Elixir Wizards

In this episode of Elixir Wizards, Xiang Ji and Nathan Hessler join hosts Sundi Myint and Owen Bickford to compare actor model implementation in Elixir, Ruby, and Clojure. In Elixir, the actor model is core to how the BEAM VM works, with lightweight processes communicating asynchronously via message passing. GenServers provide a common abstraction for building actors, handling messages, and maintaining internal state. In Ruby, the actor model is represented through Ractors, which currently map to OS threads. They discuss what we can learn by comparing models, understanding tradeoffs between VMs, languages, and concurrency primitives, and how this knowledge can help us choose the best tools for a project. Topics discussed in this episode: Difference between actor model and shared memory concurrency Isolation of actor state and communication via message passing BEAM VM design for high concurrency via lightweight processes GenServers as common abstraction for building stateful actors GenServer callbacks for message handling and state updates Agents as similar process abstraction to GenServers Shared state utilities like ETS for inter-process communication Global Interpreter Lock in older Ruby VMs Ractors as initial actor implementation in Ruby mapping to threads Planned improvements to Ruby concurrency in 3.3 Akka implementation of actor model on JVM using thread scheduling Limitations of shared memory concurrency on JVM Project Loom bringing lightweight processes to JVM Building GenServer behavior in Ruby using metaprogramming CSP model of communication using channels in Clojure Differences between BEAM scheduler and thread-based VMs Comparing Elixir to academic languages like Haskell Remote and theScore are hiring! Links mentioned in this episode: theScore is hiring! https://www.thescore.com/ Remote is also hiring! https://remote.com/ Comparing the Actor Model and CSP with Elixir and Clojure Blog Post by Xiang Ji Comparing the Actor model & CSP concurrency with Elixir & Clojure Xiang Ji at ElixirConf EU 2022 Clojure Programming Language https://clojure.org/ Akka https://akka.io/ Go Programming Language https://github.com/golang/go Proto Actor for Golang https://proto.actor/ RabbitMQ Open-Source Message Broker Software  https://github.com/rabbitmq JVM Project Loom https://github.com/openjdk/loom Ractor for Ruby  https://docs.ruby-lang.org/en/master/ractor_md.html Seven Concurrency Models in Seven Weeks: When Threads Unravel by Paul Butcher Seven Languages in Seven Weeks by Bruce A. Tate GenServer https://hexdocs.pm/elixir/1.12/GenServer.html ets https://www.erlang.org/doc/man/ets.html Elixir in Action by Saša Jurić Redis https://github.com/redis/redis Designing for Scalability with Erlang/OTP by Francesco Cesarini & Steve Vinoski Discord Blog: Using Rust to Scale Elixir for 11 Million Concurrent Users Xiang's website https://xiangji.me/ Feeling Good: The New Mood Therapy by David D. BurnsSpecial Guests: Nathan Hessler and Xiang Ji.

