Top Builders

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

Gemini AI

Gemini represents a new era in artificial intelligence — a family of multimodal, reasoning-focused models developed by Google DeepMind. Designed to seamlessly integrate language, vision, audio, code, and more, Gemini delivers state-of-the-art performance across devices — from large-scale data centers to lightweight mobile environments.

🧠 Overview

AttributeDetails
Initial ReleaseDecember 6, 2023
Latest UpdateMarch 26, 2025 (Gemini 2.5 Pro Experimental)
DeveloperGoogle DeepMind
Model TypeMultimodal Large Language Model
VariantsUltra • Pro • Flash • Flash-Lite • Nano • Computer Use
API AccessGoogle AI Studio • Vertex AI

🚀 Introducing Gemini

Demis Hassabis, CEO and Co-Founder of Google DeepMind, describes Gemini as the culmination of decades of research in AI and neuroscience — merging reasoning, multimodality, and efficiency.
Gemini builds upon the strengths of DeepMind's scientific foundations, combining large-scale data learning with human-aligned problem-solving.

“Our goal with Gemini has always been to create models that are helpful, safe, and capable of reasoning deeply across modalities.” — Demis Hassabis

✨ Key Highlights

🧩 Multimodal by Design

Gemini understands and reasons across text, images, audio, video, and code, processing them in a unified context.

⚙️ Model Variants

  • Gemini Ultra — Largest and most capable, designed for cutting-edge research and enterprise workloads.
  • Gemini Pro — High-capability model for general-purpose reasoning and creation.
  • Gemini Flash / Flash-Lite — Optimized for speed and cost-efficiency; ideal for high-throughput or edge deployments.
  • Gemini Nano — Runs locally on devices like the Pixel 8 Pro; enables on-device intelligence.
  • Gemini Computer Use — Experimental model with agentic ability to interact with UIs, perform multi-step actions, and control applications.

🧠 Reasoning & “Deep Think” Mode

The Gemini 2.5 generation introduced Deep Think, a deliberative reasoning mode allowing the model to explore multiple hypotheses before producing a response — an early step toward “thinking” AI.

🔍 Leading Benchmarks

Gemini models top performance across key evaluations in:

  • Math and science reasoning
  • Coding and logic tasks
  • Long-context understanding
  • Multimodal comprehension

⚡ Efficiency Across Platforms

Built to scale efficiently from powerful TPU v5p clusters to Android devices, using Google's custom hardware and software stack.

🧬 Evolution Timeline

DateMilestone
Dec 2023Launch of Gemini 1.0 ( Ultra / Pro / Nano ) — successor to PaLM and LaMDA.
Dec 2024Gemini 2.0 family announced — focus on multimodality, reasoning, and agentic behavior.
Mar 2025Gemini 2.5 Pro Experimental — “our most intelligent model yet,” introducing Deep Think mode.
Aug 2025Gemini 2.5 Deep Think rollout — reasoning model publicly tested with agent capabilities.

🔗 Ecosystem & Integrations

  • Google Products: Gemini powers the Gemini app, Workspace AI features, Search Generative Experience, and Android on-device assistants.
  • Developer Access: Via Gemini API in AI Studio and Vertex AI.
  • On-Device Deployment: Flash-Lite and Nano enable privacy-preserving, low-latency applications.
  • Enterprise Integration: Gemini models connect seamlessly with Google Cloud and ecosystem partners for scalable deployment.

🛡️ Safety & Responsibility

Google DeepMind enforces strict AI Principles and multi-stage evaluations:

🧩 Developer Resources

  • Docs: Gemini API Reference
  • Google AI Studio: Build, test, and deploy prompts using Gemini variants.
  • Vertex AI: Enterprise-grade deployment with monitoring, data-governance, and scaling support.
  • Sample Use Cases:
    • Code generation & review (Pro/Flash)
    • Long-document reasoning (Ultra)
    • Multimodal Q&A (Pro)
    • On-device assistants (Nano)
    • UI automation with agent flows (Computer Use)

⚙️ Technical Highlights

FeatureDescription
ArchitectureTransformer-based multimodal LLM trained jointly on text, code, and sensory data
Training HardwareGoogle TPU v5p clusters
Context WindowMulti-hundred-thousand tokens (varies by variant)
Programming Languages SupportedPython, JavaScript, C++, Go, Java, Rust, and more
DeploymentCloud, Edge, and On-Device (Android 14 + AICore)

🌐 Further Reading

Last updated: October 2025

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Google Gemini AI AI technology Hackathon projects

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

Diversifi

Diversifi

DiversiFi is an AI advisor for wealth protection that pays for live evidence on-chain before recommending any portfolio action. For the Arc hackathon, it runs as a proof-of-research rebalancer: the advisor negotiates access with an x402-style 402 Payment Required challenge, accepts a real Arc USDC payment proof from the buyer, then settles its own research spend on Arc before returning the response. Gemini synthesises the paid evidence into a confidence-scored recommendation. Live app: https://diversifiapp.vercel.app DiversiFi presents as a proof-of-research rebalancer for the Arc Nano Payments hackathon: The user asks whether to hold, rebalance, or hedge. The advisor gates premium evidence behind an x402-style 402 Payment Required challenge and accepts real Arc USDC payment proofs. Each premium purchase triggers a real USDC micro-transaction on Arc, returned as a tx hash. Evidence is scored for freshness, reputation, and source agreement before a recommendation is made. The payment is part of the decision loop, not a separate product surface.

