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Refine DuckWild project details
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src/_data/projects.json

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@@ -32,8 +32,7 @@
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"items": [
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"Traditional wildlife cameras store images locally, requiring physical retrieval",
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"Cellular models are expensive and unreliable in remote areas",
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"Most capture far more useless data than actionable insight",
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"DuckWild filters at the edge—sending structured data instead of raw images"
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"Most capture far more useless data than actionable insight"
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]
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},
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{
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"title": "Key Technical Decisions",
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"items": [
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"Reimplemented ClusterDuck in Python instead of modifying upstream C++ for maintainability and reuse",
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"Dropped hardware AI accelerators due to power cost with little benefit for solar operation",
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"Implemented fault-tolerant control software with watchdog timers and thread health monitoring to automatically detect and recover from failures, with systemd ensuring reliable system bring-up on boot and after power loss",
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"Dropped hardware AI accelerators due to power cost",
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"Motion-triggered, event-driven inference so AI only runs when needed"
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]
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},
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"Responsible for motion sensing -> camera capture pipeline",
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"Implemented a systemd service to manage dependencies, start the networking stack on boot, and automatically restart on failure, enabling reliable system bring-up",
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"Contributed to ClusterDuck packet handling and Raspberry Pi compatibility",
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"Made power and architecture decisions based on real measurements"
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"Made power and architecture decisions based on power measurements"
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]
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},
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{
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"Long-range field testing with multiple nodes",
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"Public demo video + open-source release"
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]
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},
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{
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"type": "text",
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"title": "Why This Project Matters",
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"body": "DuckWild shows how embedded systems, networking, and AI intersect in the real world. It’s not about maximizing benchmarks—it’s about making technology usable where infrastructure doesn’t exist."
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}
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]
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},

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