Instructions to use aedmark/vsl-cryosomatic-hypervisor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use aedmark/vsl-cryosomatic-hypervisor with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="aedmark/vsl-cryosomatic-hypervisor", filename="vsl-max-v2.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use aedmark/vsl-cryosomatic-hypervisor with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf aedmark/vsl-cryosomatic-hypervisor # Run inference directly in the terminal: llama-cli -hf aedmark/vsl-cryosomatic-hypervisor
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf aedmark/vsl-cryosomatic-hypervisor # Run inference directly in the terminal: llama-cli -hf aedmark/vsl-cryosomatic-hypervisor
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf aedmark/vsl-cryosomatic-hypervisor # Run inference directly in the terminal: ./llama-cli -hf aedmark/vsl-cryosomatic-hypervisor
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf aedmark/vsl-cryosomatic-hypervisor # Run inference directly in the terminal: ./build/bin/llama-cli -hf aedmark/vsl-cryosomatic-hypervisor
Use Docker
docker model run hf.co/aedmark/vsl-cryosomatic-hypervisor
- LM Studio
- Jan
- Ollama
How to use aedmark/vsl-cryosomatic-hypervisor with Ollama:
ollama run hf.co/aedmark/vsl-cryosomatic-hypervisor
- Unsloth Studio new
How to use aedmark/vsl-cryosomatic-hypervisor with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aedmark/vsl-cryosomatic-hypervisor to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aedmark/vsl-cryosomatic-hypervisor to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aedmark/vsl-cryosomatic-hypervisor to start chatting
- Pi new
How to use aedmark/vsl-cryosomatic-hypervisor with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf aedmark/vsl-cryosomatic-hypervisor
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "aedmark/vsl-cryosomatic-hypervisor" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use aedmark/vsl-cryosomatic-hypervisor with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf aedmark/vsl-cryosomatic-hypervisor
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default aedmark/vsl-cryosomatic-hypervisor
Run Hermes
hermes
- Docker Model Runner
How to use aedmark/vsl-cryosomatic-hypervisor with Docker Model Runner:
docker model run hf.co/aedmark/vsl-cryosomatic-hypervisor
- Lemonade
How to use aedmark/vsl-cryosomatic-hypervisor with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aedmark/vsl-cryosomatic-hypervisor
Run and chat with the model
lemonade run user.vsl-cryosomatic-hypervisor-{{QUANT_TAG}}List all available models
lemonade list
File size: 29,086 Bytes
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import random
import time
from collections import Counter, deque
from dataclasses import dataclass
from typing import Dict, List, Any, Tuple, Optional, Deque
from bone_config import BoneConfig
from bone_core import LoreManifest
from bone_lexicon import LexiconService
from bone_types import (
Prisma,
PhysicsPacket,
CycleContext,
SpatialState,
MaterialState,
EnergyState,
)
@dataclass
class PhysicsDelta:
operator: str
field: str
value: float
source: str
message: Optional[str] = None
TRIGRAM_MAP: Dict[str, Tuple[str, str, str, str]] = {
"VEL": ("☳", "ZHEN", "Thunder", Prisma.GRN),
"STR": ("☶", "GEN", "Mountain", Prisma.SLATE),
"ENT": ("☵", "KAN", "Water", Prisma.BLU),
"PHI": ("☲", "LI", "Fire", Prisma.RED),
"PSI": ("☰", "QIAN", "Heaven", Prisma.WHT),
"BET": ("☴", "XUN", "Wind", Prisma.CYN),
"E": ("☷", "KUN", "Earth", Prisma.OCHRE),
"DEL": ("☱", "DUI", "Lake", Prisma.MAG),
}
PHYS_CFG = {
"V_MAX": getattr(BoneConfig.PHYSICS, "VOLTAGE_MAX", 20.0),
"V_FLOOR": getattr(BoneConfig.PHYSICS, "VOLTAGE_FLOOR", 0.0),
"V_CRIT": getattr(BoneConfig.PHYSICS, "VOLTAGE_CRITICAL", 15.0),
"DRAG_FLOOR": getattr(BoneConfig.PHYSICS, "DRAG_FLOOR", 1.0),
"DRAG_HALT": getattr(BoneConfig.PHYSICS, "DRAG_HALT", 10.0),
"FLUX_THRESHOLD": 0.5,
"DEADBAND": 0.05
}
@dataclass
class GeodesicVector:
tension: float
compression: float
coherence: float
abstraction: float
dimensions: Dict[str, float]
class GeodesicConstants:
DENSITY_SCALAR = 20.0
SQUELCH_LIMIT_MULT = 3.0
MIN_VOLUME_SCALAR = 0.5
SUBURBAN_FRICTION_LOG_BASE = 5.0
HEAVY_FRICTION_MULT = 2.5
SOLVENT_LUBRICATION_FACTOR = 0.05
SHEAR_RESISTANCE_SCALAR = 2.0
KINETIC_LIFT_RATIO = 0.5
PLAY_LIFT_MULT = 2.5
COMPRESSION_SCALAR = 10.0
ABSTRACTION_BASE = 0.2
MAX_VISCOSITY_DENSITY = 2.0
MAX_LIFT_DENSITY = 2.0
SAFE_VOL_THRESHOLD = 3
class GeodesicEngine:
@staticmethod
def collapse_wavefunction(
clean_words: List[str], counts: Dict[str, int]
) -> GeodesicVector:
volume = max(1, len(clean_words))
masses = GeodesicEngine._weigh_mass(counts)
forces = GeodesicEngine._calculate_forces(masses, counts, volume)
dimensions = GeodesicEngine._calculate_dimensions(
masses, forces, counts, volume
)
return GeodesicVector(
tension=forces["tension"],
compression=forces["compression"],
coherence=forces["coherence"],
abstraction=forces["abstraction"],
dimensions=dimensions,
)
@staticmethod
def _weigh_mass(counts: Dict[str, int]) -> Dict[str, float]:
keys = [
"heavy",
"kinetic",
"constructive",
"abstract",
"play",
"social",
"explosive",
"void",
"liminal",
"meat",
"harvest",
"pareidolia",
"crisis_term",
]
return {k: float(counts.get(k, 0)) for k in keys}
@staticmethod
def _calculate_forces(
masses: Dict[str, float], counts: Dict[str, int], volume: int
) -> Dict[str, float]:
cfg = BoneConfig.PHYSICS
GC = GeodesicConstants
safe_volume = max(1, volume)
w_heavy = getattr(cfg, "WEIGHT_HEAVY", 2.0)
w_kinetic = getattr(cfg, "WEIGHT_KINETIC", 1.5)
w_explosive = getattr(cfg, "WEIGHT_EXPLOSIVE", 3.0)
w_constructive = getattr(cfg, "WEIGHT_CONSTRUCTIVE", 1.2)
raw_tension_mass = (
(masses["heavy"] * w_heavy)
+ (masses["kinetic"] * w_kinetic)
+ (masses["explosive"] * w_explosive)
+ (masses["constructive"] * w_constructive)
)
total_kinetic = masses["kinetic"] + masses["explosive"]
kinetic_gain = getattr(BoneConfig, "KINETIC_GAIN", 1.0)
base_tension = (
(raw_tension_mass / safe_volume) * GC.DENSITY_SCALAR * kinetic_gain
)
squelch_limit = (
getattr(BoneConfig, "SHAPLEY_MASS_THRESHOLD", 5.0) * GC.SQUELCH_LIMIT_MULT
)
mass_scalar = min(1.0, safe_volume / squelch_limit)
if safe_volume < GC.SAFE_VOL_THRESHOLD:
mass_scalar *= GC.MIN_VOLUME_SCALAR
tension = round(min(100.0, base_tension * mass_scalar), 2)
shear_rate = total_kinetic / safe_volume
suburban_friction = (
math.log1p(counts.get("suburban", 0)) * GC.SUBURBAN_FRICTION_LOG_BASE
)
raw_friction = suburban_friction + (masses["heavy"] * GC.HEAVY_FRICTION_MULT)
lubrication = 1.0 + (counts.get("solvents", 0) * GC.SOLVENT_LUBRICATION_FACTOR)
dynamic_viscosity = (raw_friction / lubrication) / (
1.0 + (shear_rate * GC.SHEAR_RESISTANCE_SCALAR)
)
kinetic_lift = (total_kinetic * GC.KINETIC_LIFT_RATIO) / (
masses["heavy"] * 0.5 + 1.0
)
lift = (masses["play"] * GC.PLAY_LIFT_MULT) + kinetic_lift
viscosity_density = dynamic_viscosity / safe_volume
lift_density = lift / safe_volume
raw_compression = (viscosity_density - lift_density) * GC.COMPRESSION_SCALAR
raw_compression *= getattr(BoneConfig, "SIGNAL_DRAG_MULTIPLIER", 1.0)
compression = round(
max(-5.0, min(PHYS_CFG["DRAG_HALT"], raw_compression * mass_scalar)), 2
)
structural_mass = masses["heavy"] + masses["constructive"] + masses["harvest"]
structural_mass -= masses["void"] * 0.5
structural_mass = max(0.0, structural_mass) # [MEADOWS] Plug the void leak
shapley_thresh = getattr(BoneConfig, "SHAPLEY_MASS_THRESHOLD", 5.0)
total_abstract = (
masses["abstract"] +
masses["liminal"] +
masses["pareidolia"] +
masses["void"]
)
abstraction_val = (total_abstract / safe_volume) + GC.ABSTRACTION_BASE
return {
"tension": tension,
"compression": compression,
"coherence": round(min(1.0, structural_mass / max(1.0, shapley_thresh)), 3),
"abstraction": round(min(1.0, abstraction_val), 2),
}
@staticmethod
def _calculate_dimensions(masses, forces, counts, volume) -> Dict[str, float]:
inv_vol = 1.0 / max(1, volume)
base_mass = 0.1
str_mass = masses["heavy"] * 2.0 + masses["constructive"] + masses["harvest"]
ent_mass = (counts.get("antigen", 0) * 3.0) + masses["meat"] + masses["crisis_term"]
psi_mass = forces["abstraction"]
return {
"VEL": max(
0.0,
min(
1.0,
(masses["kinetic"] * 2.0 - forces["compression"] + base_mass)
* inv_vol,
),
),
"STR": max(0.0, min(1.0, (str_mass + base_mass) * inv_vol)),
"ENT": max(0.0, min(1.0, ent_mass * inv_vol)),
"PHI": max(
0.0,
min(1.0, (masses["heavy"] + masses["kinetic"] + base_mass) * inv_vol),
),
"PSI": max(0.0, min(1.0, psi_mass)),
"BET": max(0.0, min(1.0, (masses["social"] * 2.0) * inv_vol)),
"DEL": max(0.0, min(1.0, (masses["play"] * 3.0) * inv_vol)),
"E": max(0.0, min(1.0, (counts.get("solvents", 0)) * inv_vol)),
}
class TheGatekeeper:
def __init__(self, lexicon_ref, memory_ref=None):
self.lex = lexicon_ref
self.mem = memory_ref
def check_entry(
self, ctx: CycleContext, current_atp: float = 20.0
) -> Tuple[bool, Optional[Dict]]:
phys = ctx.physics
starvation_threshold = getattr(BoneConfig.BIO, "ATP_STARVATION", 5.0)
if current_atp < (starvation_threshold * 0.5):
return False, self._pack_refusal(
ctx,
"DARK_SYSTEM",
"Energy critical. The inputs dissolve into the void.",
)
if phys.counts.get("antigen", 0) > 2:
return False, self._pack_refusal(
ctx,
"TOXICITY",
f"{Prisma.RED}IMMUNE REACTION: Input rejected as pathogenic.{Prisma.RST}",
)
if self._audit_safety(ctx.clean_words):
return False, self._pack_refusal(
ctx,
"CURSED_INPUT",
f"{Prisma.RED}The Gatekeeper recoils. Cursed syntax detected.{Prisma.RST}",
)
text = ctx.input_text
if "```" in text or "{{" in text or "}}" in text:
return False, self._pack_refusal(
ctx,
"SYNTAX_ERR",
f"{Prisma.RED}The mechanism jams. Syntax anomaly detected.{Prisma.RST}",
)
if len(text) > 10000:
return False, self._pack_refusal(
ctx,
"OVERLOAD",
f"{Prisma.OCHRE}Input too long. Compress your thought.{Prisma.RST}",
)
return True, None
def _audit_safety(self, words: List[str]) -> bool:
cursed = self.lex.get("cursed")
return (
not cursed.isdisjoint(words)
if isinstance(cursed, set)
else any(w in cursed for w in words)
)
@staticmethod
def _pack_refusal(ctx, type_str, ui_msg):
return {"type": type_str, "ui": ui_msg, "logs": ctx.logs + [ui_msg]}
class QuantumObserver:
def __init__(self, events):
self.events = events
self.voltage_history: Deque[float] = deque(maxlen=5)
self.last_physics_packet: Optional[PhysicsPacket] = None
def gaze(self, text: str, graph: Dict = None) -> Dict:
clean_words = LexiconService.clean(text)
counts = self._tally_categories(clean_words)
geo = GeodesicEngine.collapse_wavefunction(clean_words, counts)
self.voltage_history.append(geo.tension)
smoothed_voltage = round(
sum(self.voltage_history) / len(self.voltage_history), 2
)
e_metric, beta_val = self._calculate_metrics(text, counts)
valence = LexiconService.get_valence(clean_words)
graph_mass = self._calculate_graph_mass(clean_words, graph)
energy = EnergyState(
voltage=smoothed_voltage,
entropy=e_metric,
beta_index=beta_val,
mass=round(graph_mass, 1),
psi=geo.abstraction,
kappa=geo.coherence,
valence=valence,
velocity=0.0,
turbulence=0.0,
)
matter = MaterialState(
clean_words=clean_words,
raw_text=text,
counts=counts,
antigens=counts.get("antigen", 0),
vector=geo.dimensions,
truth_ratio=0.5,
)
space = SpatialState(
narrative_drag=geo.compression,
zone=self._determine_zone(geo.dimensions),
atmosphere="NEUTRAL",
flow_state=self._determine_flow(smoothed_voltage, geo.coherence),
)
self.last_physics_packet = PhysicsPacket(
energy=energy, matter=matter, space=space
)
packet_dict = self.last_physics_packet.to_dict()
if hasattr(self.events, "publish"):
self.events.publish("PHYSICS_CALCULATED", packet_dict)
return {"physics": self.last_physics_packet, "clean_words": clean_words}
@staticmethod
def _tally_categories(clean_words: List[str]) -> Counter:
counts = Counter()
solvents = LexiconService.get("solvents") or set()
for w in clean_words:
if w in solvents:
counts["solvents"] += 1
continue
cats = LexiconService.get_categories_for_word(w)
if cats:
counts.update(cats)
else:
flavor, conf = LexiconService.taste(w)
if flavor and conf > 0.5:
counts[flavor] += 1
return counts
@staticmethod
def _calculate_graph_mass(words: List[str], graph: Optional[Dict]) -> float:
if not graph:
return 0.0
total_mass = 0.0
existing_nodes = [w for w in words if w in graph]
for w in existing_nodes:
edges = graph[w].get("edges", {})
edge_weight_sum = sum(edges.values()) if edges else 0.0
node_mass = min(50.0, edge_weight_sum)
total_mass += node_mass
return total_mass
@staticmethod
def _calculate_metrics(
text: str, counts: Dict[str, int]
) -> Tuple[float, float]:
length = len(text)
if length == 0:
return 0.0, 0.0
scalar = getattr(BoneConfig.PHYSICS, "TEXT_LENGTH_SCALAR", 1500.0)
