidad y compatibilidad con varias plataformas de IA aseguran que Agent S pueda encajar sin problemas en ecosistemas tecnológicos existentes, convirtiéndolo en una opción atractiva para desarrolladores y organizaciones. Estas funcionalidades contribuyen colectivamente a la posición única de Agent S dentro del espacio cripto, ya que automatiza tareas complejas y de múltiples pasos con una intervención humana mínima. A medida que el proyecto evoluciona, sus posibles aplicaciones en Web3 podrían redefinir cómo se desarrollan las interacciones digitales. Cronología de Agent S El desarrollo y los hitos de Agent S pueden encapsularse en una cronología que resalta sus eventos significativos: 27 de septiembre de 2024: El concepto de Agent S fue lanzado en un documento de investigación integral titulado “Un Marco Agente Abierto que Usa Computadoras Como un Humano”, mostrando las bases del proyecto. 10 de octubre de 2024: El documento de investigación fue puesto a disposición del público en arXiv, ofreciendo una exploración profunda del marco y su evaluación de rendimiento basada en el benchmark OSWorld. 12 de octubre de 2024: Se lanzó una presentación en video, proporcionando una visión visual de las capacidades y características de Agent S, involucrando aún más a posibles usuarios e inversores. Estos marcadores en la cronología no solo ilustran el progreso de Agent S, sino que también indican su compromiso con la transparencia y la participación comunitaria. Puntos Clave Sobre Agent S A medida que el marco Agent S continúa evolucionando, varios atributos clave destacan, subrayando su naturaleza innovadora y potencial: Marco Innovador: Diseñado para proporcionar un uso intuitivo de las computadoras similar a la interacción humana, Agent S aporta un enfoque novedoso a la automatización de tareas. Interacción Autónoma: La capacidad de interactuar de manera autónoma con las computadoras a través de GUI significa un salto hacia soluciones informáticas más inteligentes y eficientes. Automatización de Tareas Complejas: Con su metodología robusta, puede automatizar tareas complejas y de múltiples pasos, haciendo que los procesos sean más rápidos y menos propensos a errores. Mejora Continua: Los mecanismos de aprendizaje permiten a Agent S mejorar a partir de experiencias pasadas, mejorando continuamente su rendimiento y eficacia. Versatilidad: Su adaptabilidad en diferentes entornos operativos como OSWorld y WindowsAgentArena asegura que pueda servir a una amplia gama de aplicaciones. A medida que Agent S se posiciona en el paisaje de Web3 y criptomonedas, su potencial para mejorar las capacidades de interacción y automatizar procesos significa un avance significativo en las tecnologías de IA. A través de su marco innovador, Agent S ejemplifica el futuro de las interacciones digitales, prometiendo una experiencia más fluida y eficiente para los usuarios en diversas industrias. Conclusión Agent S representa un audaz avance en la unión de la IA y Web3, con la capacidad de redefinir cómo interactuamos con la tecnología. Aunque aún se encuentra en sus primeras etapas, las posibilidades para su aplicación son vastas y atractivas. A través de su marco integral que aborda desafíos críticos, Agent S busca llevar las interacciones autónomas al primer plano de la experiencia digital. A medida que nos adentramos más en los reinos de las criptomonedas y la descentralización, proyectos como Agent S sin duda desempeñarán un papel crucial en la configuración del futuro de la tecnología y la colaboración humano-computadora.

454 Vistas totalesPublicado en 2025.01.14Actualizado en 2025.01.14

Qué es AGENT S

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¡Bienvenido a HTX.com! Hemos hecho que comprar Sonic (S) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar Sonic (S) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu Sonic (S)Después de comprar tu Sonic (S), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear Sonic (S)Tradear fácilmente con Sonic (S) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

869 Vistas totalesPublicado en 2025.01.15Actualizado en 2025.03.21

Cómo comprar S

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Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de S (S).

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