The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

August 04, 2020Supporting The Full Lifecycle Of Machine Learning Projects With Metaflow
Summary
Netflix uses machine learning to power every aspect of their business. To do this effectively they have had to build extensive expertise and tooling to support their engineers. In this episode Savin Goyal discusses the work that he and his team are doing on the open source machine learning operations platform Metaflow. He shares the inspiration for building an opinionated framework for the full lifecycle of machine learning projects, how it is implemented, and how they have designed it to be extensible to allow for easy adoption by users inside and outside of Netflix. This was a great conversation about the challenges of building machine learning projects and the work being done to make it more achievable.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!
Your host as usual is Tobias Macey and today I’m interviewing Savin Goyal about Netflix’s infrastructure for machine learning
Interview
Introductions
How did you get introduced to Python?
Can you start by describing the work you are doing at Netflix to support their machine learning workloads?
How are you addressing the impedance mismatch of machine learning/data science work between local experimentation and production deployment?
What was the motivation for building Metaflow?
How does Metaflow compare to other tools in the ecosystem such as MLFlow?
What was missing in the other available tools that made Metaflow necessary?
workflow for someone using Metaflow
How do you approach the design of the developer interface to make it approachable to machine learning engineers?
level of coupling with overall Netflix data stack
How is Metaflow implemented?
How has the architecture and design of the system evolved since you first began working on it?
supporting infrastructure/integration points
motivation/benefits of releasing it as open source
What are some of the most interesting, unexpected, or challenging lessons that you have learned while building infrastructure and tooling for machine learning?
When is Metaflow the wrong choice?
What do you have planned for the future of Metaflow and
Keep In Touch
LinkedIn
@savingoyal on Twitter
savingoyal on GitHub
Picks
Tobias
vdist
Savin
Reparing Vintage Watches
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
Metaflow
OCaml
EC2
S3
Data Lake
PyTorch
Tensorflow
Netflix Data Stack
Spinnaker
Chaos Engineering
Chaos Toolkit Podcast Episode
Chaos Monkey
Netflix Simian Army
Netflix Titus
AWS Batch
Netflix Meson
Dataflow Programming
DAG == Directed Acyclic Graph
MLFlow
DVC (Data Version Control)
Podcast Episode
CML (Continuous Machine Learning)
AWS Step Functions
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
45 min
July 28, 2020 Learning To Program By Building Tiny Python Projects
One of the best methods for learning programming is to just build a project and see how things work first-hand. With that in mind, Ken Youens-Clark wrote a whole book of Tiny Python Projects that you can use to get started on your journey. In this episode he shares his inspiration for the book, his thoughts on the benefits of teaching testing principles and the use of linting and formatting tools, as well as the benefits of trying variations on a working program to see how it behaves. This was a great conversation about useful strategies for supporting new programmers in their efforts to learn a valuable skill.
55 min
July 28, 2020Learning To Program By Building Tiny Python Projects
Summary
One of the best methods for learning programming is to just build a project and see how things work first-hand. With that in mind, Ken Youens-Clark wrote a whole book of Tiny Python Projects that you can use to get started on your journey. In this episode he shares his inspiration for the book, his thoughts on the benefits of teaching testing principles and the use of linting and formatting tools, as well as the benefits of trying variations on a working program to see how it behaves. This was a great conversation about useful strategies for supporting new programmers in their efforts to learn a valuable skill.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!
Your host as usual is Tobias Macey and today I’m interviewing Ken Youens-Clark about his book Tiny Python Projects
Interview
Introductions
How did you get introduced to Python?
What is your goal with your book of Tiny Python Projects?
What motivated you to start writing it?
Who is the target audience that you wrote the book for?
One of the notable aspects of the book is the fact that you introduce linting and testing in the first chapter. Why is that a useful subject for the first steps of someone getting started in Python?
What are some of the problems that users experience if they are introduced to these tools after they have already established a set of habits?
How did you approach the structure of the book to be approachable by newcomers to Python?
What was your process for deciding on the scope of the information to include in the book?
What are some of the challenges that you faced in identifying self-contained projects that could fit into a single chapter?
As a book that is intended to serve as a learning resource, what was your process for soliciting feedback to determine if your tone and structure is effective in teaching the reader?
What elements of the Python language and ecosystem did you consciously leave out to avoid overwhelming the readers?
What are some of the most interesting, unexpected, or challenging lessons that you learned while working on the book?
