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Autonomous driving edge cases require complex, human-like reasoning that goes far beyond legacy algorithms and models. Large language models are getting there.
Microservices break up monolithic code into discrete chunks that are easier to maintain. Here's an overview and a look at the pros and cons of migrating to a microservices architecture.
We’re getting too much latency from poorly designed, developed, and deployed cloud-based APIs and services. It’s time to brush up on testing and monitoring.
Experimental AI-powered cloud-based development environment introduces previews on mobile platforms and templates for Astro, Go, Node.js, and Python.
Oracle offers enterprises a more streamlined approach to lowering the costs of continuously training LLMs on organization knowledge, analysts say, though it lags rivals in generative AI services.
Docker Build Cloud promises to speed Docker image builds by offloading them to the cloud, whether developers build locally or through continuous integration.
The verdict is in. Most cloud computing failures can be traced back to very human mistakes. What (expensive) lessons have we learned?
Azure AI Studio, while still in preview, checks most of the boxes for a generative AI application builder, with support for prompt engineering, RAG, agent building, and low-code or no-code development.
Cloud providers are stacking up 'junk fees' and enterprises are pushing back. Here are a few tips to better negotiate and manage those fees.
Amazon simplifies writing Lambda functions in C# with features like Lambda Annotations, which uses C# source generators to generate code from a REST API path. Support for .NET 8 is coming soon.