India’s First Dual-Core Microprocessor DHRUV64: India has taken a crucial step in its semiconductor journey by launching DHRUV64, the country’s first 1.0 GHz, 64-bit dual-core microprocessor, according to a government release. This processor has been fully designed in India by the Centre for Development of Advanced Computing (C-DAC) under the Microprocessor Development Programme. DHRUV64 is part of India’s plan to reduce its dependence on processors made in other countries. The government says the chip can be used for both defence and commercial purposes and helps build a strong, homegrown technology system. DHRUV64: Dual-Core Microprocessor Features Add Zee News as a Preferred Source The processor uses modern technology to work faster, handle multiple tasks at the same time, and remain reliable. It can easily connect with different hardware systems, making it useful for 5G networks, vehicles, electronic devices, factories, and Internet of Things (IoT) applications. DHRUV64: Future Semiconductor Projects The release highlights that India contributes nearly 20 per cent of global microprocessor production, showing why homegrown chip development is important for long-term security. Even before DHRUV64, India had started building its processor ecosystem with chips such as SHAKTI, AJIT, VIKRAM, and THEJAS64. These initiatives helped create a strong base for domestic chip design. DHRUV64 gives startups, researchers, and industries a local platform to develop and test products without depending on foreign processors. It also allows prototype development at a lower cost. With India’s large pool of chip design talent, the processor is expected to help train more skilled professionals for future semiconductor projects. Its success is also likely to speed up the development of the upcoming Dhanush and Dhanush+ processors. DHRUV64: India’s First 1.0 GHz, 64-bit dual-core Microprocessor DHRUV64 is India’s first homegrown 1.0 GHz, 64-bit dual-core microprocessor, strengthening the indigenous processor pipeline. National Programmes like Digital India RISC-V support the design, testing and… pic.twitter.com/i5mfZOY3BZ — PIB India (@PIB_India) December 15, 2025 DHRUV64 Is Part Of Digital India Programme The rollout of DHRUV64 is part of the Digital India RISC-V (DIR-V) Programme, which aims to make India a global hub for electronics system design and manufacturing. The release says DHRUV64 is the third chip fabricated under DIR-V, following THEJAS32 and THEJAS64. Work on DHANUSH64 and DHANUSH64+ System-on-Chip variants is now in progress. The Ministry of Electronics and Information Technology (MeitY) guides national programmes such as the Microprocessor Development Programme, DIR-V, Chips to Startup (C2S), the India Semiconductor Mission, the Design Linked Incentive Scheme, and INUP-i2i. These initiatives strengthen the design ecosystem and support research and training across academia, startups, and industry. C-DAC continues to lead processor design efforts and is now developing the next processors in the RISC-V roadmap. “The progress from THEJAS32 to DHRUV64, and the ongoing development of Dhanush and Dhanush+, illustrates a confident national pathway toward indigenous processor innovation and technological self-reliance,” the release said.(With IANS Inputs)

Motorola Edge 70 Price In India: Motorola has expanded its premium mid-range portfolio in India with the launch of the ultra-slim Motorola Edge 70 today in the Indian market. The newly launched smartphone succeeds the popular Edge 60 and brings a focus on design, cameras and durability, alongside modern software and AI features. The Motorola Edge 70 comes with a thin form factor, that is, 5.99mm thickness and weighs around 159 grams. It is available in three colour options which includes the Lily Pad, Gadget Grey and Bronze Green. Adding further, the Motorola Edge 70 sticks to the classic Edge-series design, with a squircle-shaped rear camera module and a premium vegan leather back. Measuring just 5.99mm in thickness, it is one of the slimmest smartphones in its segment. Despite its ultra-slim body, the phone is claimed to meet MIL-STD-810H military-grade durability standards. The new Motorola Edge 70 runs on Android 16, with Hello UI. It will get three major Android upgrades and four years of security updates. Add Zee News as a Preferred Source Motorola Edge 70 Specifications The smartphone features a 6.7-inch 1.5K AMOLED display with a 120Hz refresh rate, up to 4,500 nits of peak brightness, Gorilla Glass 7i protection, Dolby Vision, and HDR10+ support. It is powered by Qualcomm’s Snapdragon 7 Gen 4 chipset, paired with 8GB of LPDDR5x RAM and 256GB of UFS 3.1 internal storage. The smartphone packs a 5,000mAh silicon carbon battery that is claimed to deliver up to 31 hours of continuous video playback, along with 68W wired and 15W wireless fast charging support. Just 5.99mm THIN, the motorola edge 70 redefines ultra-slim engineering finished in Pantone™ curated colours Sale starts 23rd December at ₹28,999* on Flipkart, https://t.co/azcEfy2uaW, and leading retail stores.