#llms

#llms

The most prominent AI systems today are Large Language Models (LLMs) like ChatGPT, Claude, Grok, Perplexity, and Gemini. These systems work through computational models that mimic the human brain's structure, thus termed "neural networks." They consist of interconnected nodes that process and learn from internet data, enabling pattern recognition and decision-making in the field of artificial intelligence called "Machine Learning." LLMs are trained on massive datasets containing billions of words from books, websites, and other text sources.

That question has become more pressing. During the company's third-quarter earnings announcement, it predicted a weaker holiday shopping season than expected, citing President Donald Trump's tariffs and their negative impact on the home furnishings category. As a result, Pinterest's fourth-quarter revenue is expected to come in between $1.31 billion and $1.34 billion, while analysts were estimating $1.34 billion, on average. The news sent the stock tumbling by more than 21% on Wednesday.

Large language models (LLMs)have become the backbone of modern software, powering everything from code assistants to data pipelines. However, until recently, building with them meant juggling multiple APIs, setting up environments, and writing extensive code just to test a single prompt. Google AI Studio changes that. It's a web-based workspace where you can prototype with the latest Gemini models, write prompts, analyze outputs, and export working code in minutes. Think of it as your personal playground for experimentation and deployment.

In 1974, economist and metalworker Harry Braverman wrote Labor and Monopoly Capital, which showed how technology under capitalism shifts knowledge from workers to management-not because automation demands it but because control-seeking managers and capitalists do. Just over a half century later, his insight remains urgent: An invention offers options, but power often determines which are pursued.

But LLMs took it a notch even further, coders have started morphing into LLM prompters today, that is primarily how software is getting produced. They still must baby sit these LLMs presently, reviewing and testing the code thoroughly before pushing it to the repo for CI/CD. A few more years and even that may not be needed as the more enhanced LLM capabilities like "reasoning", "context determination", "illumination", etc. (maybe even "engineering"!) would have become part of gpt-9

The company said the launch of Red Hat Developer Lightspeed, a portfolio of AI solutions, will equip developer teams with "intelligent, context-aware assistance" through virtual assistants. Available on the Red Hat Developer Hub, the first of these AI tools is accessible through the hub's chat interface. The company said this will help speed up non-coding-related tasks, including development of test plans, troubleshooting applications, and creating documentation. This AI assistant can be used via both publicly available and self-hosted large language models (LLMs).

So, let's return to classic literature and take a look at a 19th-century idea that feels remarkably relevant today. It's the danger of too much thought. Many writers have understood the power and peril of thought (and consciousness) long before algorithms began to mimic it. They felt, unlike the LLMs, that the very thing that makes us intelligent can also make us suffer.

What happened with AWS outage recently is only a brief foreshadow of what might eventually come to pass if this trend continues. Imagine a world where most programmers are primarily LLM prompters with a very shallow understanding of core programming skills or even operational skills pertaining to an app, framework or library. What will we do if a major outage or technical issue occurs then and no person around knows what's really going on?

"The potential use of large language models (LLMs) to simulate human cognition and behavior has been heralded as an upcoming paradigm shift in psychological and social science research," wrote lead author Eric Mayor, PhD, a senior researcher at the University of Basel, in collaboration with Lucas Bietti, PhD, an associate professor of psychology at Norwegian University of Science and Technology (NTNU), and Adrian Bangerter, PhD, a professor of psychology at the University of Neuchâtel.

Building a business has never been easier, but the landscape is also more competitive. The difference between success and failure often comes down to how quickly you can execute. You need to understand your market and know how to scale your operations. AI tools can help you get off the ground. If I had to rebuild my business from scratch today, I'd lean heavily into AI tools to make the process faster, smarter, and more efficient than ever before.

What's changed about learning Python over the last few years? What new techniques and updated advice should beginners have as they start their journey? This week on the show, Stephen Gruppetta and Martin Breuss return to discuss beginning to learn Python. We share techniques for finding motivation, building projects, and learning the fundamentals. We provide advice on installing Python and not obsessing over finding the perfect editor. We also examine incorporating LLMs into learning to code and practicing asking good questions.

