YouTube ‘Recap’ Feature: Google-owned platform has launched the first full version of YouTube ‘Recap’ feature, a personalized shareable highlight reel that sums up everything you watched throughout the year in 2025. The company’s this move clearly aimed at taking on Apple Music Replay and Spotify Wrapped. Meanwhile, the YouTube also released its annual lists of top trends, creators, songs, and podcasts that shaped the platform in 2025. Notably, the YouTube Recap feature is currently available for users in North America, with a global rollout scheduled for later this week. YouTube added this feature after nine rounds of feedback and testing more than 50 different concepts. The new feature works across both mobile and desktop. What Is YouTube ‘Recap’ Feature? Add Zee News as a Preferred Source YouTube Recap serves as a snapshot of everything users consumed on the platform throughout 2025. Recap, according to YouTube, is basically a synopsis of your 2025 viewing habits. YouTube ‘Recap’ Feature: What’s Waiting Inside For You Users will get up to 12 cards that show their favourite channels, topics, and how their watching habits changed during the year. YouTube will also give each user a personality type based on the videos they watched. Some examples of these personality types are Sunshiner, Wonder Seeker, and Connector. Others, like Philosopher and Dreamer, are less common. Moreover, if a user watched a lot of music, their Recap will also show their Top Artists and Top Songs of the year. YouTube has also shared charts that highlight the year’s most popular creators, podcasts, and songs. (Also Read: Downloaded Sanchar Saathi? Here’s What I Found: Permissions Needed, Features, 90-Day Deadline For Apple, Samsung, OnePlus, Vivo And How To Install App) YouTube ‘Recap’ Feature: How To View It Step 1: Open YouTube on your Android phone, iPhone, or desktop. Step 2: Sign in to your YouTube account. Step 3: On the homepage, tap the ‘You’ tab. Step 4: Right below your profile details, you’ll see a banner that says “Your Recap is here.” Step 5: If you don’t see the banner, you can still view your Recap by visiting youtube.com/Recap in a browser.

Seoul: The data protection regulator here said on Wednesday that e-commerce giant Coupang Inc. did not properly notify its customers of its recent major data breach, demanding a corrected notification of a personal information “leak” from an “exposure” of such data. The Personal Information Protection Commission (PIPC) made the decision in an emergency meeting after the company said last week personal information of 33.7 million customers had been compromised, including names, addresses and phone numbers, reports Yonhap news agency. While Coupang notified affected users of the breach, the PIPC said the company merely described it as personal information being exposed when it was aware that such data had been leaked. Add Zee News as a Preferred Source The regulator said Coupang also partially omitted types of data affected while announcing the breach on its website for just one to two days. It ordered the company to notify affected customers again of the leak, advise them of data protection measures, such as changing passwords, and reinspect steps to prevent harm to customers, among other measures. It demanded Coupang submit the results of its measures within one week. “(We) will swiftly and thoroughly investigate the circumstances, scope and items of Coupang’s personal information leak, as well as violations of safety duties, and will make strict punishment if violations are found,” it said in a release. Meanwhile, the regulator said it strengthened the monitoring of illegal distribution of personal information on the internet and the dark web Sunday, which will last for three months. Coupang is facing a wave of class-action lawsuits over its massive data breach that affected nearly 34 million customers. A law firm named Chung filed the first complaint against Coupang on Monday on behalf of 14 clients, seeking 200,000 won (about US$140) per person in damages. Many other law firms have also expressed their intention to participate in the class-action lawsuits and are now recruiting participants. Considering past judicial precedents, however, the compensation awarded to users whose personal information was leaked was around 100,000 won per person, legal experts said on Wednesday.

