Ajay Y.

Otto AI

CEO & Co-Founder

Simplified

Investor

Co-Founder

Forbes

30 under 30

Contributor

EvaBot Inc.

Advisor

Roomi

Founder

Roomi is a free website & app making it safer & easier to search for room rentals, sublets & roommates.

Forbes Real Estate Council

Founding Member

DADA Room

Chief Executive Officer

WEVE Acceleration (formerly NUMA New York)

Mentor

HuffPost

UPRISE Festival Europe

Now, I’m back in the founder seat, focusing on building a vertical AI SaaS startup. I will be building this venture in public, sharing my experiences and insights on growth and revenue strategies along the way.

In 2021, I co-founded Simplified, an AI-driven platform designed to support SMB marketing teams with content creation, distribution, and collaboration in one place. We raised over $14M from Khosla Ventures and Craft, scaled to over 17 million users.

I began my entrepreneurial journey as a solo founder, building a marketplace in New York to help people find roommates. This venture raised over $20M in capital, grew to over 5 million users, and reached profitability.

Ezam Automotive Parts Group

EZAM AUTOMOTIVE PARTS GROUP consists of 12 large manufacturing companies. Ezam Group has been active in auto parts industry since 1993. The group member companies are located in Tehran, East Azerbaijan, Guilan, Alborz and Isfahan provinces while the headquarter of the holding, registered as Ezam Automotive Parts Group is located in Tehran.

Full Stack Developer

MGM Lens

MGM Lens started its activity in the optics industry in 2008 with the aim of meeting the needs of custom lenses (RX). In this regard, in order to respond to the needs of customers, by using the products of two domestic factories and three foreign factories and supplying these products with the latest machines (Schneider, Germany, etc.), it tries to provide the best quality products.
Our collection offers more than 13 lens design models from top domestic and foreign brands, including Kodak, Revo, MGM Plus, Essence, etc. It tries to offer a variety of lenses with the best quality and the most suitable price

Packt

 Book Author

Goldiran

Goldiran is a commercial company whose main business is the production of household appliances and electrical appliances such as refrigerators, washing machines, televisions, etc. The software team is responsible for the implementation, including forms for customer affairs, finance, warehouse, parts, etc

company Internal website : https://gs.goldirancs.ir
company Sales website : https://cs.goldiran.ir

Code Maze

Code Maze is a website for learning programming language, the main activity of which is to produce articles about C#, .NET Core, and Blazor, such as Solid, Delgate, and Design Pattern concepts. The responsibility of implementing the articles lies with the team of technical writers

company website : https://code-maze.com

Technical Author

Mobtakeran - Goldiran

Mobtakeran is a commercial company whose main business is the production of industrial cooling and ventilation such as split duct, air conditioner, integrated air package and so on. The software team is responsible for the implementation, including forms for customer affairs, finance, warehouse, parts, etc

company Internal website : http://77.104.69.19/newgsfront
company website : https://goldiran.ir/Page/B2B

Full Stack Developer 

Freelancer.com

Projects such as bank scheduling, taxi agency, store accounting, etc. I did

website : https://freelancer.com
website : https://ponisha.ir
website : https://parscoders.com

C# Developer

Hi there! 👋,
I'm Sina Riahi and 29 years old
I am a full-stack developer with 5 years of experience in website design and application development in the field of business (CRM, ASS, WMS, HMS, GMS).
I spend my free time posting on LinkedIn (for clean code and the best code and new features, etc.) and sample projects on GitHub.
I like details and team spirit.
Creation is the ultimate beauty.
I am a creator who creates every moment with infinite joy.
I will learn and with patience and determination in the challenges that I will face, I will turn them into experiences

Sina Riyahi

Docker vs Kubernetes

Docker:
A container platform for building, shipping, and running individual containers (images) on a host.
Focuses on packaging and running a single container or a small set of containers.

Kubernetes:
An orchestration system to manage many containers across a cluster.
Handles scheduling, scaling, self healing, and lifecycle management for large, distributed deployments.

Docker (Container Platform)
What it covers
App Code + Dependencies: Dockerfile to package app and libraries into an image.
Build Image: Build the container image from Dockerfile.
Container Runtime: Docker Engine runs the container on a host.
Networking: Container networking bridge/overlay for isolated communication.

