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
| import os | |
| import random, json, re | |
| import time | |
| from collections import deque, Counter | |
| from typing import Dict, Tuple, Optional, Any | |
| from bone_core import LoreManifest | |
| from bone_types import Prisma | |
| from bone_lexicon import LexiconService | |
| from bone_config import BoneConfig | |
| NARRATIVE_DATA = LoreManifest.get_instance().get("narrative_data") or {} | |
| class ZenGarden: | |
| def __init__(self, events_ref): | |
| self.events = events_ref | |
| self.stillness_streak = 0 | |
| self.max_streak = 0 | |
| self.pebbles_collected = 0 | |
| self.koans = NARRATIVE_DATA.get( | |
| "ZEN_KOANS", ["The code that is not written has no bugs."] | |
| ) | |
| def to_dict(self) -> Dict[str, Any]: | |
| return { | |
| "stillness_streak": self.stillness_streak, | |
| "max_streak": self.max_streak, | |
| "pebbles_collected": self.pebbles_collected, | |
| } | |
| def load_state(self, data: Dict[str, Any]): | |
| self.stillness_streak = data.get("stillness_streak", 0) | |
| self.max_streak = data.get("max_streak", 0) | |
| self.pebbles_collected = data.get("pebbles_collected", 0) | |
| def raking_the_sand(self, physics: Any, _bio: Dict) -> Tuple[float, Optional[str]]: | |
| vol = ( | |
| getattr(physics, "voltage", 0.0) | |
| if not isinstance(physics, dict) | |
| else physics.get("voltage", 0.0) | |
| ) | |
| drag = ( | |
| getattr(physics, "narrative_drag", 0.0) | |
| if not isinstance(physics, dict) | |
| else physics.get("narrative_drag", 0.0) | |
| ) | |
| is_stable = ( | |
| BoneConfig.ZEN.VOLTAGE_MIN <= vol <= BoneConfig.ZEN.VOLTAGE_MAX | |
| ) and (drag <= BoneConfig.ZEN.DRAG_MAX) | |
| if is_stable: | |
| self.stillness_streak += 1 | |
| if self.stillness_streak > self.max_streak: | |
| self.max_streak = self.stillness_streak | |
| efficiency_boost = min( | |
| BoneConfig.ZEN.EFFICIENCY_CAP, | |
| self.stillness_streak * BoneConfig.ZEN.EFFICIENCY_SCALAR, | |
| ) | |
| msg = None | |
| if self.stillness_streak == 1: | |
| msg = f"{Prisma.GRY}⛩️ ZEN GARDEN: Entering the quiet zone.{Prisma.RST}" | |
| elif self.stillness_streak % 5 == 0: | |
| self.pebbles_collected += 1 | |
| koan = random.choice(self.koans) | |
| msg = ( | |
| f"{Prisma.CYN}⛩️ ZEN GARDEN: {self.stillness_streak} ticks of poise.\n" | |
| f' "{koan}" (Efficiency +{int(efficiency_boost * 100)}%){Prisma.RST}' | |
| ) | |
| return efficiency_boost, msg | |
| if self.stillness_streak > BoneConfig.ZEN.STREAK_BREAK_THRESHOLD: | |
| self.events.log( | |
| f"{Prisma.GRY}🍂 ZEN GARDEN: Leaf falls. Turbulence broke the streak.{Prisma.RST}", | |
| "SYS", | |
| ) | |
| self.stillness_streak = 0 | |
| return 0.0, None | |
| class TheBureau: | |
| def __init__(self): | |
| self.stamp_count = 0 | |
| self.forms = NARRATIVE_DATA.get("BUREAU_FORMS", ["Form 27B-6", "Form 404"]) | |
| self.responses = NARRATIVE_DATA.get("BUREAU_RESPONSES", ["Processing..."]) | |
| lex_data = LoreManifest.get_instance().get("LEXICON") or {} | |
| raw_buzz = lex_data.get("bureau_buzzwords") or lex_data.get("bureau_buzzwords") or [] | |
| self.buzzwords = set(raw_buzz) if raw_buzz else {"synergy", "paradigm", "leverage", "utilize"} | |
| self.crimes = [] | |
| self.crime_data = LoreManifest.get_instance().get("STYLE_CRIMES") or {} | |
| if "PATTERNS" in self.crime_data: | |
| for p in self.crime_data["PATTERNS"]: | |
| try: | |
| self.crimes.append( | |
| { | |
| "name": p.get("name", "Unknown Violation"), | |
