Here's a cheatsheet for choosing the right algorithm for string problems, focusing on the most asked interview string problems in Microsoft interviews:

 To automatically publish posts to a Facebook Page using the Graph API, you’ll need to follow a structured setup involving app creation, token generation, and API calls. Here's a complete guide tailored for your SaaS automation needs:

✅ 1. Create a Facebook Developer App

• Go to Meta for Developers

• Click “My Apps” → “Create App”

• Choose App Type: Business

• Fill in app name, email, and (optional) business account

✅ 2. Add Required Permissions

In App Review → Permissions and Features, request:

• pages_manage_posts

• pages_read_engagement

• pages_show_list

• pages_manage_metadata

These allow your app to read, write, and manage posts on Facebook Pages.

✅ 3. Generate a Page Access Token

Use the Graph API Explorer or your app’s OAuth flow:

• Authenticate as a user who is admin of the Page

• Select your app and request the above permissions

• Choose the Page → Click Generate Access Token

• Save the token securely

✅ 4. Get Your Page ID

Visit your Page → About → Page Transparency → Copy the numeric Page ID Or use:

GET /me/accounts?access_token=USER_ACCESS_TOKEN

✅ 5. Make the POST Request

Use the following endpoint:

POST https://graph.facebook.com/v19.0/{page_id}/feed

Payload:

{
  "message": "Hello from GlowFlow!",
  "access_token": "PAGE_ACCESS_TOKEN"
}

You can also include:

• link: for sharing URLs

• picture: for image previews

• scheduled_publish_time: for future posts (Unix timestamp)

• published: false: to schedule instead of posting immediately

🧠 Pro Tips

• Use Supabase Edge Functions or n8n to automate publishing

• Store tokens securely and refresh them periodically

• Use scheduled_publish_time to queue posts in advance

📸 How to Set Up Facebook Graph API for Instagram Publishing

Instagram automation for SaaS platforms like GlowFlow requires direct integration with Meta’s Graph API. This guide walks you through the full setup—from account linking to publishing content.

✅ Prerequisites

Before you begin, ensure you have:

• A Facebook account

• A Facebook Page (can be for testing)

• An Instagram Business or Creator account

• A Meta Developer App

Instagram Graph API only works with Professional accounts (Business or Creator) linked to a Facebook Page.

🛠 Step-by-Step Setup

1. Convert Instagram to Business Account

• Open Instagram → Settings → Account

• Tap “Switch to Professional Account”

• Choose Business, skip contact info

2. Link Instagram to Facebook Page

• Go to your Facebook Page → Settings → Linked Accounts

• Click “Connect Instagram”

• Log in and confirm the link

3. Create a Meta Developer App

• Visit Meta for Developers

• Click “My Apps” → “Create App”

• Choose App Type: Business

• Fill in app name, email, and business account (optional)

4. Add Instagram Graph API Product

• In your app dashboard, click “Add Product”

• Select Instagram Graph API

• Also add Facebook Login, Pages API, and Business Management API

5. Configure Facebook Login

• Go to Facebook Login → Settings

• Add your OAuth Redirect URI (e.g. https://yourdomain.com/auth/callback)

• Enable:

  • Client OAuth Login

  • Web OAuth Login

6. Request App Review for Permissions

Go to App Review → Permissions and Features and request:

• instagram_basic

• instagram_content_publish

• pages_show_list

• business_management

You’ll need to submit screencasts and descriptions of how your app uses these permissions.

7. Authenticate Users via OAuth

Redirect users to Meta’s login:

const redirectToMetaLogin = () => {
  const clientId = 'YOUR_APP_ID';
  const redirectUri = 'https://yourdomain.com/auth/callback';
  const scopes = [
    'instagram_basic',
    'pages_show_list',
    'instagram_content_publish',
    'business_management'
  ].join(',');

  window.location.href = `https://www.facebook.com/v19.0/dialog/oauth?client_id=${clientId}&redirect_uri=${redirectUri}&scope=${scopes}&response_type=code`;
};

8. Publish Content via Graph API

Use two endpoints:

1. Create Media Container

POST https://graph.facebook.com/v19.0/{ig_user_id}/media

Payload:

{
  "image_url": "https://yourdomain.com/image.jpg",
  "caption": "Your caption",
  "access_token": "USER_ACCESS_TOKEN"
}

2. Publish Media

POST https://graph.facebook.com/v19.0/{ig_user_id}/media_publish

Payload:

{
  "creation_id": "MEDIA_CONTAINER_ID",
  "access_token": "USER_ACCESS_TOKEN"
}

🧠 Pro Tips

• Media containers expire in 24 hours—create them just before publishing.

• Use Supabase or n8n to schedule and trigger publishing.

• Store access tokens securely and refresh them as needed.

 High-impact Cursor AI | GPT-4.1 prompts 

Here are several high-impact Cursor AI prompts to generate a modern, animated, professional landing-page layout with all key sections:

“Design a sleek, modern landing-page layout with full-width hero banner, animated headline background, and smooth scroll. Include About, Features, Getting Started steps, Pricing tiers, Testimonials slider, FAQ accordion, and a sticky Contact CTA. Add subtle CSS animations on hover and scroll-triggered fade-ins for professionalism.”

“Create a responsive, minimalist landing page: hero with animated gradient overlay, About section with slide-in images, three-step Getting Started cards with hover pulses, Pricing grid with animated price-highlight transitions, Customer logos carousel, FAQ reveal animations, and a persistent sticky signup button.”