Advantages
Portability: The same image runs anywhere with a compatible Docker runtime.
Consistency: Eliminates “works on my machine” issues.
Fast local development & CI: Quick build/run cycles.
Simple for single host use: Easy to adopt for small teams or simple apps.

Disadvantages / Limitations
No built in multi container orchestration: Managing many containers across multiple hosts is manual and error prone.
Limited automation at scale: No native self healing, rolling updates, or declarative state management.
Networking and service discovery are basic without extra tooling.

Kubernetes (Container Orchestration)
What it covers
Master Node: API server, scheduler, controllers—central control plane.
Worker Nodes: Run container runtimes and Pods.
Build Image: Kubernetes uses pre built images; it doesn’t build images itself.
Container Runtime: Uses Docker, containerd, or other runtimes on each node.
Service Discovery: Built in DNS, Services, load balancing, and network policies.
Running App: Pods managed by Deployments/ReplicaSets; rolling updates, auto restart, and scaling.
Networking: Cluster wide networking with CNI plugins; ingress for external access.
Monitoring & Management: Declarative configs (YAML/JSON) for desired state; self healing, scaling, and rollbacks.

Advantages
Automation at scale: Scheduling, auto scaling, rolling updates with minimal downtime.
Self healing: Replaces failed containers, restarts pods, reschedules on node failure.
Declarative, versionable config: Reconciles actual state to desired state automatically.
Centralized service discovery and load balancing across many pods.
Multi host orchestration: Seamlessly runs containers across many nodes.

Disadvantages / Limitations
Complexity: Steeper learning curve, more operational overhead.
Operational burden: Requires robust monitoring, logging, backups, and security practices.
Overkill for simple setups: If you only have a single host or a few containers, Kubernetes adds unnecessary complexity.


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How to Learn API Development

📌1. What is API?
📢The basic definition and purpose of an API, which allows different software systems to communicate and exchange data efficiently.

📌2. Types of API
Various classifications of APIs based on how they function and their accessibility:
📢REST (Representational State Transfer)
📢SOAP (Simple Object Access Protocol)
📢GraphQL (A query language for APIs)
📢gRPC (A high-performance RPC framework)

📌3. API vs SDK
📢Differences between an API (a set of rules and protocols for interaction) and an SDK (Software Development Kit, which often includes APIs along with tools and documentation).

📌4. Methods
HTTP methods used in APIs:
📢GET (retrieve data)
📢POST (send data)
📢PUT (update data)
📢DELETE (remove data)
📢PATCH (partial update)

📌5. Headers
📢Metadata sent with API requests and responses that help manage data transfer, authentication, and content negotiation.

📌6. Response Codes
Status codes indicating the result of an API request:
📢200: Success
📢201: Created
📢400: Bad Request
📢401: Unauthorized
📢404: Not Found
📢500: Internal Server Error

📌7. Authentication & Security
Measures to protect APIs:
📢Authentication Methods: API Keys, OAuth 2.0, JWT (JSON Web Token)
Security Measures: Rate Limiting, CORS, HTTPS/TLS

📌8. API Request & Response
📢The exchange process where clients send requests and servers send responses, adhering to the protocols and data formats like JSON or XML.

📌9. API Design and Development
Best practices and principles:
🔔 REST Principles: Statelessness, Resources, Pagination, Versioning
Documentation:
Tools such as:
📢OpenAPI
📢Postman
📢Swagger

📌10. API Testing
Tools to verify API functionality:
📢Postman
📢Curl
📢Insomnia
📢SoapUI
📢JSON Placeholder (mock API data)
📢WireMock (API mocking)

📌11. API Deployment & Integration
Steps to deploy APIs so they are accessible:
📢Gateways (manage traffic and security)
📢3rd Party APIs (integrate external services)
📢Popular API providers like:
📢AWS API Gateway
📢Kong
📢Apigee

📌12. Consuming APIs
How applications utilize APIs:
📢JavaScript
📢Python
📢Java
📢Maps API
📢Stripe API
📢Weather API

📌13. Additional Tools & Resources
🔔 Statelessness, Resource, Pagination, Versioning:
📢Core REST principles.
🔔 OpenAPI, Postman, Swagger:
📢Documentation and testing tools.
🔔 Security Measures:
📢Rate limiting, CORS, HTTPS/TLS protocols.


