QFM046: Elixir Reading List - December 2024

This article by Paweł Świątkowski discusses how techniques from functional modeling can improve coding practices in Elixir. It focuses on tackling common issues in Elixir code, such as tightly coupled modules and complex testing, by using pure functions and immutable types. The author uses a library domain example to demonstrate how functional modeling can streamline domain logic while making the code more testable and maintainable. The discussion pivots around isolating domain logic from technical concerns to enhance clarity and reduce coupling.

In the video titled 'Behaviour Stacking', Benjamin Milde delves into the nuanced world of stacked behaviours in programming, which allows developers to create more specialized code while still integrating their own business logic. This approach is exemplified through structures like Phoenix LiveView, which is built on top of Phoenix Channels, showcasing how complex functionalities can be layered effectively.

The YOLO Elixir Library provides a seamless integration of YOLO models for object detection in the Elixir programming environment. It emphasizes real-time performance, user-friendliness, and extensibility, supporting various model sizes to enhance speed and accuracy. YOLO models from the Ultralytics library need to be converted to ONNX format for use in Elixir. The library simplifies processes by allowing users to load models and detect objects with minimal code, enhancing accessibility and ease of use. The integration with tools like YoloFastNMS for faster Non-Maximum Suppression further increases efficiency, making it suitable for real-time applications.

This article by Joel Koch describes the development of a Retrieval Augmented Generation (RAG) system entirely in Elixir, overcoming previous dependencies on Python. The system uses components such as Elixir's language parser, Jina embeddings, and a local Chroma vector store, and runs machine learning models with Bumblebee. The piece provides step-by-step instructions for setting up the system, managing code ingestion, retrieval, and generation pipelines, and demonstrates a practical UI component using Phoenix Playground. The article concludes with a discussion on alternatives for running the RAG system on memory-limited machines and invites readers to engage with the author through GitHub discussions or social media.

This article explores how to convert country codes into their corresponding flag emoji using the Elixir programming language. It presents an implementation inspired by a JavaScript approach, demonstrating usage through code examples in Elixir. The article also highlights the benefits of Elixir's standard library for running tests on such conversions.

Explore the SaladUI Storybook, a comprehensive library for building LiveView component libraries. This resource offers customizable elements that help developers craft unique UI components to enhance their applications. You can browse through various UI components, examples, and core functionalities, making it ideal for developers looking to streamline their development process.

Phoenix LiveView has released version 1.0.0, introducing a new curly brace syntax for more consistent and simplified coding. This update marks a significant achievement for the Phoenix team, although it features only one backward-incompatible change and mainly includes minor enhancements and bug fixes. The curly brace syntax can replace old syntax in most places, simplifying template code while integrating seamlessly with existing features.

This article discusses the first part of a series about Retrieval Augmented Generation (RAG) information systems designed to enhance source code development teams. It introduces the concept of a RAG system that can empower development teams by providing deep insights into their codebase without the need to share sensitive information with external providers. Using LangChain, JinaAI embeddings, and Ollama, the article walks through how even a simple RAG setup for Ruby can benefit teams by efficiently indexing and retrieving contextually relevant information. The article additionally highlights the advantages of local LLMs over remote systems like Copilot, emphasizing data control and the ability to customize the information that goes into the RAG knowledge base. It gives insight into how to set up a basic RAG system and test its capabilities with programming libraries such as Discourse. The introduction into embedding models, retrieval mechanisms, and the utility of RAG systems in providing streamlined codebase knowledge helps development teams efficiently tackle coding tasks.

The article discusses the use of a Retrieval Augmented Generation (RAG) system with Elixir codebases. It explains how to adapt existing RAG systems to read and understand Elixir code by leveraging LangChain and Tree-sitter. The process involves document loading and parsing, enabling conversations about Elixir codebases with a large language model (LLM). This initiative aims to enhance team efficiencies by allowing interactive codebase discussions without third-party exchanges like OpenAI. It highlights the development of a new parser for Elixir within LangChain, enabling the parsing of Elixir code. The article provides a hands-on approach to setting up a local RAG application that allows teams to query their codebase, offering insights on code structure and features necessary for improving parsing capabilities over time.