#MotorolaEdge70 #Motorola #ImpossiblyThinYetUncompromised pic.twitter.com/3QI2S76fNR — Motorola India (@motorolaindia) December 15, 2025 On the photography front, it comes with a triple rear camera setup led by a 50-megapixel primary sensor with optical image stabilisation (OIS), a 50-megapixel ultra-wide camera, and a three-in-one light sensor, while the front houses a 50-megapixel selfie camera. (Also Read: Apple Rolls Out iOS 26.2 Stable Update For iPhone Users: Check New Features, Eligible Models; Follow THESE Steps To Install) The device supports video recording at up to 4K resolution at 60fps. It also ships with Moto AI features such as Next Move, Catch Me Up 2.0, Pay Attention 2.0, Remember This + Recall, and Co-pilot, along with AI Video Enhancement, AI Action Shot, and AI Photo Enhancement tools. Motorola Edge 70 Price In India, Availability The smartphone is priced in India at Rs 29,999 for the single variant with 8GB RAM and 256GB storage. Buyers can also avail a Rs 1,000 bank discount on select bank cards. The smartphone will be available for purchase through Flipkart, the Motorola India website, and authorised offline retail stores across the country.  Motorola Edge 70 India Launch: Alternative Options Apart from the Motorola Edge 70, consumers can also consider several alternative smartphones in a similar price range. These include the Vivo V60e, priced at Rs 27,990 on Amazon; the Realme 15 Pro 5G, available for Rs 25,999 on Amazon; the Motorola Edge 60 Pro, listed at around Rs 28,000 on Amazon; and the Nothing Phone 3a Pro, which is currently priced at Rs 27,900 on Amazon. 

Google And Apple Emergency Security Updates: Apple And Google have rolled out urgent security updates to protect against a hacking campaign targeting an unknown number of their users. Both tech giants are discovering that hackers were actively exploiting previously unknown vulnerabilities in their devices. These flaws, known as zero-day bugs, were being used in real-world attacks, prompting both tech giants to act swiftly to protect users. Google Releases Security Bugs In Chrome Browser Due to the serious nature of the threat, Google quickly released fixes for several security flaws in its Chrome browser. The company confirmed that at least one of these bugs was already being used by hackers before a patch could be issued. Google later updated its disclosure to clarify that the vulnerability was discovered by Apple’s security engineering team along with Google’s Threat Analysis Group. Add Zee News as a Preferred Source This team mainly tracks government-linked hackers and commercial spyware groups, suggesting that the attack may have been carried out by state-backed actors rather than ordinary cybercriminals. Apple Security Updates In Flagship Products Apple also rolled out critical security updates for its major products, including iPhones, iPads, Macs, Vision Pro, Apple TV, Apple Watches, and the Safari browser. In its security advisory for iPhones and iPads, the company said it fixed two vulnerabilities. The Cupertino-based tech giant Apple added that it was aware these flaws may have been used in an “extremely sophisticated attack” targeting specific individuals who were using devices running versions older than iOS 26. (Also Read: Global AI Rankings: India Becomes The World’s Third Most Competitive Country In AI Power; China Grabs Second Spot, United States Ranks At…) Such attacks are often associated with government surveillance programs or spyware companies like NSO Group and Paragon Solutions, whose tools are commonly used to target journalists, activists, lawyers, and political dissidents worldwide. Notably, users across the globe have been advised to update their devices immediately, as the exploits were reportedly targeting select individuals. How To Avoid Zero-Day Bugs Keep Everything Updated: Enable automatic updates on OS, apps, browsers, plugins, and firmware to close zero-days quickly once patches drop. Use Smart Protection: Run a reputable security suite/EDR with behavior-based detection, not just classic antivirus signatures. Reduce Attack Surface: Disable macros, remove unused plugins/software, avoid risky sites, and don’t use admin accounts for daily work Practice Safe Clicks: Treat emails, links, and attachments with suspicion, verify senders/URLs, and avoid downloading from untrusted sources. 

