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.

Black swan

Black swan

Black Swan news and market simulator is an advanced, real-time neuro-symbolic simulation engine built for the AMD Hackathon. At its core, the platform explores the intersection of generative AI, social sentiment, and complex financial systems by modeling how macroeconomic news dynamically impacts global equity markets. Powered by Google's Gemma foundation models (specifically gemma-4-31b-it and gemma-4-26b-it), the system simulates dozens of distinct behavioral retail cohorts and automated corporate PR desks that react contextually to user-defined market scenarios—such as a major technological breakthrough or a sudden supply chain disruption. Rather than relying on arbitrary price walks or fake mathematical noise, every single price movement in the simulation is 100% driven by AI agents reasoning over the incoming news feed and placing logical limit orders based on their risk tolerance and cash balances. These orders are cleared through a custom-built Python Continuous Double Auction (CDA) matching engine, while local TimesFM foundation models act as institutional quant funds performing high-speed time-series forecasting. The frontend is a sleek, highly interactive Next.js dashboard featuring a real-time generative news feed, global market visualizations, and a timeline scrubber that allows users to seamlessly travel back and forth through the simulation history to analyze exact market triggers. By unifying generative LLM workflows, time-series forecasting, and high-performance financial matching, this project provides a stunning, interactive sandbox for stress-testing market psychology and exploring AI-driven economic volatility.

ReCodeX

ReCodeX

ReCodeX is an AI-powered enterprise platform developed for the AMD Developer Hackathon: ACT II (Track 3 - Unicorn Track) designed to tackle the multi-billion dollar challenge of technical debt and legacy migration. Instead of a basic chatbot conversational layout, ReCodeX provides a complete engineering dashboard that automates the analysis, documentation, translation, and verification of aging infrastructure like legacy Java, COBOL, or unoptimized C++. The platform delivers comprehensive developer features, including: - Side-by-Side Modernization: Allows users to upload a legacy source file and view the original code side-by-side with refactored, modern, and fully test-covered code. - Automated Documentation: Provides deep semantic understanding of complex code logic to break down precisely what legacy components do. - Risk & Security Audits: Automatically generates a comprehensive Risk Report highlighting structural changes, security optimizations, and cloud-readiness metrics. The AMD Advantage & Tech Architecture: - To process heavy enterprise code demands reliably, ReCodeX relies entirely on high-performance cloud infrastructure backed by AMD hardware. - Core LLM orchestration is routed via the Fireworks AI API, utilizing high-performance AMD hardware endpoints running Google DeepMind's open-source Gemma models. - The compute layer is hosted on the AMD Developer Cloud, leveraging powerful cloud-based AMD GPUs to manage parallelized analytics, code scanning, and workflow pipeline tasks at sub-second latencies. - The entire system is packaged inside a Docker container for standardized deployment, with the frontend application built using Streamlit and React. Ultimately, ReCodeX targets tangible enterprise metrics for the Unicorn Track by reducing ongoing maintenance overhead, ensuring regulatory auditability through precise change logging, and accelerating cloud migration frictionlessly.

Kifani AI: Negotiation Dashboard

Kifani AI: Negotiation Dashboard

The Problem: The Silent Cost of Weak Negotiations In high-stakes environments—venture capital boardrooms, executive salary reviews, or billion-dollar contract closings—the difference between a "good" deal and a "legacy-defining" deal often comes down to a few seconds of psychological fortitude. Most professionals lack the real-time tactical script and the emotional composure required to push back against aggressive counterparties. Traditional executive coaching is too slow, and generic LLMs are too "polite" to win in cutthroat environments. The Solution: Kifani AI Negotiation Dashboard Kifani AI is an elite, voice-and-text enabled personal assistant designed to be the "Iron Man Suit" for business combat. By combining a 4-pillar Skill Matrix (Empathy, Persuasion, Adaptability, Composure) with high-leverage tactical frameworks, Kifani transforms raw data into execution-focused strategy. The application offers a three-stage workflow: Profiling: Users calibrate their negotiation persona to match the intensity of the situation. Intelligence Intake: Real-time voice or text input of the counterparty’s latest offer or threat. Execution Output: The AI generates "Power Move Replies"—tactical, high-leverage scripts designed to seize control—and "Blind Spots" to highlight psychological risks. Technical Architecture Built for speed and precision, the Kifani frontend utilizes Vite + React for a zero-latency UI. The intelligence engine is powered by Fireworks AI, utilizing the Gemma model hosted on ultra-high-performance AMD clusters. This stack ensures that even in the heat of a live call, the AI delivers strategic insights in milliseconds. Market Viability Kifani AI targets the $15B global executive coaching market and the burgeoning $20B LegalTech sector. By moving from "passive learning" to "active execution," Kifani provides immediate ROI for founders, sales leaders, and procurement teams who cannot afford to leave money on the table.

