Summary

Every startup begins with an idea, but that won’t get you very far without testing the feasibility of that idea. A common practice is to build a Minimum Viable Product (MVP) that addresses the problem that you are trying to solve and working with early customers as they engage with that MVP. In this episode Tony Pavlovych shares his thoughts on Python’s strengths when building and launching that MVP and some of the potential pitfalls that businesses can run into on that path.

Announcements

• Hello and welcome to Podcast.__init__, the podcast about Python’s role in data and science.
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• Your host as usual is Tobias Macey and today I’m interviewing Tony Pavlovych about Python’s strengths for startups and the steps to building an MVP (minimum viable product)

Interview

• Introductions
• How did you get introduced to Python?
• Can you describe what PLANEKS is and the story behind it?
• One of the services that you offer is building an MVP. What are the goals and outcomes associated with an MVP?
  • What is the process for identifying the product focus and feature scope?
• What are some of the common misconceptions about building and launching MVPs that you have dealt with in your work with customers?
  • What are the common pitfalls that companies encounter when building and validating an MVP?
• Can you describe the set of tools and frameworks (e.g. Django, Poetry, cookiecutter, etc.) that you have invested in to reduce the overhead of starting and maintaining velocity on multiple projects?
  • What are the configurations that are most critical to keep constant across projects to maintain familiarity and sanity for your developers? (e.g. linting rules, build toolchains, etc.)
• What are the architectural patterns that you have found most useful to make MVPs flexible for adaptation and extension?
• Once the MVP is built and launched, what are the next steps to validate the product and determine priorities?
• What benefits do you get from choosing Python as your language for building an MVP/launching a startup?
  • What are the challenges/risks involved in that choice?
• What are the most interesting, unexpected, or challenging lessons that you have learned while working on MVPs for your clients at PLANEKS?
• When is an MVP the wrong choice?
• What are the developments in the Python and broader software ecosystem that you are most interested in for the work you are doing for your team and clients?

Keep In Touch

• LinkedIn

Picks

• Tobias
  • datamodel-code-generator
• Tony
  • Screw It, Let’s Do It by Richard Branson (affiliate link)

Closing Announcements

• Thank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. The Machine Learning Podcast helps you go from idea to production with machine learning.
• 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 hosts@podcastinit.com) with your story.
• To help other people find the show please leave a review on iTunes and tell your friends and co-workers

Links

• PLANEKS
• Minimum Viable Product
• Django
• Cookiecutter
• Django Boilerplate
• OCR == Optical Character Recognition
• Tesseract OCR framework

The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA