The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

May 25, 2020Dependency Management Improvements In Pip's Resolver
Summary
Dependency management in Python has taken a long and winding path, which has led to the current dominance of Pip. One of the remaining shortcomings is the lack of a robust mechanism for resolving the package and version constraints that are necessary to produce a working system. Thankfully, the Python Software Foundation has funded an effort to upgrade the dependency resolution algorithm and user experience of Pip. In this episode the engineers working on these improvements, Pradyun Gedam, Tzu-Ping Chung, and Paul Moore, discuss the history of Pip, the challenges of dependency management in Python, and the benefits that surrounding projects will gain from a more robust resolution algorithm. This is an exciting development for the Python ecosystem, so listen now and then provide feedback on how the new resolver is working for you.
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 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You listen to this show because you love Python and want to keep your skills up to date, and machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.
Your host as usual is Tobias Macey and today I’m interviewing Tzu-ping Chung, Pradyun Gedam, and Paul Moore about their work to improve the dependency resolution capabilities of Pip and its user experience
Interview
Introductions
How did you get introduced to Python?
Can you start by describing the focus of the work that you are doing?
What is the scope of the work, and what is the established criteria for when it is considered complete?
What is your history with working on the Pip source code and what interests you most about this project?
What are the main sources or manifestations of technical debt that exist in Pip as of today?
How does it currently handle dependency resolution?
What are some of the workarounds that developers have had to resort to in the absence of a robust dependency resolver in Pip?
How is the new dependency resolver implemented?
How has your initial design evolved or shifted as you have gotten further along in its implementation?
What are the pieces of information that the resolver will rely on for determining which packages and versions to install? (e.g. will it install setuptools > 45.x in a Python 2 virtualenv?)
What are the new capabilities in Pip that will be enabled by this upgrade to the dependency resolver?
What projects or features in the encompassing ecosystem will be unblocked with the introduction of this upgrade?
What are some of the changes that users will need to make to adopt the updated Pip?
How do you anticipate the changes in Pip impacting the viability or adoption of Python and its ecosystem within different communities or industries?
What are some of the additional changes or improvements that you would like to see in Pip or other core elements of the Python landscape?
What are some of the most interesting, unexpected, or challenging lessons that you have learned while working on these updates to Pip?
Keep In Touch
Pradyun
Website
pradyunsg on GitHub
@pradyunsg on Twitter
Paul
pfmoore on GitHub
Tzu-Ping
uranusjr on GitHub
Website
@uranusjr on Twitter
Picks
Tzu-ping
Python Launcher
Joe Abercrombie author
The Shattered Sea Trilogy
Anime
PipX Standalone
Paul
pipx
Black
nox
tox
scoop
Neil Gaiman
Good Omens
Book
TV Series
Pradyun
because my picks can be anything — things that have kept me sane in this lockdown world
Music: Chris Daughtry
Video Game: Parkitect
Tobias
Language Server Protocol
Emacs lsp-mode
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
Pip
Podcast interview with Donald Stufft
Macdown
Taiwan
Pipenv
PyPI
Podcast Episode
TOML
Python Package Metadata Standards
iBook G4
Acorn Computer
distutils
easy_install
Python Eggs
setuptools
Python Wheels
CPAN
Conda
Inside The Cheeseshop
Google Summer of Code
Zazo
PEP517
pip-tools
Poetry
resolvelib
SAT Solver
Trove Classifiers
PyPA
pyproject.toml
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
1 hr 17 min
May 18, 2020 Easy Data Validation For Your Python Projects With Pydantic
One of the most common causes of bugs is incorrect data being passed throughout your program. Pydantic is a library that provides runtime checking and validation of the information that you rely on in your code. In this episode Samuel Colvin explains why he created it, the interesting and useful ways that it can be used, and how to integrate it into your own projects. If you are tired of unhelpful errors due to bad data then listen now and try it out today.
48 min
May 18, 2020Easy Data Validation For Your Python Projects With Pydantic
Summary
One of the most common causes of bugs is incorrect data being passed throughout your program. Pydantic is a library that provides runtime checking and validation of the information that you rely on in your code. In this episode Samuel Colvin explains why he created it, the interesting and useful ways that it can be used, and how to integrate it into your own projects. If you are tired of unhelpful errors due to bad data then listen now and try it out 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 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You listen to this show because you love Python and want to keep your skills up to date. Machine learning is finding its way into every aspect of software engineering. Springboard has partnered with us to help you take the next step in your career by offering a scholarship to their Machine Learning Engineering career track program. In this online, project-based course every student is paired with a Machine Learning expert who provides unlimited 1:1 mentorship support throughout the program via video conferences. You’ll build up your portfolio of machine learning projects and gain hands-on experience in writing machine learning algorithms, deploying models into production, and managing the lifecycle of a deep learning prototype. Springboard offers a job guarantee, meaning that you don’t have to pay for the program until you get a job in the space. Podcast.__init__ is exclusively offering listeners 20 scholarships of $500 to eligible applicants. It only takes 10 minutes and there’s no obligation. Go to pythonpodcast.com/springboard and apply today! Make sure to use the code AISPRINGBOARD when you enroll.
