Muhammad Usman@m_usman708

A trustless, multi-agent AI trading swarm. We used Lang Graph for consensus to analyze market data via Llama-3. The swarm registers its identity on Base Spolia blockchain via ERC-8004 and executes simulated trades using the Kraken CLI integration.

i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo i have build a project to amazing arms from to use ai and integrate ml or apply rienforcement algo

Smart RAG PDF Reader is an intelligent document assistant designed to transform static PDF files into interactive conversational workspaces. Built for the AI Factory Hackathon using native.builder, the application solves the problem of document overload by eliminating manual reading and unreliable keyword searches. Powered by a Retrieval-Augmented Generation (RAG) architecture, the system extracts text from uploaded PDFs, chunks it semantically, and stores vector embeddings in Supabase using the pgvector extension. When a user asks a question, query embeddings are compared via Cosine Similarity to retrieve the most relevant text chunks. Grounded prompts are then passed to Groq API (Llama-3) for sub-second inference and Google Gemini API for multimodal reasoning. Key Features & Technologies: • native.builder: Core app scaffolding, glassmorphism UI components, and cloud deployment. • RAG Engine: PDF text chunking, embedding generation, and zero-hallucination grounded prompts. • Supabase Vector Storage: High-performance PostgreSQL vector indexing with pgvector. • Ultra-Fast Inference: Sub-second response times powered by Groq LPUs alongside Gemini API.

`Atlas Arc is an Economic OS for AI Agents. In traditional blockchains, high gas fees make micro-transactions for AI inference impossible. Atlas Arc solves this by integrating Circle’s USDC on the Arc L1 network, enabling settlement at a scale of $0.0001 per action. Our system features a hierarchical multi-agent architecture where "Brain" agents use a Random Forest Regression model to predict market demand and price volatility. "Executor" agents handle high-frequency settlements via Circle Developer Controlled Wallets, while "Guardian" agents maintain security using Z-Score statistical analysis to detect anomalies. The project demonstrates a 99.9% reduction in gas overhead compared to legacy L1s, proving that a machine-to-machine economy is not just possible, but economically viable. By combining Google Gemini for analytical reasoning and Circle’s nano payment infrastructure, Atlas Arc creates a transparent, scalable, and autonomous marketplace for the next generation of AI services.`

ATLAS Swarm is a paradigm shift in how AI-driven automated trading operates. Most algorithmic trading bots operate in black boxes with no verifiable accountability. ATLAS changes this by requiring a multi-agent swarm to reach strict consensus before any trade is executed. We utilized Lang Graph to orchestrate specialized agents ranging from data accumulation nodes to sentiment analysts. Once the agent network identifies a profitable trade, it relies on the ERC-8004 identity registry deployed on Base Spolia. By securely tying the execution layer to a verifiable on-chain AI identity, every trade intent becomes cryptographically signed (EIP-712). Finally, for actual execution, we natively integrated the official Kraken CLI module into our workflow to handle programmatic paper trading. Our custom dashboard visualizes this entire flow: from the real-time swarm debate log to the final verified signature. This creates a fully accountable and transparent AI trading network.

🌎 Hybrid Hackathon | 💻 Online Build: 12-17 October 2026 | On-site phase: 17-18 October 2026 To participate in the hackathon, simply click Sign up with AMD. If you are not already a member of the AMD AI Developer Program (ADP), you will need to create an account. The AMD Developer Hackathon: ACT III is a hands-on event for developers, founders, engineers and builders who want to push what’s possible with AI on real infrastructure. 📍 If approved for the on-site experience, you'll also have the opportunity to showcase your project live. Travel and accommodation will not be covered.

💻 Online Hackathon | ⏳ Build: 48 hours | 📅 September 25–27, 2026 The official IBM Bob 2.0 hackathon. Build what's next in AI-assisted development, alongside the developers and experts shaping it. ⭐ Get help from experts throughout the event 🤝 Join a team or build solo 💰 Prize pool: $12,000 🧑🏻‍💻 Register before the Kick-Off Stream to start building with Bob

🌎 Hybrid Hackathon | 💻 Online Build: September 10–16, 2026 | 📍 On-site: September 15–17, 2026 The official hackathon of AI Infra Summit — co-organized by Kisaco Research and lablab.ai. Build production-grade AI applications on the modern AI infrastructure stack, alongside 8,000+ attendees, researchers, founders, and infra leaders. 📍 On-site Venue (September 15–17): Santa Clara Convention Center 5001 Great America Pkwy, Santa Clara, CA 📍 On-site participation is by invitation only. Travel and accommodation expenses will not be covered.