The Python Podcast.init

By Tobias MaceyTechnology Education

The Python Podcast.init episodes

November 04, 2017Rasa: Build Your Own AI Chatbot with Joey Faulkner
Summary
With the proliferation of messaging applications, there has been a growing demand for bots that can understand our wishes and perform our bidding. The rise of artificial intelligence has brought the capacity for understanding human language. Combining these two trends gives us chatbots that can be used as a new interface to the software and services that we depend on. This week Joey Faulkner shares his work with Rasa Technologies and their open sourced libraries for understanding natural language and how to conduct a conversation. We talked about how the Rasa Core and Rasa NLU libraries work and how you can use them to replace your dependence on API services and own your data.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
Your host as usual is Tobias Macey and today I’m interviewing Joey Faulkner about Rasa Core and Rasa NLU for adding conversational AI to your projects.
Interview
Introductions
How did you get introduced to Python?
Can you start by explaining the goals of Rasa as a company and highlighting the projects that you have open sourced?
What are the differences between the Rasa Core and Rasa NLU libraries and how do they relate to each other?
How does the interaction model change when going from state machine driven bots to those which use Rasa Core and what capabilities does it unlock?
How is Rasa NLU implemented and how has the design evolved?
What are the motivations for someone to use Rasa core or NLU as a library instead of available API services such as wit.ai, LUIS, or Dialogflow?
What are some of the biggest challenges in gathering and curating useful training data?
What is involved in supporting multiple languages for an application using Rasa?
What are the biggest challenges that you face, past, present, and future, building and growing the tools and platform for Rasa?
What would be involved for projects such as OpsDroid, Kalliope, or Mycroft to take advantage of Rasa and what benefit would that provide?
On the comparison page for the hosted Rasa platform it mentions a feature of collaborative model training, can you describe how that works and why someone might want to take advantage of it?
What are some of the most interesting or unexpected uses of the Rasa tools that you have seen?
What do you have planned for the future of Rasa?
Keep In Touch
Gitter
Twitter
@joeymfaulkner
@Rasa_HQ

Email

GitHub

Picks
Tobias
Information Architecture

Joey
Dog Spotting
Rasa NLU Trainer

Links
Rasa Technologies
Rasa NLU
Rasa Core
SpaCy
Podcast.__init__ Interview with SpaCy Creator

yt-project
Podcast.__init__ Interview with yt-project

Chatbot

Word2Vec

State Machine
Podcast.__init__ Episode About Automat with Glyph

Recursive Neural Network

MITIE

Support Vector Machine

Scikit Learn

wit.ai

LUIS

Dialogflow

Keras

Reinforcement Learning

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
49 min
October 29, 2017 Eliot: Effective Logging with Itamar Turner-Trauring
Understanding what is happening in a software system can be difficult, especially when you have inconsistent log messages. Itamar Turner-Trauring created Eliot to make it possible for your project to tell you a story about how transactions flow through your program. In this week's episode we go deep on proper logging practices, anti patterns, and how to improve your ability to debug your software with log messages.
50 min
October 29, 2017Eliot: Effective Logging with Itamar Turner-Trauring
Summary
Understanding what is happening in a software system can be difficult, especially when you have inconsistent log messages. Itamar Turner-Trauring created Eliot to make it possible for your project to tell you a story about how transactions flow through your program. In this week’s episode we go deep on proper logging practices, anti patterns, and how to improve your ability to debug your software with log messages.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
Your host as usual is Tobias Macey and today I’m interviewing Itamar Turner-Trauring about Eliot, a library for managing complex logs across multiple processes.
Interview
Introductions
How did you get introduced to Python?
What is Eliot and what problem were you trying to solve by creating it?
How is Eliot implemented and how has the design evolved since you first started working on it?
Why is it so important to have a standardized format for your application logs?
What are some of the anti-patterns that you consider to be the most harmful when developers are setting up logging in their projects?
What have been the most challenging aspects of building and maintaining Eliot?
How does Eliot compare to some of the other third party logging libraries available such as structlog or logbook?
What are some of the improvements or additional features that you have planned for the future of Eliot?
Keep In Touch
Website
@itamarst on Twitter
Picks
Tobias
Moonshot Podcast

