Video Hosting Platform FirstPartyPixel Integration/ Server Side Tracking

In the ongoing battle to abolish third-party pixels and cookies, Google and Facebook took a big step in reestablishing how companies track your online profile and habits. Facebook’s recent introduction of a first-party pixel solution to their ad platform is geared towards the online ad industry.After Google released its new feature called first-party mode, a lot of questions arose about its advantages and differences compared to server-side tracking. At first glance, both look pretty similar Custom Loader helps make Google Tag Manager and Google Analytics 4 scripts more resistant to ad blockers and Intelligent Tracking Prevention (ITP) by modifying gtag.js and gtm.js loading path. Moreover, a Custom Loader and Custom Domain help set first party cookies and prolong cookie lifetime! Work effort: Domain Management , API Integration Solution: Stape Custom GTM and GA4 Loader , FB API , Google Ads API , Google Ads Manager . Integrating with Stape API for first party domain creation , Adding CNAME records for domain and passing all tracking data to servers. Technologies: JavaScript , StapeAPI Creating a visual architecture diagram for integrating both Google and Facebook pixels with Server-Side Tracking involves a few key components. While I can’t generate actual images, I can describe how you can structure it, and you can either use this description to create the diagram yourself or work with a graphic designer to visualize it. Here’s how you can structure the architecture: — ### **1. Client-Side Tracking Layer:** – **User Browser**: This represents the user’s web browser interacting with your site. The user performs actions (page visits, clicks, etc.) on the site, triggering the need for event tracking. – **Google Pixel / Facebook Pixel** (Client-Side): These JavaScript snippets are placed in the header of your site to track client-side events. When the user interacts with the page, these pixels capture data such as page views, clicks, or form submissions. — ### **2. Server-Side Layer:** – **Web Server**: When a user performs an action, the data captured by the client-side pixels is sent to your web server. This could be an HTTP request containing tracking data. – **Server-Side Tracking**: – This is where the integration with the server-side tracking system comes in. – You could use tools like **Google Tag Manager Server-Side**, **Facebook Conversions API**, or **other server-side solutions** to capture the events coming from the user. – **Event Transformation**: – Before sending the data to Google/Facebook, you may need to transform or process the event data. – For example, you may validate or enrich the data (like adding user IDs or session data) before sending it to the respective platforms. — ### **3. Platform Integration Layer:** – **Google Server-Side Integration** (Google Tag Manager Server-Side or Google Conversions API): – Server-side events are forwarded to Google via the **Google Conversions API**. – Google can track the events like page views, add-to-carts, purchases, etc., without needing client-side scripts to handle the data. – **Facebook Server-Side Integration** (Facebook Conversions API): – Server-side events are forwarded to Facebook via the **Facebook Conversions API**. – Similar to Google, Facebook will track user behavior and activity without relying on browser-side scripts. — ### **4. Data Flow / Event Journey:** – **User Interaction**: The user performs an action on your website. – **Client-Side Pixels (Google / Facebook)**: These collect event data on the browser and send it to your web server. – **Server-Side API**: The data from client-side pixels is forwarded to your server-side infrastructure (like GTM Server-Side container or a custom API endpoint). – **Data Processing**: The server processes the data (e.g., adding more details, transforming it into the right format). – **Google/Facebook API**: Data is sent to Google and Facebook via their respective server-side APIs (Conversions API for Facebook and Conversions API for Google). — ### **5. Additional Layers:** – **Data Layer** (Optional): If you’re using a more complex server-side setup, you might have a centralized data layer that handles the synchronization of event data across multiple platforms. — ### **Example Flow Diagram (Text-based description):** 1. **User Browser** → (Interacts with website) → **Client-Side Pixels** (Google Pixel, Facebook Pixel). 2. **Client-Side Pixels** → (Sends data to Web Server) → **Server-Side Tracking System**. 3. **Server-Side Tracking System** → (Transforms and processes data) → **Google API (Conversions API)**. 4. **Server-Side Tracking System** → (Transforms and processes data) → **Facebook API (Conversions API)**. — ### **Key Points to Consider:** – **Privacy & Consent Management**: Ensure you’re complying with privacy regulations such as GDPR, CCPA. The user must consent to tracking, and you must handle the data securely. – **Data Accuracy & Latency**: Server-side tracking can improve data accuracy (for example, reducing issues caused by ad blockers) but it might introduce latency, so it’s essential to monitor the system’s performance.

Large Video Hosting Platform – Artificial Intelligence and LLM Integration

Developed OpenAI and Gemini Model integrations . Created a playground with various parameters for tweaking AI implementation parameters and prompt engineering. Work effort: Requirements and Design, Architecture, Development and Test Solution: OpenAI and ChatGPT API Integration Technologies: OpenAI API , Gemini API , Python , HTML , JavaScript

E2E Networks and Dell partner to boost AI/ML capabilities in India

E2E Networks, one of the leading hyperscaler from India with a focus on advanced cloud GPU infrastructure, is recognised for providing innovative cloud computing solutions, positioning it as a top IaaS provider in India specialising in GPU capabilities. E2E’s recent capital infusion fuels its growth and further its position as a key player in India’s AI/ML ecosystem. This investment would enable E2E to expand its cloud infrastructure, particularly focusing on next-generation GPUs and GPU clusters, which are essential for AI and machine learning workloads. E2E recognised a significant market opportunity in GenAI solutions but faced challenges in scaling due to limitations in its existing infrastructure. To address this, they partnered with Dell Technologies, leveraging Dell’s cutting-edge hardware and scalable solutions to overcome these hurdles. Dell’s AI Factory, powered by NVIDIA GPUs, now supports E2E’s efforts to meet the growing demand for AI-driven innovation across enterprises, startups, and research institutions. This collaboration aligns with Dell’s vision of advancing AI/GenAI in India, helping businesses navigate the AI journey with future-proof infrastructure and optimised solutions.