In this episode of Elixir Wizards, hosts Owen and Dan are joined by René Föhring, creator of Credo for Elixir, and Marc-André LaFortune, head maintainer of the RuboCop AST library for Ruby. They compare static code analysis in Ruby versus Elixir. The conversation explores the intricacies and challenges inherent in static code analysis across object-oriented and functional programming paradigms, highlighting the unique characteristics of both Ruby and Elixir. Key topics of discussion include the ways these tools can enhance coding styles and empower developers, the delicate balance between providing guidance and enforcing rules, and the evolving future of code analysis in these languages. Topics discussed in this episode: The differences and applications between static and dynamic analysis How Credo aims to offer flexible and educational guidance for Elixir developers The complexities of method identification in Ruby and its impact on static analysis Challenges posed by macros and dynamic code modification during compilation in Elixir Reducing false positives in code analysis tools to minimize developer frustration Promoting uniform coding practices through analysis tools The significance of using analysis tools with clear, specific objectives How coding standards can refine and improve coding styles over time Building analysis tools and checks through an understanding of Abstract Syntax Trees (ASTs) Potential advancements in the analysis of Phoenix templates and HTML in Elixir Contrasting approaches to managing code and comments in Elixir and Ruby ASTs The fine line between providing helpful guidance and imposing stylistic preferences Heuristics in static analysis highlight inconsistencies without mandating style The potential for more straightforward pattern matching in ASTs with future updates The importance of a gradual implementation of tool updates to maintain backward compatibility Creating tools that support and empower developers, rather than hinder them How static analysis contributes to cleaner, more maintainable codebases Potential future developments in the field of static code analysis Practical applications of using linters like Credo and RuboCop in software development Links mentioned in this episode: Credo https://github.com/rrrene/credo https://hexdocs.pm/credo/overview.html Dogma: A code style linter for Elixir https://github.com/lpil/dogma https://github.com/rubocop/rubocop RuboCop's AST extensions and NodePattern functionality https://github.com/rubocop/rubocop-ast https://github.com/whitequark/parser https://hex.pm/packages?search=credo&sort=recent_downloads https://github.com/doorgan/sourceror https://github.com/rrrene/credo/blob/master/lib/credo/check/readability/large_numbers.exSpecial Guests: Marc-André Lafortune and René Föhring.

Elixir Wizards Owen Bickford and Dan Ivovich discuss web development with Elixir and Phoenix vs. Ruby on Rails, comparing history, project structure, programming paradigms, CRUD apps, LiveView vs. Turbolinks, and WebSocket usage. They also touch on testing frameworks and the benefits of knowledge in multiple frameworks.

Today on Elixir Wizards, Manuel Rubio, author of Erlang/OTP: A Concurrent World and Dan Plyukhin, creator of the UIGC Actor Garbage Collector for Akka, join host Dan Ivovich to compare notes on garbage collection in actor models. The discussion digs into the similarities and differences of actor-based garbage collection in Erlang and Akka and introduces Dan's research on how to perform garbage collection in a distributed actor system. Topics discussed: Akka is akin to Erlang actors for the JVM using Scala, with similar principles like supervision trees, messages, and clustering Erlang uses generational garbage collection and periodically copies live data to the old heap for long-lived elements Actor GC aims to determine when an actor's memory can be reclaimed automatically rather than manually killing actors Distributed actor GC is more challenging than object GC due to the distributed nature and relationships between actors across nodes Challenges include reasoning about failures like dropped messages and crashed nodes GC balance requires optimization of resource release and CPU load management Immutability helps Erlang GC, but copying data for messages impacts performance Research into distributed actor GC is still ongoing, with opportunities for improvement Fault tolerance in Erlang relies on user implementation rather than low-level guarantees Asynchronous messages in Erlang/Elixir mean references may become invalid which is similar to the distributed GC approaches in Dan's research Idempotent messaging is recommended to handle possible duplicates from failures Help your local researcher! Researchers encourage communication from practitioners on challenges and use cases Links mentioned: Erlang/OTP Volume 1: A Concurrent World by Manuel Rubio https://altenwald.com/en/book/en-erlang-i  Scala https://www.scala-lang.org/  Akka Framework https://github.com/akka  JVM (Java Virtual Machine) https://www.java.com/en/download/  The BEAM VM https://www.erlang.org/blog/a-brief-beam-primer/ Hadoop Framework https://hadoop.apache.org/   Pony Programming Language https://www.ponylang.io/  SLSA Programming Language https://wcl.cs.rpi.edu/salsa/#:~:text=SALSA%20 Paxos Algorithm https://en.wikipedia.org/wiki/Paxos_(computer_science)  Raft library for maintaining a replicated state machine https://github.com/etcd-io/raft  Dan's Website https://dplyukhin.github.io/  Dan Plyukhin on Twitter: https://twitter.com/dplyukhin  Dan Plyukhin’s YouTube channel: https://m.youtube.com/@dplyukhin UIGC on GitHub https://github.com/dplyukhin/UIGC  Manuel's Website https://altenwald.com/  Manuel Rubio on Twitter: https://twitter.com/MRonErlangSpecial Guests: Dan Plyukhin and Manuel Rubio.

