CrossHair: Your Automatic Pair Programmer - Episode 302

February 16, 2021 Tobias Macey Comments Off on CrossHair: Your Automatic Pair Programmer - Episode 302

One of the perennial challenges in software engineering is to reduce the opportunity for bugs to creep into the system. Some of the tools in our arsenal that help in this endeavor include rich type systems, static analysis, writing tests, well defined interfaces, and linting. Phillip Schanely created the CrossHair project in order to add another ally in the fight against broken code. It sits somewhere between type systems, automated test generation, and static analysis. In this episode he explains his motivation for creating it, how he uses it for his own projects, and how to start incorporating it into yours. He also discusses the utility of writing contracts for your functions, and the differences between property based testing...

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Giving Your Data Science Projects And Teams A Home At DagsHub - Episode 301

February 9, 2021 Tobias Macey Comments Off on Giving Your Data Science Projects And Teams A Home At DagsHub - Episode 301

Collaborating on software projects is largely a solved problem, with a variety of hosted or self-managed platforms to choose from. For data science projects, collaboration is still an open question. There are a number of projects that aim to bring collaboration to data science, but they are all solving a different aspect of the problem. Dean Pleban and Guy Smoilovsky created DagsHub to give individuals and teams a place to store and version their code, data, and models. In this episode they explain how DagsHub is designed to make it easier to create and track machine learning experiments, and serve as a way to promote collaboration on open source data science projects.

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Exploring Literate Programming For Python Projects With nbdev - Episode 300

February 2, 2021 Tobias Macey Comments Off on Exploring Literate Programming For Python Projects With nbdev - Episode 300

Creating well designed software is largely a problem of context and understanding. The majority of programming environments rely on documentation, tests, and code being logically separated despite being contextually linked. In order to weave all of these concerns together there have been many efforts to create a literate programming environment. In this episode Jeremy Howard of fast.ai fame and Hamel Husain of GitHub share the work they have done on nbdev. The explain how it allows you to weave together documentation, code, and tests in the same context so that it is more natural to explore and build understanding when working on a project. It is built on top of the Jupyter environment, allowing you to take advantage of...

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Making The Sans I/O Ideal A Reality For The Websockets Library - Episode 299

January 26, 2021 Tobias Macey Comments Off on Making The Sans I/O Ideal A Reality For The Websockets Library - Episode 299

Working with network protocols is a common need for software projects, particularly in the current age of the internet. As a result, there are a multitude of libraries that provide interfaces to the various protocols. The problem is that implementing a network protocol properly and handling all of the edge cases is hard, and most of the available libraries are bound to a particular I/O paradigm which prevents them from being widely reused. To address this shortcoming there has been a movement towards "sans I/O" implementations that provide the business logic for a given protocol while remaining agnostic to whether you are using async I/O, Twisted, threads, etc. In this episode Aymeric Augustin shares his experience of refactoring his...

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Driving Toward A Faster Python Interpreter With Pyston - Episode 298

January 19, 2021 Tobias Macey Comments Off on Driving Toward A Faster Python Interpreter With Pyston - Episode 298

One of the common complaints about Python is that it is slow. There are languages and runtimes that can execute code faster, but they are not as easy to be productive with, so many people are willing to make that tradeoff. There are some use cases, however, that truly need the benefit of faster execution. To address this problem Kevin Modzelewski helped to create the Pyston intepreter that is focused on speeding up unmodified Python code. In this episode he shares the history of the project, discusses his current efforts to optimize a fork of the CPython interpreter, and his goals for building a business to support the ongoing work to make Python faster for everyone. This is an...

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