 "Let's Cook!": A Beginner's Guide to Making Tasty Web Projects Oct 22nd 2024, 14:00, by Filipp Shcherbanich When I was a child, I loved making pancakes with my grandmother. As time went on, I became a web developer, and now, instead of pancakes, I create various web projects with my colleagues. Every time I start a new project, I'm haunted by one question: How can I make this development "tasty" not only for the user but also for my colleagues who will work on it? This is a crucial question because over time, the development team may change, or you might decide to leave the project and hand it over to someone else. The code you create should be clear and engaging for those who join the project later. Moreover, you should avoid a situation where the current developers are dissatisfied with the final product yet have to keep adding new "ingredients" (read: functions) to satisfy the demands of the "restaurant owner." - Important note: Before I describe my recipe, I want to point out that methods can vary across different teams and, of course, they depend on their preferences. However, as we know, some people have rather peculiar tastes, so I believe it's essential to reiterate even the simplest truths.
Selecting the Ingredients: Choose Your Technology Stack Before you start cooking a dish, you usually check what ingredients you already have. If something is missing, you go to the store or look for alternative ways to acquire them, like venturing out to pick them up in the woods. The web development process is similar: before starting your work on a new project, you need to understand what resources you currently have and what you want to achieve in the end. To prepare for creating your technological masterpiece, it helps to answer a series of questions: |  Symbolic and Connectionist Approaches: A Journey Between Logic, Cognition, and the Future Challenges of AI Oct 22nd 2024, 13:00, by Frederic Jacquet This article explores two major approaches to artificial intelligence: symbolic AI, based on logical rules, and connectionist AI, inspired by neural networks. Beyond the technical aspects, the aim is to question concepts such as perception and cognition and to reflect on the challenges that AI must take up to better manage contradictions and aim to imitate human thought. Preamble French researcher Sébastien Konieczny was recently named EuAI Fellow 2024 for his groundbreaking work on belief fusion and inconsistency management in artificial intelligence. His research, focused on reasoning modeling and knowledge revision, opens up new perspectives to enable AI systems to tend to reason even more reliably in the face of contradictory information, and thus better manage the complexity of the real world. |  Platform Engineering: A Strategic Response to the Growing Complexity of Modern Software Architectures Oct 22nd 2024, 12:00, by Shubham Malhotra From monolithic applications to microservices and cloud-based architectures, the software development landscape is in constant change. These transitions have brought unprecedented opportunities but have also introduced significant complexities. Enter platform engineering: a strategic approach to managing the intricate infrastructure requirements of modern software systems. This opinion piece will explore how platform engineering is solving the challenges of today's software architectures, its evolution, and the way industry giants like Netflix, Google, Microsoft, and Apple are leveraging it to streamline their operations. Finally, we'll take a look at what the future holds for platform engineering. The Evolution of Software Architectures: From Monoliths to Microservices Software engineering has come a long way since the days of monolithic applications, where a single, unified codebase governed the entire system. While monolithic structures allowed for centralized management, they were often rigid and difficult to scale. As business needs evolved, software systems required greater agility, leading to the rise of microservices — smaller, independent units of functionality that could be developed, deployed, and scaled individually. |  AI/ML Innovation in the Kubernetes Ecosystem Oct 22nd 2024, 11:00, by Yuan Tang As organizations put artificial intelligence and machine learning (AI/ML) workloads into continuous development and production deployment, they need to have the same levels of manageability, speed, and accountability as regular software code. The popular way to deploy these workloads is Kubernetes, and the Kubeflow and KServe projects enable them there. Recent innovations like the Model Registry, ModelCars feature, and TrustyAI integrations in this ecosystem are delivering these improvements for users who rely on AI/ML. These, and other improvements, have made open source AI/ML ready for use in production. More improvements are coming in the future. Better Model Management AI/ML analyzes data and produces output using machine learning "models," which consist of code, data, and tuning information. In 2023, the Kubeflow community identified a key requirement to have better ways of distributing tuned models across large Kubernetes clusters. Engineers working on Red Hat's OpenShift AI agreed and started work on a new Kubeflow component, Model Registry. | |
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