Google’s Android 16 Update For Pixel Users: Tech giant Google has rolled out the second Android 16 update of 2025, introducing new accessibility features for the first time. The update, which is first coming to Pixel devices, marks a major change in how Android updates are delivered, as the company is shifting from one yearly update to more frequent releases. The Android 16 update brings AI-powered notification tools, expanded customization options, and streamlined parental controls. Notably, the non-Pixel smartphones will receive Android 16 according to their manufacturers’ timelines. Android 16 Update For Pixel Users: What’s New Add Zee News as a Preferred Source The new Android 16 update brings several new features that make using a phone simpler and more helpful. It now includes AI-powered notification summaries that turn long messages and group chats into short, easy-to-read notes, so you don’t have to scroll through everything. There is also a new Notification Organizer that automatically groups and silences less important alerts like promotions, news, and social updates. Android 16 also gives users more ways to customize their phones with new icon shapes, themed icons, and the ability to darken apps that don’t support dark mode. For families, a new Parental Controls section in Settings lets parents manage their child’s phone use by setting daily screen time limits, controlling which apps can be used, and creating bedtime schedules. These controls are protected by a PIN and can be managed directly from the child’s device. Overall, Android 16 makes phones easier to use, more personal, and safer for kids. (Also Read: YouTube ‘Recap’ Feature Launched: Check Top Trends, Podcasts, Songs, And Most-Watched Creators of 2025; Here’s How To View It) Google’s New Android Features Google is also introducing some new Android features that work even if you are not using Android 16. One of them is a beta feature called “Call Reason,” which lets you mark a call to your saved contacts as “urgent,” so they know it’s important. Another new feature is “Expressive Captions,” which adds emotion tags like (sad) or (joyful) to video messages or social media posts, helping you understand the tone of what someone is saying. Chrome Gets Smarter With Better Pinned Tabs Google has also improved Chrome by making pinned tabs work just like they do on a computer, so your favorite pages stay at the front and are easy to return to. The “Circle to Search” tool is also getting better, allowing you to search anything on your screen by circling, highlighting, scribbling, or tapping. And now, you don’t even need to touch your phone to use Voice Access. You can simply say, “Hey Google, start Voice Access,” and control your phone with your voice. 

Cyber Attack On Indian Airports: The government has confirmed that several major airports, including Delhi, Mumbai, and Bengaluru, detected GPS spoofing signals last month. However, it assured that flight operations were not affected. The cyberattack raises serious concerns about aviation cybersecurity and has prompted heightened vigilance at key air travel hubs. The confirmation follows multiple reports of technical anomalies, including suspected spoofing of navigational systems, at some of the country’s busiest airports. Notably, the Ministry of Civil Aviation, along with relevant security agencies, continues to monitor the situation closely to ensure smooth air traffic operations and to implement strengthened cyber countermeasures. Amid rising concerns over aviation sector vulnerabilities, industry experts have emphasised the need for stronger cyber preparedness. On this situation Evaa Saiwal, Head of Liability & Cyber Insurance at Policybazaar for Business, said, “The recent cyber-attack on Indian airports is a stark reminder that cyber incidents today are not just technical events — they impact operations, reputation, and customer trust. The ripple effect can extend to employees, partners, and entire service ecosystems”. Add Zee News as a Preferred Source He further stated that “As these threats continue to grow, cyber insurance has become a crucial layer of resilience. It helps organisations manage the tangible fallout — from operational losses and system downtime to data breaches and the high cost of investigation and recovery. More importantly, it gives businesses the confidence to get back on their feet quickly, protect stakeholders, and continue serving customers even in the face of unprecedented attacks.” What Is GPS spoofing? GPS spoofing is a cyberattack in which attackers send fake GPS signals to a device, causing it to show the wrong location, time, or route. In simple words, it fools maps, navigation tools, and tracking apps into thinking they are somewhere else. For pilots, this can affect what they see on their screens, including the aircraft’s position and speed. This is different from GPS jamming, where signals are completely blocked, making the GPS stop working and show errors like “no signal.” How