“Generate a one-page design: full-screen video background in hero, animated text overlay, About block with parallax scroll, Features icons that bounce on view, Getting Started timeline with animated progress bar, Pricing cards that flip on hover, Testimonials with fade loop, FAQ expand/collapse, and floating chat icon.”

“Build a professional landing page layout: centered headline with typing animation, About section with fade-from-left text, Feature grid with hover scale animations, Getting Started section using animated step-by-step navigation, Pricing section with animated discount ribbon, FAQ section with slide-down answers, and footer with animated social icons.”

“Produce a modern SaaS landing page: animated hero gradient text, About timeline section with scroll-triggered reveals, Features in a horizontal scroll panel, Getting Started steps that animate as you scroll, Pricing table with monthly/annual toggle and smooth transitions, Testimonials marquee animation, FAQ with tabbed animation, and an animated call-to-action footer.”

 Setting up Supabase Cron + Edge Functions is a powerful way to automate tasks like publishing, notifications, or queue processing. Here's a step-by-step guide tailored for your workflow:

⚙️ Step-by-Step: Setup Supabase Cron + Edge Function

1. Enable Required Extensions

Go to your Supabase dashboard:

• Navigate to Database → Extensions

• Enable:

  • pg_cron – for scheduling jobs

  • pg_net – for making HTTP requests to Edge Functions

  • vault – for securely storing secrets like anon_key and project_url

2. Create Your Edge Function

Use Supabase CLI:

npx supabase functions new publish-worker

This creates a function at ./supabase/functions/publish-worker/index.ts. Example:

export async function handler(req: Request): Promise<Response> {
  const payload = await req.json();
  console.log("Triggered at:", payload.time);
  // Add your publishing logic here
  return new Response("Publish task executed", { status: 200 });
}

Deploy it:

npx supabase functions deploy publish-worker

3. Store Secrets in Vault

In SQL Editor:

select vault.create_secret('https://your-project-ref.supabase.co', 'project_url');
select vault.create_secret('YOUR_SUPABASE_ANON_KEY', 'anon_key');

4. Schedule Cron Job

In SQL Editor:

select cron.schedule(
  'publish-every-5-mins',
  '*/5 * * * *',  -- every 5 minutes
  $$
  select net.http_post(
    url := (select decrypted_secret from vault.decrypted_secrets where name = 'project_url') || '/functions/v1/publish-worker',
    headers := jsonb_build_object(
      'Content-type', 'application/json',
      'Authorization', 'Bearer ' || (select decrypted_secret from vault.decrypted_secrets where name = 'anon_key')
    ),
    body := jsonb_build_object('time', now())
  )
  $$
);

Option 1: Delete Cron Job via SQL

If you know the job name (e.g. "publish-every-5-mins"), run:sql

select cron.unschedule('publish-every-5-mins');

or 

select cron.unschedule(name) from cron.job where name like 'publish-%';

 Enterprise RAG-Based Assistant Design (Azure + LLM Stack)

Objective: Design a secure, scalable enterprise assistant that allows employees to query internal documents (PDFs, meeting notes, reports) using natural language. The system returns relevant, grounded responses with references.

📆 High-Level Architecture Overview

Stack: Azure Functions, Azure AI Search (Vector), Azure OpenAI (GPT-4 + Embeddings), Semantic Kernel, Azure AD, RBAC, App Insights

💡 Core Components

1. Document Ingestion & Preprocessing

• Trigger: Upload to Azure Blob Storage / SharePoint

• Service: Azure Function (Blob Trigger)

• Processing Steps:

  • Extract text using Azure Document Intelligence

  • Chunk text into semantically meaningful segments

  • Generate embeddings using text-embedding-ada-002

2. Indexing

• Store vector embeddings + metadata in Azure AI Search

• Enable vector search on the content field

• Include filters for metadata (e.g., doc type, author, date)

3. Query Workflow

• User submits query via UI (e.g., Web App or Teams Bot)

• Query is embedded using same embedding model

• Vector search on Azure AI Search returns top-N documents

• Semantic Kernel handles:

  • Context assembly (retrieved chunks)

  • Prompt templating

  • Call to Azure OpenAI Chat Completion API

  • Response formatting (with references)

4. Semantic Kernel Role

• Provides pluggable architecture to:

  • Register skills (embedding, search, summarization)

  • Maintain short/long-term memory

  • Integrate .NET enterprise apps

• Alternative to LangChain, but better aligned with Azure

5. Security & Compliance

• Azure AD Authentication (MSAL)

• Managed Identity for Azure Functions

• RBAC to control access to Search, Blob, OpenAI

• Private Endpoints & VNet Integration

6. Monitoring & Governance

• Azure Application Insights for telemetry

• Azure Monitor for alerting & diagnostics

• Cost usage dashboard for OpenAI API

✨ Optional Extensions

• Multi-Agent Orchestration: CrewAI or LangGraph to chain agents (e.g., Search Agent → Reviewer Agent)

• Feedback Loop: Capture thumbs up/down to improve results

• SharePoint/Teams Plugin: Tight M365 integration

• Document Enrichment Pipeline using Azure Cognitive Search skillsets

🔹 Summary:

This solution leverages a robust, secure, Azure-native stack to build an enterprise-ready, LLM-powered RAG system. By combining Azure AI Search for retrieval and OpenAI GPT for reasoning, we ensure low-latency and grounded responses. Semantic Kernel enables structured orchestration and clean integration into .NET-based apps and services.

 Microservices vs Monolithic Architecture

Here’s a clear side-by-side comparison between Microservices and Monolithic architectures — from a system design and engineering perspective: