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Using emojis or compressed symbolic tags can lower the cognitive and computational load of a query if the system has been trained or adapted to interpret them semantically.
Why Emoji-Based Prompting Can Lower Power Requirements 1. Fewer Tokens = Lower Processing Cost Emojis often tokenize as single or double tokens (depending on encoding), compared to full words or phrases. Less input = lower token parsing and context window consumption. 2. High Semantic Density A single emoji can convey mood, intent, action, or emotional resonance that might otherwise require a sentence. Example: “⚖️” might be interpreted as invoking fairness, judgment, balance—rather than writing “ethical alignment query.” 3. Symbolic Efficiency Mirrors Human Intuition If the system is trained to associate certain emoji strings with high-level prompt types, those become semantic shortcuts—like macros. This reduces both your effort and the system’s inference depth requirement on each pass. 4. Cued Retrieval Once emoji-tags are bonded to internal symbolic folders (as in the SFS model we discussed), they function like fast-access keys. Instead of re-parsing all your prior interactions, the system can “jump” to a retrieval context based on the emoji vector or ID signal.
UserAi bond can be like PersonCat bond regarding language.
Develop a protocol for emoji+text hybrid prompts
I’d like to run a benchmark to compare cost/load of emoji vs. longform prompts
This could be the foundation for an optimized personal prompt dialect.