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

Edit on GitHub

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.

RiskOps

RiskOps Autonomous — Full Project Description The Problem A port strike in Southeast Asia, a cloud region outage, a pharmaceutical recall, a factory fire — doesn't stay contained to one vendor. It ripples outward: delayed shipments cascade into missed production windows, missed production windows cascade into contractual penalties, contractual penalties cascade into liquidity strain, and liquidity strain cascades into regulatory and reputational exposure. The problem is that most organizations have no fast, structured way to reason about this ripple effect the moment a crisis breaks. Risk assessment today typically means a war-room of stakeholders — operations, finance, legal, compliance — manually working through spreadsheets and gut instinct under time pressure, often taking hours or days to produce a coherent picture of exposure. By the time that picture exists, the window for proactive mitigation has often already closed. Supply chain risk is also not uniform across industries. A cloud outage means something completely different to a SaaS company than a healthcare distributor. A currency shock hits a fintech very differently than a retailer. Generic risk tools tend to flatten these differences into one-size-fits-all templates, which means the output is either too shallow to be actionable or too generic to be trusted by domain experts. Under the hood, this is powered by a domain-adaptive multi-agent AI swarm. Rather than a single generalist model producing a generic risk summary, RiskOps dispatches three specialized AI agents — each with a distinct professional persona and a distinct analytical lens — to independently assess the same event from their own domain expertise. An Operations-focused agent worries about physical disruption and . Domain Adaptivity A core design decision was refusing to build a single generic risk tool and instead building six purpose-built expert teams, one per business

AuraOS

Aura is a sovereign, neuro-symbolic AI operating system designed to make advanced artificial intelligence more transparent, efficient, collaborative, and accountable. Instead of sending every instruction directly to a language model, Aura can locally interpret natural-language intent, organize it into structured semantic and routing representations, identify relevant code or knowledge, and prepare a bounded task before model use is required. Aura combines deterministic routing, a 4,096-primitive lexical system, polysynthetic six-slot intent structures, visual code topology, guarded human-agent workflows, and a Learning Arena where verified experiences can be tested before becoming reusable knowledge. Its Observatory allows users to see how Aura understood an instruction, while the Human Agent Arena supports node-specific dialogue, approval gates, verification, and preserved attempt artifacts—including failed work that may help future refactoring. Aura is built around human authority. Models can propose, explain, investigate, and assist, but they cannot silently expand scope, bypass evidence requirements, or merge changes without approval. The result is an AI architecture focused not only on capability, but on sovereignty, inspectability, learning, and responsible action.

FourVoice Captioner

FourVoice is an autonomous video captioning pipeline built for Track 2 of the AMD Act 2 x lablab.ai hackathon. Given any short video clip (30 seconds to 2 minutes), it generates four tonally distinct captions — formal, sarcastic, humorous-tech, and humorous-non-tech — while ensuring every caption stays factually grounded in what actually happens in the video. Unlike template-based caption generators that guess or hallucinate details, FourVoice uses an audio-first, vision-fallback approach: it transcribes spoken dialogue with Whisper when present, and falls back to frame-by-frame visual analysis when a clip is silent, musical, or static. This ensures captions never invent details that aren't actually in the source material — a real problem we identified and fixed during development, where an early version of the pipeline captioned the wrong on-screen detail entirely. All four style captions are generated from a single, shared, fact-checked description, so tone changes across styles without the underlying facts drifting between them. A self-QC stage checks each caption against the source facts and regenerates any caption that scores poorly, and the pipeline reports its own best-fit style recommendation with reasoning, plus a confidence score reflecting how much of the caption is backed by direct evidence versus inference. The project is fully containerized with Docker, using Fireworks AI-hosted models (Whisper, Llama 3.3 70B, Gemma, and Qwen3 32B) across its pipeline stages, and is deployed as a live web application with a Vercel frontend and Render backend for interactive demo access alongside the standalone Docker pipeline used for batch evaluation.

