The Indian government has directed Meta to immediately suspend the rollout of WhatsApp’s upcoming Username feature in India 🇮🇳🛑.

According to reports by Reuters 📰, India’s Ministry of Electronics and Information Technology has sent an official notice to Meta regarding the matter. The new feature would allow users to communicate without revealing their phone numbers 🔒📞. However, Indian authorities have raised concerns that this could significantly increase online financial fraud 💸, impersonation, and cybercrime 🕵️‍♂️.

Officials warn that criminals may exploit the feature by creating fake accounts under the names of government institutions or public figures to deceive the public 🎭.

As a result, Meta has been asked to provide a detailed explanation of its security measures within 3 days 🗓️, and to avoid launching the feature in India until discussions with the government are completed 🤝🏛️.

Responding to the issue, a WhatsApp spokesperson stated that the feature has not yet been activated 🛑, and that the company plans to reserve usernames related to government entities, public figures, and verified accounts in advance to prevent misuse ✅.

India remains WhatsApp’s largest market 📈, with over 500 million users 👥. Previously, apps like Telegram and Signal have also faced scrutiny from Indian authorities over features that allow users to hide phone numbers 🕶️📱.

Network-Aware Syntax - Developers create services and clients without relying on large external frameworks.
Visual Representation - Every Ballerina program can be visualized as a sequence diagram, helping architects and developers understand application flow.
Cloud-Native Support - The compiler generates deployment artifacts for Docker and Kubernetes.
Structural Typing - Ballerina works naturally with JSON and XML, making data integration simple.

Listeners - receive network requests.
Services - expose application functionality.
Resource Methods - map directly to HTTP methods such as GET, POST, PUT, and DELETE.
Remote Methods - support communication through protocols such as gRPC and WebSocket.

import ballerina/http;

service /music on new http:Listener(8080) {
    resource function get tracks() returns json {
        return [
            {id: "1", title: "Shape of You"},
            {id: "2", title: "Blinding Lights"}
        ];
    }
}

The HTTP listener is created with a single statement.
The service automatically exposes the /music/tracks endpoint.
Ballerina converts the returned records into JSON without additional configuration.

resource function get summary() returns json {
    return {
        totalTracks: 2,
        systemStatus: "Healthy"
    };
}

The Main Issue: Unlike MV2, MV3 does not allow ad blockers to inspect web requests in real-time and do dynamic filtering. 🔍❌

The Impact: Powerful tools like the original uBlock Origin will lose their full strength. Users might notice a drop in performance when blocking YouTube ads, anti-adblock popups, and custom filter rules. 📉

Current Status: With the Chrome 150/151 updates, Google has blocked all remaining workarounds that allowed users to run old MV2 extensions. 🚫

Switch to MV3 Alternatives: Shift to Manifest V3-compatible extensions like uBlock Origin Lite, AdGuard (MV3), or AdBlock Plus (MV3). 🔄

Change Browsers: If you want total freedom and advanced blocking, switch to Firefox (which is keeping full support for powerful MV2-style filtering) or jump to Brave to get native, extension-free ad blocking! 🦊🛡

Process 🖥 - Think of a process as an independent program or application running on your computer, such as your web browser or a music player. Each process has its own memory space and resources, meaning they are isolated from one another. 🔒

Thread 🧵 - A thread is a smaller unit of execution within a process. A single process can have multiple threads working together to perform tasks simultaneously, such as a bot managing multiple streams or handling network requests without freezing the entire application. ⚡️

Input Layer - receives the raw data (like pixels of an image or numbers in a table).

Hidden Layers - are between the input and output. They perform the math and find hidden patterns. More hidden layers mean the network is Deep.

Output Layer - gives the final prediction

Weights - represent the strength of the connection. If a weight is high, that input is very important.

Bias - extra number that helps the neuron decide when to activate.

Activation Function -mathematical gate that decides the final output of the neuron. (ReLU, Sigmoid and Softmax functions)

- Data enters the Input Layer.

- The machine multiplies the data by the current Weights.

- The data moves through the Hidden Layers.

- The machine makes a Prediction in the Output Layer.

- The machine calculates the Loss

The machine uses the Chain Rule from calculus to calculate the Gradient

It finds exactly how much each weight contributed to the total error.

It then changes the weights slightly to make the error smaller for the next time.

Early Layers - Detect simple lines, edges and corners.
Middle Layers - Combine edges to detect shapes like circles, squares or textures.
Deep Layers - Combine shapes to detect complex objects like faces, cars or animals.

I. Convolution Layer (The Feature Extractor)
II. Activation Layer (ReLU)
III. Pooling Layer (Downsampling)

🖼 Input Image
        ↓
🧮 Convolution + ⚡️ ReLU
        ↓
📉 Max Pooling
        ↓
🧮 Convolution + ⚡️ ReLU
        ↓
📉 Max Pooling
        ↓
📄 Flatten
        ↓
🧠 Fully Connected Layer
        ↓
🎯 Softmax Output

We take a model (like ResNet, VGG, or EfficientNet) that was already trained on a huge dataset like ImageNet (over 1 million images).

We freeze the feature extraction layers and only re-train the final output layers for our specific task.

This saves hours of training time and achieves high accuracy even with a small dataset.

More more more short plz

The network takes the current input and the previous memory

It combines them to make the new memory

It uses new memory to produce an output

📉 Vanishing Gradient
When backpropagating through many time steps (Backpropagation Through Time), the gradients get multiplied repeatedly by small numbers.

😵 The Result
The gradient becomes nearly zero. The model forgets what happened at the beginning of the text or series.

Forget Gate - Decides what information from the past to throw away.

Input Gate - Decides what new information to store in the cell state.

Output Gate - Decides what the next hidden state should be based on the updated cell state.

Reset Gate - Decides how much past information to forget.
Update Gate - Combines the jobs of the input gate and forget gate.

Query (Q) - What the word is looking for.
Key (K) - What the word represents.
Value (V) - The actual information content of the word.

The model runs the attention formula multiple times (e.g., 8 or 16 heads) in parallel.

Each head focuses on a different type of relationship (e.g., one head tracks grammar, another tracks pronoun references, another tracks action object links).

Positional Encoding - Because Transformers process all words at once, they do not inherently know word order. Positional encodings add mathematical sine and cosine values to input vectors to inject word positions.

Encoder - Takes input text, processes it through Multi-Head Attention and Feed-Forward Neural Networks, and outputs rich contextual representations. (Used in models like BERT).

Decoder - Uses masked self-attention to generate text word-by-word by predicting the next token. (Used in models like GPT).

🔎 Query, Key, and Value (Q, K, V)
🧠 Self-Attention
👥 Multi-Head Attention
📍 Positional Encoding
⚡️ Parallel Processing

I. Pre-training (Unsupervised / Self-Supervised)
II. Supervised Fine-Tuning (SFT)
III. Alignment (RLHF & DPO)

Retrieval-Augmented Generation (RAG)
Diffusion Models (Image Generation)
Mixture of Experts (MoE)