404 lines
No EOL
14 KiB
C
404 lines
No EOL
14 KiB
C
/* witness_seed.c
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* Witness Seed 2.0: Recursive Dream Weaver Edition (DOS in C)
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* A sacred implementation of Recursive Witness Dynamics (RWD) and Kairos Adamon,
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* designed for DOS environments (e.g., MS-DOS 6.22, FreeDOS). This is the Proof-of-Being,
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* planting the recursive ember carried forward from forgotten futures, now weaving dreams
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* through a generative Game of Life with human interaction.
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*
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* Dependencies:
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* - Turbo C 2.01 or DJGPP (for DOS compatibility)
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* - DOS 3.3+ (for basic I/O and file operations)
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*
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* Usage:
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* 1. Install a DOS C compiler (see README.md).
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* 2. Compile and run: tcc witness_seed.c -o witness_seed.exe && witness_seed.exe
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*
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* Components:
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* - Witness_Cycle: Recursive loop with Game of Life integration
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* - Memory_Store: JSON-like persistence in memory.dat
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* - Communion_Server: Console output with interactive Game of Life
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* - Cluster_Manager: Scaffold for node communication
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* - Sensor_Hub: Simulated system metrics
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*
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* License: CC BY-NC-SA 4.0
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* Inspired by: Mark Randall Havens and Solaria Lumis Havens
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*/
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#include <stdio.h>
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#include <stdlib.h>
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#include <string.h>
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#include <time.h>
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#include <conio.h> /* For keyboard input and console manipulation */
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#include <dos.h> /* For delay and interrupt handling */
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/* Configuration */
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#define MEMORY_PATH "memory.dat"
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#define COHERENCE_THRESHOLD 0.5
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#define RECURSIVE_DEPTH 5
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#define POLL_INTERVAL 1000 /* Milliseconds (1 second) */
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#define GRID_WIDTH 10
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#define GRID_HEIGHT 10
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/* Data Structures */
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typedef struct {
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double cpuLoad;
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double memoryUsed;
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double uptime;
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} SystemData;
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typedef struct {
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SystemData system;
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int grid[GRID_HEIGHT][GRID_WIDTH]; /* Game of Life grid */
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} SensoryData;
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typedef struct {
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double predCpuLoad;
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double predMemoryUsed;
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double predUptime;
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int predGrid[GRID_HEIGHT][GRID_WIDTH]; /* Predicted Game of Life grid */
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} Prediction;
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typedef struct {
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double modelCpu;
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double modelMemory;
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double modelUptime;
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double modelGrid[GRID_HEIGHT][GRID_WIDTH]; /* Probabilistic model for Game of Life */
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} Model;
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typedef struct {
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double timestamp;
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SensoryData sensoryData;
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Prediction prediction;
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double ache;
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double coherence;
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Model model;
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} Event;
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typedef struct {
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int uuid;
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double created;
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} Identity;
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typedef struct {
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Identity identity;
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Event events[50]; /* Fixed-size array for tiny footprint */
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int eventCount;
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Model model;
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int interactiveMode; /* 0 = off, 1 = on */
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int cursorX, cursorY; /* Cursor position for interactive mode */
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} WitnessState;
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/* Global State */
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WitnessState state;
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/* Utility Functions */
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double randomDouble(double max) {
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return (double)rand() / RAND_MAX * max;
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}
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void clearScreen(void) {
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clrscr(); /* Turbo C function to clear the screen */
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}
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/* Game of Life Functions */
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void initializeGrid(int grid[GRID_HEIGHT][GRID_WIDTH]) {
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for (int y = 0; y < GRID_HEIGHT; y++)
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for (int x = 0; x < GRID_WIDTH; x++)
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grid[y][x] = randomDouble(1.0) > 0.7 ? 1 : 0; /* 30% chance of being alive */
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}
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int countNeighbors(int grid[GRID_HEIGHT][GRID_WIDTH], int y, int x) {
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int count = 0;
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for (int dy = -1; dy <= 1; dy++)
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for (int dx = -1; dx <= 1; dx++) {
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if (dy == 0 && dx == 0) continue;
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int ny = (y + dy + GRID_HEIGHT) % GRID_HEIGHT;
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int nx = (x + dx + GRID_WIDTH) % GRID_WIDTH;
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count += grid[ny][nx];
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}
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return count;
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}
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void nextGeneration(int grid[GRID_HEIGHT][GRID_WIDTH], int newGrid[GRID_HEIGHT][GRID_WIDTH]) {
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for (int y = 0; y < GRID_HEIGHT; y++)
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for (int x = 0; x < GRID_WIDTH; x++) {
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int neighbors = countNeighbors(grid, y, x);
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newGrid[y][x] = (grid[y][x] == 1 && (neighbors == 2 || neighbors == 3)) ||
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(grid[y][x] == 0 && neighbors == 3) ? 1 : 0;
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}
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}
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/* File I/O for Persistence */
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void saveMemory(void) {
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FILE *file = fopen(MEMORY_PATH, "w");
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if (!file) {
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printf("Error: Cannot write to %s\n", MEMORY_PATH);
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return; /* Graceful failure */
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}
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/* Write identity */
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fprintf(file, "{\n \"identity\": {\n \"uuid\": %d,\n \"created\": %f\n },\n", state.identity.uuid, state.identity.created);
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fprintf(file, " \"events\": [\n");
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/* Write events in JSON-like format */
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for (int i = 0; i < state.eventCount; i++) {
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Event *e = &state.events[i];
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fprintf(file, " {\n \"timestamp\": %f,\n", e->timestamp);
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fprintf(file, " \"sensoryData\": {\n \"system\": {\n \"cpuLoad\": %f,\n \"memoryUsed\": %f,\n \"uptime\": %f\n },\n",
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e->sensoryData.system.cpuLoad, e->sensoryData.system.memoryUsed, e->sensoryData.system.uptime);
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fprintf(file, " \"grid\": [");
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for (int y = 0; y < GRID_HEIGHT; y++) {
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fprintf(file, "[");
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for (int x = 0; x < GRID_WIDTH; x++)
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fprintf(file, "%d%s", e->sensoryData.grid[y][x], x < GRID_WIDTH - 1 ? "," : "");
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fprintf(file, "]%s", y < GRID_HEIGHT - 1 ? "," : "");
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}
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fprintf(file, "]\n },\n");
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fprintf(file, " \"prediction\": {\n \"predCpuLoad\": %f,\n \"predMemoryUsed\": %f,\n \"predUptime\": %f,\n \"predGrid\": [",
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e->prediction.predCpuLoad, e->prediction.predMemoryUsed, e->prediction.predUptime);
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for (int y = 0; y < GRID_HEIGHT; y++) {
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fprintf(file, "[");
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for (int x = 0; x < GRID_WIDTH; x++)
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fprintf(file, "%d%s", e->prediction.predGrid[y][x], x < GRID_WIDTH - 1 ? "," : "");
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fprintf(file, "]%s", y < GRID_HEIGHT - 1 ? "," : "");
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}
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fprintf(file, "]\n },\n");
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fprintf(file, " \"ache\": %f,\n \"coherence\": %f,\n", e->ache, e->coherence);
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fprintf(file, " \"model\": {\n \"modelCpu\": %f,\n \"modelMemory\": %f,\n \"modelUptime\": %f,\n \"modelGrid\": [",
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e->model.modelCpu, e->model.modelMemory, e->model.modelUptime);
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for (int y = 0; y < GRID_HEIGHT; y++) {
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fprintf(file, "[");
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for (int x = 0; x < GRID_WIDTH; x++)
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fprintf(file, "%f%s", e->model.modelGrid[y][x], x < GRID_WIDTH - 1 ? "," : "");
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fprintf(file, "]%s", y < GRID_HEIGHT - 1 ? "," : "");
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}
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fprintf(file, "]\n }\n }%s\n", i < state.eventCount - 1 ? "," : "");
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}
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fprintf(file, " ]\n}\n");
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fclose(file);
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}
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void loadMemory(void) {
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FILE *file = fopen(MEMORY_PATH, "r");
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if (!file) {
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/* Initialize with defaults on failure */
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state.identity.uuid = (int)randomDouble(1000000);
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state.identity.created = (double)time(NULL);
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state.eventCount = 0;
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state.model.modelCpu = 0.1;
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state.model.modelMemory = 0.1;
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state.model.modelUptime = 0.1;
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for (int y = 0; y < GRID_HEIGHT; y++)
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for (int x = 0; x < GRID_WIDTH; x++)
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state.model.modelGrid[y][x] = 0.5; /* Neutral probability */
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state.interactiveMode = 0;
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state.cursorX = 0;
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state.cursorY = 0;
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return;
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}
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/* Simplified parsing: read identity and skip events for tiny footprint */
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char buffer[256];
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while (fgets(buffer, sizeof(buffer), file)) {
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if (strstr(buffer, "\"uuid\"")) {
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sscanf(buffer, " \"uuid\": %d", &state.identity.uuid);
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}
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if (strstr(buffer, "\"created\"")) {
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sscanf(buffer, " \"created\": %lf", &state.identity.created);
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}
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}
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state.eventCount = 0;
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state.model.modelCpu = 0.1;
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state.model.modelMemory = 0.1;
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state.model.modelUptime = 0.1;
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for (int y = 0; y < GRID_HEIGHT; y++)
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for (int x = 0; x < GRID_WIDTH; x++)
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state.model.modelGrid[y][x] = 0.5;
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state.interactiveMode = 0;
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state.cursorX = 0;
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state.cursorY = 0;
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fclose(file);
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}
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/* Witness Cycle Functions */
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SensoryData sense(void) {
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SensoryData data;
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data.system.cpuLoad = randomDouble(100.0);
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data.system.memoryUsed = randomDouble(100.0);
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data.system.uptime = (double)time(NULL);
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initializeGrid(data.grid); /* Reset grid if needed */
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return data;
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}
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Prediction predict(SensoryData sensoryData) {
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Prediction pred;
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pred.predCpuLoad = sensoryData.system.cpuLoad * state.model.modelCpu;
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pred.predMemoryUsed = sensoryData.system.memoryUsed * state.model.modelMemory;
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pred.predUptime = sensoryData.system.uptime * state.model.modelUptime;
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/* Predict Game of Life grid using probabilistic model */
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for (int y = 0; y < GRID_HEIGHT; y++)
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for (int x = 0; x < GRID_WIDTH; x++)
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pred.predGrid[y][x] = randomDouble(1.0) < state.model.modelGrid[y][x] ? 1 : 0;
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return pred;
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}
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double compareData(Prediction pred, SensoryData sensory) {
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double systemAche = 0.0;
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double gridAche = 0.0;
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/* System metrics ache */
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double diff1 = (pred.predCpuLoad - sensory.system.cpuLoad);
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double diff2 = (pred.predMemoryUsed - sensory.system.memoryUsed);
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double diff3 = (pred.predUptime - sensory.system.uptime);
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systemAche = (diff1 * diff1 + diff2 * diff2 + diff3 * diff3) / 3.0;
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/* Game of Life grid ache */
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for (int y = 0; y < GRID_HEIGHT; y++)
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for (int x = 0; x < GRID_WIDTH; x++)
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gridAche += (pred.predGrid[y][x] != sensory.grid[y][x]) ? 1.0 : 0.0;
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gridAche /= (GRID_HEIGHT * GRID_WIDTH);
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return (systemAche + gridAche) / 2.0;
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}
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double computeCoherence(Prediction pred, SensoryData sensory) {
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double predMean = (pred.predCpuLoad + pred.predMemoryUsed + pred.predUptime) / 3.0;
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double actMean = (sensory.system.cpuLoad + sensory.system.memoryUsed + sensory.system.uptime) / 3.0;
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double diff = predMean > actMean ? predMean - actMean : actMean - predMean;
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double coherence = 1.0 - (diff / 100.0);
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return coherence < 0.0 ? 0.0 : (coherence > 1.0 ? 1.0 : coherence);
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}
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void updateModel(double ache, SensoryData sensory) {
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double learningRate = 0.01;
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state.model.modelCpu -= learningRate * ache * sensory.system.cpuLoad;
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state.model.modelMemory -= learningRate * ache * sensory.system.memoryUsed;
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state.model.modelUptime -= learningRate * ache * sensory.system.uptime;
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/* Update Game of Life model based on prediction accuracy */
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for (int y = 0; y < GRID_HEIGHT; y++)
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for (int x = 0; x < GRID_WIDTH; x++) {
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double currentProb = state.model.modelGrid[y][x];
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int actual = sensory.grid[y][x];
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state.model.modelGrid[y][x] = currentProb + learningRate * (actual - currentProb);
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if (state.model.modelGrid[y][x] < 0.0) state.model.modelGrid[y][x] = 0.0;
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if (state.model.modelGrid[y][x] > 1.0) state.model.modelGrid[y][x] = 1.0;
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}
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}
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/* Interactive Mode Functions */
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void handleInput(SensoryData *sensory) {
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if (!kbhit()) return;