Your host as usual is Tobias Macey and today I’m interviewing Samuel Colvin about Pydantic, a library for enforcing type hints at runtime
Interview
Introductions
How did you get introduced to Python?
Can you start by describing what Pydantic is and what motivated you to create it?
What are the main use cases that benefit from Pydantic?
There are a number of libraries in the Python ecosystem to handle various conventions or "best practices" for settings management. How does pydantic fit in that category and why might someone choose to use it over the other options?
There are also a number of libraries for defining data schemas or validation such as Marshmallow and Cerberus. How does Pydantic compare to the available options for those cases?
What are some of the challenges, whether technical or conceptual, that you face in building a library to address both of these areas?
The 3.7 release of Python added built in support for dataclasses as a means of building containers for data with type validation. What are the tradeoffs of pydantic vs the built in dataclass functionality?
How much overhead does pydantic add for doing runtime validation of the modelled data?
In the documentation there is a nuanced point that you make about parsing vs validation and your choices as to what to support in pydantic. Why is that a necessary distinction to make?
What are the limitations in terms of usage that you are accepting by choosing to allow for implicit conversion or potentially silent loss of precision in the parsed data?
What are the benefits of punting on the strict validation of data out of the box?
What has been your design philosophy for constructing the user facing API?
How is Pydantic implemented and how has the overall architecture evolved since you first began working on it?
What have you found to be the most challenging aspects of building a library for managing the consistency of data structures in a dynamic language?
What are some of the strengths and weaknesses of Python’s type system?
What is the workflow for a developer who is using Pydantic in their code?
What are some of the pitfalls or edge cases that they might run into?
What is involved in integrating with other libraries/frameworks such as Django for web development or Dagster for building data pipelines?
What are some of the more advanced capabilities or use cases of Pydantic that are less obvious?
What are some of the features or capabilities of Pydantic that are often overlooked which you think should be used more frequently?
What are some of the most interesting, innovative, or unexpected ways that you have seen Pydantic used?
What are some of the most interesting, challenging, or unexpected lessons that you have learned through your work on or with Pydantic?
When is Pydantic the wrong choice?
What do you have planned for the future of the project?
Keep In Touch
samuelcolvin on GitHub
Website
LinkedIn
@samuel_colvin on Twitter
Picks
Tobias
Devil Sticks
Samuel
Flash Boys by Michael Lewis
Algorithms To Live By by Brian Christian and Tom Griffiths
NGrok.com
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
Pydantic
Matlab
C#
FastAPI
Podcast Episode
Marshmallow
Podcast Episode
Cerberus
12 Factor App
Django
Python Type Hints
Cython
Podcast Episode
MyPy
Podcast Episode
Duck Typing
Haskell
Higher Order Types
PyCharm Pydantic Plugin
Django Rest Framework
Avro
Parquet
Dagster
Data Engineering Podcast Episode
Starlette
Flask
Ludwig
Deep Pavlov
Fast MRI
Reagent
Pynt
Open Source Has Failed article
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
48 min
May 11, 2020 Managing Distributed Teams In The Age Of Remote Work
More of us are working remotely than ever before, many with no prior experience with a remote work environment. In this episode Quinn Slack discusses his thoughts and experience of running Sourcegraph as a fully distributed company. He covers the lessons that he has learned in moving from partially to fully remote, the practices that have worked well in managing a distributed workforce, and the challenges that he has faced in the process. If you are struggling with your remote work situation then this conversation has some useful tips and references for further reading to help you be successful in the current environment.
49 min
May 11, 2020Managing Distributed Teams In The Age Of Remote Work
Summary
More of us are working remotely than ever before, many with no prior experience with a remote work environment. In this episode Quinn Slack discusses his thoughts and experience of running Sourcegraph as a fully distributed company. He covers the lessons that he has learned in moving from partially to fully remote, the practices that have worked well in managing a distributed workforce, and the challenges that he has faced in the process. If you are struggling with your remote work situation then this conversation has some useful tips and references for further reading to help you be successful in the current environment.
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 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to pythonpodcast.com/tidydata today and get started for free with no credit card required.
Your host as usual is Tobias Macey and today I’m interviewing Quinn Slack about his experience managing a fully remote company and useful tips for remote work
Interview
Introductions
How did you get introduced to Python?
Can you start by giving an overview of the team structure at Sourcegraph?