What are your thoughts on useful resources and next steps for readers who are interested in progressing in their use of Python?
Keep In Touch
kyclark on GitHub
Website
@kycl4rk on Twitter
Picks
Tobias
Marvel Cinematic Universe
Ken
Parks & Recreation TV Show
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
Tiny Python Projects
University of Arizona
BioInformatics
Perl
BioPython
Podcast Episode
Seq
Podcast Episode
Pytest
Podcast Episode
Windows Subsystem for Linux
Pylint
Podcast Episode
YAPF
Black Python Formatter
Mad Libs
Boolean Algebra
Object Oriented Programming
Delphi
OmniGraffle
Kent Beck
Test Driven Development
Clojure
Regular Expression
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
55 min
July 21, 2020 Idiomatic Functional Programming With DRY Python
Python is an intuitive and flexible language, but that versatility can also lead to problematic designs if you're not careful. Nikita Sobolev is the CTO of Wemake Services where he works on open source projects that encourage clean coding practices and maintainable architectures. In this episode he discusses his work on the DRY Python set of libraries and how they provide an accessible interface to functional programming patterns while maintaining an idiomatic Python interface. He also shares the story behind the wemake Python styleguide plugin for Flake8 and the benefits of strict linting rules to engender good development habits. This was a great conversation about useful practices to build software that will be easy and fun to work on.
48 min
July 21, 2020Idiomatic Functional Programming With DRY Python
Summary
Python is an intuitive and flexible language, but that versatility can also lead to problematic designs if you’re not careful. Nikita Sobolev is the CTO of Wemake Services where he works on open source projects that encourage clean coding practices and maintainable architectures. In this episode he discusses his work on the DRY Python set of libraries and how they provide an accessible interface to functional programming patterns while maintaining an idiomatic Python interface. He also shares the story behind the wemake Python styleguide plugin for Flake8 and the benefits of strict linting rules to engender good development habits. This was a great conversation about useful practices to build software that will be easy and fun to work on.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
This portion of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!
Your host as usual is Tobias Macey and today I’m interviewing Nikita Sobolev about his work with DRY Python and Wemake Services
Interview
Introductions
How did you get introduced to Python?
Can you start by sharing your overarching philosophies or design aesthetics for writing maintainable software?
What is your process for starting a new project, beginning at the design phase?
What are some of the challenges or shortcomings that you see in the "default" way that most developers write Python?
What is DRY Python is and how does it help in addressing those concerns?
What was your motivation for creating these projects?
There are a number of different projects that are being built under the DRY Python umbrella. Can you list the ones that are currently active and outline how they fit together?
What are some of the initial challenges that newcomers to the DRY Python libraries encounter?
How do you approach the design of the API and developer experience to make these development approaches more accessible?
What have you seen in terms of real world impact on the maintainability and extensibility of projects that you have built on top of the DRY Python components?
In addition to DRY Python you are also involved with development of the wemake-python-styleguide. Can you describe that projects goal and how it got started?
If you make the linting too restrictive then developers are likely to just ignore or disable it. What have you found to be the right balance to which rules will fail a build and which are just informational?
Why do you push the responsibility for things like formatting onto the developer, rather than an autoformatter such as YAPF or Black?
What are some of the other supporting technologies that you rely on during your development workflow?
What are some of the elements that you think are missing in the common toolbox for Python developers?
What tools are we lacking entirely?
What are the cases where DRY Python is the wrong choice?
What are your goals and plans for the future of DRY Python and the various Wemake libraries?
Keep In Touch
Blog
sobolevn on GitHub
Picks
Tobias
The Map To Everywhere
Nikita
Russian Python Week
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
DRY Python
Wemake Services
wemake-python-styleguide
Turbogears 2
Dotenv Linter
Returns
Wemake Python Package Cookiecutter Template
Test Driven Development
Requirements Analysis
RESTs
Django Rest Framework
Classes
Monads
Functors
Scala
Kotlin
Haskell
Punq dependency injection library
Flake8
Wemake Django Template
Flake8 Baseline
isort
Nitpick
Mypy
Darglint
Poetry
Pip Dependency Resolver
Podcast Episode
Hypothesis
Podcast Episode
Schemathesis
Pytest Auto Hypothesis
Typescript
Rust
Elixir
Zio Scala
GitHub Sponsors
Do Not Log blog post
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
48 min
July 13, 2020 The Past, Present, And Future Of The FLUFL: Barry Warsaw Shares His History With Python
Barry Warsaw has been a member of the Python community since the very beginning. His contributions to the growth of the language and its ecosystem are innumerable and diverse, earning him the title of Friendly Language Uncle For Life. In this episode he reminisces on his experiences as a core developer, a member of the Python Steering Committee, and his roles at Canonical and LinkedIn supporting the use of Python at those companies. In order to know where you are going it is always important to understand where you have been and this was a great conversation to get a sense of the history of how Python has gotten to where it is today.
