Top Builders

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

Google DeepMind

Google DeepMind is a world-renowned artificial intelligence research laboratory, formed from the merger of DeepMind and Google's AI division. It stands at the forefront of AI innovation, responsible for groundbreaking advancements, including the development of the Gemini series of multimodal AI models and the Gemma open-model family. DeepMind's mission is to solve intelligence to advance science and benefit humanity.

General
AuthorGoogle DeepMind
Release Date2010 (DeepMind founding)
Websitehttps://deepmind.google/
Technology TypeAI Research Organization

Key Research Areas and Achievements

  • Reinforcement Learning: Pioneering work in reinforcement learning, including AlphaGo, which defeated world champions in Go.
  • Large Language Models: Development of advanced LLMs, contributing to the Gemini and Gemma model families.
  • Scientific Discovery: Application of AI to accelerate scientific research, such as AlphaFold for protein structure prediction.
  • Safety and Ethics: Dedicated research into AI safety, ethics, and responsible deployment.

Start Exploring Google DeepMind

Google DeepMind's research underpins many of the most advanced AI systems in the world. As the organization behind foundational models like Gemini and Gemma, its work is crucial for understanding the future of AI. Developers and researchers can delve into their publications and open-source contributions to gain insights into cutting-edge AI development.

👉 DeepMind Research Publications 👉 About Google DeepMind

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Google Deepmind AI technology Hackathon projects

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

Autonomous Gemini 3.5 Market Research Agent

Autonomous Gemini 3.5 Market Research Agent

This project is an autonomous market research agent designed to generate comprehensive, up-to-date company dossiers directly from live web sources. Powered by Gemini 3.5 Flash via the official google-genai SDK, the agent orchestrates a dynamic ReAct (Reason + Act) loop to answer multi-layered queries. The data acquisition layer integrates Bright Data’s APIs to bypass common scraping hurdles like anti-bot tracking and CAPTCHAs. Specifically, it employs the SERP API to fetch clean Google search results and the Web Unlocker API to extract webpage text. To ensure high execution speed and data quality, the engine leverages a ThreadPoolExecutor to run search and scraping tasks concurrently, while utilizing BeautifulSoup to structurally parse HTML before feeding it to the model. Users interact with the agent through a streamlined Streamlit web interface. They can type in a target startup name, monitor the parallel tool execution logs, view the generated report containing key leadership, funding history, and business models, and download the output directly as a clean Markdown file. This architecture provides a functional template for real-time web-grounded AI workflows.

DUAL-BROKER SOTA ENGINE

DUAL-BROKER SOTA ENGINE

Dual-Broker SOTA Engine is an automated trading system capturing real-time arbitrage between TradFi and Web3 prediction markets (Polymarket). The project proves that combining robust web scraping with low-latency LLM intelligence creates a secure, enterprise-grade engine. **Bright Data: Bypassing the Web's Toughest Blocks** Arbitrage demands real-time data from highly protected platforms like Yahoo Finance and Polymarket, where stale data leads to losses. The engine implements a resilient 3-tier extraction fallback powered by Bright Data: - **Bright Data Scraping Browser (CDP):** Renders JS-heavy, dynamic order books and scrapes depth snapshots via Puppeteer. - **Web Unlocker:** Bypasses advanced browser fingerprinting and CAPTCHAs on news feeds to guarantee a 99.9% extraction success rate. - **Residential Proxies:** Rotates IPs across a massive pool, ensuring high-frequency scraping runs continuously without rate-limiting or bans. Standardized via a Bright Data MCP Server, this stack transforms the open web into a structured enterprise data feed. **AI/ML API: High-Concurrency, Cost-Effective Swarm Intelligence** Running financial forecasts in real-time requires a consensus mechanism that is fast and affordable. The engine deploys a 50-persona Bayesian Swarm Consensus powered by the AI/ML API: - **Ultra-Low Latency:** AI/ML API orchestrates up to 50 parallel LLM persona requests simultaneously, converging the decision matrix in under 5 seconds. - **Economic Viability:** Leveraging top-tier models (DeepSeek-V4-Pro) via the gateway keeps token costs at a fraction of a cent. - **Real-Time P&L Safeguards:** The dashboard integrates with AI/ML API's billing API to track consumption and prove positive net profitability. With Apache Flink streaming and a Saga-based transaction sandbox for atomic execution, the engine proves that web data unlocked by Bright Data and reasoned by AI/ML API is ready for enterprise production.