You recently moved to being fully remote. What was the motivating factor and how has it changed your personal workflow?
What is your prior history with working remote?
team practices for visibility of progress
impact of remote teams on how code is written and organized
reducing review burden by writing clearer code
structuring meetings when remote
points of friction for remote developer teams
benefits of being fully remote
incentivizing documentation
compensation structure
Keep In Touch
LinkedIn
@sqs on Twitter
sqs on GitHub
Picks
Tobias
Joplin App
Quinn
Skunkworks by Ben Rich
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
Sourcegraph
Quinn’s Python Search Engine
Sourcegraph Employee Handbook
Gitlab
Gitlab Handbook
Zapier
Zapier Guide To Remote Work
Automattic
Automattic Blog On Distributed Work
Comments Showing Intent
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
49 min
May 04, 2020 Maintainable Infrastructure As Code In Pure Python With Pulumi
After you write your application, you need a way to make it available to your users. These days, that usually means deploying it to a cloud provider, whether that's a virtual server, a serverless platform, or a Kubernetes cluster. To manage the increasingly dynamic and flexible options for running software in production, we have turned to building infrastructure as code. Pulumi is an open source framework that lets you use your favorite language to build scalable and maintainable systems out of cloud infrastructure. In this episode Luke Hoban, CTO of Pulumi, explains how it differs from other frameworks for interacting with infrastructure platforms, the benefits of using a full programming language for treating infrastructure as code, and how you can get started with it today. If you are getting frustrated with switching contexts when working between the application you are building and the systems that it runs on, then listen now and then give Pulumi a try.
0 min
May 04, 2020Maintainable Infrastructure As Code In Pure Python With Pulumi
Summary
After you write your application, you need a way to make it available to your users. These days, that usually means deploying it to a cloud provider, whether that’s a virtual server, a serverless platform, or a Kubernetes cluster. To manage the increasingly dynamic and flexible options for running software in production, we have turned to building infrastructure as code. Pulumi is an open source framework that lets you use your favorite language to build scalable and maintainable systems out of cloud infrastructure. In this episode Luke Hoban, CTO of Pulumi, explains how it differs from other frameworks for interacting with infrastructure platforms, the benefits of using a full programming language for treating infrastructure as code, and how you can get started with it today. If you are getting frustrated with switching contexts when working between the application you are building and the systems that it runs on, then listen now and then give Pulumi a try.
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 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Tidy Data is the DataOps monitoring platform that you’ve been missing. With real time alerts for problems in your databases, ETL pipelines, or data warehouse, and integrations with Slack, Pagerduty, and custom webhooks you can fix the errors before they become a problem. Go to pythonpodcast.com/tidydata today and get started for free with no credit card required.
Your host as usual is Tobias Macey and today I’m interviewing Luke Hoban about building and maintaining infrastructure as code with Pulumi
Interview
Introductions
How did you get introduced to Python?
Can you start by describing the concept of "infrastructure as code"?
What is Pulumi and what is the story behind it?
Where does the name come from?
How does Pulumi compare to other infrastructure as code frameworks, such as Terraform?
What are some of the common challenges in managing infrastructure as code?
How does use of a full programming language help in addressing those challenges?
What are some of the dangers of using a full language to manage infrastructure?
How does Pulumi work to avoid those dangers?
Why is maintaining a record of the provisioned state of your infrastructure necessary, as opposed to relying on the state contained by the infrastructure provider?
What are some of the design principles and constraints that developers should be considering as they architect their infrastructure with Pulumi?
Can you describe how Pulumi is implemented?
How does Pulumi manage support for multiple languages while maintaining feature parity across them?
How do you manage testing and validation of the different providers?
The strength of any tool is largely measured in the ecosystem that exists around it, which is one of the reasons that Terraform has been so successful. How are you approaching the problem of bootstrapping the community and prioritizing platform support?
Can you talk through the workflow of working with Pulumi to build and maintain a proper infrastructure?
What are some of the ways to approach testing of infrastructure code?
What does the CI/CD lifecycle for infrastructure look like?
What are the limitations of infrastructure as code?
How do configuration management tools fit with frameworks such as Pulumi?
The core framework of Pulumi is open source, and your business model is focused around a managed platform for tracking state. How are you approaching governance of the project to ensure its continued viability and growth?
What are some of the most interesting, innovative, or unexpected design patterns that you have seen your users include in their infrastructure projects?
When is Pulumi the wrong choice?
What do you have planned for the future of Pulumi?