52 min
July 13, 2020The Past, Present, And Future Of The FLUFL: Barry Warsaw Shares His History With Python
Summary
Barry Warsaw has been a member of the Python community since the very beginning. His contributions to the growth of the language and its ecosystem are innumerable and diverse, earning him the title of Friendly Language Uncle For Life. In this episode he reminisces on his experiences as a core developer, a member of the Python Steering Committee, and his roles at Canonical and LinkedIn supporting the use of Python at those companies. In order to know where you are going it is always important to understand where you have been and this was a great conversation to get a sense of the history of how Python has gotten to where it is today.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
This episode of Python Podcast is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!
Your host as usual is Tobias Macey and today I’m interviewing Barry Warsaw about his role in the Python community, past, present, and future.
Interview
Introductions
How did you get introduced to Python?
For anyone who isn’t familiar with you, how would you characterize your role in the Python language and community?
What have been your main areas of focus in your role as a core developer?
What are some of the other forms that your contributions to the language and community have taken?
What are the contributions to Python that you are most proud of?
Looking back at the past 25 years of Python, what do you find most interesting/surprising/exciting?
How has the focus of the community changed or evolved since you first began using it?
What are you currently focused on in your role in the steering council?
What are the aspects of the language and community that you think need greater attention?
What are the core strengths of the language and community that you believe will carry it through the next 25 years?
In your current and previous roles you acted as a guiding force for Python. What are the main use cases for Python at LinkedIn?
What kinds of projects are you involved with to support the other engineers in their use of Python?
How much of an impact has the invisible hand of the PSU had on the overall trajectory of Python?
Outside of Python, what are the programming languages or communities that you look to for inspiration?
What are your personal goals for the future of Python?
Keep In Touch
Website
warsaw on GitHub
warsaw on GitLab
Blog
@pumpichank on Twitter
Picks
Tobias
Hanna TV Series
Barry
Midnight Gospel
The Expanse
TV Series
Audio Books
Free 30 Day Audible Trial (Affiliate Link)
Closing Announcements
Thank you for listening! Don’t forget to check out our other show, the Data Engineering Podcast for the latest on modern data management.
Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.
If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.
To help other people find the show please leave a review on iTunes and tell your friends and co-workers
Join the community in the new Zulip chat workspace at pythonpodcast.com/chat
Links
FLUFL PEP 401
Python Steering Council
The PEP Talk episode
Usenet
BBS == Bulletin Board System
comp.lang.python
NIST == National Institute of Standards and Technology
CNRI == Corporation for National Research Initiatives
BayPIGgies
Tcl/Tk
PEP 572 := The Walrus Operator
"The Grand Renaming"
IETF == Internet Engineering Task Force
RFC
WebAssembly
Python Software Foundation
Podcast Episode
Python Black Swans keynote by Russell Keith-Magee
Followup Podcast Episode
Ewa Jodlowska
Canonical Launchpad
Mypy
Podcast Episode
Python Type Annotations
Iris Event Paging System
OnCall Pager Rotation System
Shiv
PyOxidizer
Rust
Flake8
isort
Black
Sphinx
Read The Docs
Podcast Episode
Sybil
Manuel
Doctest
Pytest
Coverage.py
Cargo package system
Tai Chi
Python Core Mentorship
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
52 min
July 06, 2020 Pure Python Configuration Management With PyInfra
Building and managing servers is a challenging task. Configuration management tools provide a framework for handling the various tasks involved, but many of them require learning a specific syntax and toolchain. PyInfra is a configuration management framework that embraces the familiarity of Pure Python, allowing you to build your own integrations easily and package it all up using the same tools that you rely on for your applications. In this episode Nick Barrett explains why he built it, how it is implemented, and the ways that you can start using it today. He also shares his vision for the future of the project and you can get involved. If you are tired of writing mountains of YAML to set up your servers then give PyInfra a try today.