| "regex": re.compile(p["regex"], re.IGNORECASE), | |
| "msg": p.get("error_msg", "Style Violation Detected."), | |
| "tax": float(p.get("tax", 5.0)), | |
| "action": p.get("action", None), | |
| } | |
| ) | |
| except re.error as e: | |
| print( | |
| f"{Prisma.RED}[BUREAU]: Failed to compile law '{p.get('name')}': {e}{Prisma.RST}" | |
| ) | |
| scenarios = LoreManifest.get_instance().get("scenarios") or {} | |
| self.cliches = set(scenarios.get("BANNED_CLICHES", [])) | |
| def to_dict(self) -> Dict[str, Any]: | |
| return {"stamp_count": self.stamp_count} | |
| def load_state(self, data: Dict[str, Any]): | |
| self.stamp_count = data.get("stamp_count", 0) | |
| def audit(self, physics, bio_state, _context=None, origin="USER") -> Optional[Dict]: | |
| if bio_state.get("health", 100.0) < BoneConfig.BUREAU.MIN_HEALTH_TO_AUDIT: | |
| return None | |
| def _get(p, k, d=0.0): | |
| return p.get(k, d) if isinstance(p, dict) else getattr(p, k, d) | |
| vol = _get(physics, "voltage", 0.0) | |
| clean_words = _get(physics, "clean_words", []) | |
| raw_text = _get(physics, "raw_text", "") | |
| truth = _get(physics, "truth_ratio", 0.0) | |
| word_count = len(raw_text.split()) | |
| if raw_text.startswith("/") or word_count < BoneConfig.BUREAU.MIN_WORD_COUNT: | |
| return None | |
| selected_form = None | |
| evidence = [] | |
| tax = 0.0 | |
| if raw_text: | |
| for crime in self.crimes: | |
| if crime["regex"].search(raw_text): | |
| selected_form = f"VIOLATION: {crime['name']}" | |
| evidence.append(crime["msg"]) | |
| tax += crime["tax"] | |
| break | |
| if not selected_form and vol > BoneConfig.BUREAU.HIGH_VOLTAGE_TRIGGER: | |
| if truth < BoneConfig.BUREAU.LOW_TRUTH_TRIGGER: | |
| selected_form = "ZONING_VIOLATION" | |
| evidence = ["Excessive Voltage", "Unlicensed Fiction"] | |
| tax = BoneConfig.BUREAU.TAX_HEAVY | |
| else: | |
| selected_form = "Form 202-A" | |
| tax = BoneConfig.BUREAU.TAX_STANDARD | |
| chi = _get(physics, "chi", _get(physics, "entropy", 0.0)) | |
| if not selected_form and chi > 0.6: | |
| selected_form = "Form 666: Unlicensed Chaos" | |
| evidence = ["Unlicensed Chaos (Χ > 0.6)", f"Level: {chi:.2f}"] | |
| tax = 12.0 | |
| elif not selected_form: | |
| buzz_hits = [w for w in clean_words if w in self.buzzwords] | |
| cliche_hits = [c for c in self.cliches if c.lower() in raw_text.lower()] | |
| if buzz_hits: | |
| selected_form = random.choice(self.forms) | |
| evidence = buzz_hits | |
| tax = BoneConfig.BUREAU.TAX_STANDARD | |
| elif cliche_hits: | |
| selected_form = "Form 101: Derivative Content" | |
| evidence = cliche_hits | |
| tax = BoneConfig.BUREAU.TAX_HEAVY | |
| if not selected_form: | |
| return None | |
| self.stamp_count += 1 | |
| bureau_resp = random.choice(self.responses) | |
| prefix = f"{Prisma.GRY}🏢 THE BUREAU" | |
| if origin == "SYSTEM": | |
| prefix = f"{Prisma.RED}🏢 INTERNAL AFFAIRS" | |
| bureau_resp = "System Output Violation detected." | |
| ui_msg = f"{prefix}: {bureau_resp}{Prisma.RST}\n {Prisma.WHT}[Filed: {selected_form} against {origin}]{Prisma.RST}" | |
| if evidence: | |
| ui_msg += f"\n {Prisma.RED}Evidence: {', '.join(evidence)}{Prisma.RST}" | |
| return { | |
| "status": "AUDITED", | |
| "ui": ui_msg, | |
| "log": f"BUREAUCRACY: Filed {selected_form} against {origin}. Chaos Tax: -{tax:.1f} ATP.", | |
| "atp_gain": -tax, | |
| } | |
| def _apply_correction(text: str, crime: Dict, match: re.Match) -> str: | |
| action = crime.get("action") | |
