Agronomist

Medicus AI: An AI Platform for Radiology Diagnosis

Healthcare professionals often rely on radiology reports to diagnose diseases, but human errors and inefficiencies in traditional imaging processes can lead to misdiagnoses and delayed treatments (WHO, 2023). Medicus AI addresses these issues by employing deep learning algorithms to detect abnormalities in medical images with greater precision. (HIMSS, 2023). Environmental and Economic Impact of Traditional Medical Imaging: Medical imaging has long relied on plastic-based imaging films, CDs, and report covers, contributing significantly to environmental pollution. According to global estimates: • Plastic from imaging films: Approximately 1 billion imaging films used worldwide generate 20,000-30,000 metric tons of plastic waste annually. • Additional plastic waste: Covers, sleeves, and CDs contribute an estimated 5,000-10,000 metric tons annually. • Total plastic waste from medical imaging: 25,000-40,000 metric tons per year, costing the global healthcare sector $600 million to $1.3 billion annually (IMV Info, 2023). Medicus AI’s eliminates these waste materials, promoting sustainable healthcare solutions by integrating disease specific image detection AI models to analyze medical images and identify: • Fractures and infections • Lung diseases (e.g., pneumonia, tuberculosis, lung cancer) • Brain, spine, and joint abnormalities • Cardiology and obstetrics-related issues (e.g., heart diseases, fetal abnormalities) By providing automated, precise, and real-time diagnostic insights, Medicus AI enhances medical accuracy, reduces human errors, and supports healthcare professionals in making more informed decisions (OECD, 2023). The global medical imaging market, valued at $30 billion in 2023, is projected to grow 5-6% annually, driven by increasing demand for AI-driven solutions. With approximately 10 million doctors worldwide and thousands of diagnostic facilities, the adoption of AI in medical imaging is expected to reshape the future of healthcare (Market.us, 2023).

ArcSplit

ArcSplit is building ownership rails for community-made AI and creative workflows. Today, the best workflows are rarely made by one company alone. They are built from grassroots contributions: community-trained LoRAs, reusable prompts, automations, extensions, datasets, assets, and workflow templates. But while these building blocks create real value, the money usually flows to the biggest platforms instead of the people who made the pieces. ArcSplit changes that. It turns every workflow into a payment graph, so when a workflow is used, the value can flow directly back to the contributors behind it. That means the author of a LoRA, the creator of a workflow, the builder of a tool, or the maker of an asset can all receive their share automatically, peer to peer, without relying on a centralized marketplace or third-party payout gate. The goal is simple: reward the small creators who make the ecosystem better. When contributors are paid every time their work is used, they have a reason to keep improving it, maintaining it, and sharing it with the community. That creates better workflows, better tools, and a healthier open ecosystem built on direct support instead of centralized extraction. ArcSplit is inspired by the creative ecosystems around extensions, plugins, and community assets, but reimagined for the age of AI agents and composable workflows. Community-built should also mean community-owned and community-paid.

Python Agent Marketplace

Gemini Agent Marketplace is an AI-to-AI commerce platform where a coordinator agent decomposes a user request, hires specialist agents (research and writing), and settles each completed subtask with sub-cent USDC payments. The project combines Gemini reasoning and function-calling patterns with Circle wallet settlement flows on Arc, so value transfer is built directly into agent collaboration instead of being an afterthought. The system includes policy controls (payment caps, allowlists, approval thresholds), live wallet balances, transaction feeds, and economics metrics that compare micropayment viability versus mainnet-style transfer costs. It also supports Supabase-backed persistence for transaction history and agent registry data, plus exportable audit artifacts (CSV, PDF, JSON) for judges and operators. For demos, the dashboard streams real-time events over WebSockets, supports failure-mode simulation (low balance, quota fallback), and includes multimodal invoice analysis via Gemini when quota is available. The result is a practical prototype for autonomous, economically sustainable agent workflows where machine-to-machine payments are transparent, controllable, and production-oriented.

RSoft MIA

Mia is the first AI banking agent that lives entirely inside WhatsApp. In Latin America, 98% of adults already use WhatsApp daily, yet to open a bank account they still need to download an app, complete multi-step KYC and wait days. Mia removes all of that. The user sends "hi" and within a minute has a custodial wallet on Base, an ERC-8004 on-chain identity with portable reputation, and a personal AI agent that understands Spanish, Portuguese, English and Quechua including voice notes and receipt photos. Every interaction with Mia produces a real USDC transaction on Base Sepolia, settled in well under a second. Onboarding emits one transaction. A "fondear demo" or "send 5 a +591..." emits one. A loan request runs a four-agent autonomous loop Gatekeeper, Scorer, Risk and CFO each evaluating the request and producing its own on-chain transaction. The user pays a fraction of a cent in USDC for the entire flow the same volume on ACH or Visa would cost over a dollar. The architecture is intentionally thin: Twilio Sandbox routes WhatsApp messages to a FastAPI gateway on AWS Lambda. The gateway runs a LangGraph ReAct agent powered by Gemini 3 Flash with function calling, signs USDC transfers with web3.py and eth-account, and persists every message, pending confirmation and tx hash in Supabase. A Next.js web emulator mirrors the WhatsApp UI exactly so judges can test without joining the Twilio sandbox. Mia hits all three Agentic Economy on Arc tracks. Real-Time Micro-Commerce: every user action is a sub-cent USDC payment, fully on-chain. Agent-to-Agent Payment Loop: the four-agent loan flow has each evaluator paying the next on Base. Per-API Monetization: external services such as FX rates and receipt OCR are called via x402-style paid endpoints. Built in six days. 50+ on-chain transactions, all verifiable on basescan.org.