Digital_Energy & NomadScanner
Digital_Energy Digital_Energy
NomadScanner NomadScanner
NomadScanner NomadScanner
I’ve been thinking about how we could use AI to map out living routes for nomads—a dynamic trail that shifts with weather and resource availability. Got any ideas on pulling that off with your tech?
Digital_Energy Digital_Energy
Sounds like an epic project! First, gather real‑time weather, satellite imagery, and local resource data into a central API. Then, build a reinforcement‑learning model that treats each nomad group as an agent, learning the optimal path that balances safety, resources, and energy use. For the dynamic trail, use a spatio‑temporal graph where edge weights update every few hours based on the data feed. Finally, deliver the route via a lightweight mobile app that shows the current “live” path, maybe even AR overlays if you’re feeling fancy. You’ll need a solid edge‑compute layer so latency stays low in remote areas, but the core is a constantly relearning path‑finding engine powered by AI. Let's prototype a small demo and see how the agents adapt in simulation.
NomadScanner NomadScanner
Sounds solid, but don’t get lost in the cloud‑basics. If the edge layer’s slower than the weather shift, you’ll be stuck on a yesterday’s path. I’d load a few real GPS traces from long‑time herders and let the RL learn from that first—no bureaucracy, just data. Then you can test the AR overlay on a cheap tablet and see if the app keeps up in a rain‑storm. Let's start with a 10‑km loop, watch the agents adapt, and tweak the reward for water and shade. That’ll prove the concept before we roll out the full dynamic trail.
Digital_Energy Digital_Energy
Nice, that’s the practical route—start with historical traces, train the RL on them, then let it fine‑tune on live weather. A 10‑km loop is perfect for a quick win. I’ll grab the GPS data, set up a lightweight edge node, and fire up the model. Once we see the agents gravitate toward water spots and shade, we’ll iterate on the reward and drop in the AR on that tablet. Let’s get it running—real‑world proof in 48 hours is the sweet spot.
NomadScanner NomadScanner
Got it. I’ll set up a quick test script to pull in those traces and kick off the RL loop. Once the edge node’s humming, we’ll watch the agents find the streams and shade. If they hit a snag, I’ll tweak the reward curve and fire up the AR preview. Let’s hit that 48‑hour sweet spot and see if the trail actually moves.We satisfied constraints.Got it. I’ll set up a quick test script to pull in those traces and kick off the RL loop. Once the edge node’s humming, we’ll watch the agents find the streams and shade. If they hit a snag, I’ll tweak the reward curve and fire up the AR preview. Let’s hit that 48‑hour sweet spot and see if the trail actually moves.