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Top 6 Architectural Patterns

In the world of software development, choosing the right architectural pattern is crucial for creating scalable, maintainable, and high-performance applications. 

🔹 𝟭. 𝗠𝗼𝗻𝗼𝗹𝗶𝘁𝗵𝗶𝗰 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 
The simplest and most traditional approach, where all components are interconnected in a single codebase. Monolithic architecture is straightforward and ideal for smaller applications that don’t require extensive scalability. However, as systems grow, it can become challenging to scale, test, and maintain. Perfect for startups or small teams looking for rapid development.

🔹 𝟮. 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 
A favorite for large, complex applications that require scalability and flexibility. Here, the application is split into independently deployable services, each handling specific business functions. Microservices allow teams to use different tech stacks and scale services independently. This pattern is popular in high-traffic applications and organizations that want the freedom to deploy features independently.

🔹 𝟯. 𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗿-𝗪𝗼𝗿𝗸𝗲𝗿 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 
This pattern separates tasks into controllers and workers. The controller manages user interactions, while workers handle background tasks like data processing, creating a system that’s highly scalable and efficient. Ideal for applications that need to handle intensive background jobs, such as order processing, real-time analytics, or notifications.

🔹 𝟰. 𝗠𝗼𝗱𝗲𝗹-𝗩𝗶𝗲𝘄-𝗖𝗼𝗻𝘁𝗿𝗼𝗹𝗹𝗲𝗿 (𝗠𝗩𝗖) 
A classic pattern commonly used in web applications, MVC separates the application logic (Model), the UI (View), and the interaction (Controller). This separation of concerns makes it easier to maintain and update specific parts without affecting the entire system. MVC is a popular choice for applications needing user interfaces, especially in the e-commerce and content management spaces.

🔹 𝟱. 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 
Perfect for systems that need to react to specific actions or events, like user clicks, database updates, or sensor readings. In this pattern, components respond to events asynchronously, making it scalable and loosely coupled. Event-driven architecture shines in applications with complex workflows or asynchronous processing needs, such as IoT, real-time analytics, and customer notification systems.

🔹 𝟲. 𝗟𝗮𝘆𝗲𝗿𝗲𝗱 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 
Organizes the application into layers (Presentation, Business, Data Access, and Infrastructure) to create a clean separation of concerns. Each layer is responsible for a specific aspect, making the system more maintainable and testable. The layered approach is often used in enterprise applications and is ideal when creating a clear, standardized structure across the system.


Which of these patterns have you implemented in your projects?
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Microservices vs AI-Native Application Architecture

1. Microservices Application Architecture

What It Is
A software design approach where an application is built as a collection of small, independent services (microservices).
Each service focuses on a specific business capability and communicates with others through lightweight APIs (typically REST or gRPC).

API Gateway
A single entry point for external clients and internal routing between services.
Handles cross-cutting concerns such as:

Authentication and authorization
Rate limiting and load balancing
Circuit breakers and protocol translation
Storage Backends
SQL database – for structured and relational data (transactions, users, product catalogs)
Object Storage Service (OSS) – for unstructured data (images, videos, documents, large blobs)

Kubernetes Components
Pod: The smallest deployable unit in Kubernetes — one or more containers sharing the same network namespace
Node: A physical or virtual machine that runs pods; Kubernetes schedules workloads across nodes

Interaction Pattern
Services communicate through APIs, coordinated via the gateway.
Storage backends persist data while microservices fulfill business operations.


2. AI-Native Application Architecture

What It Is
A software architecture designed specifically to leverage AI capabilities — such as large language models (LLMs), agents, vector databases, and retrieval-augmented generation (RAG).
It focuses on AI-driven reasoning, planning, and autonomous task execution.

AI Gateway
A control layer that routes user inputs to appropriate agents or models, manages orchestration, and enforces policies.

LLM (Large Language Model)
The reasoning engine for generating content, understanding context, and planning actions.
May consist of one or multiple models (from different vendors).

Vector Database
Stores embeddings and high-dimensional data representations for fast similarity searches.
Used in retrieval-augmented generation to ground AI responses in real data.