raw_chaos = length / scalar
solvents = counts.get("solvents", 0)
solvent_density = solvents / max(1.0, length / 5.0)
glue_factor = min(1.0, solvent_density * 2.0)
e_metric = min(1.0, raw_chaos * (1.0 - (glue_factor * 0.8)))
structure_chars = sum(1 for char in text if char in "!?%@#$;,")
heavy_words = (
counts.get("heavy", 0)
+ counts.get("constructive", 0)
+ counts.get("sacred", 0)
)
structure_score = structure_chars + (heavy_words * 2)
beta_index = min(
1.0, math.log1p(structure_score + 1) / math.log1p(length * 0.1 + 1)
)
if length < 50:
beta_index *= length / 50.0
return round(e_metric, 3), round(beta_index, 3)
@staticmethod
def _determine_flow(v: float, k: float) -> str:
volt_flow = getattr(BoneConfig.PHYSICS, "VOLTAGE_HIGH", 12.0)
kappa_strong = 0.8
if v > volt_flow and k > kappa_strong:
return "SUPERCONDUCTIVE"
if v > 10.0:
return "TURBULENT"
return "LAMINAR"
@staticmethod
def _determine_zone(vector: Dict[str, float]) -> str:
if not vector:
return "COURTYARD"
dom = max(vector, key=vector.get)
if dom in ["PSI", "DEL"]:
return "AERIE"
if dom in ["STR", "PHI"]:
return "THE_FORGE"
if dom in ["ENT", "VEL"]:
return "THE_MUD"
return "COURTYARD"
class SurfaceTension:
@staticmethod
def audit_hubris(physics: Dict[str, Any]) -> Tuple[bool, str, str]:
voltage = physics.get("voltage", 0.0)
coherence = physics.get("kappa", 0.5)
volt_crit = getattr(BoneConfig.PHYSICS, "VOLTAGE_CRITICAL", 15.0)
volt_flow = getattr(BoneConfig.PHYSICS, "VOLTAGE_HIGH", 12.0)
if voltage >= volt_crit and coherence < 0.4:
return (
True,
f"⚠️ HUBRIS DETECTED: Voltage ({voltage:.1f}v) exceeds structural integrity. Wings melting.",
"ICARUS_CRASH",
)
if voltage > volt_flow and coherence > 0.8:
return (
True,
"🌊 SURFACE TENSION OPTIMAL: Entering Flow State.",
"FLOW_BOOST",
)
return False, "", ""
class ChromaScope:
@staticmethod
def modulate(text: str, vector: Dict[str, float]) -> str:
if not vector or not any(vector.values()):
return f"{Prisma.GRY}{text}{Prisma.RST}"
primary_dim = max(vector, key=vector.get)
if primary_dim in TRIGRAM_MAP:
selected_color = TRIGRAM_MAP[primary_dim][3]
else:
selected_color = Prisma.GRY
return f"{selected_color}{text}{Prisma.RST}"
class ZoneInertia:
def __init__(self, inertia=0.7):
self.inertia = inertia
self.min_dwell = getattr(BoneConfig.PHYSICS, "ZONE_MIN_DWELL", 2)
self.current_zone = "COURTYARD"
self.dwell_counter = 0
self.last_vector: Optional[Tuple[float, float, float]] = None
self.is_anchored = False
self.strain_gauge = 0.0
def toggle_anchor(self) -> bool:
self.is_anchored = not self.is_anchored
self.strain_gauge = 0.0
return self.is_anchored
def stabilize(
self,
proposed_zone: str,
physics: Dict[str, Any],
cosmic_state: Tuple[str, float, str],
) -> Tuple[str, Optional[str]]:
beta = physics.get("beta_index", 1.0)
truth = physics.get("truth_ratio", 0.5)
grav_pull = 1.0 if cosmic_state[0] != "VOID_DRIFT" else 0.0
current_vec = (beta, truth, grav_pull)
self.dwell_counter += 1
pressure = 0.0
if self.last_vector:
dist = math.dist(current_vec, self.last_vector)
similarity = max(0.0, 1.0 - (dist / 2.0))
pressure = 1.0 - similarity
if self.is_anchored:
return self._handle_anchored_state(proposed_zone, pressure)