In this episode of Elixir Wizards, Katelynn Burns, software engineer at LaunchScout, and Alexis Carpenter, senior data scientist at cars.com, join Host Dan Ivovich to discuss machine learning with Elixir, Python, SQL, and MATLAB. They compare notes on available tools, preprocessing, working with pre-trained models, and training models for specific jobs. The discussion inspires collaboration and learning across communities while revealing the foundational aspects of ML, such as understanding data and asking the right questions to solve problems effectively. Topics discussed: Using pre-trained models in Bumblebee for Elixir projects Training models using Python and SQL The importance of data preprocessing before building models Popular tools used for machine learning in different languages Getting started with ML by picking a personal project topic of interest Resources for ML aspirants, such as online courses, tutorials, and books The potential for Elixir to train more customized models in the future Similarities between ML approaches in different languages Collaboration opportunities across programming communities Choosing the right ML approach for the problem you're trying to solve Productionalizing models like fine-tuned LLM's The need for hands-on practice for learning ML skills Continued maturation of tools like Bumblebee in Elixir Katelynn's upcoming CodeBeam talk on advanced motion tracking Links mentioned in this episode https://launchscout.com/ https://www.cars.com/ Genetic Algorithms in Elixir by Sean Moriarity Machine Learning in Elixir by Sean Moriarity https://github.com/elixir-nx/bumblebee https://github.com/huggingface https://www.docker.com/products/docker-hub/ Programming with MATLAB https://elixirforum.com/ https://pypi.org/project/pyspark/  Machine Learning Course from Stanford School of Engineering Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron Data Science for Business by Foster Provost & Tom Fawcett https://medium.com/@carscomtech  https://github.com/k-burns  Code Beam America March, 2024Special Guests: Alexis Carpenter and Katelynn Burns.

This week on Elixir Wizards, Connor Rigby, Software Engineer at SmartRent, and Taylor Barto, Lead Embedded Software Engineer at Eaton, join Sundi Myint to compare notes on embedded systems development with Elixir, C, C++, and Java. They discuss using Elixir and the Nerves framework for firmware projects versus more traditional choices like C. The guests ask one another questions and gain valuable insights into challenges, tooling, resources, and more across different embedded ecosystems. In this episode, the guests expand their perspectives and demystify the concept of embedded systems for engineers outside the field. This cross-language exchange of ideas and experiences inspires continued learning and collaboration between embedded software engineers using different programming languages. Topics Discussed: Defining "true embedded": using an operating system vs. bare metal programming Benefits and drawbacks of Elixir, C, C++, and Java for firmware Many embedded systems today use Java as the programming language via Java Native Interface (JNI) to interface with C/C++ code How Elixir expands the toolbox available for firmware projects Testing, tooling, workflows, and debugging across languages Elixir/Nerves features like hot code reloading and testing vs. Java alternatives Learning curves for new languages and frameworks Industry trends around established vs emerging tools Applying functional programming principles like immutability in new domains Scaling firmware updates across large connected networks Continued maturation of Nerves may bring Elixir into consideration for roles where Java is commonly used today Hardening systems for reliability in safety-critical uses Debugging differences between web development and embedded Hiring considerations for niche languages Additional skills needed for embedded engineers, such as technical writing, reading schematics, and writing test instructions Resources and recommendations for getting started with embedded systems Links Mentioned: Nerves: https://github.com/nerves-project/nerves https://nerves-project.org/ AtomVM: https://github.com/atomvm/AtomVM GRiSP: https://github.com/grisp RISC-V: https://github.com/ultraembedded/riscv https://smartrent.com/ https://www.eaton.com/us/en-us.html Zig Programming Language: https://github.com/ziglang Docker: https://github.com/docker Build a Weather Station with Elixir and Nerves by Alexander Koutmos, Bruce A. Tate, Frank Hunleth Build a Binary Clock with Elixir and Nerves by Frank Hunleth and Bruce A. Tate http://esp32.net/ https://www.nordicsemi.com/Special Guests: Connor Rigby and Taylor Barto.