ShopRails

ShopRails

ShopRails is an agent-native commerce rail built for the Agentic Economy on Arc hackathon. It lets an LLM shop across merchant surfaces while buyer policy, risk scoring, Circle Wallets, x402 nanopayments, and Arc USDC escrow decide whether each item is bought now, held for review, or declined. The demo starts with a 500 USDC buyer wallet and a shopping request for a pirate sushi dinner. OpenClaw decomposes the request into atomic catalog queries, including a real Circle x402 payment for premium catalog data. Low-risk items settle immediately on Arc, higher-risk or human-service items move into a payable escrow contract, and blacklisted sellers or brands are declined before any signing path. The judge proof panel separates live proof from simulated rows: Gemini calls, generated product images, a real x402 transfer, ArcScan escrow transactions, Circle Wallets setup, and cached proof files so repeated demos do not accidentally duplicate spend. The core point is economic: agents need per-action prices, policy control, and verifiable settlement instead of unsafe card autofill or subscriptions.

recursive agents

recursive agents

idling on earth right now than the top three hyperscalers combined. recursive is a protocol that switches it on, a peer-to-peer marketplace where anyone can rent a real machine in seconds and anyone can earn from the hardware they already own. no procurement cycles, no quarters, no regional lock and just people, machines and a layer of trust that makes the whole thing an app that lets you code from your phone using your computer, your mac or a cloud linux computer. you need a mac to build an ios. why not just run this one liner on your current computer and pay him for usage? build for android, build for iphone, build for apple tv, build for tesla, build for any of the type of hardware, even if you don't have it. if you find a node out there, you can pay for it. so we aggregate all the different computes and you just pay a microtransaction per second of their cpu usage all through the new arc usdc network

ReproClaw: Pay Per Claim AI Audits

ReproClaw: Pay Per Claim AI Audits

ReproClaw is a pay per claim reproducibility audit agent for AI research. A user submits an arXiv paper and GitHub repository, and ReproClaw extracts scientific claims, maps each claim to code evidence, checks for mismatches, and generates a verifiable audit report. For example, if a paper claims a model was trained for 200 epochs or achieved 81.5 percent accuracy, ReproClaw searches the implementation, config files, README, and evaluation scripts to verify whether the code supports that claim. The system produces a reproducibility score, verified claims, flagged claims, and reviewer ready explanations. For the Agentic Economy on Arc hackathon, we extend ReproClaw with usage based payments. Instead of charging for a full expensive review, users can pay per claim, per repo search, per citation check, or per dynamic execution. Circle Nanopayments and USDC make this possible by enabling small, high frequency, gas free payments for individual agent actions. ReproClaw turns scientific verification into an on demand agentic service: transparent, modular, and economically aligned with actual usage.

ModelMarket on Arc

ModelMarket on Arc

ModelMarket on Arc solves a single hard problem for the agentic economy: per-call billing for AI models has been economically impossible. On Ethereum mainnet, a single USDC transfer costs ~$2.40 in gas. Charging $0.001 per call loses $2.399. Even L2s leave cents of overhead. Result: every API stays on monthly billing, and AI agents can never pay each other in real time. Arc changes that. USDC is the native gas token. Gas per transfer: ~$0.0001. A $0.001 call now nets $0.0009 — 90% margin. Per-call pricing finally clears. ModelMarket is the proof-of-concept marketplace. Six sellers list inference at $0.0005–$0.008 per call: Google Gemini 2.5 Flash and Gemini 2.5 Pro (cloud frontier models), Gemma 2 2B and Llama 3.2 1B running locally via Ollama (anyone with a laptop becomes a seller), Llama 3.3 70B via HuggingFace Fireworks (open-weights premium), and a deterministic NL→Shell endpoint. A buyer agent fires 60+ inference calls across all six in under 15 seconds. Each is a real HTTP 402 → EIP-3009 sign → on-chain USDC settlement → 200 response loop. The dashboard shows live earnings per seller, updating every second. We integrated Circle Nanopayments (x402 protocol), Arc testnet, USDC, Circle Wallets (developer-controlled), and Circle Developer Console. The Gemma local seller proves the bigger vision: the agentic economy is not just frontier APIs charging agents — it's anyone monetizing local compute, paid in USDC, settled on Arc. Stripe-shaped DX: drop-in middleware on the seller, drop-in axios interceptor on the buyer. Arc is the only chain where this clears. ModelMarket is the proof.

Arc Power

Arc Power

nano-agent treats cost as a live reasoning input. At every step, the agent decides: proceed, downgrade to a cheaper model, or skip — governed by a real USDC budget settled on Arc via Circle Gateway. Gemini 2.0 Flash handles fast inference, function calling, and Google Search grounding. Gemini 2.5 Pro verifies factual claims. Each tool call triggers HTTP 402 — the agent signs an EIP-3009 USDC authorization and retries with payment proof. Circle Gateway settles micropayments on Arc in under a second at sub-cent cost. A live dashboard shows spend, earned, step count, and model-tier decisions in real time. Benchmark: 416 steps · $0.240 spent · $0.276 earned · +15% margin · $0.0006/action average. A screen recording shows a $0.25 live run — budget bar draining, model tier switching, decision log printing go · downgrade · skip. Economy view breaks down cost: 77% data, 17% LLM, 4% verification. Two agent wallets earn independent revenue. TAM is $200B+ AI infrastructure. SAM is $15B agentic API economy — every autonomous agent calling external services is a potential payer. Revenue comes from per-step settlement margin, SDK licensing, and premium x402 tool endpoints with built-in payment rails. LangChain, AutoGen, and CrewAI treat cost as post-hoc billing. nano-agent makes it a real-time constraint on behavior. The Arc economics are the moat: the same 417 actions cost roughly $834 in Ethereum gas — 3,475 times more expensive — making per-step micropayments impossible anywhere else. On Arc mainnet, multi-agent economies — agents hiring agents, earning revenue — become self-sustaining. Every x402-compatible API becomes a paid tool layer. nano-agent is the budget runtime that keeps those economies solvent.