ShieldRadius AI

ShieldRadius AI

ShieldRadius AI is a multi-agent platform that autonomously monitors the open web for vendor security breaches and compliance issues. Upon detection, it assesses the enterprise's risk, identifies affected customer accounts, and triggers automated Go-To-Market (GTM) outreach workflows. Developed with Node.js, TypeScript, and Supabase (PostgreSQL), the system is designed for serverless execution and operates as an asynchronous, event-driven state machine to manage tasks efficiently. The core functionality consists of four agents: - Threat Intelligence Agent: Monitors web for new threat indicators and regulations. - Risk Assessment Agent: Evaluates intelligence against compliance standards like GDPR and SOC2, assigning a risk score from 0–100. - GTM Enrichment Agent: Gathers firmographic data through asynchronous web scraping. - Outreach Engine Agent: Compiles client profiles and generates personalized email notifications. Relying on third-party integrations like Bright Data and DeepSeek for bypassing anti-bot protections, the platform features an interactive State-Machine Dashboard. This includes a “Layman Mode” for non-technical users, presenting data in an accessible format with visual risk indicators and structured tables, while allowing developers to access raw JSON for debugging.

OmniClaims Adjuster

OmniClaims Adjuster

OmniClaims Adjuster: El Futuro de la Liquidación de Siniestros En la actualidad, el procesamiento de reclamos de seguros es un proceso manual, lento y propenso a errores. OmniClaims Adjuster revoluciona el sector Insurtech mediante una arquitectura multi-agente totalmente autónoma construida sobre la familia de modelos Gemini 3.1 de Google. Diseñado para la AI Agent Olympics Hackathon, este sistema actúa como un ajustador de seguros experto. En lugar de depender de un solo modelo monolítico, el flujo de trabajo orquesta múltiples agentes especializados trabajando en paralelo y en tiempo real: 1. Agente de Extracción: Transforma las narrativas no estructuradas del cliente en datos estructurados estandarizados bajo esquemas estrictos de Pydantic. 2. Agente de Pólizas: Analiza los términos contractuales (PDFs) verificando límites, deducibles, exclusiones y coberturas con precisión milimétrica. 3. Agente de Visión (Daños): Aprovecha la multimodalidad nativa de Gemini 3.1 Pro para examinar fotografías de evidencias, evaluando la severidad y la congruencia del daño reportado. 4. Agente Antifraude: Detecta anomalías cruzando variables (ej. inconsistencias entre la historia del cliente y la evidencia visual) para emitir una puntuación de riesgo. 5. Agente Orquestador: Consolida todos los análisis en una decisión final holística (Aprobado, Rechazado o Revisión Manual). A nivel técnico, la plataforma cuenta con un backend en FastAPI y una interfaz Gradio con diseño premium glassmorphism. Priorizando la explicabilidad (AI Transparency), el sistema expone en la UI todo el Chain of Thought (Razonamiento) de los agentes. OmniClaims Adjuster no reemplaza al ajustador humano; lo empodera resolviendo automáticamente el 80% de los casos claros y entregando un dossier procesado de alta inteligencia para los reclamos complejos.

Cortex

Cortex

Across Europe, SMBs are losing enormous economic potential—not because of competition, but because founders are trapped in daily operations. In Italy alone, SMEs represent 95% of companies and employ nearly 80% of the workforce, yet most founders spend 70–80% of their time handling repetitive operational tasks instead of focusing on growth and strategy. The consequences are severe: businesses leave 15–35% of potential revenue unrealized, lose €50K–€300K annually due to delayed decisions, and suffer valuation discounts because companies depend too heavily on a single founder. Cortex solves this problem by giving every SMB access to an autonomous AI-powered executive board. The platform includes five specialized AI executives: • CEO — strategy and market analysis • CFO — forecasting and cash-flow management • CSO — sales optimization and conversion strategy • CMO — customer acquisition and growth campaigns • CTO — infrastructure audits and software scaling Through an immersive dark-mode interface, founders can text or speak directly to executives, assign workflows, compare recommendations, and receive real-time, boardroom-ready insights. Unlike generic AI chatbots, Cortex operates as a decentralized multi-agent ecosystem where executives collaborate independently, validate strategies together, and execute workflows asynchronously before presenting solutions. Cortex is powered by cutting-edge infrastructure: • Speechmatics enables real-time voice onboarding and hands-free interaction • Google Gemini 2.0 Flash powers rapid, high-context reasoning • Featherless AI routes advanced tasks across 27,000+ open-source models including DeepSeek • Vultr Cloud delivers enterprise-grade speed, scalability, and security Built for SMB founders, startups, and lean teams spending heavily on consultants—or operating without strategic guidance entirely—Cortex delivers a scalable AI workforce and executive-level decision making at a fraction of traditional costs.