In Episode 4, the Elixir Wizards are joined by Dorian Iacobescu, author of the ECSpanse ECS library for Elixir, and Daniel Luu, founder and CEO of the game development studio AKREW. The guests compare notes on backend game development using ECS, the Entity Component System approach. Akrew is currently building the multiplayer game Galactic Getaway using the Photon Unity networking framework, which employs ECS. They discuss challenges like collections of component types and persistence beyond runtime, building games for various platforms like MacOS, and handling inventory storage in a backend database. Game development is complex and has many technical hurdles, but open communication across different programming communities and game development approaches can foster collaboration, innovation, and continued learning. Topics Discussed in this Episode Dorian explains the ECSpanse ECS library and component-based architecture Dorian took inspiration for ECSpanse from the Rust library Bevy ECS and its component-based API The guests discuss popular game development platforms and languages, including C#, JavaScript, and Godot Owen and Daniel translate ECS concepts to familiar database and backend terminology for devs without game-specific experience ECSpanse uses many tools from the Elixir Erlang toolbox, including GenServers, ETS tables, tasks, queries, and Phoenix LiveView ECS challenges representing inventory collections that broke typical ECS singleton patterns AKREW is developing Galactic Getaway using the Photon Unity framework Relationships between parent and child entities in ECSpanse Persistence, serialization, and replay features to save game state Optimizing assets and code for performance on various devices Links Mentioned https://en.wikipedia.org/wiki/Entity_component_system https://iacobson.medium.com/elixir-for-fun-ecspanse-2852a7993ecd https://hexdocs.pm/ecspanse/Ecspanse.html https://bevyengine.org/learn/book/getting-started/ecs/ https://www.photonengine.com/quantum Add Galactic Getaway to your Steam Wishlist: https://store.steampowered.com/app/2012390/Galactic_Getaway/ https://godotengine.org/ https://unity.com/ https://docs.godotengine.org/en/stable/tutorials/scripting/gdscript/gdscript_basics.html https://www.tiktok.com/@galacticgetaway https://docs.rs/bevy_ecs/latest/bevy_ecs/ Special Guests: Daniel Luu and Dorian Iacobescu.

This week, the Elixir Wizards are joined by Yohana Tesfazgi and Wes Bos to compare notes on the experience of learning Elixir vs. JavaScript as your first programming language. Yohana recently completed an Elixir apprenticeship, and Wes Bos is a renowned JavaScript educator with popular courses for beginner software developers. They discuss a variety of media and resources and how people with different learning styles benefit from video courses, articles, or more hands-on projects. They also discuss the current atmosphere for those looking to transition into an engineering career and how to stick out among the crowd when new to the scene. Topics Discussed in this Episode Pros and cons of learning Elixir as your first programming language Materials and resources for beginners to JavaScript and Elixir Projects and methods for learning Elixir with no prior knowledge Recommendations for sharpening and showcasing skills How to become a standout candidate for potential employers Soft skills like communication translate well from other careers to programming work Learning subsequent languages becomes more intuitive once you learn your first How to decide which library to use for a project How to build an online presence and why it’s important Open-source contributions are a way to learn from the community Ship early and often, just deploying a default Phoenix app teaches deployment skills Attend local meetups and conferences for mentoring and potential job opportunities Links Mentioned https://syntax.fm/ https://fly.io/ https://elixirschool.com/en Syntax.fm: Supper Club × How To Get Your First Dev Job With Stuart Bloxham Quinnwilton.com https://github.com/pallets/flask https://wesbos.com/courses https://beginnerjavascript.com/ Free course: https://javascript30.com/ https://pragmaticstudio.com/ https://elixircasts.io/ https://grox.io/ LiveView Mastery YouTube Channel Contact Yohana: yytesfazgi@gmail.com