FourCast — The Caption Factory

FourCast — The Caption Factory

Most caption pipelines are a prompt wrapper. FourCast is a caption factory with quality control. Frames are sampled straight off the video URL (ffmpeg remote seek — no full download), and Kimi K2.6 writes a strict Scene Report: subjects, actions, on-screen text, distinctive details. Facts only — if it isn't visiFireworksble, it doesn't exist. GLM 5.2 then writes three candidates per style against explicit style contracts; humorous_non_tech must additionally survive a regex jargon firewall — one tech word and the candidate dies. A separate model family (gpt-oss-120b) judges every candidate on the exact two axes the official leaderboard uses — caption accuracy and style match — picks the winner, and sends weak ones back for a critique-guided rewrite. The container is engineered for the evaluation harness: results are atomically rewritten after every clip (a timeout still leaves valid partial output), every style always gets a grounded caption (never a missing key), output JSON is ASCII-escaped, and one bad clip can't crash the batch. Three example clips process in ~55 seconds; a 12-clip hidden set fits comfortably inside the 10-minute budget. On the sample clips, our internal judge scores land at 0.85–1.00 on both axes — e.g. "Kitten boots from a stationary state, then begins a slow forward deploy toward the camera, tail raised high as a status indicator." Built on Fireworks AI, with serverless models hosted on AMD hardware.

CaptionMan

CaptionMan

CaptionMan is an evidence-first video captioning system built for AMD Developer Hackathon ACT II Track 2. Its moat is the Caption Court pipeline: the system does not simply ask a model for captions and return the first answer. It extracts visual evidence from the video, generates multi-style caption candidates, checks them for grounding and quality, repairs weak outputs, and selects a final verdict-ready caption for each requested style. The project supports the official judged Docker workflow. The public linux/amd64 image reads /input/tasks.json, writes /output/results.json, validates the Track 2 schema, and uses a temporary hackathon-only Fireworks credential for runtime inference. CaptionMan is designed for hidden judging clips, with safeguards against generic captions, filename leakage, contact-sheet artifacts, frame-count language, unfinished sentences, and overly artificial phrasing. CaptionMan also includes Gemma-aware specialist routing. Gemma is configured as part of the repair/model-routing strategy, while the production route uses a champion model setup selected for reliability on the final Docker submission. This gives the system a practical balance: strong visual understanding, polished caption writing, and a transparent repair/verdict layer that makes the final output more dependable than a basic one-shot caption generator.

OmniEdge RouteIQ — Local-First Intelligent Router

OmniEdge RouteIQ — Local-First Intelligent Router

OmniEdge RouteIQ is a general-purpose AI agent built for AMD ACT II Track 1. It processes eight benchmark categories: factual knowledge, mathematical reasoning, sentiment classification, summarization, named entity recognition, code debugging, logical reasoning, and code generation. The scoring agent runs inside a public Linux AMD64 Docker container. At runtime, it reads tasks from /input/tasks.json, uses only the models supplied through ALLOWED_MODELS, routes all requests through the evaluator-provided FIREWORKS_BASE_URL, and writes structured answers to /output/results.json while preserving each task_id. RouteIQ follows a Gemma-first model policy. When an approved Gemma 4 model is available, it is selected before permitted fallback models. The implementation avoids hardcoded credentials and model assumptions, supports evaluator-controlled API access, and writes progressive results so completed work survives interruptions. The wider project also includes a FastAPI routing backend and a Next.js command dashboard for demonstrating route decisions, provider status, simulations, metrics, and audit visibility. The benchmark container remains self-contained so automated judging can pull and run it independently.