Top Builders

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

Google's Generative AI Studio

Experience the power of Google's Vertex AI through Generative AI Studio, a managed environment that streamlines the interaction, customization, and deployment of foundation models for production applications.

General
Release date2023
AuthorGoogle
DocumentationLink
TypeGenerative AI Model Management

Start building with Generative AI Studio

Explore the best Generative AI Studio resources and libraries to help you get started with building projects using Google's Vertex AI today.

A curated list of libraries and resources to help you build outstanding projects with Generative AI Studio.

Edit on GitHub

Google Generative AI Studio AI technology Hackathon projects

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

Claption

Claption

Claption is a containerized video captioning app built for the AMD Developer Hackathon ACT II Video Captioning track. It processes short video clips by sampling frames with FFmpeg, generating a grounded visual fact sheet with Fireworks AI, and then rewriting that factual summary into four required caption styles: formal, sarcastic, humorous-tech, and humorous-non-tech. To improve leaderboard performance, Claption separates factual understanding from tone generation, then uses an internal LLM judge loop to score captions for accuracy, tone match, humor quality, and hallucination risk. Weak captions are automatically repaired before final export. The project includes a Next.js demo app, batch CLI, Docker container, sample outputs, and judge-ready setup instructions.

SimuFactory

SimuFactory

Project Description: SimuFactory The Problem In the world of industrial automation, training technicians on live PLC (Programmable Logic Controller) systems is high-risk and high-cost. A single wrong command in a real factory can lead to catastrophic hardware failure, expensive production downtime, or severe safety hazards. However, traditional simulators are often static and fail to teach the "critical thinking" required for real-world troubleshooting. SimuFactory is a high-fidelity, AI-powered training ecosystem that implements a Cognitive Digital Twin of an industrial cell. Instead of a simple "correct/incorrect" feedback loop, SimuFactory utilizes a Socratic Coaching Engine. Powered by Llama-3, the AI acts as a senior engineer, guiding the trainee through complex faults using targeted questions and contextual hints, forcing the user to diagnose the root cause rather than simply following a manual. To ensure industrial-grade safety, we implemented a sophisticated Dual-LLM Architecture. While Llama-3 handles the pedagogical coaching, we integrated Gemma-2 as a dedicated Real-time Safety Auditor. This "Watchdog" model intercepts every terminal command, analyzing it against the current factory state to detect ISO 26262 safety violations. If a command is deemed dangerous, the auditor blocks the action and triggers a "Thermal Trip" or "E-Stop" simulation, teaching the trainee the real-world consequences of unsafe practices. SimuFactory is designed for Edge Deployment on AMD hardware. By leveraging the AMD ROCm™ stack and the massive memory bandwidth of the Instinct™ MI300X, we achieve ultra-low latency inference that allows the simulation and AI to react in real-time. Most importantly, our architecture ensures Data Sovereignty. In a real-world deployment, sensitive factory telemetry and PLC logic never leave the local network, removing the security risks associated with cloud-based AI and making SimuFactory a viable solution for high-security industrial environments

Autonomous Product Studio

Autonomous Product Studio

Autonomous Product Studio is an AI-powered multi-agent platform that transforms a single startup idea into a complete, evidence-backed execution package. Instead of acting as a chatbot or coding assistant, it behaves like a virtual startup team with specialized agents for market research, competitor analysis, product management, software architecture, execution planning, and presentation. Starting from one idea, the system autonomously researches market demand, identifies user pain points, analyzes competitors, generates Product Requirements Documents (PRDs), Technical Requirements Documents (TRDs), system architecture, API designs, implementation roadmaps, sprint plans, and investor-ready pitch materials. Each agent operates in an isolated context with its own model-selected tools, enabling long-horizon workflows spanning 25–35 tool calls while maintaining coherent reasoning. The platform includes over 50 model-callable tools across research, analysis, product, architecture, execution, and presentation domains. Rather than relying on a single AI response, agents collaborate through structured artifacts and typed outputs, where each stage consumes the results of the previous one. This creates a composable workflow that mirrors how real product teams operate. All recommendations are grounded in evidence collected from multiple sources, including GitHub, Reddit, Hacker News, Stack Exchange, Wikipedia, arXiv, package registries, trends data, and web search, providing transparent and explainable decision-making. Built with LangGraph, FastAPI, React, and Pydantic, the system incorporates production-grade engineering practices such as structured logging, retries, rate limiting, schema validation, evaluation harnesses, and comprehensive testing. The result is an autonomous product organization that converts ideas into actionable startup blueprints instead of simply generating code or reports.