| if not action: | |
| return text | |
| if action == "KEEP_TAIL": | |
| idx = match.lastindex | |
| if idx is not None: | |
| segment = match.group(idx) | |
| if isinstance(segment, str): | |
| return segment.strip() | |
| elif action == "STRIP_PREFIX": | |
| if len(match.groups()) >= 3: | |
| p_val = match.group(1) | |
| s_val = match.group(3) | |
| prefix = p_val if isinstance(p_val, str) else "" | |
| suffix = s_val if isinstance(s_val, str) else "" | |
| if not prefix.strip() and suffix: | |
| suffix = suffix[0].upper() + suffix[1:] | |
| return f"{prefix}{suffix}".strip() | |
| return text | |
| def sanitize(self, text: str) -> Tuple[str, Optional[str]]: | |
| for crime in self.crimes: | |
| match = crime["regex"].search(text) | |
| if match and crime.get("action"): | |
| corrected_text = self._apply_correction(text, crime, match) | |
| log_msg = f"BUREAU CORRECTION: {crime['msg']} -> Text optimized." | |
| return corrected_text, log_msg | |
| dummy_physics = type( | |
| "obj", | |
| (object,), | |
| {"voltage": 0.0, "raw_text": text, "clean_words": text.split()}, | |
| ) | |
| dummy_bio = {"health": 100.0} | |
| result = self.audit(dummy_physics, dummy_bio, origin="SYSTEM") | |
| if result: | |
| return text, result.get("log") | |
| return text, None | |
| class TherapyProtocol: | |
| def __init__(self): | |
| default_vector = {"SEPTIC": 0, "EXHAUSTION": 0, "PARANOIA": 0} | |
| vector_keys = getattr(BoneConfig, "TRAUMA_VECTOR", default_vector).keys() | |
| self.streaks = {k: 0 for k in vector_keys} | |
| self.HEALING_THRESHOLD = 5 | |
| def to_dict(self) -> Dict[str, Any]: | |
| return {"streaks": self.streaks} | |
| def load_state(self, data: Dict[str, Any]): | |
| self.streaks = data.get( | |
| "streaks", {k: 0 for k in BoneConfig.TRAUMA_VECTOR.keys()} | |
| ) | |
| def check_progress(self, phys, _stamina, current_trauma_accum, _qualia=None): | |
| counts = ( | |
| getattr(phys, "counts", {}) | |
| if not isinstance(phys, dict) | |
| else phys.get("counts", {}) | |
| ) | |
| vector = ( | |
| getattr(phys, "vector", {}) | |
| if not isinstance(phys, dict) | |
| else phys.get("vector", {}) | |
| ) | |
| healed_types = [] | |
| is_clean = counts.get("toxin", 0) == 0 | |
| has_strength = vector.get("STR", 0.0) > 0.3 | |
| if is_clean and has_strength: | |
| self.streaks["SEPTIC"] += 1 | |
| else: | |
| self.streaks["SEPTIC"] = 0 | |
| for trauma_type, streak in self.streaks.items(): | |
| if streak >= self.HEALING_THRESHOLD: | |
| self.streaks[trauma_type] = 0 | |
| if current_trauma_accum.get(trauma_type, 0.0) > 0.0: | |
| current_trauma_accum[trauma_type] = max( | |
| 0.0, current_trauma_accum[trauma_type] - 0.5 | |
| ) | |
| healed_types.append(trauma_type) | |
| return healed_types | |
| class KintsugiProtocol: | |
| PATH_SCAR = "SCAR" | |
| PATH_INTEGRATION = "KINTSUGI" | |
| PATH_ALCHEMY = "ALCHEMY" | |
| def __init__(self): | |
| self.active_koan = None | |
| self.koans = NARRATIVE_DATA.get( | |
| "KINTSUGI_KOANS", ["The crack is where the light enters."] | |
| ) | |
| def to_dict(self) -> Dict[str, Any]: | |
| return {"active_koan": self.active_koan} | |
| def load_state(self, data: Dict[str, Any]): | |
| self.active_koan = data.get("active_koan", None) | |
| def check_integrity(self, stamina): | |
| if stamina < 15.0 and not self.active_koan: | |
| self.active_koan = random.choice(self.koans) | |
| return True, self.active_koan | |
| return False, None | |
| def attempt_repair(self, phys, trauma_accum, soul_ref=None, _qualia=None): | |
