Let me cut to the chase here – I love this mouse. I mean, I really, really love this mouse. It’s the Logitech MX Master 3S, the latest version of the MX Master series, and let me tell you, Logitech has managed to make an already fantastic mouse even better.
The moment you hold this mouse, you’ll notice how nicely it sits in your hand. It’s like it was made for you. The MX Master series of mice are known for their unrivalled comfort. Gone are the days of aching wrists after long hours in front of the screen. And those buttons? They’re right where you’d naturally rest your fingers, making them easy to access.
Apple is known for introducing innovative products to the market and revolutionising the tech industry, most notably with the iPod and then the iPhone. The latest buzz is about the upcoming Apple augmented reality (AR) glasses being dubbed Apple Glasses. AR technology superimposes digital elements in the real world, creating an interactive and immersive experience.
The idea of AR glasses is not new, but Apple’s entry into the market could be a game-changer if they do it right. The company has a loyal fan base, and its products have a reputation for being high-quality and user-friendly.
Despite the monumental leaps in artificial intelligence (AI) we’ve witnessed in recent years, the prospect of Artificial General Intelligence (AGI)—machines possessing the ability to understand, learn, and perform any intellectual task precisely as a human can—remains a far-off goal. Yes, advancements have been made with tools like GPT-4, Alpha Go, and Gato, contributing to the foundation of AGI. However, these are still considered early examples of AGI and not fully formed AGI systems.
Introduction
In data science and machine learning, cosine similarity is a measure that calculates the cosine of the angle between two vectors. This metric reflects the similarity between the two vectors, and it’s used extensively in areas like text analysis, recommendation systems, and more. This post delves into the intricacies of implementing cosine similarity checks using TypeScript.
Understanding Cosine Similarity and Its Applications in AI
Cosine similarity measures two non-zero vectors of an inner product space. It is defined as the cosine of the angle between them, which is calculated using this formula:
While working on an AI-related app recently using vectors, I found myself with a 115MB file in my repository. While attempting to push to GitHub, I got an error in GitKraken about a hook failing. I didn’t have any hooks locally or remotely, so the issue was a bit perplexing. After trying a few things, I resorted to command-line Git, and that’s when I saw the problem.
GitHub blocks files larger than 100 megabytes. They recommend using Git Large File Storage, but I decided maybe I shouldn’t be adding large vector files into my repository anyway.
I’ve got a confession to make. I miss buttons. You know, the kind in cars, where you press one and something actually happens. No swiping, no squinting, no guessing if you hit the right part of the screen. Just good old-fashioned, satisfying, clicky buttons.
Remember when touchscreens started becoming a thing in cars? There was this sense of “Wow, it’s like driving in the future!” But after the novelty wore off, we were left playing a dangerous game of ‘Whack-A-Mole’ on the highway. You just wanted to adjust the fan speed, but instead found yourself in a high-stakes game of find-the-menu, all while keeping an eye on the road. Not exactly the stress-free driving experience we were promised.
Picture this: It’s 3 AM, and you’re staring at your computer screen, bleary-eyed, as you struggle to solve that pesky bug in your code. You can almost feel the weight of the digital cobwebs piling up on StackOverflow as you sift through outdated answers and snarky comments. But what if I told you that the days of scouring through StackOverflow’s seemingly endless abyss might be numbered? Enter ChatGPT, Google Bard, and a whole new breed of AI-powered chatbots revolutionising how developers find answers to their coding conundrums.
Rate limiting is a crucial aspect of building scalable and secure web applications. It helps prevent abuse and ensures the fair usage of resources. In this blog post, we will explore how to implement token bucket rate limiting in a NestJS application with a reset interval of 24 hours.
What is Token Bucket Rate Limiting? Token bucket rate limiting is an algorithm that controls the rate at which a system processes requests. The main idea is that a fixed number of tokens are added to a bucket regularly. When a request arrives, a token is removed from the bucket. If there are no tokens left, the request is rejected.
Remote work, or working from home, has become increasingly popular. With the COVID-19 pandemic, remote work has become the new norm for many employees. However, remote work had gained traction even before the pandemic due to its numerous benefits.
While remote work has many benefits, it’s important to note that it may not be suitable for every job or company. Some jobs require in-person collaboration or access to specialised equipment only available in a traditional office setting.
In the past year, a surge of AI tools has hit the market, with many identifying as AI startups. The advent of OpenAI’s ChatGPT, including GPT-3.5 and GPT-4 models, has revolutionised how we interact with technology. However, amidst this excitement, a trend needs addressing: the phenomenon of “API wrappers” masquerading as AI startups.
While it’s true that many of these products utilize the power of OpenAI’s GPT APIs, it’s essential to take a step back and consider the implications of relying solely on an external API for your business. Does wrapping GPT APIs and selling a service based on them warrant the label of an AI startup? Let’s take a closer look at the potential downsides of this approach.