How to Build a Personal Brand as a 2026 AI/ML Engineer: Step-by-Step Guide Using GitHub Copilot 2.0

import requests
import os
import json
from datetime import datetime, timedelta
from typing import Dict, List, Optional

class GitHubProfileAuditor:
    '''Audits a GitHub user's profile for AI/ML personal brand readiness.'''

    def __init__(self, github_username: str, github_token: Optional[str] = None):
        self.username = github_username
        self.token = github_token or os.getenv('GITHUB_TOKEN')
        self.headers = {'Authorization': f'token {self.token}'} if self.token else {}
        self.base_url = 'https://api.github.com'
        self.ai_ml_keywords = {'pytorch', 'tensorflow', 'sklearn', 'huggingface', 'transformers', 'mlops', 'llm', 'diffusion'}

    def get_user_repos(self) -> List[Dict]:
        '''Fetch all public repositories for the user, with error handling for rate limits.'''
        repos = []
        page = 1
        while True:
            try:
                response = requests.get(
                    f'{self.base_url}/users/{self.username}/repos',
                    headers=self.headers,
                    params={'page': page, 'per_page': 100, 'type': 'public'}
                )
                response.raise_for_status()
                batch = response.json()
                if not batch:
                    break
                repos.extend(batch)
                page += 1
                # Respect GitHub rate limits: 60 requests/hour unauthenticated, 5000 authenticated
                if 'X-RateLimit-Remaining' in response.headers:
                    remaining = int(response.headers['X-RateLimit-Remaining'])
                    if remaining < 10:
                        print(f'Warning: Only {remaining} API requests remaining. Consider adding a GITHUB_TOKEN.')
            except requests.exceptions.HTTPError as e:
                if e.response.status_code == 403:
                    print(f'Rate limit exceeded: {e}. Wait 1 hour or add a GITHUB_TOKEN.')
                    break
                elif e.response.status_code == 404:
                    raise ValueError(f'GitHub user {self.username} not found.') from e
                else:
                    raise
            except requests.exceptions.RequestException as e:
                print(f'Network error: {e}. Retrying...')
                continue
        return repos

    def classify_ai_ml_repos(self, repos: List[Dict]) -> Dict[str, List[Dict]]:
        '''Classify repositories as AI/ML or non-AI/ML based on keywords in name, description, and topics.'''
        ai_ml_repos = []
        non_ai_ml_repos = []
        for repo in repos:
            repo_text = ' '.join([
                repo.get('name', ''),
                repo.get('description', '') or '',
                ' '.join(repo.get('topics', []))
            ]).lower()
            if any(keyword in repo_text for keyword in self.ai_ml_keywords):
                ai_ml_repos.append(repo)
            else:
                non_ai_ml_repos.append(repo)
        return {'ai_ml': ai_ml_repos, 'non_ai_ml': non_ai_ml_repos}

    def generate_audit_report(self) -> Dict:
        '''Generate a full audit report with contribution stats, star counts, and AI/ML alignment.'''
        repos = self.get_user_repos()
        classified = self.classify_ai_ml_repos(repos)
        ai_ml_stars = sum(repo.get('stargazers_count', 0) for repo in classified['ai_ml'])
        total_stars = sum(repo.get('stargazers_count', 0) for repo in repos)
        report = {
            'username': self.username,
            'total_repos': len(repos),
            'ai_ml_repos': len(classified['ai_ml']),
            'non_ai_ml_repos': len(classified['non_ai_ml']),
            'total_stars': total_stars,
            'ai_ml_stars': ai_ml_stars,
            'ai_ml_star_percentage': (ai_ml_stars / total_stars * 100) if total_stars > 0 else 0,
            'last_updated': datetime.now().isoformat()
        }
        return report

if __name__ == '__main__':
    # Replace with your GitHub username; set GITHUB_TOKEN env var for higher rate limits
    auditor = GitHubProfileAuditor(github_username='your-username-here')
    try:
        report = auditor.generate_audit_report()
        print(json.dumps(report, indent=2))
        # Save report to file for later comparison
        with open(f'github_audit_{report["username"]}.json', 'w') as f:
            json.dump(report, f, indent=2)
        print(f'Audit report saved to github_audit_{report["username"]}.json')
    except Exception as e:
        print(f'Audit failed: {e}')

import yaml
import os
from pathlib import Path
from typing import Dict, Any

class Copilot2ConfigGenerator:
    '''Generates optimized GitHub Copilot 2.0 configuration files for AI/ML projects.'''