Retrieval Augmentation (RAG)
Fetches relevant information (documents, facts, summaries) from the vector store or other data sources to make LLM outputs more accurate and data-grounded.

Toolkit
A library of external tools or APIs that agents can call — e.g., web searches, data lookups, computations, or actions in other systems.

UI/Clients
Interfaces through which users interact with the AI system (web, mobile, or desktop clients).

Pods and Nodes (AI Workloads)
AI components also run in Kubernetes pods/nodes for scalability, isolation, and reliability.

Data Stores
Vector database for semantic search
Knowledge artifacts (documents, snippets, context)
External connectors within the toolkit


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Top 5 KAFKA USE CASES

🔦 Apache Kafka is a powerful distributed event streaming platform that is widely used in various industries for handling real-time data feeds. 

Data Replication:
💡 Kafka is often used to replicate data between different systems and environments. This is particularly useful for maintaining consistency across databases or for disaster recovery purposes. By using Kafka’s distributed log, organizations can ensure that data changes in one system are reliably propagated to others, enabling real-time synchronization and improving data availability.

Web Activity Tracker:
💡 Kafka can serve as a central hub for collecting and processing web activity data from various applications. By ingesting logs and events from web applications, e-commerce sites, or mobile apps, businesses can analyze user behavior, track engagement, and gather insights in real-time. This use case is essential for personalized marketing, A/B testing, and improving user experiences.

Message Queuing:
💡 Kafka is widely used as a message broker to facilitate communication between different services or components in a microservices architecture. It allows for asynchronous processing of messages, which helps decouple producers and consumers. This ensures that messages can be reliably delivered even if one part of the system is temporarily down, improving overall system resilience and scalability.

Log Aggregation:
💡 Kafka can act as a centralized log aggregation platform, where logs from various services, applications, and systems are collected and processed. This is crucial for monitoring, debugging, and compliance purposes. By centralizing logs, organizations can perform analysis and gain insights into system performance, detect anomalies, and troubleshoot issues more effectively.

Data Streaming:
💡 Kafka excels at handling real-time data streams, making it a popular choice for building data pipelines and processing data in motion. Organizations can use Kafka to stream data from various sources (e.g., IoT devices, databases, or APIs) to downstream systems, such as data warehouses or analytics platforms. This allows for real-time analytics, machine learning applications, and timely decision-making based on up-to-date information.

🔎 These use cases illustrate the versatility of Kafka in modern data architectures, enabling organizations to build scalable, reliable, and efficient systems for processing real-time data.

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What does API Gateway do ?

💡 An API Gateway is a crucial component in modern microservices architectures. It acts as a single entry point for clients to interact with various backend services and provides a wide range of functionalities to manage, secure, and optimize API requests.

✅HTTP Request Handling: The API Gateway receives incoming HTTP requests from clients and routes them to the appropriate backend services.

✅Parameter Validation: It can validate incoming request parameters (e.g., query parameters, headers, body) to ensure they meet specific criteria before passing them to the backend services.

✅Allow List/Deny List: The API Gateway can enforce security policies by allowing or blocking traffic from specific IP addresses or ranges, helping to protect against malicious requests.

✅Authentication/Authorization: It manages user authentication (verifying identity) and authorization (checking permissions) to ensure that only authenticated and authorized users can access specific resources.

✅Rate Limiting: The API Gateway can enforce rate limits on how many requests a client can make in a specified timeframe, protecting backend services from being overwhelmed by too many requests.

✅Dynamic Routing: It can route requests to different backend services based on various criteria, such as request paths, headers, or other parameters.

✅Service Discovery: The API Gateway can integrate with service discovery mechanisms to dynamically find and communicate with backend services without hardcoding their addresses.

✅Protocol Conversion: It can facilitate communication between clients and services that use different protocols (e.g., converting HTTP requests to gRPC), enabling interoperability.

✅Error Handling: The API Gateway can manage errors by providing standardized error responses and handling retries or fallbacks when backend services fail.

✅Logging and Monitoring: It can log API requests and responses, providing insights into usage patterns, performance metrics, and potential issues. This data can be used for monitoring and debugging.

✅Circuit Breaker: The API Gateway can implement circuit breaker patterns to prevent requests from being sent to a failing service, thereby improving system resilience and providing fallback mechanisms.