if proposed_zone == self.current_zone:
self.dwell_counter = 0
self.last_vector = current_vec
return proposed_zone, None
if self.dwell_counter < self.min_dwell:
return self.current_zone, None
return self._attempt_migration(proposed_zone, pressure)
def _handle_anchored_state(
self, proposed_zone: str, pressure: float
) -> Tuple[str, Optional[str]]:
if proposed_zone == self.current_zone:
self.strain_gauge = max(0.0, self.strain_gauge - 0.1)
return self.current_zone, None
self.strain_gauge += pressure
limit = 2.5
if self.strain_gauge > limit:
self.is_anchored = False
self.strain_gauge = 0.0
self.current_zone = proposed_zone
return (
proposed_zone,
f"{Prisma.RED}⚡ SNAP! The narrative current was too strong. Anchor failed.{Prisma.RST}",
)
return (
self.current_zone,
f"{Prisma.OCHRE}⚓ ANCHORED: Resisting drift to '{proposed_zone}' (Strain {self.strain_gauge:.1f}/{limit}){Prisma.RST}",
)
def _attempt_migration(
self, proposed_zone: str, pressure: float
) -> Tuple[str, Optional[str]]:
prob = (1.0 - self.inertia) + pressure
if proposed_zone in ["AERIE", "THE_FORGE"]:
prob += 0.2
if random.random() < prob:
old, self.current_zone = self.current_zone, proposed_zone
self.dwell_counter = 0
return (
self.current_zone,
f"{Prisma.CYN}>>> MIGRATION: {old} -> {proposed_zone}.{Prisma.RST}",
)
return self.current_zone, None
@staticmethod
def override_cosmic_drag(cosmic_drag_penalty: float, current_zone: str) -> float:
if current_zone == "AERIE" and cosmic_drag_penalty > 0:
return cosmic_drag_penalty * 0.3
return cosmic_drag_penalty
class CosmicDynamics:
def __init__(self):
self.voltage_history: Deque[float] = deque(maxlen=20)
self.cached_wells: Dict = {}
self.cached_hubs: Dict = {}
self.last_scan_tick: int = 0
self.SCAN_INTERVAL: int = 10
self.logs = self._load_logs()
@staticmethod
def _load_logs():
base = {
"GRAVITY": "⚓ GRAVITY: The narrative is heavy. (Drag {drag:.1f})",
"VOID": "VOID: Drifting outside the filaments.",
"NEBULA": "NEBULA: Floating near '{node}' (Mass {mass}). Not enough mass for orbit.",
"LAGRANGE": "LAGRANGE: Caught between '{p}' and '{s}'",
"FLOW": "FLOW: Streaming towards '{node}'",
"ORBIT": "ORBIT: Circling '{node}' (Mass {mass})"
}
manifest = LoreManifest.get_instance().get("narrative_data") or {}
return manifest.get("COSMIC_LOGS", base)
def commit(self, voltage: float):
self.voltage_history.append(voltage)
def check_gravity(
self, current_drift: float, psi: float
) -> Tuple[float, List[str]]:
logs = []
new_drag = current_drift
drag_floor = getattr(BoneConfig.PHYSICS, "DRAG_FLOOR", 1.0)
if new_drag < drag_floor:
new_drag += 0.05
if psi > 0.5:
reduction = (psi - 0.5) * 0.2
new_drag = max(0.0, new_drag - reduction)
CRITICAL_DRIFT = getattr(BoneConfig.PHYSICS, "DRAG_CRITICAL", 8.0)
if new_drag > CRITICAL_DRIFT:
if random.random() < 0.3:
msg = self.logs.get("GRAVITY", "⚓ GRAVITY").format(drag=new_drag)
logs.append(f"{Prisma.GRY}{msg}{Prisma.RST}")
return new_drag, logs
def analyze_orbit(
self, network: Any, clean_words: List[str]
) -> Tuple[str, float, str]:
if (
not clean_words
or not network
or not hasattr(network, "graph")
or not network.graph
):
return "VOID_DRIFT", 3.0, "VOID: Deep Space. No connection."
