Muhammad Usman@m_usman708
2
Events attended
2
Submissions made
Pakistan
3+ years of experience
About me
I am an AI developer focused on building autonomous agent systems and real-world machine learning applications. I specialize in AutoML, MCP architectures, and multi-step agent workflows. Currently building AutoDS-MCP โ an autonomous data science agent that performs dataset analysis, model training using AutoGluon, generates insights, and produces automated PDF reports. Passionate about agentic AI, system design, and turning complex problems into automated intelligent solutions.
๐ค Latest Submissions
Atlas Arc: Decentralized AI Agentic Economy
`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 Circleโs 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 Circleโs nano payment infrastructure, Atlas Arc creates a transparent, scalable, and autonomous marketplace for the next generation of AI services.`
26 Apr 2026
ATLAS AI TRADING AGENT
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.
12 Apr 2026
