Podcast.__init__('Python')

Podcast.__init__('Python')
By Tobias Macey
About this podcast
This is a podcast about the Python programming language, its ecosystem, and its community. We conduct interviews about projects and topics that are of particular interest to people who are interested in and use Python.

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By Michael Kennedy and Brian Okken
By Kenneth Reitz & Co-Host
By Sean Bradley | Exotics by Nature Co. | EbNMedia.tv
By 听学程序
Latest episodes
Nov. 19, 2017
Summary Do you know what is happening in your production systems right now? If you have a comprehensive metrics platform then the answer is yes. If your answer is no, then this episode is for you. Jason Dixon and Dan Cech, core maintainers of the Graphite project, talk about how graphite is architected to capture your time series data and give you the ability to use it for answering questions. They cover the challenges that have been faced in evolving the project, the strengths that have let it stand the tests of time, and the features that will be coming in future releases. 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. Now is a good time to start planning your conference schedule for 2018. To help you out with that, guest Jason Dixon is offering a $100 discount for Monitorama in Portland, OR on June 4th – 6th and guest Dan Cech is offering a €50 discount to Grafanacon in Amsterdam, Netherlands March 1st and 2nd. There is also still time to get your tickets to PyCascades in Vancouver, BC Canada January 22nd and 23rd. All of the details are in the show notes Your host as usual is Tobias Macey and today I’m interviewing Jason Dixon and Dan Cech about Graphite Interview Introductions How did you get introduced to Python? What is Graphite and how did you each get involved in the project? Why should developers be thinking about collecting and reporting on metrics from their software and systems? How do you think the Graphite project has contributed to or influenced the overall state of the art in systems monitoring? There are a number of different projects that comprise a fully working Graphite deployment. Can you list each of them and describe how they fit together? What are some of the early design choices that have proven to be problematic while trying to evolve the project? What are some of the challenges that you have been faced with while maintaining and improving the various Graphite projects? What will be involved in porting Graphite to run on Python 3? If you were to start the project over would you still use Python? What are the options for scaling Graphite and making it highly available? Given the level of importance to a companies visibility into their systems, what development practices do you use to ensure that Graphite can operate reliably and fail gracefully? What are some of the biggest competitors to Graphite? When is Graphite not the right choice for tracking your system metrics? What are some of the most interesting or unusual uses of Graphite that you are aware of? What are some of the new features and enhancements that are planned for the future of Graphite? Keep In Touch Jason @obfuscurity on Twitter Website obfuscurity on GitHub Dan @dancech on Twitter Website DanCech on GitHub Picks Tobias Archery Jason Rocket League Monitorama $100 Discount (First 100 People) Dan Home Assistant Podcast.init Interview GrafanaCon €50 discount with PODCASTINIT2018 Links Graphite Sensu Monitorama RainTank Grafana Labs Librato GitHub Dyn Telemetry Perl PHP React O’Reilly Graphite Book Time Series RRDTool InfluxDB Adrian Cockcroft NVMe Prometheus CNCF ASAP Smoothing PyCascades The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Nov. 11, 2017
Summary A relevant and timely recommendation can be a pleasant surprise that will delight your users. Unfortunately it can be difficult to build a system that will produce useful suggestions, which is why this week’s guest, Nicolas Hug, built a library to help with developing and testing collaborative recommendation algorithms. He explains how he took the code he wrote for his PhD thesis and cleaned it up to release as an open source library and his plans for future development on it. 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 Nicolas Hug about Surprise, a scikit library for building recommender systems Interview Introductions How did you get introduced to Python? What is Surprise and what was your motivation for creating it? What are the most challenging aspects of building a recommender system and how does Surprise help simplify that process? What are some of the ways that a user or company can bootstrap a recommender system while they accrue data to use a collaborative algorithm? What are some of the ways that a recommender system can be used, outside of the typical ecommerce example? Once an algorithm has been deployed how can a user test the accuracy of the suggestions? How is Surprise implemented and how has it evolved since you first started working on it? What have been the most difficult aspects of building and maintaining Surprise? competitors? What are the attributes of the system that can be modified to improve the relevance of the recommendations that are provided? For someone who wants to use Surprise in their application, what are the steps involved? What are some of the new features or improvements that you have planned for the future of Surprise? Keep In Touch Website @hug_nicolas on Twitter nicolashug on GitHub Picks Tobias Silk profiler for Django Links Surprise Gridsearch Cold Start Problem Content-Based Recommendation Ensemble Learning Spotlight Lightfm Pandas The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Nov. 4, 2017
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
Oct. 29, 2017
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
Oct. 22, 2017
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.podcastinit.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
Oct. 15, 2017
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
Oct. 8, 2017
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.podcastinit.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
Oct. 1, 2017