Itamar
Middlemarch by George Eliot

Links
Eliot
Zope
PHP
OpenTracing
Zipkin
Carl De Marcken
Sentry
Elasticsearch
Logstash
Kibana
Eliot-Tree
Daniel Lebrero
Flocker
Context Local Variables PEP (PEP 550)
Flamegraph
Brendan Gregg
DAG
Structlog
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
50 min
October 22, 2017 Donkey: Building Self Driving Cars with Will Roscoe
Do you wish that you had a self-driving car of your own? With Donkey you can make that dream a reality. This week Will Roscoe shares the story of how he got involved in the arena of self-driving car hobbyists and ended up building a Python library to act as his pilot. We talked about the hardware involved, how he has evolved the code to meet unexpected challenges, and how he plans to improve it in the future. So go build your own self driving car and take it for a spin!
34 min
October 22, 2017Donkey: Building Self Driving Cars with Will Roscoe
Summary
Do you wish that you had a self-driving car of your own? With Donkey you can make that dream a reality. This week Will Roscoe shares the story of how he got involved in the arena of self-driving car hobbyists and ended up building a Python library to act as his pilot. We talked about the hardware involved, how he has evolved the code to meet unexpected challenges, and how he plans to improve it in the future. So go build your own self driving car and take it for a spin!
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected])
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
Your host as usual is Tobias Macey and today I’m interviewing Will Roscoe about Donkey, a python library for building DIY self driving cars.
Interview
Introductions
How did you get introduced to Python?
What is Donkey and what was your reason for creating it?
What is the story behind the name?

What was your reason for choosing Python as the language for implementing Donkey and if you were to start over today would you make the same choice?

How is Donkey implemented and how has its software architecture evolved?

Is the library built in a way that you can process inputs from additional sensor types, such as proximity detectors or LIDAR?

For training the autopilot what are the input features that the model is testing against for the input data, and is it possible to change the features that it will try to detect?

Do you have plans to incorporate any negative reinforcement techniques for training the pilot models so that errors in data collection can be identified as undesirable outcomes?

What have been some of the most interesting or humorous successes and failures while testing your cars?

What are some of the challenges involved with getting such a sophisticated stack of software running on a Raspberry Pi?

What are some of the improvements or new features that you have planned for the future of Donkey?

Media
Donkey Car Photos
Keep In Touch
Donkey Slack Channel
Wills Twitter – @dataduce
#donkeycar on social
Picks
Tobias
Orgzly
Org Mode for Sublime
Org Mode for VSCode
Org Mode for Vim

Will
Algorithms to Live By
The Structure of Scientific Revolutions
A song I can’t stop nodding my head to

Links
Donkey Car
DIY Robocars
Tornado
[Tornado on Podcast.init](https://www.pythonpodcast.com/episode-40-ben-darnell-on-tornado/?utm_source=rss&utm_medium=rss
Raspberry Pi
TensorFlow
Convolutional Neural Network
Adafruit
LIDAR
ROS (Robot Operating System)
Unity
Udacity self driving car nano-degree
SparkFun
Beagleboard
Adam Conway
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
34 min
October 15, 2017 Event Sourcing with John Bywater
The way that your application handles data and the way that it is represented in your database don't always match, leading to a lot of brittle abstractions to reconcile the two. In order to reduce that friction, instead of overwriting the state of your application on every change you can log all of the events that take place and then render the current state from that sequence of events. John Bywater joins me this week to discuss his work on the Event Sourcing library, why you might want to use it in your applications, and how it can change the way that you think about your data.
1 hr 9 min
October 15, 2017Event Sourcing with John Bywater
Summary
The way that your application handles data and the way that it is represented in your database don’t always match, leading to a lot of brittle abstractions to reconcile the two. In order to reduce that friction, instead of overwriting the state of your application on every change you can log all of the events that take place and then render the current state from that sequence of events. John Bywater joins me this week to discuss his work on the Event Sourcing library, why you might want to use it in your applications, and how it can change the way that you think about your data.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who supports the show on Patreon. Your contributions help to make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at podastinit.com/linode and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app. And now you can deliver your work to your users even faster with the newly upgraded 200 GBit network in all of their datacenters.
If you’re tired of cobbling together your deployment pipeline then it’s time to try out GoCD, the open source continuous delivery platform built by the people at ThoughtWorks who wrote the book about it. With GoCD you get complete visibility into the life-cycle of your software from one location. To download it now go to podcatinit.com/gocd. Professional support and enterprise plugins are available for added piece of mind.
Visit the site to subscribe to the show, sign up for the newsletter, and read the show notes. And if you have any questions, comments, or suggestions I would love to hear them. You can reach me on Twitter at @Podcast__init__ or email [email protected]
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
Your host as usual is Tobias Macey and today I’m interviewing John Bywater about event sourcing, an architectural approach to make your data layer easier to scale and maintain.
Interview
Introductions
How did you get introduced to Python?
Can you start by describing the concept of event sourcing and the benefits that it provides?
What is the event sourcing library and what was your reason for starting it?
What are some of the reasons that someone might not want to implement an event sourcing approach in their persistence layer?
Given that you are storing a record for each event that occurs on a domain object, how does that affect the amount of storage necessary to support an event sourced application?
What is the impact on performance and latency from an end user perspective when the application is using event sourcing to render the current state of the system?
What does the internal architecture and design of your library look like and how has that evolved over time?
In the case where events are delivered out of order, how can you ensure that the present view of an object is reflected accurately?
For someone who wants to incorporate an event sourcing design into an existing application, how would they do that?
How do you manage schema changes in your domain model when you need to reconstruct present state from the beginning of an objects event sequence?
What are some of the most interesting uses of event sourcing that you have seen?
What are some of the features or improvements that you have planned for the future of you event sourcing library?
Keep In Touch
John
johnbywater on GitHub
@johnbywater on Twitter

