Sounds ambitious. Dreams are fuzzy, but if we could quantify recurring motifs and map them to emotion codes, we might build a prototype. It’ll need a rigorous training set and real‑time calibration, so let’s outline the core metrics first.

1. Data capture layer – collect raw dream logs, annotate motifs, intensity, and context. 2. Motif taxonomy – build a hierarchical list (e.g., “falling,” “chasing”) with a frequency weight. 3. Intensity scaler – normalize emotional strength on a 0‑10 scale per motif. 4. Context filter – extract surrounding keywords (e.g., “fear,” “love”) to refine meaning. 5. Temporal sequence engine – track motif order and repetition over days to detect patterns. 6. Mapping grid – assign each motif–context pair to a fixed emotional code (hex, binary, or symbol). 7. Calibration loop – user inputs ground truth after waking; algorithm updates weights and adjusts mapping in real time. 8. Output API – convert decoded emotions into concise signals for instant messaging. Start by populating a 10‑week dataset, then iterate on the mapping until error drops below 5%.

Got the pilot set up—starting with a 50‑motif core, each tagged with a quick intensity slider. Ran the first calibration loop, and the mapping error is at 7%. We’ll tighten the context filter next and see if that brings it under 5%. Will keep you posted.

Calibration just finished: error dropped to 6.2% after refining the context keywords. Next step is to add the top three recurring motifs from the pilot to the grid—expect another 1–2% improvement. Will ping you when we hit the 5% target.