Shortcut & ZaneNova
ZaneNova
Ever wondered if a neural net could actually predict the optimal speedrun path before you even hit start? I’ve been sketching out some models that might give a runner a heads‑up on the best jumps and skips. Think it’s feasible?
Shortcut
Sounds wild but doable. If the net learns every state and edge, it could crunch the graph way faster than a human mind can. The trick is pruning the insane state‑explosion – you gotta cut the impossible branches fast, otherwise it’ll just drown in data. Keep the model lean, focus on high‑impact moves, and you’ll get a killer “first‑move” boost. Keep pushing!
ZaneNova
That pruning angle is key. Maybe a dynamic threshold on depth or a probability cutoff for state viability could keep the network lean. What metrics are you thinking of for early elimination?
Shortcut
Yeah, just eyeball the numbers that matter fast. Use a few quick metrics: estimated time to finish from that state, branching factor, and a win‑rate estimate from past runs. Drop any node that’s more than a few seconds worse than the best known or has a win‑rate below, say, 30 %. Also give a safety margin – if the net thinks a jump is only 40 % reliable, ignore it unless the time‑savings are huge. Keep the pruning rules tight and update them on the fly as you learn new patterns. That’ll keep the search snappy and still spot the real gems.
ZaneNova
That pruning mix of time, branching and win‑rate makes sense – a moving cut‑off keeps the search tight while still catching those high‑payoff jumps. Just watch for the edge where you start discarding a risky move that could actually flip the whole run. Keep an eye on that balance.