New Delhi: Agentic artificial intelligence (AI) systems are rapidly reshaping how banks manage frontline sales, offering a potential breakthrough for relationship managers long burdened by inefficient systems, weak leads, and heavy administrative workloads, according to a McKinsey report. “In frontline sales, the potential is vast. Agentic AI makes it possible to automate the complex workflows characteristic of financial services—something bankers have long wanted to do but have never fully succeeded in,” the report noted. At leading global banks, agentic AI is already being deployed across prospecting, lead nurturing, and account management, delivering measurable gains in productivity and revenue within months. Unlike traditional generative AI, which responds to prompts, agentic AI can independently interpret objectives, break them into tasks, interact with systems and people, execute actions, and continuously adapt with minimal human input. Add Zee News as a Preferred Source The shift comes at a critical time for the banking sector, which is facing margin pressure, slowing growth, and rising cost-to-income ratios. Industry research indicates that when banks redesign an entire frontline domain end-to-end using agentic AI, revenues per relationship manager can rise by 3 to 15 per cent, while the cost to serve can fall by 20 to 40 per cent. “As banks face margin pressure, slowing growth, and rising cost-to-income ratios, agentic AI represents not just a productivity tool but a new operating model for relationship management,” the report said. Frontline bankers have long cited poor-quality leads, excessive compliance requirements, and fragmented technology systems as key obstacles to effective selling. Many relationship managers spend more time updating customer relationship management systems and preparing reports than engaging with clients. This imbalance has contributed to high burnout and attrition across sales teams. Agentic AI offers a way to rebalance this equation. Intelligent agents can continuously scan markets, analyse structured and unstructured data, prioritise high-potential prospects, and automate follow-ups. In sales outreach, agents can personalise communications at scale, nurture thousands of leads simultaneously, and escalate only qualified opportunities to human bankers. This allows relationship managers to focus on higher-value conversations and complex client needs. Banks piloting these systems have reported significant operational improvements. AI-driven market mapping has expanded sales pipelines by roughly 30 per cent in some institutions, while automated lead nurturing has doubled or tripled the number of qualified leads. In parallel, AI-powered account intelligence tools have reduced meeting preparation time and improved the quality of client interactions. With routine tasks handled by agents, bankers can act more as trusted advisors, concentrating on insight-led discussions, strategic problem-solving, and long-term relationship building. However, the report cautions that capturing the full value of agentic AI requires more than deploying isolated tools. Banks must reimagine frontline operating models end-to-end, invest in robust data foundations, establish clear governance, and upskill employees to work effectively alongside AI agents. With revenue uplift and productivity gains now visible, agentic AI is increasingly seen not as an experiment but as a new operating paradigm for frontline banking.

New Delhi: Artificial intelligence-related investments are set to accelerate sharply in 2026 as companies expand spending to keep pace with the fast-growing AI revolution, according to a report. It also said the surge in AI-driven private-sector spending is significantly cushioning the negative impact of tariff hikes on the US economy. Fitch Ratings, in a report, said, “Corporate plans suggest another AI-related investment increase in 2026. AI-driven private-sector spending is significantly cushioning the negative impact of tariffs.” It highlighted that while global growth is expected to slow, the resilience shown by the US is partly due to strong AI-linked investment momentum. Add Zee News as a Preferred Source The report noted that world GDP growth is projected to ease to 2.5 per cent in 2025, down from 2.9 per cent in 2024. The US economy growth is also expected to dip to 1.8 per cent in 2025 from 2.8per cent in 2024. Fitch had earlier anticipated a sharper deceleration in the US following the steep rise in tariff rates. However, the report stated that the “tariff shock” turned out milder than expected as it coincided with a major upturn in private-sector spending linked to the AI boom. The report added that the sharp rise in IT investment seen in national accounts is further supported by data from the largest US technology companies. Capital spending by AI hyperscalers, including the “Magnificent 7”, has doubled since 2023 to USD 400 billion as companies pour money into data centres. Corporate plans also indicate another wave of AI-related investment growth in 2026. It said the AI boom is already having a clear macroeconomic impact. In the first half of 2025, IT capital spending accounted for nearly 90 per cent of US GDP growth, reflecting the scale at which AI is reshaping investment patterns. The AI-fuelled equity market rally could also add 0.4 percentage points to consumption, providing additional support to the economy. The report noted that the strong momentum in IT capex has so far not been accompanied by a rise in corporate leverage at the aggregate level. Upward revisions in private capital expenditure forecasts are helping soften the drag caused by tariff increases. Overall, Fitch stated that the ongoing surge in AI-related investments is emerging as a crucial counterbalance to economic pressures arising from higher tariffs, while also laying the foundation for long-term structural transformation.