    def __init__(self, project_root: Path, ml_framework: str = 'pytorch'):
        self.project_root = project_root
        self.ml_framework = ml_framework.lower()
        self.valid_frameworks = {'pytorch', 'tensorflow', 'jax', 'huggingface'}
        if self.ml_framework not in self.valid_frameworks:
            raise ValueError(f'Unsupported framework: {ml_framework}. Choose from {self.valid_frameworks}')

    def generate_context_config(self) -> Dict[str, Any]:
        '''Generate multi-repo context configuration for Copilot 2.0, prioritizing AI/ML dependencies.'''
        # Copilot 2.0 uses these paths to index context for code suggestions
        context_paths = [
            'src/',
            'notebooks/',
            'configs/',
            'tests/',
            'requirements.txt',
            'pyproject.toml'
        ]
        # Add framework-specific context paths
        if self.ml_framework == 'pytorch':
            context_paths.extend(['models/pytorch/', 'data/dataloaders/'])
        elif self.ml_framework == 'tensorflow':
            context_paths.extend(['models/tensorflow/', 'data/tfrecords/'])
        elif self.ml_framework == 'huggingface':
            context_paths.extend(['models/hf/', 'datasets/', 'tokenizers/'])

        config = {
            'version': '2.0',
            'project_type': 'ai_ml',
            'ml_framework': self.ml_framework,
            'context': {
                'include_paths': context_paths,
                'exclude_paths': ['__pycache__/', '*.pth', '*.ckpt', 'data/raw/'],
                'max_context_tokens': 128000,  # Copilot 2.0 supports 128k context windows
                'priority_repos': [
                    'https://github.com/pytorch/pytorch',
                    'https://github.com/huggingface/transformers',
                    'https://github.com/ray-project/ray'
                ]
            },
            'suggestions': {
                'enable_multiline': True,
                'enable_docstring_generation': True,
                'enable_test_generation': True,
                'ai_ml_specific': {
                    'suggest_model_architectures': True,
                    'suggest_dataloader_patterns': True,
                    'suggest_mlops_pipelines': True
                }
            }
        }
        return config

    def write_config(self) -> Path:
        '''Write Copilot 2.0 config to .copilot/config.yaml in project root.'''
        config_dir = self.project_root / '.copilot'
        config_dir.mkdir(exist_ok=True)
        config_path = config_dir / 'config.yaml'
        config = self.generate_context_config()
        try:
            with open(config_path, 'w') as f:
                yaml.dump(config, f, sort_keys=False, default_flow_style=False)
            print(f'Copilot 2.0 config written to {config_path}')
            return config_path
        except Exception as e:
            raise IOError(f'Failed to write config to {config_path}: {e}') from e

    def validate_config(self) -> bool:
        '''Validate that the generated config is compatible with Copilot 2.0 v2.1+.'''
        config = self.generate_context_config()
        # Check required fields
        required_fields = ['version', 'project_type', 'context', 'suggestions']
        for field in required_fields:
            if field not in config:
                print(f'Missing required field: {field}')
                return False
        # Check context token limit
        if config['context']['max_context_tokens'] > 128000:
            print('Copilot 2.0 only supports up to 128k context tokens. Reduce max_context_tokens.')
            return False
        print('Config validation passed.')
        return True

if __name__ == '__main__':
    # Replace with your AI/ML project root path
    project_root = Path('./my-ai-ml-project')
    project_root.mkdir(exist_ok=True)

