The 2026 Full-Stack AI Architecture Blueprint: Scaling beyond the MVP

import { openai } from '@ai-sdk/openai';
import { streamText } from 'ai';

export const maxDuration = 30; // Extend duration for deep reasoning loops

export async function POST(req: Request) {
  const { messages } = await req.json();

  // Initialize the stream using the OpenAI API at the Edge
  const result = await streamText({
    model: openai('gpt-4o'),
    messages,
    system: 'You are an elite system architect. Format your responses with markdown and structural tables.',
    temperature: 0.2,
  });

  // Return the streamed response immediately with appropriate headers
  return result.toDataStreamResponse({
    headers: {
      'Cache-Control': 'no-cache',
      'Connection': 'keep-alive',
      'Content-Type': 'text/event-stream',
    }
  });
}

-- Enable the vector extension for high-performance semantic search
CREATE EXTENSION IF NOT EXISTS vector;

-- Core documents table with structural metadata
CREATE TABLE documents (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    user_id UUID NOT NULL,
    title TEXT NOT NULL,
    content TEXT NOT NULL,
    created_at TIMESTAMP WITH TIME ZONE DEFAULT timezone('utc'::text, now()) NOT NULL,
    updated_at TIMESTAMP WITH TIME ZONE DEFAULT timezone('utc'::text, now()) NOT NULL
);

-- Vectors table linking to documents for dense search queries
CREATE TABLE document_embeddings (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    document_id UUID REFERENCES documents(id) ON DELETE CASCADE,
    embedding VECTOR(1536), -- 1536 dimensions matches OpenAI's text-embedding-3-small
    chunk_index INT NOT NULL,
    chunk_content TEXT NOT NULL
);

-- Create an HNSW index to ensure lightning-fast vector search speeds
CREATE INDEX document_embeddings_hnsw_idx 
ON document_embeddings 
USING hnsw (embedding vector_cosine_ops);

import { createClient } from '@supabase/supabase-base-js';

const supabase = createClient(
  process.env.NEXT_PUBLIC_SUPABASE_URL!,
  process.env.SUPABASE_SERVICE_ROLE_KEY!
);

export async function queryUserDocuments(userId: string, queryEmbedding: number[], matchThreshold = 0.7, matchCount = 5) {
  const { data, error } = await supabase.rpc('match_documents', {
    p_user_id: userId,
    p_embedding: queryEmbedding,
    p_match_threshold: matchThreshold,
    p_match_count: matchCount
  });

  if (error) throw new Error(error.message);
  return data;
}

import { Metadata } from 'next';
import { getProjectBySlug } from '@/lib/api';

type Props = {
  params: { slug: string };
};

export async function generateMetadata({ params }: Props): Promise<Metadata> {
  const project = await getProjectBySlug(params.slug);
  if (!project) return { title: 'Project Not Found' };

  return {
    title: `${project.title} | Premium AI Case Study`,
    description: project.summary,
    alternates: {
      canonical: `https://mahenoorsalat.com/work/${project.slug}`,
    },
    openGraph: {
      title: project.title,
      description: project.summary,
      type: 'website',
      images: [{ url: `https://mahenoorsalat.com/api/og?title=${encodeURIComponent(project.title)}` }]
    }
  };
}

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Antigravity AI Sentinel",
  "operatingSystem": "All",
  "applicationCategory": "DeveloperApplication",
  "offers": {
    "@type": "Offer",
    "price": "0.00",
    "priceCurrency": "USD"
  },
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.9",
    "ratingCount": "184"
  }
}