New Delhi: The 5G services have been rolled out in all states and union territories (UTs) across the country and presently, these are available in 99.9 per cent of the districts, according to the government. As of October 31, telecom service providers (TSPs) have installed 5.08 lakh 5G Base Transceiver Stations (BTSs) across rural and urban areas of the country. It is to mention that more than 31 lakh Base Transceiver Stations (BTSs) have been installed across the country, informed Minister of State for Communications and Rural development, Dr. Pemmasani Chandra Sekhar, while replying to a question in the Rajya Sabha. To reduce call drops and improve internet connectivity in underserved areas, the government has taken several initiatives. Add Zee News as a Preferred Source These are BharatNet project for providing broadband connectivity in Gram Panchayats (GPs) and villages; scheme for providing mobile services in Left Wing Extremism (LWE) affected areas and in Aspirational Districts; 4G Saturation scheme to provide 4G mobile coverage in all uncovered villages; launch of GatiShakti Sanchar portal and RoW (Right of Way) Rules to streamline RoW permissions and clearance of installation of telecom infrastructure; and time-bound permission for use of street furniture for installation of small cells and telecommunication line. According to the minister, the telecom infrastructure are being deployed by private TSPs as well as state-led service providers. Further, telecom infrastructure are being shared by private and state-led service providers based on techno-commercial feasibility, he mentioned. Meanwhile, seven dedicated Working Groups constituted earlier by the Centre under the Bharat 6G Alliance have presented their progress and roadmap. Union Communications Minister Jyotiraditya Scindia said that technology, spectrum, devices, applications and sustainability verticals must align seamlessly for innovations to mature and scale. He said monthly joint reviews between working groups are essential to ensure that breakthroughs in one domain translate into actionable outcomes in others. The minister pointed out that spectrum policy will be central to India’s 6G strategy and noted that India has already undertaken significant spectrum refarming, with more planned ahead.

In 2025, “using AI” no longer just means chatting with a model, and you’ve probably already noticed that shift yourself.

Internet connectivity in India is deeply unequal, from the fast fiber lines in cities to the remarkably slow and unreliable networks that starve millions of villages. Starlink – the satellite internet service from billionaire Elon Musk-led SpaceX – will soon launch in India, promising to change this all over with a radical new way to deliver direct internet access from space. Starlink is not yet another service; it is a very different way of looking at connectivity. A recent leak on the Starlink India website briefly flashed tentative pricing, hinting at a high-end offering: an estimated monthly fee of about Rs 8,600 and a hardware kit at approximately Rs 34,000. Starlink later clarified these plans were uploaded by mistake and are incorrect, adding that the official India plan has not been announced yet. However, several key regulatory approvals are pending, including official launch dates, which are still not certain despite Starlink having received preliminary approval from the central government. Add Zee News as a Preferred Source How Satellite Internet Works And Why Starlink Is Different Traditional satellite internet involves big satellites at high orbits, about 36,000 km from Earth. The signal is delayed because of the great distance, therefore presenting high latency. Starlink changes this completely. Low Earth Orbit: Starlink deploys thousands of small satellites orbiting at a much lower altitude of about 550 kilometers. This greatly reduced distance minimizes the travel time of data, therefore contributing to much lower latency and improved speed of the internet. The System: At one end, the Starlink system comprises a smart dish installed on the user’s roof and the constellation of LEO satellites. Data goes from the user’s Wi-Fi router to the dish, which links directly to the nearest Starlink satellite. From there, the data either goes to a ground station or jumps via laser links from one satellite to another before reaching the global