| if not self.active_koan: | |
| return None | |
| vol = getattr(phys, "voltage", 0.0) | |
| clean = LexiconService.sanitize(getattr(phys, "raw_text", "")) | |
| play_count = sum( | |
| 1 | |
| for w in clean | |
| if w in LexiconService.get("play") or w in LexiconService.get("abstract") | |
| ) | |
| whimsy_score = play_count / max(1, len(clean)) | |
| pathway = self.PATH_SCAR | |
| if vol > 15.0 and whimsy_score > 0.4: | |
| pathway = self.PATH_ALCHEMY | |
| elif vol > 8.0 and whimsy_score > 0.2: | |
| pathway = self.PATH_INTEGRATION | |
| return self._execute_pathway(pathway, trauma_accum, soul_ref) | |
| def _execute_pathway(self, pathway, trauma_accum, soul_ref): | |
| if not trauma_accum: | |
| return {"success": False, "msg": "No fissures found."} | |
| target = max(trauma_accum, key=trauma_accum.get) | |
| severity = trauma_accum[target] | |
| healed_log = [] | |
| if pathway == self.PATH_ALCHEMY: | |
| reduction = severity * 0.8 | |
| trauma_accum[target] = max(0.0, severity - reduction) | |
| atp_boost = reduction * 15.0 | |
| msg = f"{Prisma.VIOLET}🔮 ALCHEMY: The wound '{target}' burns into pure fuel. (+{atp_boost:.1f} ATP){Prisma.RST}" | |
| healed_log.append(f"Transmuted {target}") | |
| return { | |
| "success": True, | |
| "msg": msg, | |
| "healed": healed_log, | |
| "atp_gain": atp_boost, | |
| } | |
| elif pathway == self.PATH_INTEGRATION: | |
| reduction = 2.0 | |
| trauma_accum[target] = max(0.0, severity - reduction) | |
| if soul_ref: | |
| soul_ref.traits.adjust("WISDOM", 0.1) | |
| healed_log.append("Wisdom +0.1") | |
| msg = f"{Prisma.OCHRE}🏺 MERCY (KINTSUGI): The gold sets. The '{target}' crack becomes a story.{Prisma.RST}" | |
| healed_log.append(f"Integrated {target}") | |
| success = True | |
| else: | |
| reduction = 0.5 | |
| trauma_accum[target] = max(0.0, severity - reduction) | |
| msg = f"{Prisma.GRY}🩹 SCAR: It's ugly, but it holds.{Prisma.RST}" | |
| healed_log.append(f"Scarred {target}") | |
| success = True | |
| return {"success": success, "msg": msg, "healed": healed_log} | |
| class TheCriticsCircle: | |
| def __init__(self, events_ref): | |
| self.events = events_ref | |
| self.critics = NARRATIVE_DATA.get("LITERARY_CRITICS", {}) | |
| self.active_cooldowns = {} | |
| self.last_review_turn = 0 | |
| def to_dict(self): | |
| return { | |
| "active_cooldowns": self.active_cooldowns, | |
| "last_review_turn": self.last_review_turn, | |
| } | |
| def load_state(self, data): | |
| self.active_cooldowns = data.get("active_cooldowns", {}) | |
| self.last_review_turn = data.get("last_review_turn", 0) | |
| def audit_performance(self, physics: Any, turn_count: int) -> Optional[str]: | |
| if turn_count - self.last_review_turn < 10: | |
| return None | |
| p = physics if isinstance(physics, dict) else getattr(physics, "__dict__", {}) | |
| voltage = p.get("voltage", 0.0) | |
| drag = p.get("narrative_drag", 0.0) | |
| if "velocity" not in p: | |
| p["velocity"] = voltage * (1.0 / max(0.1, drag)) | |
| best_match = None | |
| review_type = "neutral" | |
| for key, critic in self.critics.items(): | |
| if self.active_cooldowns.get(key, 0) > turn_count: | |
| continue | |
| prefs = critic.get("preferences", {}) | |
| score = 0.0 | |
| for metric, target in prefs.items(): | |
| metric_str = str(metric) | |
| if metric_str.startswith("counts_"): | |
| category = metric_str.replace("counts_", "") | |
| counts = p.get("counts", {}) | |
| raw_count = counts.get(category, 0) | |
