Google AI Studio

Top Builders

Explore the top contributors showcasing the highest number of app submissions within our community.

Google AI Studio

Google AI Studio is a free, web-based development environment that simplifies the process of building and prototyping generative AI applications. It allows developers to quickly experiment with prompts, test various models, and integrate with the Gemini API without needing complex setup. This tool is designed to accelerate the development lifecycle for AI-powered features and applications.

General
Author	Google
Release Date	2023
Website	https://ai.google.dev/ai-studio
Documentation	https://ai.google.dev/gemini-api/docs/ai-studio-quickstart
Technology Type	Developer Tool

Key Features

Prompt Engineering Interface: A user-friendly workspace for designing, testing, and iterating on prompts for generative AI models.
Gemini API Integration: Seamless connection to the Gemini API, providing access to Google's most advanced models.
Multi-modal Support: Experiment with text, image, and other data types to build rich AI applications.
Code Generation: Automatically generates code snippets in various languages (Python, Node.js, etc.) for easy integration into projects.
No-Cost Access: Free to use for rapid prototyping and development, lowering the barrier to entry for AI innovation.

Start Building with Google AI Studio

Google AI Studio is an invaluable tool for developers looking to quickly build and test applications using generative AI, particularly with the Gemini API. Its intuitive interface and direct integration capabilities enable rapid experimentation and deployment of AI-powered features. Start prototyping your ideas and bring your generative AI applications to life.

👉 Google AI Studio Quickstart Guide 👉 Explore Gemini API Models

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Google AI Studio AI technology Hackathon projects

Discover innovative solutions crafted with Google AI Studio AI technology, developed by our community members during our engaging hackathons.

Doen AI

Doen AI is a real-time AI guidance system designed to transform how humans perform skilled physical work. Using a small wearable camera, our AI sees what the worker sees and provides step-by-step instructions, from identifying worn parts to guiding precise actions, reducing months of training to minutes. The system is ideal for industries like field services, manufacturing, healthcare, and construction, where skilled labor shortages are a critical problem. By combining multimodal AI, computer vision, and AR-enabled devices, Doen AI empowers workers to learn on the job, improve efficiency, and maintain high-quality standards, all while ensuring safety and scalability.

Adaptifleet

Traditional warehouse automation has improved efficiency, yet many systems remain rigid, expensive, and difficult to adapt when workflows or layouts change. Even small adjustments often require specialized expertise or time-consuming reprogramming. This creates a disconnect between what operators need robots to do and how easily they can communicate those needs — a challenge we call the “Human Intent Gap.” AdaptiFleet was designed to close this gap by enabling intuitive, AI-driven fleet control. Instead of relying on complex interfaces or predefined scripts, users interact with autonomous robots using natural language. Commands such as “Get me three bags of chips and a cold drink” are interpreted and translated into structured robotic tasks automatically. At its core, AdaptiFleet leverages Gemini-powered Vision Language Models (VLMs) to understand user intent and visual context. Robots operate within a dynamic decision framework, allowing them to adapt to changing environments rather than follow rigid, pre-programmed routes. The platform integrates a digital twin simulation stack built on Isaac Sim, enabling teams to validate behaviors, test workflows, and optimize multi-robot coordination before live deployment. Once deployed, ROS2 and Nav2 provide robust navigation, dynamic path planning, and collision avoidance. The VLM orchestration layer continuously analyzes visual inputs to support scene understanding, anomaly detection, and proactive hazard awareness. When conditions change, AdaptiFleet autonomously re-plans routes and tasks, reducing downtime and operational disruption. By combining conversational interaction, real autonomy, and simulation-driven validation, AdaptiFleet simplifies robotic deployments while improving efficiency and visibility. The result is an automation system that is adaptive, scalable, and aligned with how people naturally work.