GPS Spoofing Works? GPS satellites send very weak signals to Earth, which devices use to calculate their location, speed, and time. In a GPS spoofing attack, an attacker uses special radio transmitters or software to create stronger fake GPS signals that look like the real ones. The device connects to these fake signals instead of the real satellites, causing it to show the wrong location, route, speed, or time even though it has not actually moved. GPS Spoofing: Where It is Used GPS spoofing can affect navigation and transport by misleading ships, aircraft, drones, trucks, and cars, causing them to deviate from their routes or hide their actual movements. It can also impact smartphones and apps, allowing users to fake their location in ride-hailing, gaming, financial, or social apps, sometimes to commit fraud or bypass geo-restrictions. In the field of security and defense, state or sophisticated actors may use GPS spoofing around sensitive areas to protect VIPs, conceal military activity, or disrupt enemy drones. The incident has intensified calls for advanced monitoring, greater redundancy and stricter controls across aviation and related sectors. Highlighting the emerging threat landscape, Sameer Yadav, Founder at Netforchoice Datacentre, said: “The GPS spoofing at key airports in India is a pointer to the way cyber threats are morphing from digital hits on IT systems into cyber-physical strikes that aim to rupture the integrity of critical infrastructure. He further stated that “Today’s adversaries use APT methods in spoofing PNT/GNSS signals and exploiting OT-IT that could disrupt navigation, timing and safety of operations. With data centers and sat-based timing along with IAM and interconnected supply chain, this next attack suggests a need for redundancy, zero-trust architecture – continuous threat telemetry. In today’s world, cyber insurance is a must-have; not just as monetary protection but as a layer of resiliency that helps in forensic response, business interruption, compliance liability and fast recovery.” Recent Delhi Airport And Airbus A320 Glitch Scare The government’s statement comes just weeks after more than 400 flights were delayed at Delhi airport because of a technical problem in the Air Traffic Control system. The issue was linked to the Automatic Message Switching System, which sends important flight plan data to the Auto Track System. The GPS spoofing incident also follows global flight disruptions that happened a few days earlier, caused by a software update needed for Airbus A320 airplanes. 

Google Workspace Studio: Google has officially launched Workspace Studio, a new automation tool that helps users design, manage and share AI agents inside Google Workspace. The platform is powered by Gemini 3, Google’s latest advanced AI model. It is Designed for Easy Automation.  Workspace Studio was first teased and announced earlier this year. It is now available for business users who want to automate everyday work without coding. The tool follows a simple “if this, then that” automation model. It connects directly with Google apps like Gmail, Chat, Drive, Docs, and Sheets. It also supports third-party tools, including Asana, Jira, Mailchimp and Salesforce. Add Zee News as a Preferred Source Google claims that users can build AI agents in just a few minutes by typing natural language instructions instead of writing code. (Also Read: Spotify Wrapped And Apple Music Replay: Rapper Bad Bunny Is World’s Top Artist; Check Your Favourites) Agents With AI Reasoning and Context Understanding Workspace Studio replaces older rule-based automation tools. The new AI agents can: Understand context Reason through tasks Generate responses and actions Google says this allows more flexible workflows, compared to older tools that needed strict, technical commands. These agents can handle repetitive tasks such as organising emails, scheduling events, or sending follow-ups — freeing up teams to focus on important work. Built Into Google Workspace Apps A new shortcut icon will appear in the top-right corner of web apps, next to the Gemini button. Users will find three main tabs: Discover — ready-made automations My Agents — agents you create Activity — tasks performed by agents Workspace Studio is mainly for companies and professional teams. Helpful but Still Developing Google highlights Workspace Studio as a major step in the AI competition. The company aims to help organisations close the “automation gap” by making advanced automation available to everyone. However, the rollout comes with some limitations. Features like a public agent marketplace will not be ready until 2026, and companies still need to manage security, governance and cost controls carefully. Farhaz Karmali, Google Workspace product director, said the aim is to eliminate time-consuming office tasks, “You can delegate these repetitive tasks to agents that can reason, understand context and handle the work that used to slow you down,” the director said. 