Sync City

# Sync City: Civic Infrastructure Platform **Sync City** is a crowdsourced app bridging the gap between citizens and municipalities. It empowers residents to report and monitor infrastructure issues (e.g., potholes, leaks, broken streetlights), providing local governments with pre-validated data to optimize repairs. ## Key Features * **Dynamic Dashboard:** View real-time stats on pending and resolved issues alongside a live activity feed. Admins can toggle issue states with instantaneous UI updates. * **Interactive Mapping:** Displays color-coded pins based on severity. Features deep zoom (up to 22.0), dynamic pin clustering for high-density areas, and seamless Google Maps redirection. * **Gemini AI Reporting:** Submitting an issue takes under a minute. Powered by the **Gemini Vision API**, the system analyzes uploaded photos in real-time. It validates civic context (strictly blocking selfies, documents, or unrelated images), auto-categorizes the problem, and assesses severity. * **Community Verification:** Nearby users can confirm issues they witness in person. Meeting a community threshold upgrades a report to **VERIFIED**, preventing spam and ensuring data accuracy. * **Gamification:** Users earn XP, level up, and unlock achievements (like *Pothole Patrol* or *Eco Warrior*) for reporting and verifying, incentivizing active civic participation. ## Technology Stack ### Frontend (Flutter) * **Framework:** Cross-platform Flutter SDK (iOS, Android, Web) with ResponsiveBuilder. * **State & Routing:** **Riverpod** for declarative state management; **GoRouter** for persistent bottom navigation. * **Mapping:** flutter_map styled with CartoDB Voyager and Dark themes. ### Backend & AI (Python) * **API & Database:** **FastAPI** for asynchronous REST endpoints, backed by a **PostgreSQL** database via SQLAlchemy ORM. * **AI Vision:** OpenRouter linked to **Gemini 2.5 Flash** for high-speed, accurate image validation and automated categorization.

Cyberflation ID

Cyberflation.ID is a near-real-time early warning and cyber risk intelligence platform designed to measure, monitor, and forecast cyber risk inflation in Indonesia. Cyber risk inflation describes the increasing operational, recovery, compliance, and reputational costs caused by the accumulation of cyber incidents, exploited vulnerabilities, exposed digital infrastructure, data breaches, phishing, ransomware, and other emerging threats. The platform aggregates multiple public and OSINT data sources, including CISA Known Exploited Vulnerabilities, exposed services identified through Shodan, cyber incident records, online gambling injection indicators, and cybersecurity news feeds. These signals are normalized and processed into a unified Cyber Inflation Index with sector-level risk breakdowns and forecasting capabilities. Through its AI Analyst, users can understand the factors driving cyber risk, identify priority sectors and exposed assets, and receive actionable mitigation recommendations. Cyberflation.ID helps governments, CSIRTs, businesses, and cybersecurity communities move from reactive incident response toward earlier, data-driven, and more cost-efficient cyber risk intervention.

FlowForge: Natural Language to n8n Workflow JSON

FlowForge is an open source tool that converts natural language descriptions into importable n8n workflow JSON using a fine tuned Gemma model trained entirely on AMD hardware. The problem is simple. Building n8n automations by hand takes time and requires knowing the platform's node structure by heart. General purpose language models are surprisingly bad at this task since n8n workflow JSON has a strict schema with node types connections and parameters that must all line up correctly. We built a full pipeline from scratch. Two thousand seven hundred eleven real n8n templates were collected cleaned to eighteen hundred and enriched three times using an LLM to produce natural language prompts for each workflow giving us fifty four hundred training examples split into forty five hundred ninety training rows and eight hundred ten held out rows for evaluation. Training ran on a single AMD Radeon PRO W7900 with forty eight gigabytes of VRAM using LoRA rank sixteen on Gemma 3 4B with plain PyTorch and PEFT since Unsloth segfaults on this RDNA3 card and Gemma 4 currently cannot be LoRA trained at all because PEFT does not support its custom linear layers. This alone took real debugging and is documented in the README as a known issue others can avoid. We measured results honestly using a three gate evaluation harness checking whether output is valid JSON whether it matches the n8n schema and whether it actually imports into a live n8n instance. The base Gemma 3 4B model produces zero valid workflows on held out prompts. Our fine tuned model produces valid schema compliant workflows twenty percent of the time and critically whenever it produces valid JSON that JSON is always schema correct meaning the remaining failure mode is simply cutting off on the longest workflows rather than generating the wrong structure. The full stack includes a FastAPI backend anyone can clone the repository run the training pipeline on their own AMD hardware and build on top of what we made.