Apple is set to overtake Samsung as the world’s leading smartphone manufacturer in 2025, according to industry forecasts. The company’s success will be fueled by its strategic moves in both mature and emerging markets, along with the anticipated release of its budget-friendly iPhone SE 4.

Apple Expected to become largest smartphone maker in 2025

According to data from Counterpoint, Samsung currently holds a 3% lead over Apple in global market share as of the second quarter of this year. However, the company has historically performed well in the third and fourth quarters, benefitting from the annual iPhone release cycle.

Techinsights predicts that Apple will maintain its momentum through the first half of the year. If this forecast holds, the tech giant will dethrone Samsung, a position it has held for years.

Apple’s rise to becoming the largest smartphone maker in 2025 is driven by strong sales of older iPhone models. Emerging markets like India and China are leading this trend. The upcoming budget-friendly iPhone SE 4, priced at $499, will likely boost Apple’s market share further.

The innovations, including the rumored ultra-slim iPhone 17, will play a crucial role in this growth. The advanced Apple Intelligence platform will also impact this trend. These technologies are expected to speed up the iPhone replacement cycle. This strategy encourages users to upgrade to the latest models.

Apple’s focus on AI with its Apple Intelligence platform aims to deepen its integration with iOS and macOS.

In mature markets, Apple’s growth will benefit from favorable economic conditions. Exchange rates will also play a supportive role. These factors are expected to make Apple’s high-end devices more accessible. This accessibility will likely increase sales in regions where demand for premium smartphones remains strong.

Competitive Landscape: Samsung and Huawei’s Challenges

Huawei is also facing challenges as Apple gains ground. The company has been resilient, especially in recent years, but economic pressures and manufacturing constraints in China are expected to slow its momentum. These issues could limit Huawei’s ability to compete at the same level in the global market. Apple’s rise in 2025 may further widen the gap between Huawei and its rivals.

Microsoft is expanding Xbox Cloud Gaming’s streaming capabilities to support titles that aren’t on Xbox Game Pass, reports. The company has stated its plans to let users stream games from their own libraries numerous times over the last few years, going back to the announcement of , but the feature hasn’t yet materialized. Now, Microsoft is ready to start testing it among Xbox Insiders in November, with plans to ultimately support thousands of games, according to The Verge, which cites sources “familiar with Microsoft’s plans.”

Xbox players have long been asking for the option to stream games that they’ve purchased, rather than only those that are available on Xbox Game Pass. If the testing period goes as planned, more players could soon see the ability to do this. The report comes on the heels of Xbox president Sarah Bond’s announcement on Friday that Android users will be able to starting next month.

By now, large language models (LLMs) like ChatGPT and Claude have become an everyday word across the globe. Many people have started worrying that AI is coming for their jobs, so it is ironic to see almost all LLM-based systems flounder at a straightforward task: Counting the number of “r”s in the word “strawberry.” They are not exclusively failing at the alphabet “r”; other examples include counting “m”s in “mammal”, and “p”s in “hippopotamus.” In this article, I will break down the reason for these failures and provide a simple workaround.

LLMs are powerful AI systems trained on vast amounts of text to understand and generate human-like language. They excel at tasks like answering questions, translating languages, summarizing content and even generating creative writing by predicting and constructing coherent responses based on the input they receive. LLMs are designed to recognize patterns in text, which allows them to handle a wide range of language-related tasks with impressive accuracy.

Despite their prowess, failing at counting the number of “r”s in the word “strawberry” is a reminder that LLMs are not capable of “thinking” like humans. They do not process the information we feed them like a human would.

Almost all the current high performance LLMs are built on transformers. This deep learning architecture doesn’t directly ingest text as their input. They use a process called tokenization, which transforms the text into numerical representations, or tokens. Some tokens might be full words (like “monkey”), while others could be parts of a word (like “mon” and “key”). Each token is like a code that the model understands. By breaking everything down into tokens, the model can better predict the next token in a sentence. 

LLMs don’t memorize words; they try to understand how these tokens fit together in different ways, making them good at guessing what comes next. In the case of the word “hippopotamus,” the model might see the tokens of letters “hip,” “pop,” “o” and “tamus”, and not know that the word “hippopotamus” is made of the letters — “h”, “i”, “p”, “p”, “o”, “p”, “o”, “t”, “a”, “m”, “u”, “s”.

