#llms

#llms

As AI chat interfaces become more popular, users increasingly rely on AI outputs to make decisions. Without explanations, AI systems are black boxes. Explaining to people how an AI system has reached a particular output helps users form accurate mental models, prevents the spread of misinformation, and helps users decide whether to trust an AI output. However, the explanations currently offered by large language models (LLMs) are often inaccurate, hidden, or confusing.

Tiiny AI, a US-based deep-tech startup, has unveiled the Pocket Lab, officially verified as the "world's smallest personal AI supercomputer." This palm-sized device, no larger than a typical power bank, is capable of running large language models (LLMs) with up to 120 billion parameters entirely on-device, without relying on cloud servers or external GPUs. Designer: Tiiny AI At its core, the Pocket Lab aims to make advanced artificial intelligence both personal and private.

Harvey on Thursday confirmed it closed a round of funding, led by Andreessen Horowitz, that values the legal AI startup at $8 billion after reports of the funding leaked in October. The startup raised $160 million in the round. This latest capital infusion came just months after it raised a $300 million in a Series E round at a $5 billion valuation in June. And that was just months after raising a Sequoia-led $300 million Series D at a $3 billion valuation in February.

"A press release really lends itself to AI, because if you think about it, if you're talking about your company or your you're putting out expert knowledge," Jeppsen explained during a recent Tech Talk at Ragan's Future of Communications Conference. "You are the domain expert. You are factual. You've got a framework ... that resonates, and not only humans read it that way, but then AI tries to read it like a human."

Context engineering has emerged as one of the most critical skills in working with large language models (LLMs). While much attention has been paid to prompt engineering, the art and science of managing context-i.e., the information the model has access to when generating responses-often determines the difference between mediocre and exceptional AI applications. After years of building with LLMs, we've learned that context isn't just about stuffing as much information as possible into a prompt.

"We are dabbling in using AI," Houser said, "but the truth is a lot of it's not as useful as some of the companies would have you believe."

While AI tutors can provide personalized feedback, they cannot yet replicate what human tutors do best: connect, empathize, and build trust. AI can simulate dialogue, but it lacks emotional understanding. Human tutors perceive tone, hesitation, and body language, nonverbal cues that reveal engagement and comprehension. They also navigate ethical and cultural complexities, exercising moral judgment that AI simply doesn't possess.

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.

Service design is evolving and we're quickly moving past static screens and pages toward dynamic, contextual experiences. In my previous article, Service Design in the Era of AI Agents, I discussed this evolution in detail. After reading it, many people reached out with one specific question: what exact design patterns will we use for GenUI apps? In this article, I decided to discuss specific foundational patterns for GenUI apps.

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.

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.