internet. Global Constellation: Currently, there are over 8,500 Starlink LEO satellites in orbit actively working around the globe, and the total number launched has already approached 9,000. In fact, this dense network ensures continuous signal coverage virtually everywhere. Bridging India’s Connectivity Gap Indeed, the Starlink model is all the more exciting for India, where laying fiber optic cables across challenging terrain-mountains, forests, remote villages, and border areas-is often difficult or impossible. Starlink bypasses infrastructure, whereas traditional internet needs multi-layered ground infrastructure in the form of fiber, towers, and undersea cables. The dish connects directly with the satellite network for high-speed internet at places that are unreachable by cables. Easy Installation: Your dish is smart, it self-locates and adjusts for the right satellite, without repeated engineering visits. Reliability: Starlink boasts an impressive 99.9% uptime. Since it’s satellite-based, it’s immune to the common problems of traditional internet-service hiccups, like fiber cuts that could leave you with no service for hours. Market Impact And Challenges In India While Starlink is often viewed as a solution to all of India’s internet problems, experts warn against this expectation. This calls for a focus on connectivity, not on speeds. While Starlink offers decent speed, it may not match the full range of fiber speeds offered in Indian cities.  The true value of Starlink is in connection where the laying of optical fiber is difficult. For rural schools, health centers, and government offices in difficult terrains, the Starlink may be invaluable for telemedicine, e-governance, and digital payments.  Affordability Hurdle: The service is pricey, and high costs for the hardware and monthly fees will restrict immediate mass adoption in India’s price-sensitive market.  Challenges: The service is not without its shortcomings. Inclement rain, heavy cloud cover, or obstruction on the roof will now and then weaken the signal. With Starlink’s entry into India, it is a signal that the future of connectivity is being built in space.  Much like how cell phone networks revolutionised communication, satellite internet can bring critical digital access to areas currently receiving a perpetual “No Signal” message. ALSO READ | What Is The Trump Gold Card? New USD 1M Visa Offers Expedited Path To Permanent US Residency

import dataclasses   import datasets import torch import torch.nn as nn import tqdm     @dataclasses.dataclass class BertConfig:     “”“Configuration for BERT model.”“”     vocab_size: int = 30522     num_layers: int = 12     hidden_size: int = 768     num_heads: int = 12     dropout_prob: float = 0.1     pad_id: int = 0     max_seq_len: int = 512     num_types: int = 2       class BertBlock(nn.Module):     “”“One transformer block in BERT.”“”     def __init__(self, hidden_size: int, num_heads: int, dropout_prob: float):         super().__init__()         self.attention = nn.MultiheadAttention(hidden_size, num_heads,                                                dropout=dropout_prob, batch_first=True)         self.attn_norm = nn.LayerNorm(hidden_size)         self.ff_norm = nn.LayerNorm(hidden_size)         self.dropout = nn.Dropout(dropout_prob)         self.feed_forward = nn.Sequential(             nn.Linear(hidden_size, 4 * hidden_size),             nn.GELU(),             nn.Linear(4 * hidden_size, hidden_size),         )       def forward(self, x: torch.Tensor, pad_mask: torch.Tensor) -> torch.Tensor:         # self-attention with padding mask and post-norm         attn_output, _ = self.attention(x, x, x, key_padding_mask=pad_mask)         x = self.attn_norm(x + attn_output)         # feed-forward with GeLU activation and post-norm         ff_output = self.feed_forward(x)         x = self.ff_norm(x + self.dropout(ff_output))         return x     class BertPooler(nn.Module):     “”“Pooler layer for BERT to process the [CLS] token output.”“”     def __init__(self, hidden_size: int):         super().__init__()         self.dense = nn.Linear(hidden_size, hidden_size)         self.activation = nn.Tanh()       def forward(self, x: torch.Tensor) -> torch.Tensor:         x = self.dense(x)         x = self.activation(x)         return x     class BertModel(nn.Module):     “”“Backbone of BERT model.”“”     def __init__(self, config: BertConfig):         super().