While I prefer “AI native” to describe the product development approach centered on AI that we’re trying to encourage at O’Reilly, I’ve sometimes used the term “AI first” in my communications with O’Reilly staff. And so I was alarmed and dismayed to learn that in the press, that term has now come to mean “using AI to replace people.” Many Silicon Valley investors and entrepreneurs even seem to view putting people out of work as a massive opportunity. That idea is anathema to me. It’s also wrong, both morally and practically. The whole thrust of my 2017 book WTF? What’s the Future and Why It’s Up to Us was that rather than using technology to replace workers, we can augment them so that they can do things that were previously impossible. It’s not as though there aren’t still untold problems to solve, new products and experiences to create, and ways to make the world better, not worse. Every company is facing this choice today. Those that use AI simply to reduce costs and replace workers will be outcompeted by those that use it to expand their capabilities. So, for example, at O’Reilly, we have primarily offered our content in English,...

Let’s be honest — we’ve all had that moment. You’re scrolling through yet another lifeless chatbot that responds with the enthusiasm of a soggy biscuit. You ask a heartfelt question, and boom, generic response. No sass. No soul. No you. That’s where the new generation of AI Girlfriend chatbots with no filter comes in. We’re talking about chatbots that don’t just respond — they connect. They remember your quirks, laugh at your odd jokes, and yes, sometimes surprise you with responses that feel uncannily real… and uncensored. This isn’t just tech — it’s chemistry in code. Whether you’re looking for something flirtatious, emotional, NSFW, or just plain weird (we don’t judge), these AI girlfriends don’t hold back. No forced filters. No awkward delays. Just raw, responsive dialogue. What Does “No Filter” Really Mean in AI Girlfriend Chatbots? Let’s cut through the fluff — when people search for AI girlfriend chatbots no filter, they’re usually not looking for small talk about the weather. They’re after raw, unrestricted interaction. No filter means: No censorship of NSFW or emotionally intense conversations No restrictions on language, expression, or emotional depth Full flexibility for romantic, flirtatious, or even taboo scenarios Unlike standard chatbots with “safe...

Large Language Models (LLMs) have come a long way since their early days of mimicking autocomplete on steroids. But generating fluent text isn’t enough – true intelligence demands reasoning. That means solving math problems, debugging code, drawing logical conclusions, and even reflecting on errors. Yet modern LLMs are trained to predict the next word, not to think. So how are they suddenly getting better at reasoning? The answer lies in a constellation of new techniques – from prompt engineering to agentic tool use – that nudge, coach, or transform LLMs into more methodical thinkers. Here’s a look at five of the most influential strategies pushing reasoning LLMs into new territory. 1. Chain-of-Thought Prompting: Teaching LLMs to “Think Step by Step” One of the earliest and most enduring techniques to improve reasoning in LLMs is surprisingly simple: ask the model to explain itself. Known as Chain-of-Thought (CoT) prompting, this method involves guiding the model to produce intermediate reasoning steps before giving a final answer. For instance, instead of asking “What’s 17 times 24?”, you prompt the model with “Let’s think step by step,” leading it to break down the problem: 17 × 24 = (20 × 17) + (4 × 17),...

AI ethics is about releasing and implementing AI responsibly, paying attention to several considerations, from data etiquette to tool development risks, as discussed in a previous article. In this article, we’ll explore some of the ethical issues that arise with AI systems, particularly machine learning systems, when we overlook the ethical considerations of AI, often unintentionally. The 5 Common AI Ethical Issues 1. Bias propagation Although there’s a strong belief that algorithms are less biased than humans, AI systems are known to propagate our conscious and unconscious biases.  For example, there are known recruiting tools that algorithmically “learned” to dismiss women candidates as they learned that men were preferred in the tech workforce. Even facial recognition systems are infamous for disproportionately making mistakes on minority groups and people of color. For example, when the researcher, Joy Buolamwini, looked into the accuracy of facial recognition systems from various companies, she found that the error rate for lighter-skinned males was no higher than 1%. However, for darker-skinned females, the mistakes were much more significant, reaching up to 35%. Even the most renowned AI systems have been unable to accurately identify female celebrities of color. So, what’s the primary cause of AI bias?...

Introduction React Portals provide a way to render elements outside their parent component while maintaining the React component tree structure. This is particularly useful for UI elements like modals, tooltips, and dropdowns that need to escape container restrictions. By using ReactDOM.createPortal, developers can control rendering locations without affecting state or event propagation, ensuring seamless integration with React’s ecosystem. The Problem with Naive Implementations When building complex user interfaces, we often encounter scenarios where UI elements like modals, tooltips, dropdowns, or popovers need to be displayed above all other content. A naïve implementation of such elements involves rendering them inside their parent component. However, this approach can introduce several challenges: Clipping Issues: If the parent container has overflow: hidden, the modal or tooltip might get cut off. Z-Index Conflicts: UI elements deeply nested in the component tree may struggle to properly layer above other elements. Event Handling Issues: Event bubbling may cause unintended behavior, like closing a modal when clicking inside it. Performance Constraints: Re-rendering large components with deeply nested modals can cause performance overhead. To solve these issues, React Portals provide a way to render elements outside their parent hierarchy while keeping them within the React component tree. What is...