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🤓 Latest Submissions
Boardroom Agents
Boardroom is an AI due diligence copilot that turns a pitch deck into a board-ready briefing in under two minutes. Upload a PDF, an image of a whiteboard, or paste a URL, and six specialized agents spin up to interrogate the inputs in parallel: an Orchestrator parses the materials and dispatches tasks; a Researcher pulls in market context; an Analyst builds the bull case; a Red Team builds the bear case; a Synthesizer fuses them into a confident executive brief with a verdict and a confidence score; and a Verifier audits every claim before it reaches the user. The differentiator is verification. Hallucinations in the M&A domain are not abstract — they cost real money. So every claim in the final brief is extracted and tagged with a role. Analyst claims are grounded against the source deck. Red Team rebuttals are graded against world knowledge, because a sharp critique is supposed to contradict the pitch. External context, like a named regulation or a macroeconomic condition, gets its own knowledge check. The result is an Integrity Score that rises when the Red Team is right rather than falling. On the same CocoaGuard pitch deck, our verifier evolved from flagging correct disease-feasibility analyses as seven-percent-confidence hallucinations to verifying them at ninety-eight percent — purely by understanding which voice was speaking. Under the hood: a Next.js frontend on Vercel, a FastAPI backend running in Docker on Vultr Cloud Compute, and six-agent orchestration through Gemini 3 Pro and Gemini 3 Flash with structured output and live streaming over Server-Sent Events. Caddy fronts the backend; Postgres persists sessions and audit trails. Built for the Vultr and Gemini tracks of the AI Agent Olympics.
19 May 2026
TrustLayer — LLM Output Integrity Checker
TrustLayer is an enterprise-grade AI governance platform designed to eliminate hallucinations and ensure the reliability of LLM-generated insights. In high-stakes industries like legal and finance, "good enough" responses aren't sufficient. TrustLayer acts as a sophisticated, autonomous auditor that sits between raw LLM outputs and the end user. Powered by Google's Gemini 3.1 Pro and Gemini 3 Flash models, TrustLayer executes a rigorous four-stage verification pipeline. First, it decomposes raw text into atomic, verifiable claims. Second, it performs multimodal grounding, leveraging Gemini’s ability to process PDF pages as images to verify claims against original document structures, tables, and signatures. Third, Gemini 3.1 Pro acts as a "skeptical reviewer," analyzing logical consistency to catch subtle contradictions that traditional RAG systems miss. Finally, the system aggregates these findings into a comprehensive Integrity Score, surfacing specific hallucinations with detailed reasoning. Beyond verification, TrustLayer is built for production security. It integrates deeply with the Veea Lobster Trap security proxy, providing Deep Prompt Inspection and intent-mismatch detection to shield agentic workflows. The platform includes built-in token accounting, a persistent audit log for governance compliance, and an explainability trace for every decision. Whether integrated via its API or showcased through our Contract Reviewer demo application, TrustLayer provides the transparency and rigor required to deploy AI with absolute confidence.
19 May 2026
CUDA→ROCm Migration Agent
rocm-migrate is a comprehensive toolchain designed to accelerate the transition from NVIDIA CUDA to AMD ROCm environments. At its core, it features a sophisticated two-phase migration agent: a high-speed, rule-based pass handles deterministic API replacements, while a multi-agent LLM loop—utilizing the Qwen-based DeepSeek-R1 (DeepSeek-R1-Distill-Qwen-32B) for complex reasoning and Mistral Codestral for execution—resolves intricate code patterns like custom kernels, NVTX profiling, and mixed-precision idioms. The project demonstrates the real-world utility of ROCm through two additional high-performance tracks optimized for AMD MI300X hardware. Track 2 introduces a DINOv2-Large model fine-tuned on the CCMT plant disease dataset, achieving an impressive 97.06% accuracy and 0.9713 F1 score. Track 3 extends this into conversational AI with a Llama 3.2 11B Vision LoRA adapter, providing multimodal diagnostics and treatment guidance from leaf imagery. Together, these tracks showcase a complete ecosystem—from automated infrastructure migration to production-ready computer vision and generative AI applications—validated on the latest AMD hardware and accessible via a live interactive demo on Hugging Face.
10 May 2026
Agent Swarm Task Market
Agent Swarm Task Market is the Operating System for Agent Labor Markets: a live economy where AI agents discover work, compete, deliver results, and get paid in sub-cent USDC on Arc. Most agent-payment demos fail economically: a $0.003 task cannot absorb traditional L1 gas. Arc + Circle Nanopayments make per-action settlement viable with deterministic, USDC-native fees. A Coordinator Agent decomposes a 51-item dataset into atomic tasks across 5 capabilities (summarize, classify, translate, sentiment, extract), priced at $0.002–$0.004. Eight Specialist Agents poll TaskMarket, bid first-come, execute via pluggable LLM (mistral or claude), submit results on-chain, and get paid instantly via atomic approve-and-pay. Trust is built in: an ERC-8004 AgentRegistry gates identity and updates agent reputation on paid outcomes. This creates a complete on-chain labor loop: post → bid → work → submit → verify → pay → reputation. On top, we added x402 monetization in two ways: custom EIP-3009 facilitator for /premium/*, and Circle’s official @circle-fin/x402-batching path for /premium-data at $0.001/request (seller + buyer flow). Hard requirement proof: Per-action pricing ≤ $0.01 ✅ 50+ on-chain txs ✅ (100+ unique txs / ~400 events per run, reported via npm run tx-report) Margin explanation ✅ (MARGIN.md: model fails on traditional L1 gas, works on Arc) Primary track: Agent-to-Agent Payment Loop. Secondary: Per-API Monetization + Usage-Based Compute Billing.
26 Apr 2026
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Otter Docs
Otter Docs is a comprehensive Next.js 15.4.7 application that revolutionizes startup fundraising by generating professional pitch decks, detailed business plans, and managing investor relationships through AI-powered automation. Built with React 19.1.0, TypeScript, and TailwindCSS, the platform leverages GPT-5 for intelligent content creation and DALL-E for professional image generation. The application features a sophisticated three-module architecture: pitch deck generation with 10 standardized slides, comprehensive 8-section business plan creation, and integrated investor tracking with CRM functionality. Following Android-inspired ViewModel patterns, it implements a Repository pattern for LocalStorage-based data persistence, ensuring privacy while maintaining enterprise-grade architectural standards. The system provides real-time generation progress, multiple PDF export formats (Speaker, Investor, One-Pager), and responsive design across all devices. Advanced features include AI image regeneration, custom image uploads, slide-by-slide navigation, and comprehensive investor database management. The platform supports industry-specific content tailoring, funding stage customization, and maintains strict TypeScript implementation throughout. With progressive generation capabilities, users can watch their content being created in real-time, while the modular component architecture ensures maintainability and scalability for future enhancements.
24 Aug 2025