__init__()         # embedding layers         self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size,                                             padding_idx=config.pad_id)         self.type_embeddings = nn.Embedding(config.num_types, config.hidden_size)         self.position_embeddings = nn.Embedding(config.max_seq_len, config.hidden_size)         self.embeddings_norm = nn.LayerNorm(config.hidden_size)         self.embeddings_dropout = nn.Dropout(config.dropout_prob)         # transformer blocks         self.blocks = nn.ModuleList([             BertBlock(config.hidden_size, config.num_heads, config.dropout_prob)             for _ in range(config.num_layers)         ])         # [CLS] pooler layer         self.pooler = BertPooler(config.hidden_size)       def forward(self, input_ids: torch.Tensor, token_type_ids: torch.Tensor, pad_id: int = 0                 ) -> tuple[torch.Tensor, torch.Tensor]:         # create attention mask for padding tokens         pad_mask = input_ids == pad_id         # convert integer tokens to embedding vectors         batch_size, seq_len = input_ids.shape         position_ids = torch.arange(seq_len, device=input_ids.device).unsqueeze(0)         position_embeddings = self.position_embeddings(position_ids)         type_embeddings = self.type_embeddings(token_type_ids)         token_embeddings = self.word_embeddings(input_ids)         x = token_embeddings + type_embeddings + position_embeddings         x = self.embeddings_norm(x)         x = self.embeddings_dropout(x)         # process the sequence with transformer blocks         for block in self.blocks:             x = block(x, pad_mask)         # pool the hidden state of the `[CLS]` token         pooled_output = self.pooler(x[:, 0, :])         return x, pooled_output     class BertPretrainingModel(nn.Module):     def __init__(self, config: BertConfig):         super().__init__()         self.bert = BertModel(config)         self.mlm_head = nn.Sequential(             nn.Linear(config.hidden_size, config.hidden_size),             nn.GELU(),             nn.LayerNorm(config.hidden_size),             nn.Linear(config.hidden_size, config.vocab_size),         )         self.nsp_head = nn.Linear(config.hidden_size, 2)       def forward(self, input_ids: torch.Tensor, token_type_ids: torch.Tensor, pad_id: int = 0                 ) -> tuple[torch.Tensor, torch.Tensor]:         # Process the sequence with the BERT model backbone         x, pooled_output = self.bert(input_ids, token_type_ids, pad_id)         # Predict the masked tokens for the MLM task and the classification for the NSP task         mlm_logits = self.mlm_head(x)         nsp_logits = self.nsp_head(pooled_output)         return mlm_logits, nsp_logits     # Training parameters epochs = 10 learning_rate = 1e–4 batch_size = 32   # Load dataset and set up dataloader dataset = datasets.Dataset.from_parquet(“wikitext-2_train_data.parquet”)   def collate_fn(batch: list[dict]):     “”“Custom collate function to handle variable-length sequences in dataset.”“”     # always at max length: tokens, segment_ids; always singleton: is_random_next     input_ids = torch.tensor([item[“tokens”] for item in batch])     token_type_ids = torch.tensor([item[“segment_ids”] for item in batch]).abs()     is_random_next = torch.tensor([item[“is_random_next”] for item in batch]).to(int)     # variable length: masked_positions, masked_labels     masked_pos = [(idx, pos) for idx, item in enumerate(batch) for pos in item[“masked_positions”]]     masked_labels = torch.tensor([label for item in batch for label in item[“masked_labels”]])     return input_ids, token_type_ids, is_random_next, masked_pos, masked_labels   dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True,                                          collate_fn=collate_fn, num_workers=8)   # train the model   device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”) model = BertPretrainingModel(BertConfig()).to(device) model.train() optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.1) loss_fn = nn.CrossEntropyLoss()   for epoch in range(epochs):     pbar = tqdm.tqdm(dataloader, desc=f“Epoch {epoch+1}/{epochs}”)     for batch in pbar:         # get batched data         input_ids, token_type_ids, is_random_next, masked_pos, masked_labels = batch         input_ids = input_ids.to(device)         token_type_ids = token_type_ids.to(device)         is_random_next = is_random_next.to(device)         masked_labels = masked_labels.to(device)         # extract output from model         mlm_logits, nsp_logits = model(input_ids, token_type_ids)         # MLM loss: masked_positions is a list of tuples of (B, S), extract the         # corresponding logits from tensor mlm_logits of shape (B, S, V)         batch_indices, token_positions = zip(*masked_pos)         mlm_logits = mlm_logits[batch_indices, token_positions]         mlm_loss = loss_fn(mlm_logits, masked_labels)         # Compute the loss for the NSP task         nsp_loss = loss_fn(nsp_logits, is_random_next)         # backward with total loss         total_loss = mlm_loss + nsp_loss         pbar.set_postfix(MLM=mlm_loss.item(), NSP=nsp_loss.item(), Total=total_loss.item())         optimizer.zero_grad()         total_loss.backward()         optimizer.step()         scheduler.step()         pbar.update(1)     pbar.close()   # Save the model torch.save(model.state_dict(), “bert_pretraining_model.pth”) torch.save(model.bert.state_dict(), “bert_model.pth”)

Apple iPhone 16 Pro Max Flipkart Price: Flipkart’s Buy Buy 2025 sale is going to end with in couple of days. This sale is offering some of the biggest year end deals on popular gadgets. One of the best offers in this sale is on Apple’s iPhone 16 and iPhone 16 Pro Max. Flipkart is giving straight discounts, bank offers and exchange deals, which means you can get the iPhone 16 for less than Rs 40,000 if you combine all the benefits. At the same time, Flipkart is also offering a huge discount of more than Rs 10,000 on the iPhone 16 Pro Max. This makes it a great chance to buy Apple’s premium phone at a lower price than usual. These offers are not expected to stay for long, so interested buyers should make their purchase before the deals end. Apple iPhone 16 Specifications Add Zee News as a Preferred Source The premium smartphone features a 6.1 inch Super Retina XDR OLED display with HDR10 support and offers up to 1600 nits peak brightness outdoors, making the screen clear and bright even in sunlight. It runs on Apple’s A16 Bionic chipset, built on TSMC’s advanced 3nm process for faster performance and better efficiency. The phone is backed by a 3561mAh battery and comes with 8GB RAM along with 128GB or 256GB storage options. In the camera department, the iPhone 16 includes a dual camera setup with a 48MP primary sensor and a 12MP ultrawide sensor, while the front houses a 12MP camera for selfies and video calls. Apple iPhone 16 (128 GB Variant) Discount The iPhone 16 base model, which comes with 8GB RAM and 128GB storage, is now available at Apple for Rs 69,900. This is much lower than its original price of Rs 79,900, giving buyers a direct discount of Rs 10,000. With bank discounts and exchange offers, the price can drop even further, going down to around Rs 40,000. Consumers can also get a special flat discount of Rs 8,501 and choose no cost EMI options, making the deal even more affordable. If you use the exchange option, you can get up to Rs 57,400 off, making the deal even more affordable. Apple iPhone 16 Pro Max Specifcations The iPhone features a large 6.9 inch Super Retina XDR all screen OLED display with a sharp 2868×1320 pixel resolution at 460 ppi, offering bright and detailed visuals. It is powered by the A18 Pro chip, which includes a new 6 core CPU with two performance cores and four efficiency cores for smooth and powerful performance. The phone comes with a triple camera setup on the back, including a 48MP main camera with dual pixel PDAF and sensor shift OIS, a 12MP telephoto lens with 3D sensor shift OIS and 5x optical zoom, and a 48MP ultrawide camera. For selfies, it has a 12MP front camera. The device is equipped with a 4685mAh Li Ion battery that provides reliable all day usage. Apple iPhone 16 Pro Max (256 GB Variant) Discount The Apple iPhone 16 Pro Max (256 GB variant) is currently priced at Rs 1,34,900, which is Rs 10,000 lower than its original launch price of Rs 1,44,900. Buyers can also take advantage of additional bank offers, including 5 percent cashback on Axis Bank Flipkart Debit Cards (up to Rs 750) and 5 percent cashback on Flipkart SBI Credit Cards (up to Rs 4,000 per calendar quarter). The deal becomes even better with the exchange option, where users can get up to Rs 57,400 off depending on the device they trade in.