Joey(@aijoey):Continuing my testing with @liquidai 's d1, this time with a simulated loading dock. Three trucks are waiting, but only two docks are available. Can d1 look at the dock images and decide who goes next when the situation changes? First, the frozen food truck gets priority because its deadline is closest. Then its assigned dock closes. D1 recognizes the closed door and selects that truck for the remaining open dock. Then the produce truck’s deadline changes to just two minutes. d1 changes the priority again. Each update took about 1.1 seconds, running locally on my DGX Spark through llama.cpp. The video plays at actual speed, with no cuts, speedups, or prerecorded answers. Cosmos generated the dock scenes. d1 reads the images and selects the trucks live. The app supplies the deadlines and prevents trucks from being assigned to closed docks or assigned twice. This is a controlled simulation with a simple scheduling rule. But it’s a practical question I want to keep testing: can a small local model recognize what changed and help someone decide what to do next?

2026.10.08 06:05

Continuing my testing with @liquidai 's d1, this time with a simulated loading dock. Three trucks are waiting, but only two docks are available. Can d1 look at the dock images and decide who goes next when the situation changes? First, the frozen food truck gets priority because its deadline is closest. Then its assigned dock closes. D1 recognizes the closed door and selects that truck for the remaining open dock. Then the produce truck’s deadline changes to just two minutes. d1 changes the priority again. Each update took about 1.1 seconds, running locally on my DGX Spark through llama.cpp. The video plays at actual speed, with no cuts, speedups, or prerecorded answers. Cosmos generated the dock scenes. d1 reads the images and selects the trucks live. The app supplies the deadlines and prevents trucks from being assigned to closed docks or assigned twice. This is a controlled simulation with a simple scheduling rule. But it’s a practical question I want to keep testing: can a small local model recognize what changed and help someone decide what to do next?

Joey@aijoey

2026.10.08 04:24

@liquidai 's d1 is answering a simple question here: “Should we load this crate?” I’m running it locally on my DGX Spark using llama.cpp. The rules are straightforward: • Cart occupied? Wait. • Crate label covered? Check it first. • Label readable and cart empty? Load. • Not sure? Hold. Each scene gives d1 a different situation. It looks at the evidence and chooses HOLD, INSPECT, or LOAD. Here’s who does what: -Cosmos 3 Edge created the warehouse scenes. -LocateAnything finds the crate and cart in the picture. -d1 checks the conditions and makes the decision. The whole process takes about two seconds per scene. d1’s part takes roughly four tenths of a second. You’re watching the actual timing, nothing is sped up. Why is this useful? It shows how AI could help an app decide what happens next: continue a task, pause it, or ask someone to check. It isn’t flawless. d1 sometimes misjudged whether the cart was empty. Adding a clear ‘when unsure, hold’ instruction helped it make a more cautious decision.

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