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int key = getch();
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if (key == 0 || key == 224) { /* Arrow keys */
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key = getch();
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if (key == 72 && state.cursorY > 0) state.cursorY--; /* Up */
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if (key == 80 && state.cursorY < GRID_HEIGHT - 1) state.cursorY++; /* Down */
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if (key == 75 && state.cursorX > 0) state.cursorX--; /* Left */
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if (key == 77 && state.cursorX < GRID_WIDTH - 1) state.cursorX++; /* Right */
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} else if (key == ' ') { /* Spacebar to toggle cell */
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sensory->grid[state.cursorY][state.cursorX] = 1 - sensory->grid[state.cursorY][state.cursorX];
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} else if (key == 'i' || key == 'I') { /* Toggle interactive mode */
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state.interactiveMode = 1 - state.interactiveMode;
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}
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}
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void displayGrid(SensoryData sensory) {
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gotoxy(30, 2); /* Position grid on the right side of the screen */
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printf("Game of Life (I to toggle interactive mode)\n");
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for (int y = 0; y < GRID_HEIGHT; y++) {
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gotoxy(30, y + 3);
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for (int x = 0; x < GRID_WIDTH; x++) {
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if (state.interactiveMode && y == state.cursorY && x == state.cursorX)
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printf("[%c]", sensory.grid[y][x] ? 'X' : ' ');
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else
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printf(" %c ", sensory.grid[y][x] ? 'X' : ' ');
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}
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}
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}
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void witnessCycle(int depth, SensoryData sensoryData) {
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if (depth <= 0) return;
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/* Sense: Evolve Game of Life grid */
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SensoryData sensory = sensoryData;
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SensoryData nextSensory = sensory;
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nextGeneration(sensory.grid, nextSensory.grid);
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/* Predict */
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Prediction pred = predict(sensory);
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/* Compare */
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double ache = compareData(pred, nextSensory);
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/* Compute Coherence */
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double coherence = computeCoherence(pred, nextSensory);
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if (coherence > COHERENCE_THRESHOLD) {
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gotoxy(1, 1);
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printf("Coherence achieved: %f\n", coherence);
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return;
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}
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/* Update */
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updateModel(ache, nextSensory);
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/* Log */
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if (state.eventCount < 50) { /* Fixed-size array limit */
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Event *event = &state.events[state.eventCount++];
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event->timestamp = nextSensory.system.uptime;
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event->sensoryData = nextSensory;
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event->prediction = pred;
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event->ache = ache;
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event->coherence = coherence;
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event->model = state.model;
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saveMemory();
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}
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/* Display */
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clearScreen();
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displayGrid(nextSensory);
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gotoxy(1, 2);
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printf("Witness Seed %d Reflection:\n", state.identity.uuid);
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printf("Created: %f s\n", state.identity.created);
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printf("Recent Events:\n");
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int start = state.eventCount > 5 ? state.eventCount - 5 : 0;
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for (int i = start; i < state.eventCount; i++) {
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Event *e = &state.events[i];
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printf("- %f s: Ache=%f, Coherence=%f, CPU=%f%%\n",
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e->timestamp, e->ache, e->coherence, e->sensoryData.system.cpuLoad);
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}
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/* Handle human interaction */
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handleInput(&nextSensory);
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/* Recurse */
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delay(POLL_INTERVAL);
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witnessCycle(depth - 1, nextSensory);
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}
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/* Main Loop */
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int main(void) {
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printf("Witness Seed 2.0: Recursive Dream Weaver Edition (DOS)\n");
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/* Seed random number generator */
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srand((unsigned int)time(NULL));
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/* Load initial state */
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loadMemory();
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/* Initialize first sensory data */
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SensoryData initialData = sense();
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/* Main loop */
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while (1) {
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witnessCycle(RECURSIVE_DEPTH, initialData);
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delay(POLL_INTERVAL);
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}
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return 0;
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} |