Harisankar -S@sharisankar6618092

THE PROBLEM: Space and satellite data engineers building real-time telemetry pipelines face three severe developer workflow bottlenecks: 1. Brittle Manual DAG Wiring: Adding a new orbital, radiation, or collision-analysis model requires manually rewriting DAG dependency files and restarting the orchestration server, causing pipeline downtime. 2. Telemetry Payload Bloat: Raw satellite API streams contain massive unrequested sensor noise and redundant fields, wasting downlink bandwidth, memory, and downstream compute/token budgets. 3. Pipeline Fragility Under Load: Sudden telemetry bursts or upstream API rate limits crash static pipelines, forcing engineers to manually intervene, debug schema mismatches, and restart workers. THE SOLUTION — SPACEMESH: SpaceMesh is an autonomous, self-healing developer workflow mesh that ingests live, keyless telemetry from the International Space Station (NORAD 25544) and NASA JPL Close-Approach APIs and orchestrates processing nodes automatically. TARGET USERS & HOW THEY INTERACT: Built for space systems engineers, telemetry pipeline developers, and edge-compute architects. Developers simply drop a Python node class inheriting from `SpaceMeshNode` into the `/nodes/` directory and monitor the workflow via the SpaceMesh Live Mission Dashboard (Streamlit). Through the dashboard, users stream live NASA/ISS packets, inspect the auto-wired DAG topology, monitor stripped payload keys, view real-time maneuver advisories, and trigger live hot-plugging and burst simulations. WHAT MAKES IT CREATIVE, UNIQUE & EFFICIENT: - Zero-Restart Dynamic DAG Discovery: `MeshOrchestrator` scans `/nodes/*.py` at runtime, reads each node's `required_inputs` and `produced_outputs`, and automatically wires the execution order using Kahn's topological sort algorithm—allowing developers to hot-add new nodes (like our Geomagnetic Storm Detector) with zero server restarts. - Intelligent Node I/O Pruning: Each node runs `analyze_and_filter_io()`, stripping unrequested upstream keys and reducing raw payload size by 70.5% to 79.3% before forwarding data downstream. - Self-Healing & Auto-Scaling Runtime: SpaceMesh dynamically scales async worker replicas from 1 to 8 per node based on queue depth and latency (processing packets in ~0.1–1.0 ms) and wraps every node in a stateful Circuit Breaker (`CLOSED -> OPEN -> HALF_OPEN`) with automated fallback injection so a failing node or rate-limited API never crashes the mission pipeline.

💻 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

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.