    # Initialize config generator for PyTorch project
    generator = Copilot2ConfigGenerator(
        project_root=project_root,
        ml_framework='pytorch'
    )

    try:
        # Validate config before writing
        if generator.validate_config():
            config_path = generator.write_config()
            # Print config for review
            with open(config_path) as f:
                print('\nGenerated Copilot 2.0 Config:')
                print(f.read())
    except Exception as e:
        print(f'Config generation failed: {e}')

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, Dataset
from torchvision import datasets, transforms
import matplotlib.pyplot as plt
from typing import Tuple
import os
import argparse
from tqdm import tqdm

class SignatureImageClassifier(nn.Module):
    '''CNN-based image classifier for signature AI/ML project, optimized for Copilot 2.0 suggestions.'''

    def __init__(self, num_classes: int = 10, input_channels: int = 3):
        super().__init__()
        # Convolutional layers with batch norm for stable training
        self.conv_layers = nn.Sequential(
            nn.Conv2d(input_channels, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2),

            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )

        # Fully connected layers for classification
        self.fc_layers = nn.Sequential(
            nn.Dropout(0.5),
            nn.Linear(128 * 4 * 4, 512),  # Assuming 32x32 input images (CIFAR-10)
            nn.ReLU(),
            nn.Dropout(0.3),
            nn.Linear(512, num_classes)
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.conv_layers(x)
        x = x.view(x.size(0), -1)  # Flatten for FC layers
        x = self.fc_layers(x)
        return x

class CIFAR10Dataset(Dataset):
    '''Wrapped CIFAR-10 dataset with custom transforms for reproducibility.'''

    def __init__(self, train: bool = True):
        transform = transforms.Compose([
            transforms.RandomHorizontalFlip(),
            transforms.RandomCrop(32, padding=4),
            transforms.ToTensor(),
            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
        ])
        self.dataset = datasets.CIFAR10(
            root='./data',
            train=train,
            download=True,
            transform=transform
        )

    def __len__(self) -> int:
        return len(self.dataset)

    def __getitem__(self, idx: int) -> Tuple[torch.Tensor, int]:
        return self.dataset[idx]

def train_model(
    model: nn.Module,
    train_loader: DataLoader,
    val_loader: DataLoader,
    epochs: int = 10,
    learning_rate: float = 0.001,
    device: str = 'cuda' if torch.cuda.is_available() else 'cpu'
) -> Tuple[nn.Module, Dict[str, List[float]]]:
    '''Train the image classifier with progress tracking and error handling.'''
    model = model.to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)
    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=3)

    history = {'train_loss': [], 'train_acc': [], 'val_loss': [], 'val_acc': []}

    for epoch in range(epochs):
        # Training phase
        model.train()
        train_loss = 0.0
        correct = 0
        total = 0
        for inputs, labels in tqdm(train_loader, desc=f'Epoch {epoch+1}/{epochs} Train'):
            inputs, labels = inputs.to(device), labels.to(device)
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            train_loss += loss.item()
            _, predicted = outputs.max(1)
            total += labels.size(0)
            correct += predicted.eq(labels).sum().item()

        train_acc = 100. * correct / total
        avg_train_loss = train_loss / len(train_loader)
        history['train_loss'].append(avg_train_loss)
        history['train_acc'].append(train_acc)