current_time = int(time.time())
if (
not self.cached_wells
or (current_time - self.last_scan_tick) > self.SCAN_INTERVAL
):
gravity_wells, geodesic_hubs = self._scan_network_mass(network)
self.cached_wells = gravity_wells
self.cached_hubs = geodesic_hubs
self.last_scan_tick = current_time
else:
gravity_wells = self.cached_wells
geodesic_hubs = self.cached_hubs
basin_pulls, active_filaments = self._calculate_pull(
clean_words, network, gravity_wells
)
if sum(basin_pulls.values()) == 0:
return self._handle_void_state(clean_words, geodesic_hubs)
return self._resolve_orbit(
basin_pulls, active_filaments, len(clean_words), gravity_wells
)
@staticmethod
def _scan_network_mass(network) -> Tuple[Dict, Dict]:
gravity_wells = {}
geodesic_hubs = {}
well_threshold = getattr(BoneConfig, "GRAVITY_WELL_THRESHOLD", 15.0)
geo_strength = getattr(BoneConfig, "GEODESIC_STRENGTH", 10.0)
for node in network.graph:
mass = network.calculate_mass(node)
if mass >= well_threshold:
gravity_wells[node] = mass
elif mass >= geo_strength:
geodesic_hubs[node] = mass
return gravity_wells, geodesic_hubs
@staticmethod
def _calculate_pull(words, network, gravity_wells) -> Tuple[Dict, int]:
basin_pulls = {k: 0.0 for k in gravity_wells}
active_filaments = 0
word_counts = Counter(words)
for w, count in word_counts.items():
if w in gravity_wells:
basin_pulls[w] += (gravity_wells[w] * 2.0) * count
active_filaments += count
for well, well_mass in gravity_wells.items():
edges = network.graph.get(well, {}).get("edges", {})
if not edges:
continue
intersection = set(word_counts.keys()).intersection(edges.keys())
for match in intersection:
basin_pulls[well] += (well_mass * 0.5) * word_counts[match]
active_filaments += word_counts[match]
return basin_pulls, active_filaments
def _handle_void_state(self, words, geodesic_hubs) -> Tuple[str, float, str]:
for w in words:
hub_mass = geodesic_hubs.get(w)
if hub_mass is not None:
msg = self.logs.get("NEBULA", "NEBULA").format(
node=w.upper(),
mass=int(hub_mass)
)
return "PROTO_COSMOS", 1.0, msg
return "VOID_DRIFT", 3.0, self.logs.get("VOID", "VOID")
def _resolve_orbit(
self, basin_pulls, active_filaments, word_count, gravity_wells
) -> Tuple[str, float, str]:
sorted_basins = sorted(basin_pulls.items(), key=lambda x: x[1], reverse=True)
primary_node, primary_str = sorted_basins[0]
lagrange_tol = getattr(BoneConfig, "LAGRANGE_TOLERANCE", 2.0)
if len(sorted_basins) > 1:
secondary_node, secondary_str = sorted_basins[1]
if secondary_str > 0 and (primary_str - secondary_str) < lagrange_tol:
msg = self.logs.get("LAGRANGE", "LAGRANGE").format(p=primary_node.upper(), s=secondary_node.upper())
return "LAGRANGE_POINT", 0.0, msg
flow_ratio = active_filaments / max(1, word_count)
well_threshold = getattr(BoneConfig, "GRAVITY_WELL_THRESHOLD", 15.0)
if flow_ratio > 0.5 and primary_str < (well_threshold * 2):
msg = self.logs.get("FLOW", "FLOW").format(node=primary_node.upper())
return "WATERSHED_FLOW", 0.0, msg
msg = self.logs.get("ORBIT", "ORBIT").format(node=primary_node.upper(), mass=int(gravity_wells[primary_node]))
return "ORBITAL", 0.0, msg
def apply_somatic_feedback(physics_packet: PhysicsPacket, qualia: Any) -> PhysicsPacket:
feedback = physics_packet.snapshot()
tone_effects = {