SANCTUMSIM

SANCTUM-SIM is a simulation-first robotics platform that demonstrates autonomous infrastructure management under real-world constraints. In a simulated geothermal and logistics facility, mobile inspection and maintenance robots operate in dynamic heat zones, energy limits, and failure-prone environments. Unlike scripted demos, each agent continuously evaluates tradeoffs between energy use, time, and operational risk using cost functions and a planner-based decision engine. Every action generates a structured, explainable rationale—showing why a robot rerouted, delayed, escalated, or conserved power. The system introduces sensor noise, randomized failures, and environmental volatility to demonstrate robustness, recovery, and graceful degradation. A browser dashboard visualizes agent states, decisions, and performance metrics in real time. SANCTUM-SIM enables energy operators and infrastructure teams to simulate, validate, and build trust in autonomous systems before real-world deployment.

FleetMind

Operating robot fleets traditionally requires complex dashboards, rigid scripts, and specialized ROS knowledge. FleetMind democratizes robotics by allowing operators to control multiple agents using simple natural language commands like "Patrol the north sector" or "Inspect the warehouse center." Our core innovation is a Hybrid AI Architecture that solves the gap between high-level reasoning and low-level control: The Brain (Gemini 2.0 Flash): Acts as the strategic commander. It parses vague human intents, resolves ambiguity (e.g., "Go back" implies memory), and outputs precise JSON mission parameters for multiple robots simultaneously. The Reflex (Reinforcement Learning): Local Q-Learning agents run on each robot to handle real-time survival. Robots autonomously decide to abort missions and seek charging stations when battery is critical—demonstrating self-optimization without hard-coded rules. Digital Twin: A high-fidelity 3D simulation (built with Three.js and React Three Fiber) visualizes the fleet in real-time, featuring simulated physics for collision detection and battery drain. FleetMind also integrates Voice Control via the WebSpeech API and simulated Telemetry streams, providing a complete "Mission Control" experience accessible from any web browser.

RoboGripAI

This project presents a simulation-first robotic system designed to perform structured physical tasks through reliable interaction with objects and its environment. The system focuses on practical task execution rather than complex physics modeling, ensuring repeatability, robustness, and measurable performance across varied simulated conditions. Simulation-first robotic system performing structured physical tasks such as pick-and-place, sorting, and simple assembly. Designed for repeatable execution under varied conditions, with basic failure handling, environmental interaction, and measurable performance metrics. A key emphasis of the system is reliability under dynamic conditions. The simulation introduces variations such as object position changes, minor environmental disturbances, and task sequence modifications. The robot is designed to adapt to these variations while maintaining consistent task success rates. Basic failure handling mechanisms are implemented, including reattempt strategies for failed grasps, collision avoidance corrections, and task state recovery protocols. The framework incorporates structured task sequencing and state-based control logic to ensure deterministic and repeatable behavior. Performance is evaluated using clear metrics such as task completion rate, execution time, grasp accuracy, recovery success rate, and system stability across multiple trials. The modular system design allows scalability for additional tasks or integration with advanced planning algorithms. By prioritizing repeatability, robustness, and measurable outcomes, this solution demonstrates practical robotic task automation in a controlled simulated environment, aligning with real-world industrial and research use cases. Overall, the project showcases a dependable robotic manipulation framework that bridges perception, decision-making, and action in a simulation-first setting, delivering consistent and benchmark-driven task execution.

Proof of Skill Network

Proof of Skill Network (PSN) is an AI-powered platform that verifies real skills through live trials, voice interviews, and competitive arenas. Resumes and certificates are often unreliable, and AI makes traditional tests easy to fake. PSN ensures skills are proven in a controlled, robotics-inspired simulation. In Start New Trials, users complete challenges while the system monitors lighting, devices, and presence. It detects tab switching, copy-paste attempts, and other suspicious behaviors. AI voice interviews ask dynamic spoken questions, requiring real-time answers to prevent cheating. The Live Arena allows users to compete globally, demonstrating skills under pressure and earning rankings. PSN is more than a testing platform; it combines behavioral monitoring, robotics-style simulation, and real-time performance tracking. In the future, it could become a global standard for hiring, education, and certification, helping organizations identify real talent and giving users a reliable way to prove their abilities.