A model architecture that can directly look at individual letters without tokenizing them may potentially not have this problem, but for today’s transformer architectures, it is not computationally feasible.

Further, looking at how LLMs generate output text: They predict what the next word will be based on the previous input and output tokens. While this works for generating contextually aware human-like text, it is not suitable for simple tasks like counting letters. When asked to answer the number of “r”s in the word “strawberry”, LLMs are purely predicting the answer based on the structure of the input sentence.

Here’s a workaround

While LLMs might not be able to “think” or logically reason, they are adept at understanding structured text. A splendid example of structured text is computer code, of many many programming languages. If we ask ChatGPT to use Python to count the number of “r”s in “strawberry”, it will most likely get the correct answer. When there is a need for LLMs to do counting or any other task that may require logical reasoning or arithmetic computation, the broader software can be designed such that the prompts include asking the LLM to use a programming language to process the input query.

Conclusion

A simple letter counting experiment exposes a fundamental limitation of LLMs like ChatGPT and Claude. Despite their impressive capabilities in generating human-like text, writing code and answering any question thrown at them, these AI models cannot yet “think” like a human. The experiment shows the models for what they are, pattern matching predictive algorithms, and not “intelligence” capable of understanding or reasoning. However, having a prior knowledge of what type of prompts work well can alleviate the problem to some extent. As the integration of AI in our lives increases, recognizing its limitations is crucial for responsible usage and realistic expectations of these models.

 Chinmay Jog is a senior machine learning engineer at Pangiam.

The dispute between WordPress founder Matt Mullenweg and hosting provider WP Engine continues, with Mullenweg announcing that WordPress is “forking” a plugin developed by WP Engine.

Specifically, Advanced Custom Fields — a plugin making it easier for WordPress users to customize their edit screens — is being taken out of WP Engine’s hands and updated as a new plugin called Secure Custom Fields.

Mullenweg wrote that this step was necessary “to remove commercial upsells and fix a security problem.”

The Advanced Custom Fields team responded on X, describing this as a situation where a plugin “under active development” has been “unilaterally and forcibly taken away from its creator without consent,” which it said has never happened “in the 21 year history of WordPress.”

“This essential community promise has been violated, and we ask everyone to consider the ethics of such an action, and the new precedent that has been set,” the ACF team wrote.

Both Mullenweg’s blog post and a reply from WordPress claim that similar situations have, in fact, happened before, though Mullenweg added, “This is a rare and unusual situation brought on by WP Engine’s legal attacks, we do not anticipate this happening for other plugins.”

They also pointed to WordPress’ plugin guidelines, which give WordPress the right to disable or remove any plugin, remove developer access, or change a plugin “without developer consent, in the name of public safety.”

Some background: WordPress is a free, open source content management system used by many websites (including TechCrunch), while companies like WP Engine and Mullenweg’s Automattic offer hosting and other commercial services on top. 

Last month, Mullenweg published a blog post criticizing WP Engine as a “cancer to WordPress.” His criticisms covered everything from WP Engine’s lack revision history to its investor Silver Lake, but he also suggested that its “WP” branding confuses customers, making it sound like the company is officially connected to WordPress.

Cease-and-desist letters have gone both ways, with WP Engine claiming Mullenweg threatened to take a “scorched earth nuclear approach” unless the company paid to license the WordPress trademark.

WordPress banned WP Engine from accessing WordPress.org, briefly lifted the ban, then imposed it again. This essentially prevents WP Engine from updating the plugin through WordPress.org — so it can’t offer easy updates to address security issues. WP Engine has, however, published a workaround for users who want to update the plugin and continue using ACF. (It says the workaround is only necessary for ACF’s free users, as pro users will continue to receive updates from the ACF website.)

Moving forward, Mullenweg wrote that Secure Custom Fields will be available as a non-commercial plugin:
“If any developers want to get involved in maintaining and improving it, please get in touch.”

When we reviewed the Nvidia Shield TV Pro back in 2021, we said that it was the best streaming box on the market at the time. It’s still a great streamer thanks to its flexibility and power, but it’s been a little neglected. However, Nvidia has issued an update at last – the first such update since 2023. If you’re encountering bugs or glitches that are taking the shine off your Shield, this new update should hopefully address them.

The new updates are detailed in a post on the Nvidia website, but the short version is that it enhances the Match Frame Rate, fixes a GeForce Now crash bug and addresses some irritating video and audio issues.

What’s new in the Nvidia Shield TV update