Architecting resilient backends & intelligent web systems.

// Express SSE Streaming Handler
app.post('/api/chat/stream', async (req, res) => {
  res.writeHead(200, {
    'Content-Type': 'text/event-stream',
    'Cache-Control': 'no-cache',
    'Connection': 'keep-alive',
  });

  const { prompt, persona, history } = req.body;
  const stream = await geminiModel.generateContentStream({
    contents: [...history, { role: 'user', parts: [{ text: prompt }] }],
    systemInstruction: getPersonaPrompt(persona)
  });

  for await (const chunk of stream.stream) {
    res.write(`data: ${JSON.stringify({ text: chunk.text() })}\n\n`);
  }
  res.write('data: [DONE]\n\n');
  res.end();
});

import numpy as np

def retrieve_grounded_context(query_vec: np.ndarray, doc_embeddings: np.ndarray, chunks: list[str], top_k: int = 3):
    """Calculates cosine similarity ranking locally with zero cloud transmission."""
    norm_query = query_vec / np.linalg.norm(query_vec)
    norm_docs = doc_embeddings / np.linalg.norm(doc_embeddings, axis=1, keepdims=True)
    similarity_scores = np.dot(norm_docs, norm_query)
    
    top_indices = np.argsort(similarity_scores)[::-1][:top_k]
    return [{"chunk": chunks[i], "score": float(similarity_scores[i])} for i in top_indices]

CREATE TABLE properties (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  title VARCHAR(255) NOT NULL,
  price_cents BIGINT NOT NULL CHECK (price_cents > 0),
  location VARCHAR(120) NOT NULL,
  status VARCHAR(30) DEFAULT 'available' CHECK (status IN ('available', 'pending', 'leased')),
  created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Compound index for sub-50ms multi-facet filtering
CREATE INDEX idx_properties_lookup ON properties (status, location, price_cents);

class MilestoneStateMachine {
    private const ALLOWED_TRANSITIONS = [
        'draft'     => ['submitted'],
        'submitted' => ['under_review', 'revision_requested'],
        'under_review' => ['approved', 'rejected'],
        'approved'  => ['paid']
    ];

    public function transition(Milestone $milestone, string $targetState, User $actor): bool {
        if (!in_array($targetState, self::ALLOWED_TRANSITIONS[$milestone->status] ?? [])) {
            throw new InvalidStateTransitionException("Cannot move from {$milestone->status} to {$targetState}");
        }
        
        DB::transaction(function() use ($milestone, $targetState, $actor) {
            $milestone->update(['status' => $targetState]);
            AuditLog::create([
                'entity_id' => $milestone->id,
                'from_state' => $milestone->getOriginal('status'),
                'to_state' => $targetState,
                'actor_id' => $actor->id
            ]);
        });
        return true;
    }
}

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

class HybridRecommender:
    def __init__(self, user_item_matrix, item_features):
        self.matrix = user_item_matrix
        self.item_sim = cosine_similarity(item_features)

    def predict_top_k(self, user_vector: np.ndarray, k: int = 5):
        # Weighted dot-product across interaction history and item feature space
        scores = user_vector.dot(self.item_sim) / (np.linalg.norm(user_vector) + 1e-9)
        ranked_indices = np.argsort(scores)[::-1][:k]
        return ranked_indices, scores[ranked_indices]

export interface TaskPayload {
  title: string;
  priority: 'low' | 'medium' | 'high' | 'critical';
  deadline: string;
}

export function validateTaskPayload(payload: Partial): TaskPayload {
  if (!payload.title || payload.title.trim().length < 3) {
    throw new ValidationError('Task title must be at least 3 characters.');
  }
  const validPriorities = ['low', 'medium', 'high', 'critical'];
  if (!payload.priority || !validPriorities.includes(payload.priority)) {
    throw new ValidationError('Invalid priority tier.');
  }
  return payload as TaskPayload;
}

class MenuIndexer {
  constructor(menuItems) {
    this.cache = new Map();
    menuItems.forEach(item => {
      const list = this.cache.get(item.category) || [];
      list.push(item);
      this.cache.set(item.category, list);
    });
  }

  filterCategory(category) {
    const items = category === 'all' 
      ? Array.from(this.cache.values()).flat() 
      : (this.cache.get(category) || []);
    
    // Minimal reflow batch update
    requestAnimationFrame(() => renderMenuDOM(items));
  }
}