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With Microsoft discontinuing App Center support on March 31, this blog shows how to automate Mendix native mobile builds for Android and iOS using Azure DevOps. This solution is ideal for Mendix…
Nowadays mobile device is one of the most used technologies that ever invented. Everyone has access to internet in their phone and search their needs on google and downloads various apps that are…
In today's rapidly evolving AI landscape, one persistent challenge is equipping language models with robust decision-making abilities that extend beyond single-turn interactions. Traditional large language models (LLMs) excel at generating coherent responses but often struggle with multi-step problem solving or interacting with dynamic environments. This shortfall largely stems from the nature of the training data, which rarely reflects the structured, interactive experiences that real-world scenarios demand. Moreover, directly deploying models to gather real-world interaction data can be both costly and risky. Hence, there is a clear need for methodologies that teach LLMs to explore, gather relevant information, and make thoughtful,