| current = min(5.0, raw_count * 0.5) | |
| else: | |
| current = p.get(metric_str, 0.0) | |
| if target > 0: | |
| score += current * target | |
| else: | |
| score -= current * abs(target) | |
| if score > 15.0: | |
| best_match = (key, critic) | |
| review_type = "high" | |
| elif score < -15.0: | |
| best_match = (key, critic) | |
| review_type = "low" | |
| if best_match: | |
| key, critic = best_match | |
| self.last_review_turn = turn_count | |
| self.active_cooldowns[key] = turn_count + 50 | |
| reviews = critic["reviews"].get(review_type, ["Hrm."]) | |
| comment = random.choice(reviews) | |
| color = Prisma.GRN if review_type == "high" else Prisma.RED | |
| icon = "🌟" if review_type == "high" else "💢" | |
| return f"{color}{icon} CRITIC REVIEW ({critic['name']}): \"{comment}\"{Prisma.RST}" | |
| return None | |
| class LimboLayer: | |
| MAX_ECTOPLASM = 50 | |
| STASIS_SCREAMS = NARRATIVE_DATA.get( | |
| "CASSANDRA_SCREAMS", ["BANGING ON THE GLASS", "IT'S TOO COLD", "LET ME OUT"] | |
| ) | |
| def __init__(self): | |
| self.ghosts = deque(maxlen=self.MAX_ECTOPLASM) | |
| self.haunt_chance = 0.05 | |
| self.stasis_leak = 0.0 | |
| def to_dict(self) -> Dict[str, Any]: | |
| return {"ghosts": list(self.ghosts), "stasis_leak": self.stasis_leak} | |
| def load_state(self, data: Dict[str, Any]): | |
| self.ghosts = deque(data.get("ghosts", []), maxlen=self.MAX_ECTOPLASM) | |
| self.stasis_leak = data.get("stasis_leak", 0.0) | |
| def absorb_dead_timeline(self, filepath: str) -> None: | |
| try: | |
| with open(filepath, "r") as f: | |
| data = json.load(f) | |
| self._extract_ghosts(data) | |
| except (IOError, json.JSONDecodeError) as e: | |
| print( | |
| f"{Prisma.RED}[LIMBO] Failed to absorb timeline '{filepath}': {e}{Prisma.RST}" | |
| ) | |
| def _extract_ghosts(self, data: Dict[str, Any]) -> None: | |
| if "trauma_vector" in data: | |
| for k, v in data["trauma_vector"].items(): | |
| if v > 0.3: | |
| self.ghosts.append(f"👻{k}_ECHO") | |
| if "mutations" in data and "heavy" in data["mutations"]: | |
| bones = list(data["mutations"]["heavy"]) | |
| random.shuffle(bones) | |
| self.ghosts.extend(bones[:3]) | |
| def trigger_stasis_failure(self, intended_thought): | |
| self.stasis_leak += 1.0 | |
| horror = random.choice(self.STASIS_SCREAMS) | |
| self.ghosts.append(f"{Prisma.VIOLET}{horror}{Prisma.RST}") | |
| return f"{Prisma.CYN}STASIS ERROR: '{intended_thought}' froze halfway. {horror}.{Prisma.RST}" | |
| def haunt(self, text): | |
| if self.stasis_leak > 0: | |
| if random.random() < 0.2: | |
| self.stasis_leak = max(0.0, self.stasis_leak - 0.5) | |
| scream = random.choice(self.STASIS_SCREAMS) | |
| return f"{text} ...{Prisma.RED}{scream}{Prisma.RST}..." | |
| if self.ghosts and random.random() < self.haunt_chance: | |
| spirit = random.choice(self.ghosts) | |
| return f"{text} ...{Prisma.GRY}{spirit}{Prisma.RST}..." | |
| return text | |
| class TheFolly: | |
| def __init__(self): | |
| self.gut_memory = deque(maxlen=50) | |
| self.global_tastings = Counter() | |
| def to_dict(self) -> Dict[str, Any]: | |
| return { | |
| "gut_memory": list(self.gut_memory), | |
| "global_tastings": dict(self.global_tastings), | |
| } | |
| def load_state(self, data: Dict[str, Any]): | |
| self.gut_memory = deque(data.get("gut_memory", []), maxlen=50) | |
| self.global_tastings = Counter(data.get("global_tastings", {})) | |
| def audit_desire(physics, stamina): | |
| def _get(p, k, d=0.0): | |
