Latest in Planet Python: Open Source, SaaS and Monetization; Some Python Guides
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Open Source, SaaS and Monetization
When you're reading this blog post Sentry which I have been working on for the last few years has undergone a license change. Making money with Open Source has always been a complex topic and over the years my own ideas of how this should be done have become less and less clear. The following text is an attempt to summarize my thoughts on it an to put some more clarification on how we ended up picking the BSL license for Sentry.
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Open Source is pretty clear cut: it does not discriminate. If you get the source, you can do with it what you want (within the terms of the license) and no matter who you are (within the terms of the license). However as Open Source is defined — and also how I see it — Open Source comes with no strings attached. The moment we restrict what you can do with it — like not compete — it becomes something else.
The license of choice is the BSL. We looked at many things and the one we can to is the idea of putting a form of natural delay into our releases and the BSL does that. We make sure that if time passes all we have, becomes Open Source again but until that point it's almost Open Source but with strings attached. This means for as long as we innovate there is some natural disadvantage for someone competing with the core product while still ensuring that our product stays around and healthy in the Open Source space.
If enough time passes everything becomes available again under the Apache 2 license.
This ensures that no matter what happens to Sentry the company or product, it will always be there for the Open Source community. Worst case, it just requires some time.
I'm personally really happy with the BSL. I cannot guarantee that after years no better ideas came around but this is the closest I have seen that I feel very satisfied with where I can say that I stand behind it.
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How to Handle Coroutines with asyncio in Python
When a program becomes very long and complex, it is convenient to divide it into subroutines, each of which implements a specific task. However, subroutines cannot be executed independently, but only at the request of the main program, which is responsible for coordinating the use of subroutines.
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When to Use a List Comprehension in Python
Python is famous for allowing you to write code that’s elegant, easy to write, and almost as easy to read as plain English. One of the language’s most distinctive features is the list comprehension, which you can use to create powerful functionality within a single line of code. However, many developers struggle to fully leverage the more advanced features of a list comprehension in Python. Some programmers even use them too much, which can lead to code that’s less efficient and harder to read.
By the end of this tutorial, you’ll understand the full power of Python list comprehensions and how to use their features comfortably. You’ll also gain an understanding of the trade-offs that come with using them so that you can determine when other approaches are more preferable.
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LWN: The return of Python dictionary "addition"
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