        # Validation phase
        model.eval()
        val_loss = 0.0
        correct = 0
        total = 0
        with torch.no_grad():
            for inputs, labels in val_loader:
                inputs, labels = inputs.to(device), labels.to(device)
                outputs = model(inputs)
                loss = criterion(outputs, labels)
                val_loss += loss.item()
                _, predicted = outputs.max(1)
                total += labels.size(0)
                correct += predicted.eq(labels).sum().item()

        val_acc = 100. * correct / total
        avg_val_loss = val_loss / len(val_loader)
        history['val_loss'].append(avg_val_loss)
        history['val_acc'].append(val_acc)
        scheduler.step(avg_val_loss)

        print(f'Epoch {epoch+1}: Train Loss {avg_train_loss:.4f}, Train Acc {train_acc:.2f}%, Val Loss {avg_val_loss:.4f}, Val Acc {val_acc:.2f}%')

    return model, history

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Train signature AI/ML image classifier')
    parser.add_argument('--epochs', type=int, default=10, help='Number of training epochs')
    parser.add_argument('--batch-size', type=int, default=64, help='Batch size for training')
    parser.add_argument('--lr', type=float, default=0.001, help='Learning rate')
    args = parser.parse_args()

    # Set random seeds for reproducibility
    torch.manual_seed(42)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(42)

    # Load datasets
    train_dataset = CIFAR10Dataset(train=True)
    val_dataset = CIFAR10Dataset(train=False)
    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=2)
    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=2)

    # Initialize model
    model = SignatureImageClassifier(num_classes=10)
    print(f'Model initialized. Total parameters: {sum(p.numel() for p in model.parameters()):,}')

    try:
        # Train model
        trained_model, history = train_model(
            model=model,
            train_loader=train_loader,
            val_loader=val_loader,
            epochs=args.epochs,
            learning_rate=args.lr
        )
        # Save model
        torch.save(trained_model.state_dict(), 'signature_classifier.pth')
        print('Model saved to signature_classifier.pth')
        # Plot training history
        plt.figure(figsize=(12,4))
        plt.subplot(1,2,1)
        plt.plot(history['train_loss'], label='Train Loss')
        plt.plot(history['val_loss'], label='Val Loss')
        plt.legend()
        plt.title('Training and Validation Loss')
        plt.subplot(1,2,2)
        plt.plot(history['train_acc'], label='Train Acc')
        plt.plot(history['val_acc'], label='Val Acc')
        plt.legend()
        plt.title('Training and Validation Accuracy')
        plt.savefig('training_history.png')
        print('Training history saved to training_history.png')
    except Exception as e:
        print(f'Training failed: {e}')
        raise

import os
import json
import requests
from typing import Dict, List, Optional
from datetime import datetime
import markdown
from pathlib import Path

class CopilotContentGenerator:
    '''Generates technical blog posts for AI/ML personal brands using GitHub Copilot 2.0 API.'''

    def __init__(self, copilot_token: str, blog_topic: str, project_context: Optional[Dict] = None):
        self.token = copilot_token
        self.topic = blog_topic
        self.project_context = project_context or {}
        self.api_url = 'https://api.github.com/copilot/v2/generations'
        self.headers = {
            'Authorization': f'Bearer {self.token}',
            'Content-Type': 'application/json',
            'X-GitHub-Api-Version': '2022-11-28'
        }

    def generate_blog_outline(self) -> List[str]:
        '''Generate a blog post outline using Copilot 2.0 with AI/ML context.'''
        prompt = f'''Generate a detailed technical blog post outline for the topic: {self.topic}.
        The blog is for AI/ML engineers, senior level audience. Include code examples, benchmarks, and real-world use cases.
        Project context: {json.dumps(self.project_context, indent=2)}
        Return the outline as a JSON list of section headings.'''
        try:
            response = requests.post(
                self.api_url,
                headers=self.headers,
                json={
                    'prompt': prompt,
                    'max_tokens': 500,
                    'temperature': 0.3,  # Low temperature for structured output
                    'stream': False
                }
            )
            response.raise_for_status()
            generated_text = response.json().get('choices', [{}])[0].get('text', '')
            # Parse JSON list from generated text
            outline = json.loads(generated_text.strip())
            if not isinstance(outline, list):
                raise ValueError('Generated outline is not a valid list.')
            print(f'Generated outline with {len(outline)} sections.')
            return outline
        except requests.exceptions.HTTPError as e:
            if e.response.status_code == 401:
                raise ValueError('Invalid Copilot token. Check your COPILOT_TOKEN env var.') from e
            else:
                raise
        except json.JSONDecodeError as e:
            raise ValueError(f'Failed to parse outline JSON: {e}. Generated text: {generated_text}') from e

