Sajid Hameed@SajidHameed

We built an end-to-end AI stock signal pipeline that turns live market data and news headlines into explainable trade signals — fully accelerated on AMD Instinct MI300X via ROCm + PyTorch. The system runs three cooperating agents in an agentic workflow: 1. Signal Agent — pulls live OHLCV and headlines via yfinance, computes rolling-volatility regimes (LOW/MED/HIGH), and runs batched market inference on GPU. 2. Sentiment Agent — a fine-tuned DistilBERT classifier from Hugging Face Hub (POS/NEU/NEG with signed scores) running batched ROCm inference for thousands of headlines per second. 3. Reasoning Agent — Qwen3-8B generates a natural-language explanation for each BUY / SELL / HOLD decision, with entry, stop-loss, and target levels. A simulated execution engine then runs an OPEN-to-CLOSED trade lifecycle with slippage and P&L, surfacing win-rate, average return, and a cumulative P&L curve. The Streamlit dashboard exposes a live GPU diagnostics banner, KPI tiles, signal table, sector heatmap, sentiment analytics, and CPU-vs-GPU throughput charts. Verified on AMD Developer Cloud (ROCm 6.2 + PyTorch 2.5.1+rocm6.2): 17.0x speedup on 100-batch market pipeline, 208.0x on 1000-batch market pipeline, 14.49x on sentiment batch — all from the exact same code path that runs on CPU. AMD-first, IP-safe, judge-friendly, and built to scale to multi-GPU MI300X clusters.

AutoPilot is a full-stack platform designed to monitor, manage, and audit autonomous treasury rebalancing agents. The dashboard interface allows users to oversee critical system metrics such as active agents, queue depth, execution success rates, and error rates. By presenting each metric in an intuitive, analytics-driven UI, AutoPilot improves operational awareness and decision-making for teams managing automated treasury workflows. The platform includes dedicated sections for tasks, executions, approvals, audit logs, agent health checks, and system settings. Each module is structured to streamline oversight of automated processes while providing transparency, accountability, and performance insights. The system also includes real-time status indicators, activity trends, and agent-level health reports to ensure reliability at scale. The goal of AutoPilot is to provide a unified operational interface for teams working with autonomous financial or treasury automation agents, reducing human error while enhancing control, efficiency, and visibility across the entire automation pipeline.

We propose using the LLaMA 3.1:1B model as a local proxy server to manage caches with JSON responses. Here's how the llama model can help us do just that: Query analysis and optimization Smart data management in the cache Optimization of communication with API Create intelligent cache management policies. Enrich responses and adding a layer of security and privacy to the application. Understand user behavior and tailor data to their needs. We can also approach the problem using the cloud model, when we do not have enough RAM to be able to run llama 3.1:1B. We can then send queries from time to time to the server, which would decide on the cache hierarchy, which would be the most important, and which items would already have a deletion time.

📅 May 11–19, 2026 • May 11 – 19: Online Build Phase. • May 18 (Hybrid Build Day – Online & Onsite) • May 19 (Demos & Awards – Online & Onsite) 🌍 Hybrid event. Join online from anywhere or build onsite at the venue. 🏆 Winners will be announced live on stage at the AI & Big Data Expo North America on May 19 📍 On-site Venue (May 18–19): San Jose McEnery Convention Center, CA, USA 🤝 Join solo or with a team. 🎟 FREE AI & Big Data Expo North America ticket included for participants that are able to join the on-site phase. 🏆Compete for a $10,000 prize pool 🧑‍💻 Apply now to design, prototype, and showcase enterprise-ready innovation.

Grab your ideas, spin up compute, and start building what’s next! Start with $100 in AMD Developer Cloud credits and begin building right away. Get access to hands-on tutorials, workshops, and curated learning resources to help you build faster and go further. To unlock credits and join the hackathon, sign up for the AMD AI Developer Program. 🤝 Go solo or team up — ship something real. 💰 $21,500+ total prize pool & 1 × AMD Radeon™ AI PRO R9700 GPU up for grabs. 📍 On-site Venue (May 9-10): MindsDB SF AI Collective 3154 17th St, San Francisco, California, USA 📍On-site participation is by invitation only. Travel and accommodation expenses will not be covered. 🧑‍💻 Developers, builders, and innovators - bring your boldest ideas and let’s turn them into real AI systems on AMD.

Teams work together with multiple AI agents in shared workspaces to design, build, and launch complete AI products — from initial idea to market-ready app. 📅 February 23– March 1, 2026 ⏱️ 6 days to build and ship an end-to-end agentic AI product 🤝 Work together in shared AI workspaces where your team and AI agents collaborate in real time 🔧 Build with multiple AI agents that handle development, design, testing, and documentation together 🎯 Create everything in one place — from ideation to product to marketing materials, all by prompting and observing 🚀 Focus on real-world products that teams could actually ship and use 🏆 $3,500 in prizes + premium 3-month Complete.dev access for top teams 🧑‍💻 Join your team workspace and start building together