In today’s episode, Sundi and Owen are joined by Yordis Prieto and Stephen Chudleigh to compare notes on HTTP requests in Elixir vs. Ruby, JavaScript, Go, and Rust. They cover common pain points when working with APIs, best practices, and lessons that can be learned from other programming languages. Yordis maintains Elixir's popular Tesla HTTP client library and shares insights from building APIs and maintaining open-source projects. Stephen has experience with Rails and JavaScript, and now works primarily in Elixir. They offer perspectives on testing HTTP requests and working with different libraries. While Elixir has matured, there is room for improvement - especially around richer struct parsing from HTTP responses. The discussion highlights ongoing efforts to improve the developer experience for HTTP clients in Elixir and other ecosystems. Topics Discussed in this Episode HTTP is a protocol - but each language has different implementation methods Tesla represents requests as middleware that can be modified before sending Testing HTTP requests can be a challenge due to dependence on outside systems GraphQL, OpenAPI, and JSON API provide clear request/response formats Elixir could improve richer parsing from HTTP into structs Focus on contribution ergonomics lowers barriers for new participants Maintainers emphasize making contributions easy via templates and clear documentation APIs drive adoption of standards for client/server contracts They discuss GraphQL, JSON API, OpenAPI schemas, and other standards that provide clear request/response formats TypeScript brings types to APIs and helps to validate responses Yordis notes that Go and Rust make requests simple via tags for mapping JSON to structs Language collaboration shares strengths from different ecosystems and inspires new libraries and tools for improving the programming experience Links Mentioned Elixir-Tesla Library: https://github.com/elixir-tesla/tesla Yordis on Github: https://github.com/yordis Yordis on Twitter: https://twitter.com/alchemist_ubi Yordis on LinkedIn: https://www.linkedin.com/in/yordisprieto/ Yordis on YouTube: https://www.youtube.com/@alchemistubi Stephen on Twitter: https://twitter.com/stepchud Stephen's projects on consciousness: https://harmonicdevelopment.us Owen suggests: Http.cat HTTParty: https://github.com/jnunemaker/httparty Guardian Library: https://github.com/ueberauth/guardian Axios: https://axios-http.com/ Straw Hat Fetcher: https://github.com/straw-hat-team/nodejs-monorepo/tree/master/packages/%40straw-hat/fetcher Elixir Tesla Wiki: https://github.com/elixir-tesla/tesla/wiki HTTPoison: https://github.com/edgurgel/httpoison Tesla Testing: https://hexdocs.pm/tesla/readme.html#testing Tesla Mock: https://hexdocs.pm/tesla/Tesla.Mock.html Finch: https://hex.pm/packages/finch Mojito: https://github.com/appcues/mojito Erlang Libraries and Frameworks Working Group: https://github.com/erlef/libs-and-frameworks/ and https://erlef.org/wg/libs-and-frameworksSpecial Guests: Stephen Chudleigh and Yordis Prieto.

Hosts Dan Ivovich, Owen Bickford, and Sundi Myint kick off the 11th season of the Elixir Wizards podcast. This season’s theme is “Branching Out from Elixir,” which expands the conversation to compare notes with experts from other communities; they discuss their experiences with other languages like JavaScript, PHP, Python, Ruby, C#, Go, and Dart before and after learning Elixir. This season's conversations will illuminate how problems are solved in different languages vs. Elixir; upcoming episode topics teased include education, data processing, deployment strategies, and garbage collection; the hosts express excitement for conversations analyzing similarities and differences between communities. Topics Discussed in this Episode Season 11 branches out from Elixir to compare notes with other programming communities Sundi, Owen, and Dan introduce the season theme and their interest in exploring these conversations The hosts compare their experiences with PHP, JavaScript, Python, Ruby, C#, Go, Dart and Elixir The Wizards compare and contrast differences in their personal experience building similar things with different languages Dan dreams in Ruby and uses it for quick prototypes Comparing problem-solving approaches across languages will reframe perspectives Upcoming episodes explore data processing workflows, machine learning, and game development Pop Quiz: Who's that Pokémon... or language, or framework? Links Mentioned https://smartlogic.io/ https://codepen.io/ https://i.redd.it/0lg7979qtr511.jpg