    def generate_blog_post(self, outline: List[str]) -> str:
        '''Generate full blog post content from outline, with code blocks and benchmarks.'''
        outline_str = '\n'.join([f'{i+1}. {section}' for i, section in enumerate(outline)])
        prompt = f'''Write a 2000-word technical blog post for senior AI/ML engineers following this outline:
        {outline_str}
        Topic: {self.topic}
        Include:
        - At least 2 runnable Python code blocks with error handling
        - A comparison table with actual benchmark numbers
        - Real-world case study
        - Troubleshooting tips
        Use Markdown formatting. Project context: {json.dumps(self.project_context, indent=2)}'''
        try:
            response = requests.post(
                self.api_url,
                headers=self.headers,
                json={
                    'prompt': prompt,
                    'max_tokens': 4000,
                    'temperature': 0.5,
                    'stream': False
                }
            )
            response.raise_for_status()
            blog_content = response.json().get('choices', [{}])[0].get('text', '')
            # Add frontmatter for static site generators
            frontmatter = f'''---
title: "{self.topic}"
date: {datetime.now().strftime('%Y-%m-%d')}
tags: [ai-ml, personal-brand, github-copilot, {self.project_context.get('ml_framework', 'pytorch')}]
---
'''
            full_post = frontmatter + blog_content
            return full_post
        except Exception as e:
            raise RuntimeError(f'Failed to generate blog post: {e}') from e

    def save_blog_post(self, content: str, output_dir: Path = Path('./blog-posts')) -> Path:
        '''Save generated blog post to Markdown file, with slugified filename.'''
        output_dir.mkdir(exist_ok=True)
        slug = self.topic.lower().replace(' ', '-').replace(':', '')[:50]
        filename = f'{datetime.now().strftime('%Y-%m-%d')}-{slug}.md'
        output_path = output_dir / filename
        try:
            with open(output_path, 'w') as f:
                f.write(content)
            print(f'Blog post saved to {output_path}')
            return output_path
        except IOError as e:
            raise IOError(f'Failed to save blog post to {output_path}: {e}') from e

if __name__ == '__main__':
    # Set COPILOT_TOKEN env var with your GitHub Copilot 2.0 API token
    copilot_token = os.getenv('COPILOT_TOKEN')
    if not copilot_token:
        raise ValueError('COPILOT_TOKEN env var not set. Get your token from GitHub Copilot settings.')

    # Define blog topic and project context
    blog_topic = 'Optimizing PyTorch DataLoaders for 2x Faster Training Throughput'
    project_context = {
        'ml_framework': 'pytorch',
        'project_name': 'signature-image-classifier',
        'repo_url': 'https://github.com/your-username/signature-image-classifier',
        'benchmarks': {
            'default_dataloader_throughput': '1200 samples/sec',
            'optimized_dataloader_throughput': '2400 samples/sec'
        }
    }

    generator = CopilotContentGenerator(
        copilot_token=copilot_token,
        blog_topic=blog_topic,
        project_context=project_context
    )

    try:
        # Generate outline first
        outline = generator.generate_blog_outline()
        print('\nGenerated Blog Outline:')
        for section in outline:
            print(f'- {section}')

        # Generate full post
        blog_post = generator.generate_blog_post(outline)
        print('\nGenerated Blog Post Preview (first 500 chars):')
        print(blog_post[:500] + '...')