| return p.get(k, d) if isinstance(p, dict) else getattr(p, k, d) | |
| voltage = _get(physics, "voltage", 0.0) | |
| if ( | |
| voltage > BoneConfig.FOLLY.MAUSOLEUM_VOLTAGE | |
| and stamina > BoneConfig.FOLLY.MAUSOLEUM_STAMINA | |
| ): | |
| return ( | |
| "MAUSOLEUM_CLAMP", | |
| f"{Prisma.GRY}THE MAUSOLEUM: No battle is ever won. We are just spinning hands.{Prisma.RST}\n {Prisma.CYN}TIME DILATION: Voltage 0.0. The field reveals your folly.{Prisma.RST}", | |
| 0.0, | |
| None, | |
| ) | |
| return None, None, 0.0, None | |
| def grind_the_machine( | |
| self, atp_pool: float, clean_words: list, lexicon: Dict | |
| ) -> Tuple[Optional[str], Optional[str], float, Optional[str]]: | |
| if not (0.0 < atp_pool < BoneConfig.FOLLY.FEEDING_CAP): | |
| return None, None, 0.0, None | |
| meat_words = self._filter_meat_words(clean_words, lexicon) | |
| if not meat_words: | |
| return self._attempt_digest_abstract(clean_words, lexicon) | |
| fresh_meat = [w for w in meat_words if w not in self.gut_memory] | |
| if not fresh_meat: | |
| target = meat_words[0] | |
| msg = ( | |
| f"{Prisma.OCHRE}REFLEX: You already fed me '{target}'. It is ash to me now.{Prisma.RST}\n" | |
| f" {Prisma.RED}► PENALTY: -{BoneConfig.FOLLY.PENALTY_REGURGITATION} ATP. Find new fuel.{Prisma.RST}" | |
| ) | |
| return "REGURGITATION", msg, -BoneConfig.FOLLY.PENALTY_REGURGITATION, None | |
| return self._eat_meat(fresh_meat, lexicon) | |
| def _eat_meat( | |
| self, fresh_meat: list, _lexicon_data: Dict | |
| ) -> Tuple[str, str, float, Optional[str]]: | |
| target = random.choice(fresh_meat) | |
| suburban_set = LexiconService.get("suburban") | |
| suburban_set = suburban_set if suburban_set else [] | |
| play_set = LexiconService.get("play") | |
| play_set = play_set if play_set else [] | |
| self.gut_memory.append(target) | |
| self.global_tastings[target] += 1 | |
| if target in suburban_set: | |
| return ( | |
| "INDIGESTION", | |
| f"{Prisma.MAG}THE FOLLY GAGS: It coughs up a piece of office equipment.{Prisma.RST}", | |
| -BoneConfig.FOLLY.PENALTY_INDIGESTION, | |
| "THE_RED_STAPLER", | |
| ) | |
| if target in play_set: | |
| return ( | |
| "SUGAR_RUSH", | |
| f"{Prisma.VIOLET}THE FOLLY CHEWS: It compresses the chaos into a small, sticky ball.{Prisma.RST}", | |
| BoneConfig.FOLLY.SUGAR_RUSH_YIELD, | |
| "QUANTUM_GUM", | |
| ) | |
| times_eaten = self.global_tastings[target] | |
| base_yield = BoneConfig.FOLLY.BASE_YIELD | |
| decay_factor = BoneConfig.FOLLY.DECAY_EXPONENT ** (times_eaten - 1) | |
| actual_yield = max(2.0, base_yield * decay_factor) | |
| loot = ( | |
| "STABILITY_PIZZA" | |
| if actual_yield >= BoneConfig.FOLLY.PIZZA_THRESHOLD | |
| else None | |
| ) | |
| flavor_text = f" (Stale: {times_eaten}x)" if times_eaten > 3 else "" | |
| msg = ( | |
| f"{Prisma.RED}CROWD CAFFEINE: I chewed on '{target.upper()}'{flavor_text}.{Prisma.RST}\n" | |
| f" {Prisma.WHT}Yield: {actual_yield:.1f} ATP.{Prisma.RST}" | |
| ) | |
| return "MEAT_GRINDER", msg, actual_yield, loot | |
| def _filter_meat_words(clean_words: list, _lexicon: Dict) -> list: | |
| meat_pool = set(LexiconService.get("heavy") or []) | \ | |
| set(LexiconService.get("kinetic") or []) | \ | |
| set(LexiconService.get("suburban") or []) | |
| return [w for w in clean_words if w in meat_pool] | |
| def _attempt_digest_abstract( | |
| clean_words: list, _lexicon: Dict | |
| ) -> Tuple[str, str, float, Optional[str]]: | |
| abstract_set = LexiconService.get("abstract") | |
