Fixing the code signing and notarization issues of Unreal Engine (5.3+) projects
Starting from Unreal Engine 5.3, Epic Games added support for the so-called modern Xcode workflow. This workflow allows the Unreal Build Tool (UBT) to be more consistent with the standard Xcode app projects, and to be compliant with the Apple requirements for distributing applications... In theory! 😅 In practice this workflow is flawed: both the code signing and the framework supports are not correctly implemented, making the creation of working apps and their distribution impossible. In this article, we'll go through the problems faced during the packaging, code signing, and notarization of an Unreal Engine application on macOS and end up with the step-by-step process to solve them all.The (Hidden?) Costs of Vertex AI Resource Pools: A Cautionary Tale
In the article "Custom model training & deployment on Google Cloud using Vertex AI in Go" we explored how to leverage Go to create a resource pool and train a machine learning model using Vertex AI's allocated resources. While this approach offers flexibility, there's a crucial aspect to consider: the cost implications of resource pools. This article details my experience with a sudden price increase in Vertex AI and the hidden culprit – a seemingly innocuous resource pool.Building a RAG for tabular data in Go with PostgreSQL & Gemini
In this article we explore how to combine a large language model (LLM) with a relational database to allow users to ask questions about their data in a natural way. It demonstrates a Retrieval-Augmented Generation (RAG) system built with Go that utilizes PostgreSQL and pgvector for data storage and retrieval. The provided code showcases the core functionalities. This is an overview of how the "chat with your data" feature of fitsleepinsights.app is being developed.Using Gemini in a Go application: limits and details
This article explores using Gemini within Go applications via Vertex AI. We'll delve into the limitations encountered, including the model's context window size and regional restrictions. We'll also explore various methods for feeding data to Gemini, highlighting the challenges faced due to these limitations. Finally, we'll briefly introduce RAG (Retrieval-Augmented Generation) as a potential solution, but leave its implementation details for future exploration.
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