Summary 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. 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.podcastinit.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 Antoine Nguyen about Modoboa, a project to make mail hosting simple. Interview Introductions How did you get introduced to Python? What is Modoboa and what is the problem that you were trying to solve when you started it? Where does the name come from? Self-hosting an email server was a common activity during the early stages of the internet, what are some of the reasons that someone should consider running their own mail server now that there are so many options for third-party hosting such as Gmail and Outlook? Email hosting has become more complicated in recent years with the need to jump through a lot of hoops to maintain a sufficient reputation to keep your messages from being flagged as spam. Are there any utilities in Modoboa to assist with that process? There are a lot of components that you have brought together for running an email server. Can you describe how the different pieces fit together and what layers you have built on top to help make the overall system more manageable? What does the scaling strategy look like for Modoboa? What is the most challenging aspect of building and maintaining Modoboa? What are some of the features that you have planned for the future of Modoboa? Keep In Touch Email @antngu on Twitter Picks Tobias Dropbox Paper Antoine Capoeira Links PyTk Postfix Dovecot Nextcloud Owncloud SPF Records DKIM DMARC SMTP IMAP Apache Libcloud Amavis Mail Transfer Agent Radicale Ansible Docker Gentoo Packer Synology Drobo Prosody Lua XMPP The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Sept. 24, 2017
Summary The future of computation and our understanding of the world around us is driven by the quantum world. This week Paul Nation explains how the Quantum Toolbox in Python (QuTiP) is being used in research projects that are expanding our knowledge of the physical universe. 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.podcastinit.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 Paul Nation about QuTIP, the quantum toolbox in Python. Interview Introductions How did you get introduced to Python? Before we start talking about QuTiP, can you provide us with a baseline definition of what quantum mechanics is? What is QuTIP and how did the project get started? Is QuTiP used purely in academics, or are there other users? What are some of the practical innovations that have been created as a result of research into different areas of quantum optics? How do you foresee the advent of practical quantum computers impacting the state of quantum mechanical research? Given the inherent complexity of the subject matter that you are dealing with, how do you approach the challenge of trying to present a usable API to users of QuTiP while not inhibiting their ability to operate at a low level when necessary? What is the process for incorporating new understandings of quantum mechanical theory into the QuTiP package? What are some of the most difficult aspects of simulating quantum systems in a standard computational environment? What is the most enjoyable aspect of working on QuTiP, what is the least enjoyable? What are some of the most notable research results that you are aware of which used QuTiP as part of their studies? What are some resources that you can recommend for anyone who wants to learn more about quantum mechanics? Keep In Touch QuTiP QuSTaR Picks Tobias edx.org Paul Cython Matplotlib Cheyenne Mountain Zoo Links Quantum Optics 2 Level System Complex Numbers Qubit Quantum Computing Harmonic Oscillator Nature Scientific Journal IBM Quantum Experience D-Wave Rigetti Quantum Computing Quantum Supremacy Hamiltonian Sparse Matrix Richard Feynman Dask Project Q Quantum State Transfer via Noisy Photonic and Phononic Waveguides paper by Peter Zoller Extending the lifetime of a quantum bit with error correction in superconducting circuits paper by Rob Shoelkopf (Yale) QuTiP Documentation The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Sept. 17, 2017
Summary Do you like Legos, robots, and Python? This week I am joined by David Lechner and Denis Demidov to talk about the ev3dev project and how you can program your Lego Mindstorms with Python! 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.podcastinit.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 David Lechner and Denis Demidov about using Python with the ev3dev platform for programming LEGO robots Interview Introductions How did you get introduced to Python? Can you explain what the ev3dev project is and some of the story about how and why it got started? What is LEGO’s opinion of the ev3dev project? For anyone who isn’t familiar with the MINDSTORMS EV3 product from LEGO, can you give a brief overview of the hardware that they come with? Other than allowing users to program in environments other than the block-based editor that LEGO provides, what capabilities does the ev3dev project add to the MINDSTORMS EV3 platform? How are the language bindings generated and how do the different implementations compare to each other? What are the most challenging aspects of building and maintaining the ev3dev distribution and various language bindings? One of the things that my son is curious about is the possibility for integrating his MINDSTORMS with projects such as Kalliope or Mycroft to allow for voice controlled robots. Are you aware of anyone having done so or how you would approach something like that? What are some of the most interesting or innovative projects that you have seen people make with the MINDSTORMS platform running ev3dev? Why would someone want to use MINDSTORMS instead of any of the other robotics platforms that are available? For someone who is interested in learning more about intermediate and advanced robotics, what are some resources that you would recommend? Keep In Touch Denis @denis_demidov on Twitter ddemidov on Github David dlech on Github Website Picks Tobias Raspberry Pi Kalliope Denis pybind11 David Local food LocalHarvest Links ev3dev Lego MINDSTORMS BeagleBone Lego Mindstorms Community C++ Jupyter Notebooks Ralph Hempel Forth RCX NXT EV3 ARMv5 Debian PiStorms BrickPi EVB UART EV3 Schematics Look for “EV3 Hardware Developer Kit” in “Advanced Users” section. I2C RPyC Laurens Valk Liquid Templates Delta Robot Quest For Space Lego Technic Mindsensors.com Cool robots built with ev3dev Micropython The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
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