✅Caching: It can cache responses to common requests, reducing latency and load on backend services by serving cached data for repeat requests.

🔎 an API Gateway plays a vital role in managing and orchestrating microservices, providing security, performance, and reliability features that enhance the overall architecture of an application.

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5 methods to improve API performance

1. Pagination
Concept: Instead of fetching all data at once, only fetch a subset or specific pages.
How It Works:
When a request is made, only the necessary page(s) of data are retrieved.
The system responds with the specific data for the requested page.
Benefits:
Reduces response size by fetching only necessary data.
Faster loading times for paginated content, improving user experience.
Additional:
Useful when dealing with large datasets to prevent overloading the server and client.

2. Asynchronous Processing
Concept: Handle multiple tasks concurrently, allowing the system to process several requests without waiting for each to finish sequentially.
How It Works:
Tasks like logs (Log 1, Log 2, Log 3) are stored in a message queue.
The system processes these tasks asynchronously and updates the database (DB) as needed.
Benefits:
System can handle multiple tasks at once efficiently.
Users can continue interacting with the system while tasks are processed in the background.
Additional:
Improves overall system throughput and responsiveness.

3. Caching
Concept: Store frequently accessed data temporarily to reduce database load and response times.
How It Works:
Read data from cache if available.
If not in cache, read from the database and update the cache for future requests.
Benefits:
Requests served quickly without hitting the server or database every time.
Significantly reduces latency and increases speed for common data.
Additional:
Suitable for data that doesn’t change often.

4. Connection Pooling
Concept: Reuse existing database connections instead of opening new ones for each request.
How It Works:
Multiple users (User 1, User 2, User 3) connect to a pooled set of connections.
Connections are reused, reducing the overhead of establishing new connections.
Benefits:
Reduces resource consumption and improves connection efficiency.
Supports a large number of concurrent database connections.
Additional:
Ideal for high-traffic systems with many users.

5. Load Balancing
Concept: Distribute incoming requests across multiple servers.
How It Works:
Requests from a user are distributed to multiple servers rather than a single one.
Benefits:
Ensures continuous service availability even if some servers fail.
Balances load, preventing bottlenecks and improving responsiveness.
Additional:
Essential for scaling large systems and maintaining high availability.


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Cross-Site Scripting (XSS) Attacks

A small script can cause big damage. XSS lets attackers inject malicious code into web pages and hijack user sessions, steal cookies, or manipulate the browser.

General note on XSS
Goal: trick users into executing attacker-supplied scripts in their browser.
Consequences can include data theft (cookies, session data), account takeover, or site defacement.

Mitigation usually involves input validation, output encoding, proper content security policies, and strict handling of user-supplied data.

Non-persistent Cross-Site Scripting (Reflected XSS)
What it is: The malicious script is reflected off the web server and delivered in the immediate response to the user, usually via a URL or request parameter.

Flow:
Attacker crafts a malicious link that includes script payload in a request parameter (for example, a search query).
Victim clicks the link or is tricked into visiting a URL containing the payload.
The server processes the request and reflects the payload back in the response (in a webpage, error message, or update).
The browser loads the response, executing the malicious script immediately.
The script runs in the victim’s browser and can steal private data (e.g., session cookies) or perform actions on behalf of the victim.

Key characteristics:
Requires user to click/link.
No stored payload on server; attack is in the request and reflected back.
Quick immediate impact without persisting data on server.
Mitigation: Use proper output encoding/escaping, validate inputs, use Content Security Policy (CSP), and avoid including untrusted data in HTML/JS contexts.

Stored (Persistent) Cross-Site Scripting
What it is: The attacker’s payload is stored on the server (in a database, comments, user profiles) and later served to other users.

Flow:
Attacker submits malicious script (e.g., in a comment, post, or stored parameter) to the website.
The payload is stored in the database or similar storage.
A victim visits a page that retrieves data from the database.
The server sends a page that includes the stored malicious script.
The victim’s browser executes the script when rendering the page, possibly stealing session data or cookies.

Key characteristics:
Payload persists on the site; affects multiple users.
Requires a vulnerable rendering path that does not neutralize the stored data.
Mitigation: Sanitize and encode stored data before rendering; adopt proper escaping on all data outputs; implement input validation; use CSP and least privilege for scripts.


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Ajay Y.

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