Agentic AI has become a hot topic amongst software developers in recent months. As usage of LLMs has become increasingly popular, many developers are switching to agentic AI services to build projects. One point of contention with agentic AI is that productivity is always limited without proper context. Solid context is crucial in streamlining agentic AI tools, as it guards against things like hallucinations and inefficiencies with software development.

If your brand isn't being mentioned on credible media outlets, industry lists or podcast transcripts, it's less likely to get pulled into LLM answers. LLMs lean on the same principles as traditional SEO. And earned media remains essential for authority and discoverability, whether it's through an LLM or SEO efforts. In traditional SEO, that authority helps content rank higher. With AI, trusted brand mentions have even greater influence, directly shaping how and where a brand appears in AI-generated results.

The Russian troll farm that in the lead-up to the 2024 US presidential election posted a bizarro video claiming Democratic candidate Kamala Harris was a rhino poacher, is back with hundreds of new fake news websites serving up phony political commentary with an AI assist. In a paper published today, Recorded Future's Insikt Group threat researchers also unveil evidence that the pro-Putin posters known as CopyCop, aka Storm-1516,

Large language models (LLMs) are much the same. They carry vast general knowledge yet lack the specific context that makes them immediately valuable to your organization. Just like new hires go through the onboarding ropes, LLMs need structured access to your business's data, tools, and workflows to become truly useful. That's where Model Context Protocol (MCP) comes in. MCP enables communication between AI applications, AI agents, applications and data sources.

In today's dynamic work environment, personalized learning isn't a luxury-it's an expectation. Learners across regions, roles, and functions crave content that feels relevant, specific, and immediately applicable to their day-to-day reality. But traditional personalization strategies-building five versions of every course, rewording every scenario, translating every line-are time-consuming and costly. This is where prompt-powered personalization comes in. By leveraging Large Language Models (LLMs), Learning and Development (L&D) teams can now instantly adapt content for different learner personas using smart prompt templates

Cohere, the Toronto-based startup building large language models for business customers, has long had a lot in common with its hometown hockey team, the Maple Leafs. They are a solid franchise and a big deal in Canada, but they've not made a Stanley Cup Final since 1967. Similarly, Cohere has built a string of solid, if not spectacular, LLMs and has established itself as the AI national champion of Canada.

My name is Mark Kurtz. I was the CTO at a startup called Neural Magic. We were acquired by Red Hat end of last year, and now working under the CTO arm at Red Hat. I'm going to be talking about GenAI at scale. Essentially, what it enables, a quick overview on that, costs, and generally how to reduce the pain. Running through a little bit more of the structure, we'll go through the state of LLMs and real-world deployment trends.

Google's AI Mode is good at finding answers, but what happens when you need to do something with those answers? The thing is, different AI tools excel at different things. Some are built for seamless app integration, others crush data analysis, and a few specialize in understanding your industry context. If you're looking for Google AI Mode alternatives that can handle the heavy lifting in your daily work, these options bring something unique to the table.​​​​​​​​​​​​​​​​ 🎯

MCP gives all three context. MCP stands for Model Context Protocol and was developed and open-sourced by Anthropic to standardize integrations between AI and the tools and data sources that can provide critical information in ways that enable LLMs to understand and take action. Instead of every service building out an integration for every AI agent, MCP defines a protocol where any application can maintain a single MCP server implementation that exposes its functionality,

AI Overviews offered incorrect information about the game to some players, as well as the crew at Spilt Milk Studios when they tested the responses. For instance, AI Overviews suggested that a player could damage a trinket when they were removing debris from it, which is not true. It also in some cases delivered the correct information, but pointed the user to an incorrect source.

The 5th International Conference on Computing and Communication Networks (ICCCNet-2025) concluded on a high note at Manchester Metropolitan University, solidifying its reputation as a premier platform for global innovation. From August 1-3, 2025, the conference became a crucible for ideas, bringing together brilliant minds from academia, industry, and government to forge the future of technology. The prestigious best paper awards, announced at the close of the event, weren't just accolades; they were a roadmap to a more intelligent, sustainable, and equitable world.