This is the safe demo version of our DSL prototype — a controlled preview of how LIL Quantum structures identity‑based instructions into a machine‑readable format.
Only the surface is visible; the core remains protected.
What this demo is:
A simplified Structured Expression Layer (SEL) — the safe, public-facing part of our DSL architecture.
It shows how text‑based identity expressions can be transformed into structured data blocks.
What you can do with it:
You can run a small DSL‑formatted text block and instantly see how the system maps it into four structured categories - without using Any AI
• geometry — structural identity markers
• color — chromatic attributes
• light — illumination cues
• uniform — symbolic identity codes
This lets developers and researchers explore the concept without accessing the real DSL key or mathematical engine.
Why it’s interesting:
It demonstrates a new way to encode identity — not through images or prompts, but through structured language that becomes machine‑interpretable meaning.
This demo reveals only the SEL layer; the true LIL Quantum core remains fully protected.
# LIL Quantum Identity Systems LTD © 2026
# Visual DSL Demonstration Endpoint (Safe Version)
# Contains no core DSL logic or internal mathematical structures.
from fastapi import FastAPI
from pydantic import BaseModel
import re
app = FastAPI(
title="LIL Quantum Identity Systems · Visual DSL Demo",
description="Demonstration endpoint for safe, non-core DSL parsing.",
version="1.0-demo",
contact={
"name": "LIL Quantum Identity Systems LTD",
"url": "https://lil-quantum.com"
}
)
class DSLInput(BaseModel):
dsl_block: str
class SceneOutput(BaseModel):
geometry: dict
color: dict
light: dict
uniform: dict
def safe_dsl_parser(dsl_block: str) -> SceneOutput:
has_geometry = bool(re.search(r"G_face|G_body|G_uniform", dsl_block))
has_color = bool(re.search(r"C_skin|C_hair|C_uniform", dsl_block))
has_light = bool(re.search(r"L_key|L_fill|L_shadow", dsl_block))
return SceneOutput(
geometry={"valid": has_geometry, "face": "demo_face", "body": "demo_body"},
color={"valid": has_color, "skin": "neutral", "hair": "dark"},
light={"valid": has_light, "key": "soft", "fill": "low"},
uniform={"code": "DEMO_UNIFORM"}
)
@app.post("/api/dsl/visual/parse", response_model=SceneOutput)
def parse_visual_dsl(input_data: DSLInput):
return safe_dsl_parser(input_data.dsl_block)