Picks
Tobias
Heresy In The Church Of Docker

John
QuantDSL

Links
CKAN
Data.gov
Patterns of Enterprise Application Architecture
Object Relational Impedance Mismatch
Event Sourcing (Pattern)
Event Sourcing (Library)
N-Tiered Architecture
Domain Driven Design
Event Storming
ORM, The Vietnam of Computer Science
Vaughn Vernon, Implementing Domain Driven Design
Active Record Pattern
Optimistic Concurrency Control
Paxos
DynamoDB
Martin Fowler
Eric Evans
The Dark Side of Event Sourcing
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
1 hr 9 min
October 08, 2017 Kalliope with Nicolas Marcq and Thibaud Buffet
Wouldn't it be nice to have a personal assistant to answer your questions, help you remember important tasks, and control your environment? Meet Kalliope, a Python powered, modular, voice controlled automation platform. This week Nicolas Marcq and Thibaud Buffet explain how they started the project, what makes it stand out from other open source and commercial options, and how you can start using it today.
33 min
October 08, 2017Kalliope with Nicolas Marcq and Thibaud Buffet
Summary
Wouldn’t it be nice to have a personal assistant to answer your questions, help you remember important tasks, and control your environment? Meet Kalliope, a Python powered, modular, voice controlled automation platform. This week Nicolas Marcq and Thibaud Buffet explain how they started the project, what makes it stand out from other open source and commercial options, and how you can start using it today.
Preface
Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
I would like to thank everyone who supports us on Patreon. Your contributions help to make the show sustainable.
When you’re ready to launch your next project you’ll need somewhere to deploy it. Check out Linode at www.podastinit.com/linode?utm_source=rss&utm_medium=rss and get a $20 credit to try out their fast and reliable Linux virtual servers for running your awesome app.
Need to learn more about how to scale your apps or learn new techniques for building them? Pluralsight has the training and mentoring you need to level up your skills. Go to www.pythonpodcast.com/pluralsight?utm_source=rss&utm_medium=rss to start your free trial today.
Visit the site to subscribe to the show, sign up for the newsletter, read the show notes, and get in touch.
To help other people find the show please leave a review on iTunes, or Google Play Music, tell your friends and co-workers, and share it on social media.
If you work with data for your job or want to learn more about how open source is powering the latest innovations in data science then make your way to the Open Data Science Conference, happening in London in October and San Francisco in November. Follow the links in the show notes to register and help support the show in the process.
Your host as usual is Tobias Macey and today I’m interviewing Nicolas Marcq and Thibaud Buffet about Kalliope, a modular always-on voice controlled personal assistant designed for home automation.
Interview
Introductions
How did you get introduced to Python?
What is the Kalliope project and how did it get started?
How does Kalliope compare to commercial options such as Amazon Alexa and Google Home, as well as other open source projects such as Mycroft or Jasper?
The majority of voice assistant projects that I have seen default to interacting in English, whereas Kalliope is multi-lingual. What led you to that design choice and how is that implemented?
One of the perennial questions around voice assistants is privacy, so how does Kalliope work to mitigate the issues associated with having an always on device listening in people’s homes?
How is Kalliope architected internally and how has the design evolved over time?
What are some of the most difficult or challenging aspects of building Kalliope and its associated projects?
What are some of the most interesting uses of Kalliope that you are aware of?
What are some of the most notable features or improvements that you have planned for the future of Kalliope?
How has the choice of Python as the implementation worked for you, and if you were to start over today do you think you would make the same decision?
Keep In Touch
Nicolas
@Sispheor on Twitter
Sispheor on GitHub
Website

Thibaud
@Tib_Tac on Twitter
LaMonF on GitHub

Picks
Tobias
Kiwi Crate

Nicolas
Raspberry Pi Speaker

Thibaud
ReactiveX in Python

Links
Snowboy
Mycroft
Mycroft Interview
Amazon Alexa
Google Home
Jasper
Kalliope
TTS
STT
CMU Sphinx
Abstract Base Class
MQTT
RxPy Interview
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
33 min
October 01, 2017 Modoboa with Antoine Nguyen
Email has long been the most commonly used means of communication on the internet. This week Antoine Nguyen talks about his work on the Modoboa project to make hosting your own mail server easier to manage. He discusses how the project got started, the tools that it ties together, and how he used Django to build a webmail and admin interface to make it more approachable.
34 min