Struggling to find the perfect cross-platform tool? Skip might be the game-changer you’ve been looking for—write your app in Swift and watch it seamlessly transform into a Kotlin-based Android app. With native access to APIs and a single codebase for both iOS and Android, Skip simplifies development while offering a polished, native feel across platforms.
Learn how mastering multi-MCU systems can streamline your embedded designs by addressing firmware management, power control, and recovery strategies.
Navigating the world of generative AI can feel like exploring uncharted waters, especially when it comes to bringing a model into production. This blog dives into the crucial steps of selecting the right Large Language Model (LLM) for your specific needs, drawing insights from a recent AWS conference. From understanding the nuances of each model to leveraging an AI Gateway for a smoother deployment, this guide offers practical advice to ensure your generative AI application is both robust and future-proof.
We are excited to announce we have a new team member with us! And to celebrate we wanted to share with you a little bit about him. Welcome to the team Josh! His role at DiUS: I’ve joined as Lead Software Engineer Consultant.
Join Trevor on a two-week adventure with Kotlin Multiplatform (KMP) as he tackles the challenges of enhancing a Flutter-based health and wellness app. Faced with complex processing tasks and AWS Amplify authentication hurdles, the journey is filled with unexpected challenges and creative solutions.
Hear from our experience design expert Anthony Vermeulen to learn how building real-world solutions with generative AI is a journey of discovery and adaptation. While there’s plenty of information on using generative AI tools, there’s less on designing for generative AI experiences. After wrapping up a project focused on increasing internal productivity through generative AI proof of concepts, Anthony shares key learnings and guidance for designers venturing into this innovative space.
Explore how to harness the best of traditional machine learning by generating synthetic data with LLMs for training compact, efficient ML models. To illustrate this, I ran an experiment using GPT-4o to generate synthetic data for three text classification tasks: spam detection, product classification, and sentiment analysis. I’ve also set out some recommendations for anyone looking to leverage synthetic data generated by LLMs for training efficient ML models.
Recently, DiUS encountered a flurry of questions about multilingual chatbots from two clients in different industries. A healthcare provider needed to communicate with diverse, non-English speaking patients, while a travel company sought to enhance experiences for non-English speaking visitors. Both scenarios highlighted a common need: breaking down language barriers to improve user experience. Multilingual chatbots powered by generative AI offer a promising solution for seamless global communication.
Get data ready: Data readiness isn’t merely about having a vast amount of data; it’s about having the right data, properly prepared and processed. In the journey towards successful ML implementation, the way data is managed from collection to analysis plays a pivotal role. Here’s a detailed look at each aspect of data readiness.
Gaining stronger control over your data is a frequent topic in our conversations with clients. Rather than starting with the technology, the aim should be to get value from your data, transforming raw information into actionable insights. Unlike dark data, which is collected but not utilised, activated data is analysed and used to inform decisions, create new business models, and enhance customer experiences.