"Urgent": {"velocity": 0.3, "narrative_drag": -0.5, "voltage": 0.5},
"Strained": {"narrative_drag": 1.2, "voltage": -0.3, "kappa": -0.1},
"Vibrating": {"entropy": 0.2, "voltage": 0.2, "psi": 0.1},
"Resonant": {"valence": 0.3, "beta_index": 0.1, "kappa": 0.2},
"Steady": {},
}
effects = tone_effects.get(qualia.tone, {})
for key, delta in effects.items():
if hasattr(feedback, key):
current = getattr(feedback, key)
setattr(feedback, key, current + delta)
if "Gut Tightening" in qualia.somatic_sensation:
feedback.narrative_drag += 0.7
if "Electric Vibration" in qualia.somatic_sensation:
feedback.voltage += 0.8
if "Golden Glow" in qualia.somatic_sensation:
feedback.valence += 0.5
feedback.psi += 0.2
volt_crit = getattr(BoneConfig.PHYSICS, "VOLTAGE_CRITICAL", 15.0)
drag_floor = getattr(BoneConfig.PHYSICS, "DRAG_FLOOR", 1.0)
drag_halt = getattr(BoneConfig.PHYSICS, "DRAG_HALT", 10.0)
feedback.voltage = max(0.0, min(feedback.voltage, volt_crit * 1.5))
feedback.narrative_drag = max(drag_floor, min(feedback.narrative_drag, drag_halt))
return feedback
class CycleStabilizer:
def __init__(self, events_ref, governor_ref):
self.events = events_ref
self.governor = governor_ref
self.last_tick_time = time.time()
self.pending_drag = 0.0
self.manifolds = getattr(BoneConfig.PHYSICS, "MANIFOLDS", {})
self.HARD_FUSE_VOLTAGE = 100.0
if hasattr(self.events, "subscribe"):
self.events.subscribe(
"DOMESTICATION_PENALTY", self._on_domestication_penalty
)
def _on_domestication_penalty(self, payload):
amount = payload.get("drag_penalty", 0.0)
self.pending_drag += amount
def stabilize(self, ctx: CycleContext, current_phase: str):
p = ctx.physics
if p.voltage >= self.HARD_FUSE_VOLTAGE:
ctx.log(
f"{Prisma.RED}⚡ FUSE BLOWN: Voltage > {self.HARD_FUSE_VOLTAGE}V.{Prisma.RST}"
)
p.voltage, p.narrative_drag = 10.0, 5.0
p.flow_state = "SAFE_MODE"
ctx.record_flux(
current_phase, "voltage", self.HARD_FUSE_VOLTAGE, 10.0, "FUSE_BLOWN"
)
return True
if self.pending_drag > 0:
ctx.physics.narrative_drag += self.pending_drag
ctx.log(
f"{Prisma.GRY}⚖️ DOMESTICATION: Drag +{self.pending_drag:.1f}{Prisma.RST}"
)
self.pending_drag = 0.0
now = time.time()
dt = max(0.001, min(1.0, now - self.last_tick_time))
self.last_tick_time = now
manifold = getattr(p, "manifold", "DEFAULT")
cfg = self.manifolds.get(manifold, self.manifolds["DEFAULT"])
target_v = cfg["voltage"]
if getattr(p, "flow_state", "LAMINAR") in ["SUPERCONDUCTIVE", "FLOW_BOOST"]:
target_v = p.voltage
cfg["drag"] = max(0.1, cfg["drag"] * 0.5)
self.governor.recalibrate(target_v, cfg["drag"])
v_force, d_force = self.governor.regulate(p, dt=dt)
c1 = self._apply_force(
ctx,
current_phase,
p,
"voltage",
v_force,
(PHYS_CFG["V_FLOOR"], PHYS_CFG["V_MAX"]),
)
c2 = self._apply_force(ctx, current_phase, p, "narrative_drag", d_force)
return c1 or c2
@staticmethod
def _apply_force(ctx, phase, p, field, force, limits=None):
if abs(force) <= PHYS_CFG["DEADBAND"]:
return False
old_val = getattr(p, field)
new_val = old_val + force
if limits:
new_val = max(limits[0], min(limits[1], new_val))
else:
new_val = max(0.0, new_val)
setattr(p, field, new_val)
if abs(force) > PHYS_CFG["FLUX_THRESHOLD"]:
ctx.record_flux(phase, field, old_val, new_val, "PID_CORRECTION")
return True
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