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AI engineers operate at a higher level of abstraction than Machine Learning (ML) engineers or
large language model (LLM) engineers and don't necessarily need to know how to build an LLM or a ML model.
AI engineering builds upon ML systems[^1], but with a focus on large scale, ready made models (aka. base models).
The distinct skills that AI engineers need to know include prompt engineering,
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Since the recent release of Catalina, macOS has shipped with the ability to allow iOS/iPAD apps to run on macOS without any modification via a featureset known as Project Catalyst.
This is exciting, as writing React Native + Clojurescript apps as a target for the desktop is much more compelling than a pure Electron app (imo).
Many people have been getting confused over what CQRS is. They look at CQRS as being an architecture; it is not. CQRS is a very simple pattern that enables many opportunities for architecture that may otherwise not exist. CQRS is not eventual consistency, it is not eventing, it is not messaging, it is not having separated models for reading and writing, nor is it using event sourcing. I want to take a few paragraphs to describe first exactly what CQRS is and then how it relates to other patterns.
CQRS Command and Query Responsibility Segregation
Starting with CQRS, CQRS is simply the creation of two objects where there was previously only one. The separation occurs based upon whether the methods are a command or a query (the same definition that is used by Meyer in Command and Query Separation, a command is any method that mutates state and a query is any method that returns a value).
Additive scene loading over network with Unity UNet
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