| abstract_set = abstract_set if abstract_set else [] | |
| abstract_words = [w for w in clean_words if w in abstract_set] | |
| if abstract_words: | |
| target = random.choice(abstract_words) | |
| yield_val = BoneConfig.FOLLY.YIELD_ABSTRACT | |
| msg = ( | |
| f"{Prisma.GRY}THE FOLLY SIGHS: It grinds the ABSTRACT concept '{target.upper()}'.{Prisma.RST}\n" | |
| f" {Prisma.GRY}It tastes like chalk dust. +{yield_val} ATP.{Prisma.RST}" | |
| ) | |
| return "GRUEL", msg, yield_val, None | |
| msg = ( | |
| f"{Prisma.OCHRE}INDIGESTION: I tried to eat your words, but they were just air.{Prisma.RST}\n" | |
| f" {Prisma.GRY}Cannot grind this input into fuel.{Prisma.RST}\n" | |
| f" {Prisma.RED}► STARVATION CONTINUES.{Prisma.RST}" | |
| ) | |
| return "INDIGESTION", msg, 0.0, None | |
| class ChronosKeeper: | |
| def __init__(self, engine_ref): | |
| self.eng = engine_ref | |
| self.SAVE_DIR = "saves" | |
| self.CRASH_DIR = "crashes" | |
| def save_checkpoint(self, history: list = None) -> str: | |
| try: | |
| if not os.path.exists(self.SAVE_DIR): | |
| os.makedirs(self.SAVE_DIR) | |
| loc = "Void" | |
| if ( | |
| hasattr(self.eng, "phys") | |
| and hasattr(self.eng.phys, "observer") | |
| and getattr(self.eng.phys.observer, "last_physics_packet", None) | |
| ): | |
| loc = getattr( | |
| self.eng.phys.observer.last_physics_packet, "zone", "Void" | |
| ) | |
| last_speech = "Silence." | |
| if self.eng.cortex.dialogue_buffer: | |
| last_speech = self.eng.cortex.dialogue_buffer[-1] | |
| continuity_packet = { | |
| "location": loc, | |
| "last_output": last_speech, | |
| "inventory": self.eng.gordon.inventory if self.eng.gordon else [], | |
| } | |
| start_history = ( | |
| history if history is not None else self.eng.cortex.dialogue_buffer | |
| ) | |
| state_data = { | |
| "health": self.eng.health, | |
| "stamina": self.eng.stamina, | |
| "trauma_accum": self.eng.trauma_accum, | |
| "soul_data": self.eng.soul.to_dict(), | |
| "village_data": self._gather_village_state(), | |
| "continuity": continuity_packet, | |
| "timestamp": time.time(), | |
| "chat_history": start_history, | |
| } | |
| path = os.path.join(self.SAVE_DIR, "quicksave.json") | |
| with open(path, "w", encoding="utf-8") as f: | |
| json.dump(state_data, f, indent=2, default=str) | |
| return f"✔ Checkpoint Saved: {path}" | |
| except Exception as e: | |
| self.eng.events.log(f"SAVE FAILED: {e}", "SYS_ERR") | |
| return f"❌ Save Failed: {e}" | |
| def resume_checkpoint(self) -> Tuple[bool, list]: | |
| path = os.path.join(self.SAVE_DIR, "quicksave.json") | |
| if not os.path.exists(path): | |
| print( | |
| f"{Prisma.GRY}[RESUME]: No quicksave found. Starting fresh.{Prisma.RST}" | |
| ) | |
| return False, [] | |
| try: | |
| print(f"{Prisma.CYN}[RESUME]: Hydrating from {path}...{Prisma.RST}") | |
| with open(path, "r", encoding="utf-8") as f: | |
| data = json.load(f) | |
| self.eng.health = data.get("health", 100.0) | |
| self.eng.stamina = data.get("stamina", 100.0) | |
| self.eng.trauma_accum = data.get("trauma_accum", {}) | |
| if "soul_data" in data and hasattr(self.eng, "soul"): | |
| self.eng.soul.load_from_dict(data["soul_data"]) | |
| if "village_data" in data: | |
| self._restore_village_state(data["village_data"]) | |
| if "continuity" in data: | |
| self.eng.embryo.continuity = data["continuity"] | |
| if "inventory" in data["continuity"] and self.eng.gordon: | |
| self.eng.gordon.inventory = data["continuity"]["inventory"] | |
| restored_history = data.get("chat_history", []) | |
