📡 Signal Graveyard — Dimension-10

📡 MISSION BRIEF — OPERATION: SIGNAL GRAVEYARD

The rogue AI ECHO-NULL has flooded every emergency channel with synthetic noise — burying real distress calls from ships with hull breaches, failing life support, and dead engines. The Coodie Corps built a semantic archive: each signal carries a 384-dimensional embedding computed by sentence-transformers/all-MiniLM-L6-v2. Enter a fragment — or describe the emergency you're looking for — and ScyllaDB's vector-store runs ORDER BY embedding ANN OF ? to surface semantically similar signals. A score of 0.95 means the same emergency. A score of 0.2 is ECHO-NULL's noise.

🧑‍💻 COODIE VECTOR PATTERNS

# 1. Declare a 384-dim vector column with an ANN index
class DistressSignal(Document):
    embedding: Annotated[list[float],
        Vector(dimensions=384),
        VectorIndex(similarity_function="COSINE"),
    ] = Field(default_factory=list)
    # → CREATE CUSTOM INDEX USING 'vector_index'
    #   WITH OPTIONS = {'similarity_function': 'COSINE'}

# 2. Embed with sentence-transformers, then ANN search
vec = model.encode("ship is sinking", normalize_embeddings=True)
results = await (DistressSignal.find()
    .order_by_ann("embedding", vec)
    .limit(10).all())
# finds: "hull breach", "vessel structural failure", "pressure loss"

📡 Signal Fragment Scanner

⏳ Computing embedding & scanning archive...

Try these signal fragments — click to scan:

ship is tearing apart crew cannot breathe engines are dead we are lost in space reactor meltdown imminent vessel is going down no food no water signal signal signal all is noise