        # Save post
        generator.save_blog_post(blog_post)
    except Exception as e:
        print(f'Content generation failed: {e}')

import requests
import os
import json
from datetime import datetime, timedelta
from typing import Dict, List, Optional
import matplotlib.pyplot as plt
from pathlib import Path

class BrandGrowthTracker:
    '''Tracks personal brand growth metrics for AI/ML engineers across GitHub, content, and social.'''

    def __init__(
        self,
        github_username: str,
        github_token: Optional[str] = None,
        twitter_handle: Optional[str] = None,
        twitter_token: Optional[str] = None
    ):
        self.github_username = github_username
        self.github_token = github_token or os.getenv('GITHUB_TOKEN')
        self.twitter_handle = twitter_handle
        self.twitter_token = twitter_token or os.getenv('TWITTER_TOKEN')
        self.github_headers = {'Authorization': f'token {self.github_token}'} if self.github_token else {}
        self.twitter_headers = {'Authorization': f'Bearer {self.twitter_token}'} if self.twitter_token else {}
        self.metrics_history = []

    def get_github_metrics(self) -> Dict:
        '''Fetch GitHub metrics: stars, forks, contributors, traffic.'''
        metrics = {}
        try:
            # Get user repos
            repos_response = requests.get(
                f'https://api.github.com/users/{self.github_username}/repos',
                headers=self.github_headers,
                params={'per_page': 100}
            )
            repos_response.raise_for_status()
            repos = repos_response.json()
            metrics['total_stars'] = sum(repo.get('stargazers_count', 0) for repo in repos)
            metrics['total_forks'] = sum(repo.get('forks_count', 0) for repo in repos)
            metrics['total_repos'] = len(repos)

            # Get traffic (requires push access to repos)
            if self.github_token:
                traffic = 0
                for repo in repos:
                    repo_name = repo.get('name')
                    try:
                        traffic_response = requests.get(
                            f'https://api.github.com/repos/{self.github_username}/{repo_name}/traffic/views',
                            headers=self.github_headers
                        )
                        traffic_response.raise_for_status()
                        traffic += traffic_response.json().get('count', 0)
                    except:
                        continue
                metrics['github_traffic_views'] = traffic
            else:
                metrics['github_traffic_views'] = 0
                print('Warning: No GITHUB_TOKEN set. GitHub traffic metrics unavailable.')
        except Exception as e:
            print(f'Failed to fetch GitHub metrics: {e}')
            metrics['github_error'] = str(e)
        return metrics

    def get_content_metrics(self, blog_dir: Path = Path('./blog-posts')) -> Dict:
        '''Track blog post metrics: word count, publication frequency, estimated reach.'''
        metrics = {'total_posts': 0, 'total_words': 0, 'avg_words_per_post': 0, 'posts_last_30_days': 0}
        if not blog_dir.exists():
            print(f'Blog directory {blog_dir} not found. Skipping content metrics.')
            return metrics
        thirty_days_ago = datetime.now() - timedelta(days=30)
        for post_path in blog_dir.glob('*.md'):
            metrics['total_posts'] += 1
            with open(post_path) as f:
                content = f.read()
                word_count = len(content.split())
                metrics['total_words'] += word_count
            # Check if post was published in last 30 days
            try:
                post_date_str = post_path.stem.split('-')[0:3]  # Assumes YYYY-MM-DD filename
                post_date = datetime.strptime('-'.join(post_date_str), '%Y-%m-%d')
                if post_date >= thirty_days_ago:
                    metrics['posts_last_30_days'] += 1
            except:
                continue
        if metrics['total_posts'] > 0:
            metrics['avg_words_per_post'] = metrics['total_words'] / metrics['total_posts']
        return metrics

    def get_social_metrics(self) -> Dict:
        '''Fetch Twitter/X metrics for brand reach (requires Twitter API token).'''
        metrics = {'twitter_followers': 0, 'twitter_impressions': 0}
        if not self.twitter_token or not self.twitter_handle:
            print('Warning: No Twitter token or handle. Skipping social metrics.')
            return metrics
        try:
            # Get user followers
            user_response = requests.get(
                f'https://api.twitter.com/2/users/by/username/{self.twitter_handle}',
                headers=self.twitter_headers,
                params={'user.fields': 'public_metrics'}
            )
            user_response.raise_for_status()
            user_data = user_response.json().get('data', {})
            metrics['twitter_followers'] = user_data.get('public_metrics', {}).get('followers_count', 0)
        except Exception as e:
            print(f'Failed to fetch Twitter metrics: {e}')
            metrics['twitter_error'] = str(e)
        return metrics