Spotify Wrapped And Apple Music Replay: As the year comes to an end, music lovers around the world are celebrating their listening habits through Spotify Wrapped and Apple Music Replay. Both platforms have released their yearly reports, highlighting the most-streamed artists, songs, albums, and genres of 2025. Spotify released its Wrapped feature on Wednesday, started at 13:00 GMT. This yearly experience gives users a personalised summary of all the music and podcasts they enjoyed throughout the year. Anyone with a Spotify account, including free users, can check their Wrapped. Spotify suggests that users update the app to the latest version for a smoother experience. Inside the app, fans will find slides showing their top tracks, favourite genres, most-played artists, and even the podcasts they listened to the most. Add Zee News as a Preferred Source Bad Bunny Leads Global Charts Puerto Rican superstar Bad Bunny is the world’s most-streamed artist on Spotify in 2025, earning more than 19.8 billion streams this year. His hit album Debí Tirar Más Fotos, which celebrates Puerto Rican musical culture, was also named the world’s biggest album of 2025. With this success, Bad Bunny is set to perform at the Super Bowl halftime show next year. UK Fans Choose Alex Warren’s ‘Ordinary’ In the United Kingdom, listeners chose Alex Warren’s track “Ordinary” as the top song of the year. The popular single stayed at number one for 13 weeks and was among the few newly released songs to make it into Spotify’s Top 10 tracks in the UK. Several older hits remained popular throughout the year, including songs from Lola Young, Billie Eilish, and Gigi Perez, which continued to hold strong positions on the charts. Chappell Roan’s song Pink Pony Club, first released in 2020, also returned to the spotlight as the fourth most-streamed song of 2025. Top UK Album: Sabrina Carpenter’s ‘Short’n’Sweet’ American singer Sabrina Carpenter dominated UK album streams. Her 2024 album Short’n’Sweet, powered by viral hits like Taste, Espresso, and Please Please Please, became the most-streamed album in the UK this year. However, her latest album Man’s Best Friend did not enter the Top 10 list. (Also Read: Google Workspace Studio: From Automatic Emails And Chats To Building AI Agents In Minutes; Check How This New Tool Works) Special Wrapped Experiences for Creators Spotify is not limiting Wrapped to listeners. The platform has also created personalised versions for artists, songwriters, podcasters, authors, and advertisers. Through special microsites, creators can see detailed insights about how fans around the world enjoyed their content. Spotify is also bringing Wrapped into real-world spaces with around 50 pop-up experiences in major cities. These installations will highlight popular artists like Oasis in Manchester, JENNIE in Seoul, Bad Bunny in Mexico City, and Chappell Roan in New York City. Apple Music Replay Apple Music offers a similar year-end feature called Replay. It provides users with a full summary of their listening activity, including total minutes played and their most-streamed songs, albums, and artists. Apple Music starts collecting listening data in February and creates a detailed annual report by December. Replay began as a weekly update in 2019 and evolved in 2022 to become a yearly listening review similar to Spotify Wrapped. iPhone users can access Apple Music Replay directly through the Music app under the Home tab. Those without iPhones can still view their Replay by visiting replay.music.apple.com and logging in with their Apple ID.