NLP Execution Router: Hybrid Token-Efficient Agent

NLP Execution Router: Hybrid Token-Efficient Agent

Project Overview NLP Execution Router is a high-precision cost and latency optimizer designed for enterprise AI systems. Hosting premium proprietary models for every single request can be incredibly expensive and slow. Our intelligent agent solves this by serving as a hybrid router between local lightweight heuristics and cloud-hosted LLM proxies. How It Works The agent dynamically inspects incoming task payloads and automatically categorizes them across multiple core capability domains. For tasks requiring lower cognitive loads, such as Category 3 (Sentiment Analysis), the system leverages ultra-fast, zero-token local classification models directly inside the container. Complex operational tracks—including mathematical reasoning, code generation, summarization, and logic puzzles—are efficiently routed to cloud-based Fireworks AI models. Key Features & Benefits Extreme Token Efficiency: Achieves an estimated 63%+ token savings by safely catching and resolving conversational metadata at the local edge. Ultra-Low Latency: Local rules execute in under 10ms, drastically outperforming internet round-trips. Production-Ready & Deterministic: Utilizes temperature 0.0 configurations for external API calls, enforcing strict constraints, absolute brevity, and eliminating conversational waste to guarantee an optimized leaderboard ranking.

. SmartTender A

. SmartTender A

BidCraft AI is an intelligent assistant that helps businesses prepare stronger, more compliant tender and procurement bids using artificial intelligence. Users simply upload a tender document in PDF or Word format, and the platform automatically extracts key requirements, identifies mandatory documents, generates a compliance checklist, summarizes eligibility criteria, and drafts proposal sections. AI also analyzes the tender to highlight missing information, suggest improvements, and estimate the likelihood of meeting evaluation requirements. The platform is designed for small businesses, startups, consultants, and organizations that want to reduce errors, save time, and improve their chances of winning government and private-sector contracts. Built with Next.js, Supabase, and modern AI models, BidCraft AI transforms the traditionally complex tender preparation process into a fast, accurate, and user-friendly experience while making procurement more accessible to everyone.

Setu Guardian AI

Setu Guardian AI

Setu is a privacy firewall for LLM applications: detect → redact → verify → explain. Millions of people receive documents — prescriptions, bills, government forms — they can't safely or easily understand, and existing "AI helper" tools require sending that sensitive content to a third party unredacted. Setu solves this differently. PII detection and redaction runs locally on an AMD GPU (confirmed via torch.cuda.is_available() = True, device "AMD Radeon Graphics", Navi31 architecture), using Gemma 3 4B-IT via PyTorch ROCm 6.2. Only the redacted transcript — with names, phone numbers, and ID numbers already replaced — ever crosses to the reasoning stage, which generates a plain-language explanation and regional-language translation. We verified real GPU utilization during inference: 99% GPU load, 130W power draw, 2236MHz clock speed, captured live via rocm-smi. Gemma 3 4B-IT was downloaded, licensed, and run directly on AMD hardware in bfloat16 precision, producing correct redaction output (verified against float16, which caused numerical instability on this architecture — a real ROCm/Navi31 compatibility finding). The redaction engine is built as a standalone, reusable component (gemma_guard), not app-specific code — proven by running it against unrelated documents outside the main demo flow. The document-explainer app is one proof-of-concept built on top of this middleware; the underlying pattern — redact before you reason — is intended to be reusable by any team building LLM applications on sensitive data. Self-verification (an independent re-scan of redacted output for leaks) and a downloadable SHA-256 verification receipt provide auditable evidence of what was and wasn't redacted, rather than asking users to trust an unverifiable claim. Built solo under significant time constraints, with an honest account of what's fully verified versus in-progress included in the project documentation.