Effective error handling ensures that a program can gracefully handle unexpected situations and errors, making the software more robust and reliable. Well-designed error messages help users understand what went wrong and how to correct it, and contribute to the overall user experience using the library or the API. In this article I’ll gradually go through a number of options of handling errors in Rust and try to explain the benefits of using a method vs the other.

The Builder Pattern is a design pattern that provides a way to construct a complex object step by step, focusing on breaking down the construction of the object into smaller, more manageable steps. It separates the construction of a object from its representation, allowing the same construction process to create different variations or representations of the final object. This helps improve code readability, especially when dealing with objects that have a large number of attributes or configurations.

In this article I’m exploring the process of implementing a Fan-Out/ Fan-In pipeline in Go and Rust. Fan-Out is a term to describe the process of starting multiple workers to handle input from the pipeline, and Fan-In is a term to describe the process of combining multiple results into one channel. I’m assuming that you have some familiarity with both Go and Rust languages. I’m new to Rust also, therefore it’s fair to say that this is about concurrency in Go & Rust through the eyes of a GO developer.

This is the continuation of the previous post. I would strongly recommend reading Part 1 first then continue here as the description of the program, the intent and the current state are relevant as we move forward. Iter 3 - Remove Contention # Iter 3 code on Github Reading line by line from the stream and sending those over to the goroutines to be processed works, but may not be the most efficient way to process a large file.

In this blog post I’ll iterate over a simple script and refactor it each time to improve its performance. Before diving into the details I would quote a couple of well known individuals from the GO Community. “You want to write code that is optimized for correctness. Don’t make coding decisions based on what you think might perform better. You must benchmark or profile to know if code is not fast enough.

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks and it is used for machine learning applications such as neural networks. Tensorflow has grown to become one of the widely adopted ML platforms in the world. TensorFlow offers multiple levels of abstraction so you can choose the right one for your needs. You can build and train models by using the high-level Keras API, which makes getting started with TensorFlow and machine learning easy.

gRPC describes itself as: gRPC is a modern open source high performance RPC framework that can run in any environment. It can efficiently connect services in and across data centers with pluggable support for load balancing, tracing, health checking and authentication. It is also applicable in last mile of distributed computing to connect devices, mobile applications and browsers to backend services. This is Part 3 of a series of three articles describing gRPC functionality.