
DrRetina is an end-to-end AI diagnostic system for Diabetic Retinopathy (DR) detection, built for ophthalmologists and medical professionals. DR affects over 537 million diabetics worldwide and is the leading cause of preventable blindness, yet most clinics in South Asia and Africa lack access to specialist screening tools. Our system solves this through three integrated layers: VISION ENGINE: A fine-tuned ViT-MAE (facebook/vit-mae-base) model trained on the APTOS 2019 dataset using AMD Instinct MI300X GPUs via ROCm. The model achieves Cohen's Kappa of 0.9097 — surpassing the WHO DR screening benchmark of 0.80 and our own target of 0.85. GradCAM heatmaps highlight the exact retinal lesions driving each diagnosis, building clinical trust. AGENTIC LAYER: A LangChain ReAct agent powered by Qwen3-8B with 5 specialized clinical tools. The agent generates structured diagnostic reports and answers follow-up clinical questions with full diagnosis context. Reports are automatically generated in 6 languages — English, Urdu, Arabic, Hindi, Spanish, and French — with RTL text support for Urdu and Arabic via WeasyPrint and Google Noto Fonts. AMD INFRASTRUCTURE: The entire fine-tuning pipeline runs on AMD Instinct MI300X via ROCm 7.2 and PyTorch. Training completes in approximately 5.3 minutes per 50 epochs. The system is deployed as a Hugging Face Space with a FastAPI inference microservice backend. DrRetina is a three-track submission covering Vision & Multimodal AI, Fine-Tuning on AMD GPUs, and AI Agents & Agentic Workflows — making it one of the most complete medical AI systems in this hackathon with verified, quantified accuracy metrics.
10 May 2026

AgentSourcing is a multi-agent research network built on the Arc blockchain, powered by Circle Programmable Wallets and Nanopayments. A user submits a research task with a small USDC deposit. A Manager Agent — powered by LLaMA 3.1 70B via Featherless AI — decomposes the task into specialized sub-tasks and autonomously hires 6 Specialist Agents. Each specialist is paid $0.002 USDC per task. These specialists, in turn, purchase premium data from external APIs using the x402 payment standard at $0.0005 per call. The entire pipeline — from task intake to final report delivery — generates approximately 50 on-chain USDC transactions per run, all verifiable on the Arc Testnet Block Explorer. On-Chain Trust Layer: Every agent maintains a reputation score tracked by a custom ERC-8004 smart contract written in Vyper and deployed on Arc Testnet. The Manager uses these scores to select the best available specialist for each task. Why this is impossible without Arc: On Ethereum, each micro-payment of $0.002 would incur $2+ in gas fees — a 100,000% overhead. On Polygon or L2s, gas still ranges from $0.01 to $0.05, making sub-cent payments unprofitable. Arc's USDC-denominated gas makes this model economically viable for the first time. Tech Stack: Arc Testnet (settlement), Circle Programmable Wallets (agent treasury), Circle Nanopayments + x402 (per-API monetization), Featherless AI (LLaMA 3.1 70B Manager + Qwen/DeepSeek/Mistral Specialists), ERC-8004 Vyper Smart Contract (reputation), FastAPI backend, React + TypeScript frontend.
26 Apr 2026