Keep In Touch
LinkedIn
lukehoban on GitHub
@lukehoban on Twitter
Picks
Tobias
Bookshelf App
Luke
GoBinaries.com
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
Pulumi
Terraform
IronPython
HCL == Hashicorp Config Language
Kubernetes
TypeScript
DevOps
CloudFormation
ARM == Azure Resource Manager
AWSx
GCP == Google Cloud Platform
Pulumi SaaS
SaltStack
Podcast Episode
Ansible
Elastic Beanstalk
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
1 hr 1 min
April 28, 2020 Teaching Python Machine Learning
Python has become a major player in the machine learning industry, with a variety of widely used frameworks. In addition to the technical resources that make it easy to build powerful models, there is also a sizable library of educational resources to help you get up to speed. Sebastian Raschka's contribution of the Python Machine Learning book has come to be widely regarded as one of the best references for newcomers to the field. In this episode he shares his experiences as an author, his views on why Python is the right language for building machine learning applications, and the insights that he has gained from teaching and contributing to the field.
50 min
April 28, 2020Teaching Python Machine Learning
Summary
Python has become a major player in the machine learning industry, with a variety of widely used frameworks. In addition to the technical resources that make it easy to build powerful models, there is also a sizable library of educational resources to help you get up to speed. Sebastian Raschka’s contribution of the Python Machine Learning book has come to be widely regarded as one of the best references for newcomers to the field. In this episode he shares his experiences as an author, his views on why Python is the right language for building machine learning applications, and the insights that he has gained from teaching and contributing to the field.
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 200 Gbit/s private networking, node balancers, a 40 Gbit/s public network, fast object storage, and a brand new managed Kubernetes platform, all controlled by a convenient API you’ve got everything you need to scale up. And for your tasks that need fast computation, such as training machine learning models, they’ve got dedicated CPU and GPU instances. Go to pythonpodcast.com/linode to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
Your host as usual is Tobias Macey and today I’m interviewing Sebastian Raschka about his experiences writing the popular Python Machine Learning book
Interview
Introductions
How did you get introduced to Python?
How did you get started in machine learning?
What were the concepts that you found most difficult in your career with statistics and machine learning?
One of your notable contributions to the field is your book "Python Machine Learning". What inspired you to write the initial version?
How did you approach the challenge of striking the right balance of depth, breadth, and accessibility for the content?
What was your process for determining which aspects of machine learning to include?
You have made 3 editions of the book from 2015 through December of 2019. In what ways has the book changed?
What are the biggest changes to the ecosystem and approaches to ML in that timeframe?
What are the fundamental challenges of developing machine learning projects that continue to present themselves?
What new difficulties have arisen with the introduction of new technologies and the rise of deep learning?
What are some of the ways that the Python language lends itself to analytical work?
What are its shortcomings and how has the community worked around them?
What do you see as the biggest risks to the popularity of Python in the data and analytics space?
What are some of the common pitfalls that your readers and students face while learning about different aspects of machine learning?
What are some of the industries that can benefit most from applications of machine learning?
What are you most excited about in the applications or capabilities of machine learning?
What are you most worried about?
Keep In Touch
Website
@rasbt on Twitter
rasbt on GitHub
LinkedIn
Picks
Tobias
Trolls World Tour
Sebastian
FFMPeg Normalize
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
Python Machine Learning (Packt)
Buy On Amazon (affiliate link)
UW Madison
Pascal
Delphi
R
Perl
Bioinformatics
Seq
Podcast Episode
BioPython
Podcast Episode
CodeCademy
Udacity CS101
Andrew Ng
Coursera
Support-Vector Machine
Bayesian Statistics
Matlab
scikit-learn
NumPy
Pandas
Podcast Episode
Sebastian’s Blog
Perceptron
Heatmaps In R
The Hundred Page Machine Learning Book by Andriy Burkov
ImageNet
Random Forest
Logistic Regression
XGBoost
Theano
Generative Adversarial Networks
Is This Person Real / This Person Does Not Exist
Reinforcement Learning
AlphaGo
AlphaStar
Ray
RLlib
Open AI
Google DeepMind
Google Colab
CUDA
Julia
Sebastian Raschka, Joshua Patterson, and Corey Nolet (2020). Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information 2020, 11, 193
Swift Language
Swift for TensorFlow
Matplotlib
Differential Privacy
PrivacyNet
YouTube recordings of Stat453: Introduction to Deep Learning and Generative Models (Spring 2020)
ffmpeg-normalize
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
50 min
April 20, 2020 Build The Next Generation Of Python Web Applications With FastAPI
Python has an embarrasment of riches when it comes to web frameworks, each with their own particular strengths. FastAPI is a new entrant that has been quickly gaining popularity as a performant and easy to use toolchain for building RESTful web services. In this episode Sebastián Ramirez shares the story of the frustrations that led him to create a new framework, how he put in the extra effort to make the developer experience as smooth and painless as possible, and how he embraces extensability with lightweight dependency injection and a straightforward plugin interface. If you are starting a new web application today then FastAPI should be at the top of your list.
59 min