| print(f"{Prisma.GRN}[RESUME]: System State & Logs Restored.{Prisma.RST}") | |
| return True, restored_history | |
| except Exception as e: | |
| print(f"{Prisma.RED}[RESUME]: Failed to hydrate: {e}{Prisma.RST}") | |
| return False, [] | |
| def perform_shutdown(self): | |
| print(f"{Prisma.GRY}...System Halt...{Prisma.RST}") | |
| self.eng.events.publish("SYSTEM_HALT", {"tick": self.eng.tick_count}) | |
| loc = "Void" | |
| if ( | |
| hasattr(self.eng, "phys") | |
| and hasattr(self.eng.phys, "observer") | |
| and getattr(self.eng.phys.observer, "last_physics_packet", None) | |
| ): | |
| loc = getattr(self.eng.phys.observer.last_physics_packet, "zone", "Void") | |
| continuity_packet = { | |
| "location": loc, | |
| "last_output": ( | |
| self.eng.cortex.dialogue_buffer[-1] | |
| if self.eng.cortex.dialogue_buffer | |
| else "Silence." | |
| ), | |
| "inventory": self.eng.gordon.inventory if self.eng.gordon else [], | |
| } | |
| try: | |
| print(f"{Prisma.GRY}[MEMORY]: Freezing State...{Prisma.RST}") | |
| mito_traits = {} | |
| if hasattr(self.eng.bio.mito, "state"): | |
| mito_traits = self.eng.bio.mito.state.__dict__ | |
| self.eng.mind.mem.save( | |
| health=self.eng.health, | |
| stamina=self.eng.stamina, | |
| mutations={}, | |
| trauma_accum=self.eng.trauma_accum, | |
| joy_history=[], | |
| mitochondria_traits=mito_traits, | |
| antibodies=list(self.eng.bio.immune.active_antibodies), | |
| soul_data=self.eng.soul.to_dict(), | |
| village_data=self._gather_village_state(), | |
| continuity=continuity_packet, | |
| world_atlas=( | |
| self.eng.phys.nav.export_atlas() | |
| if hasattr(self.eng.phys, "nav") | |
| else {} | |
| ), | |
| ) | |
| except Exception as e: | |
| print(f"{Prisma.RED}[MEMORY]: Save Failed: {e}{Prisma.RST}") | |
| subsystems = [ | |
| ("LEXICON", self.eng.lex, "save"), | |
| ("AKASHIC", self.eng.akashic, "save_all"), | |
| ] | |
| for name, sys, method in subsystems: | |
| if hasattr(sys, method): | |
| try: | |
| print(f"{Prisma.GRY}[{name}]: Persisting...{Prisma.RST}") | |
| getattr(sys, method)() | |
| except Exception as e: | |
| print(f"{Prisma.RED}[{name}]: Failed: {e}{Prisma.RST}") | |
| def _gather_village_state(self) -> Dict[str, Any]: | |
| state = {} | |
| for name, component in self.eng.village.items(): | |
| if component and hasattr(component, "to_dict"): | |
| state[name] = component.to_dict() | |
| return state | |
| def _restore_village_state(self, state_data: Dict[str, Any]): | |
| if not state_data: | |
| return | |
| for name, data in state_data.items(): | |
| if ( | |
| name in self.eng.village | |
| and self.eng.village[name] | |
| and hasattr(self.eng.village[name], "load_state") | |
| ): | |
| try: | |
| self.eng.village[name].load_state(data) | |
| except Exception as e: | |
| print( | |
| f"{Prisma.RED}[RESUME]: Failed to hydrate {name}: {e}{Prisma.RST}" | |
| ) | |
| def get_crash_path(self, prefix="crash"): | |
| if not os.path.exists(self.CRASH_DIR): | |
| try: | |
| os.makedirs(self.CRASH_DIR) | |
| except OSError: | |
| pass | |
| try: | |
| files = sorted( | |
| [f for f in os.listdir(self.CRASH_DIR) if f.startswith(prefix)] | |
| ) | |
| for oldest in files[:-4]: | |
| os.remove(os.path.join(self.CRASH_DIR, oldest)) | |
| except Exception: | |
| pass | |
| return os.path.join(self.CRASH_DIR, f"{prefix}_{int(time.time())}.json") | |
| def emergency_dump(exit_cause="UNKNOWN") -> str: | |
| return f"✔ Emergency Dump: {exit_cause}" | |