Realme P4x Launched: Realme has introduced its latest smartphone, the Realme P4x, in India. The company has positioned the phone for users who want solid performance and long-lasting battery backup while staying within a budget. Alongside the phone, Realme has also launched the Realme Watch 5. The Realme P4x will go on sale on December 10, starting at 12 PM for a 12-hour first sale window. It will be available on realme.com, Flipkart, and authorised retail stores. Prices:  Add Zee News as a Preferred Source 6GB RAM + 128GB storage: Rs 15,999 8GB RAM + 128GB storage: Rs 17,499 8GB RAM + 256GB storage: Rs 19,499 Design and Build Realme claims the P4x comes with an “aerospace-inspired design.” The rear camera module features a vertical pill-shaped cutout, accompanied by the company’s branding. The phone measures 8.39mm in thickness and weighs 208 grams. Display and Audio The smartphone features a 6.72-inch display with a 144Hz refresh rate for smoother scrolling and gaming visuals. It supports up to 1,000 nits of peak brightness, allowing for better visibility in outdoor light. The phone also includes a dual-speaker setup for stereo sound. Performance and Gaming The P4x is powered by the MediaTek Dimensity 7400 Ultra 5G chipset. Realme claims the device has achieved over 7,80,000 points on AnTuTu.  The company also states that users can enjoy gaming at: 90 FPS on BGMI and Call of Duty Mobile Up to 120 FPS on Free Fire To manage heat during extended gaming, the phone includes a vapour cooling chamber. (Also Read: Google Workspace Studio: From Automatic Emails And Chats To Building AI Agents In Minutes; Check How This New Tool Works) Cameras The smartphone comes with a 50MP AI main camera on the rear that supports 4K video recording. AI tools such as Eraser, Motion Deblur, and Glare Remover are included to improve images. It comes with a 8MP front camera for selfies and video calls. Battery and Charging One of the key highlights of the Realme P4x is its 7,000mAh battery, which is much larger than what is typically found in this segment. It supports 45W fast charging, and Bypass Charging is also available for users who prefer to game while charging without heating the device too much.

Language model training is slow, even when your model is not very large. This is because you need to train the model with a large dataset and there is a large vocabulary. Therefore, it needs many training steps for the model to converge. However, there are some techniques known to speed up the training process. In this article, you will learn about them. In particular, you will learn about: Using optimizers Using learning rate schedulers Other techniques for better convergence or reduced memory consumption Let’s get started. How to Speed-Up Training of Language ModelsPhoto by Emma Fabbri. Some rights reserved. Overview This article is divided into four parts; they are: Optimizers for Training Language Models Learning Rate Schedulers Sequence Length Scheduling Other Techniques to Help Training Deep Learning Models Optimizers for Training Language Models Adam has been the most popular optimizer for training deep learning models. Unlike SGD and RMSProp, Adam uses both the first and second moment of the gradient to update the parameters. Using the second moment can help the model converge faster and more stably, at the expense of using more memory. However, when training language models nowadays, you will usually use AdamW, the Adam optimizer with weight decay. Weight decay is a regularization technique to prevent overfitting. It usually involves adding a small penalty to the loss function. But in AdamW, the weight decay is applied directly to the weights instead. This is believed to be more stable because the regularization term is decoupled from the calculated gradient. It is also more robust to hyperparameter tuning, as the effect of the regularization term is applied explicitly to the weight update. In formula, AdamW weight update algorithm is as follows: $$\begin{aligned}g_t &= \nabla_\theta L(\theta_{t-1}) \\m_t &= \beta_1 m_{t-1} + (1 – \beta_1) g_t \\v_t &= \beta_2 v_{t-1} + (1 – \beta_2) g_t^2 \\\hat{m_t} &= m_t / (1 – \beta_1^t) \\\hat{v_t} &= v_t / (1 – \beta_2^t) \\\theta_t &= \theta_{t-1} – \alpha \Big( \frac{\hat{m_t}}{\sqrt{\hat{v_t}} + \epsilon} + \lambda \theta_{t-1} \Big)\end{aligned}$$ The model weight at step $t$ is denoted by $\theta_t$. The $g_t$ is the computed gradient from the loss function $L$, and $g_t^2$ is the elementwise square of the gradient. The $m_t$ and $v_t$ are the moving average of the first and second moment of the gradient, respectively. Learning rate $\alpha$, weight decay $\lambda$, and moving average decay rates $\beta_1$ and $\beta_2$ are hyperparameters. A small value $\epsilon$ is used to avoid division by zero. A common choice would be $\beta_1 = 0.9$, $\beta_2 = 0.999$, $\epsilon = 10^{-8}$, and $\lambda = 0.1$. The key of AdamW is the $\lambda \theta_{t-1}$ term in the gradient update, instead of in the loss function. AdamW is not the only choice of optimizer. Some newer optimizers have been proposed recently, such as Lion, SOAP, and AdEMAMix. You can see the paper Benchmarking Optimizers for Large Language Model Pretraining for a summary. Learning Rate Schedulers A learning rate scheduler is used to adjust the learning rate during training. Usually, you would prefer a larger learning rate for the early training steps and reduce the learning rate as training progresses to help the model converge. You can add a warm-up period to increase the learning rate from a small value to the peak over a short period (usually 0.1% to 2% of total steps), then the learning rate is decreased over the remaining training steps. A warm-up period usually starts with a near-zero learning rate and increases linearly to the peak learning rate. A model starts with randomized initial weights. Starting with a large learning rate can cause poor convergence, especially for big models, large batches, and adaptive optimizers. You can see the need for warm-up from the equations above. Assume the model is uncalibrated; the loss may vary greatly between subsequent steps. Then the first and second moments $m_t$ and $v_t$ will be fluctuating greatly, and the gradient update $\theta_t – \theta_{t-1}$ will also be fluctuating greatly. Hence, you would prefer the loss to be stable and move slowly so that AdamW can build a reliable running average. This can be easily achieved if $\alpha$ is small. At the learning rate reduction phase, there are a few choices: cosine decay: $LR = LR_{\max} \cdot \frac12 \Big(1 + \cos \frac{\pi t}{T}\Big)$ square-root decay: $LR = LR_{\max} \cdot \sqrt{\frac{T – t}{T}}$ linear decay: $LR = LR_{\max} \cdot \frac{T – t}{T}$ Plot of the three decay functions A large learning rate can help the model converge faster while a small learning rate can help the model stabilize. Therefore, you want the learning rate to be large at the beginning when the model is still uncalibrated, but small at the end when the model is close to its optimal state. All decay schemes above can achieve this, but you would not want the learning rate to become “too small too soon” or “too large too late”. Cosine decay is the most popular choice because it drops the learning rate more slowly at the beginning and stays longer at a low learning rate near the end, which are desirable properties to help the model converge faster and stabilize respectively. n PyTorch, you have the CosineAnnealingLR scheduler to implement cosine decay. For the warm-up period, you need to combine with the LinearLR scheduler. Below is an example of the training loop using AdamW, CosineAnnealingLR, and LinearLR: import torch import torch.nn as nn import torch.optim as optim from torch.optim.lr_scheduler import LinearLR, CosineAnnealingLR, SequentialLR # Example setup model = torch.nn.Linear(10, 1) X, y = torch.randn(5, 10), torch.randn(5) loss_fn = nn.MSELoss() optimizer = optim.AdamW(model.parameters(), lr=1e-2, betas=(0.9, 0.999), eps=1e-8, weight_decay=0.1) # Define learning rate schedulers warmup_steps = 10 total_steps = 100 min_lr = 1e-4 warmup_lr = LinearLR(optimizer, start_factor=0.1, end_factor=1.0, total_iters=warmup_steps) cosine_lr = CosineAnnealingLR(optimizer, T_max=total_steps – warmup_steps, eta_min=min_lr) combined_lr = SequentialLR(optimizer, schedulers=[warmup_lr, cosine_lr], milestones=[warmup_steps]) # Training loop for step in range(total_steps): # train one epoch y_pred = model(X) loss = loss_fn(y_pred, y) # print loss and learning rate print(f”Step {step+1}/{total_steps}: loss {loss.item():.4f}, lr {combined_lr.get_last_lr()[0]:.4f}”) # backpropagate and update weights optimizer.zero_grad()

