Dinner at Six

Dinner at Six: a home robot cooks dinner over a ten-hour day on a small Liquid model running on a laptop, and keeps its memory outside the model. A robot can’t start a new chat. One goal at 8am: dinner for seven at 6pm. The day is one session: a scene every minute, a vegan guest, a broken dryer, an extra guest, and a power cut with the roast in an oven that has no timer. We ran the same robot through the same day four times, changing only its memory: full history, sliding window, rolling summary, and our task board: a to-do/doing/done board with take-out timers, a few memories picked by a small Liquid model, and the last few events, saved to Tinybird’s RawTree so it survives the power cut. Results, same model and 4K budget: task board 4/6 checks (prompt ≤802 tokens), sliding window 1/6, rolling summary 1/6, full history 0/6: it hit the 32K window at 15:17 and couldn’t act. The task board’s misses were the small planner misreading facts it still had; the others had lost the facts. Tech: Liquid LFM2.5-8B-A1B plans every step locally via Ollama; LFM2.5-1.2B curates memory. Tinybird RawTree stores the board and event logs, restores the robot after the power cut, and powers a live SQL dashboard. Black Forest Labs FLUX.2 draws each robot’s 6pm table from its actual actions. Three.js replays the kitchen. Every model call is cached, so the whole day replays in ~50s. https://drive.google.com/file/d/1DiGdwWWs00TEp2jGwGa3UGrEMa76elXk/view?usp=sharing