44 min
July 06, 2020Pure Python Configuration Management With PyInfra
Summary
Building and managing servers is a challenging task. Configuration management tools provide a framework for handling the various tasks involved, but many of them require learning a specific syntax and toolchain. PyInfra is a configuration management framework that embraces the familiarity of Pure Python, allowing you to build your own integrations easily and package it all up using the same tools that you rely on for your applications. In this episode Nick Barrett explains why he built it, how it is implemented, and the ways that you can start using it today. He also shares his vision for the future of the project and you can get involved. If you are tired of writing mountains of YAML to set up your servers then give PyInfra a try today.
Announcements
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
When you’re ready to launch your next app or want to try a project you hear about on the show, you’ll need somewhere to deploy it, so take a look at our friends over at Linode. With the launch of their managed Kubernetes platform it’s easy to get started with the next generation of deployment and scaling, powered by the battle tested Linode platform, including simple pricing, node balancers, 40Gbit networking, dedicated CPU and GPU instances, and worldwide data centers. Go to pythonpodcast.com/linode and get a $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
This portion of Podcast.__init__ is brought to you by Datadog. Do you have an app in production that is slower than you like? Is its performance all over the place (sometimes fast, sometimes slow)? Do you know why? With Datadog, you will. You can troubleshoot your app’s performance with Datadog’s end-to-end tracing and in one click correlate those Python traces with related logs and metrics. Use their detailed flame graphs to identify bottlenecks and latency in that app of yours. Start tracking the performance of your apps with a free trial at datadog.com/pythonpodcast. If you sign up for a trial and install the agent, Datadog will send you a free t-shirt.
You listen to this show to learn and stay up to date with the ways that Python is being used, including the latest in machine learning and data analysis. For more opportunities to stay up to date, gain new skills, and learn from your peers there are a growing number of virtual events that you can attend from the comfort and safety of your home. Go to pythonpodcast.com/conferences to check out the upcoming events being offered by our partners and get registered today!
Your host as usual is Tobias Macey and today I’m interviewing Nick Barrett about PyInfra, a pure Python framework for agentless configuration management
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what PyInfra is and its origin story?
There are a number of options for configuration management of various levels of complexity and language options. What are the features of PyInfra that might lead someone to choose it over other systems?
What do you see as the major pain points in dealing with infrastructure today?
For someone who is using PyInfra to manage their servers, what is the workflow for building and testing deployments?
How do you handle enforcement of idempotency in the operations being performed?
Can you describe how PyInfra is implemented?
How has its design or focus evolved since you first began working on it?
What are some of the initial assumptions that you had at the outset which have been challenged or updated as it has grown?
The library of available operations seems to have a good baseline for deploying and managing services. What is involved in extending or adding operations to PyInfra?
With the focus of the project being on its use of pure Python and the easy integration of external libraries, how do you handle execution of python functions on remote hosts that requires external dependencies?
What are some of the other options for interfacing with or extending PyInfra?
What are some of the edge cases or points of confusion that users of PyInfra should be aware of?
What has been the community response from developers who first encounter and trial PyInfra?
What have you found to be the most interesting, unexpected, or challenging aspects of building and maintaining PyInfra?
When is PyInfra the wrong choice for managing infrastructure?
What do you have planned for the future of the project?
Keep In Touch
Fizzadar on GitHub
Website
@Fizzadar on Twitter
LinkedIn
Picks
Tobias
My Spy
Nick
Das Keyboard Ultimate
Korean Short Ribs
Kimchi Fried Rice
Links
PyInfra
Oxygem
WordPress
Lua
Gary’s Mod
Java
Ansible
SaltStack
Chef
Puppet
EC2
Boto 3
Hashicorp Vault
Vagrant
Docker
Testinfra
SaltStack Testinfra Plugin
Dockerfile
Idempotence
Nginx
POSIX
gevent
Jinja2
Click
Zero Tier
BSD
AST Module
RedBaron
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
44 min
June 30, 2020 Build Your Own Domain Specific Language in Python With textX
Programming languages are a powerful tool and can be used to create all manner of applications, however sometimes their syntax is more cumbersome than necessary. For some industries or subject areas there is already an agreed upon set of concepts that can be used to express your logic. For those cases you can create a Domain Specific Language, or DSL to make it easier to write programs that can express the necessary logic with a custom syntax. In this episode Igor Dejanović shares his work on textX and how you can use it to build your own DSLs with Python. He explains his motivations for creating it, how it compares to other tools in the Python ecosystem for building parsers, and how you can use it to build your own custom languages.
55 min