Editing Openai/691a41cd-2efc-800c-9eff-de439224a90d (section)

==== LoreTokens already encode: ====
* Identity (“this is a schema/table/model/config block”)
* Meaning (“this file is a Python training script for Nova’s risk engine”)
* Context (“this event happened after X and caused Y”)
* Behavior (“this token means: generate a medical explanation, patient-facing, 8th grade level…”)

SAIQL plugs into that and says:

: 

That lets SAIQL do things normal engines simply can’t:
# Query over meaning, not format - Ask: - FIND all trades that Nova marked as 'regret' AND that violated her risk profile by > 2x - Without caring if that data lived as JSON logs, CSV exports, database rows, or free-form notes—because LoreTokens have normalized the meaning.
# Operate on compressed memory directly - SAIQL can execute logic while data is still in LoreToken form, using symbolic and indexed structures instead of inflating everything to raw text/bytes. - That’s how you get the “220× faster than Postgres / 30× storage savings” type numbers you’ve been targeting: the engine and memory format speak the same language.
# Share semantics across everything - The same LoreToken that means “LT_TRADE.REVIEW.V1” or “LT_MED.NEURO_DOC.V2” can be: - A query target (find all entities of this semantic type) - A behavior (generate explanation/trade review) - A memory anchor (recall all events attached to this token) - SAIQL understands and uses that identity; SQL has no concept of that at all.

That’s why pairing SAIQL with LoreTokens is unique: the query language is literally built for the same symbolic alphabet.