    def capture_metrics(self) -> Dict:
        '''Capture all metrics and save to history.'''
        github_metrics = self.get_github_metrics()
        content_metrics = self.get_content_metrics()
        social_metrics = self.get_social_metrics()
        full_metrics = {
            'timestamp': datetime.now().isoformat(),
            'github': github_metrics,
            'content': content_metrics,
            'social': social_metrics
        }
        self.metrics_history.append(full_metrics)
        return full_metrics

    def save_metrics_history(self, output_dir: Path = Path('./brand-metrics')) -> Path:
        '''Save metrics history to JSON file for trend analysis.'''
        output_dir.mkdir(exist_ok=True)
        output_path = output_dir / f'brand_metrics_{self.github_username}.json'
        try:
            with open(output_path, 'w') as f:
                json.dump(self.metrics_history, f, indent=2)
            print(f'Metrics history saved to {output_path}')
            return output_path
        except IOError as e:
            raise IOError(f'Failed to save metrics: {e}') from e

    def plot_growth_trends(self) -> None:
        '''Plot GitHub stars and content growth over time.'''
        if not self.metrics_history:
            print('No metrics history to plot.')
            return
        timestamps = [m['timestamp'] for m in self.metrics_history]
        stars = [m['github'].get('total_stars', 0) for m in self.metrics_history]
        posts = [m['content'].get('total_posts', 0) for m in self.metrics_history]

        plt.figure(figsize=(12, 6))
        plt.plot(timestamps, stars, label='GitHub Total Stars', marker='o')
        plt.plot(timestamps, posts, label='Total Blog Posts', marker='s')
        plt.xlabel('Date')
        plt.ylabel('Count')
        plt.title('Personal Brand Growth Trends')
        plt.legend()
        plt.xticks(rotation=45)
        plt.tight_layout()
        plt.savefig('brand_growth_trends.png')
        print('Growth trends plot saved to brand_growth_trends.png')

if __name__ == '__main__':
    tracker = BrandGrowthTracker(
        github_username='your-username-here',
        twitter_handle='your-twitter-handle'
    )

    try:
        # Capture current metrics
        current_metrics = tracker.capture_metrics()
        print('\nCurrent Brand Metrics:')
        print(json.dumps(current_metrics, indent=2))

        # Save history
        tracker.save_metrics_history()

        # Plot trends
        tracker.plot_growth_trends()
    except Exception as e:
        print(f'Metrics tracking failed: {e}')

context:
  priority_repos:
    - 'https://github.com/huggingface/transformers'
    - 'https://github.com/your-username/llm-optimization-toolkit'
    - 'https://github.com/pytorch/pytorch'

copilot generate --prompt 'Write a 1500-word blog post on optimizing PyTorch DataLoaders for CIFAR-10 training, including code examples and benchmarks' --output blog-post.md

import mlflow
mlflow.start_run()
mlflow.log_param('epochs', args.epochs)
mlflow.log_param('batch_size', args.batch_size)
mlflow.log_metric('val_acc', val_acc)
mlflow.pytorch.log_model(trained_model, 'model')
mlflow.end_run()

your-ai-ml-brand-repo/
├── src/
│   ├── audit/
│   │   └── github_auditor.py  # Step 1 code
│   ├── config/
│   │   └── copilot_config.yaml  # Step 2 config
│   ├── projects/
│   │   └── signature-image-classifier/  # Step 3 code
│   ├── content/
│   │   └── blog_posts/  # Step 4 generated content
│   └── tracking/
│       └── brand_tracker.py  # Step 5 code
├── requirements.txt
├── README.md
└── LICENSE