New Delhi: India Inc. is rapidly scaling AI, fueled by global tailwinds, competition and advances in GenAI technologies, a report said on Wednesday, noting that AI now cuts across customer engagement, operational optimisation and mission-critical processes in multiple sectors. Yet adoption remains fragmented, with only 15 per cent of organisations having extensive enterprise-wide AI deployment. “While AI will continue growing, oversight is not keeping pace with it. In many organisations, AI infrastructure is expanding faster than the governance, security and ethical safeguards needed, creating widening gaps in accountability and risk management,” Alvarez & Marsal (A&M), a global professional services firm, said in its report. Meanwhile, governance maturity remains limited despite rising usage. While 60 per cent of organisations have introduced basic governance or acceptable-use policies, only 19 per cent have carried out detailed risk assessments, and 81 per cent still lack full visibility of how their AI systems are monitored or governed, the report noted. Add Zee News as a Preferred Source With many AI initiatives developed in silos, accountability and standards vary widely, especially when third-party and in-house models coexist. The report highlighted the need for integrated, organisation-wide governance frameworks that embed transparency, oversight and clear role ownership. “AI is now embedded deeper into business processes and decision systems than ever before. India’s AI opportunity is substantial, but its long-term gains depend on how effectively organisations govern and secure the systems they deploy,” said Dhruv Phophalia, MD and India Lead – Disputes and Investigations, Alvarez & Marsal. Those who invest early in these foundations will be best placed to unlock the full economic and competitive potential of AI, he added. According to the report, responsible AI principles are widely acknowledged; however, their implementation remains limited. Fewer than 20 per cent of organisations have deployed mechanisms for explainability, bias detection or fairness, and 60 per cent lack any formal process to validate model integrity. Data governance shows similar gaps, with only 26 per cent having integrated data masking and PII-scanning within AI workflows, and 60 per cent perform no structured dataset validation. The report also highlighted that as more complex AI models go into production, security across the AI lifecycle will be imperative. While 52 per cent of enterprises have secure development environments with basic controls, fewer than 30 per cent conduct penetration testing or red-teaming, and only 19 per cent have safeguards to detect data poisoning during model training.

New Delhi: The total number of Internet subscribers in India increased from 1002.85 million at the end of the April-June quarter (Q1 FY26) to 1017.81 million at the end of the July-September period (Q2 FY26), registering a quarterly growth of 1.49 per cent, data from Telecom Regulatory Authority of India (TRAI) showed on Wednesday. As per the data, out of 1,070.81 million internet subscribers, the number of Wired Internet subscribers is 44.42 million, and the number of Wireless Internet subscribers is 973.39 million. Meanwhile, the broadband Internet subscriber base increased by 1.63 per cent from 979.71 million at the end of June to 995.63 million at the end of September this year. The narrowband Internet subscriber base decreased from 23.14 million in the June quarter to 22.18 million at the end of the September quarter. Add Zee News as a Preferred Source At the same time, wireline subscribers decreased from 47.49 million at the end of the April-June quarter to 46.61 million at the end of the September quarter, with a quarterly rate of decline of 1.84 per cent. On a year-on-year (YoY) basis, wireline subscriptions increased by 26.21 per cent at the end of the July-September quarter. Wireline Tele-density decreased from 3.36 per cent at the end of Q1 FY26 to 3.29 per cent at the end of Q2 FY26, with a quarterly rate of decline of 2.06 per cent. Meanwhile, the monthly average revenue per user (ARPU) for wireless service increased by 2.34 per cent, from Rs 186.62 in the first quarter to Rs 190.99 in the second quarter of this fiscal. Monthly ARPU for wireless service increased by 10.67 per cent YoY in this quarter as well. The ARPU per month for the pre-paid segment is Rs 189.69, and the ARPU per month for the post-paid segment is Rs 204.55 for the quarter under review. On an all-India average, the overall MOU per month decreased by 0.10 per cent from 1006 in the April-June period to 1005 at the end of the July-September period. The total Internet subscriber base comprises a Broadband Internet subscriber base of 995.63 million and a Narrowband Internet subscriber base of 22.18 million.