I think most relevant data that provides best answers lives in GitHub. Sometimes in code, sometimes in issues or discussions. Many libs have their docs there as well. But the information is scattered and not easy to find, and often you need multiple sources to come up with a solution to some problem.
The main reason is that most real blockers are tied to a specific language ecosystem. Even if the high-level ideas transfer across languages, the actual fix usually depends on the language’s APIs, tooling, and conventions. When someone is searching, they typically need something that fits the environment they are working in at the moment.
So the language choice in GitHits mainly steers the system toward code that is immediately usable.
Another part of the story is that finding patterns across languages is a much harder problem. It requires a level of semantic, cross-language search that does not really exist yet in a reliable way. I would love to reach that point, but today the best results come from staying within one ecosystem at a time.
Under the hood, there are several search modes, and not all of them are strictly language specific. The language selection guides the search, but it does not fully constrain it. And at some point, there might be a more generic search mode that is not tied to any single language at all, but that will take more research and iteration.
Thanks! While those languages are not yet "officially" supported, GitHits works with them as well. The results might not be that good as with the ones I have enabled officially since the search and final output is partially steered by the selected language.
Many projects are dropping support for old Python versions very fast. It's not surprising given the history of the Python 2 -> 3 transition. No one wants to end up in that situation anymore. For example, NumPy has already dropped 3.6 support (3.6 will be EOL later this year).
I don't know about "very fast", Python 3.6 was released in 2016 giving people plenty of time to update from 3.5. Those are backward-compatible too.
If you haven't updated in that time, you probably have a systemic problem which shouldn't be blamed on Python moving too fast (in fact I suspect those people don't keep up with bugfixes either, so nudging them to upgrade when they try to update flask/numpy is a good idea).
I have currently Sony XA2 with Sailfish OS. Just ordered Xperia 10 II so I have it ready when they publish the official version for it. I have been using Sailfish as my daily driver since the original Jolla phone was released in 2013.
The only issue that I might see as a blocker for some users is that the Android layer is not perfect. It does not support Bluetooth properly so pretty much any Android app that connects to some external peripheral like smart watch will not work. Additionally, you might need to install for example microG to run some apps via the Android layer.
Redux Toolkit is great and removes most of the usual Redux boilerplate which seems to be the most often used argument against Redux. Additionally, I usually use a function which uses createEntityAdapter, createSlice and createAsyncThunk methods to create Ducks bundles for each REST API resource automatically. As a result I get all async action creators, reducers and basic selectors for some REST API resource with a couple of lines of code.
If you like the stuff in RTK so far, I think you're going to like our upcoming "RTK Query" API, which adds a React Query-inspired data fetching abstraction:
Azure Function App development experience is indeed pretty nice at least when using .NET Core. There are some issues, like loading secrets to the local dev environment from Key Vault has to be done manually and easy auth (App Service Authentication) does not work locally.
If the Falcon 9 landings feel mundane, I would recommend to follow Starship development. Starship SN6 might do a 150 meter hop later today: https://www.youtube.com/watch?v=Ky5l9ZxsG9M
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