regret | You made a thing, but now you wish it'd go away Deprecations, a love story

I have some code that uses the module Big_int, like in:

I have a container

And I have 3

This is my code, I'm using React:

I've been trying to come up with the best way to handle configurations in Python projects and I decided to go with creating a config.py and just put all the configurations as classes.

However, I found out that:

1. It is good to have some form of "base" configuration if you want to have multiple configurations.
2. Sometimes getting configurations in dict comes in handy when you have to put it in some function parameter.

So I create my own way of handling configurations with config.py by:

1. Inheriting from BaseConfig when I create any new configuration like NewConfig
2. Inheriting @classmethod that returns all the class variables as dict.

However, since I created this code from scratch I would like to know if there's any possible side-effect that can later make me regret.

One thing I'm unsure about is whether it is a good idea to inherit class variables at all.

Also, I managed to make get_dict() work but I don't know if its mechanics is good and scalable.

Below is my code:

My chrome extension was removed from google chrome store and I don't know why, I'm not using remote hosted code. I am using manifest V2

Does anyone can suggest why they removed my extension?

Time line (emails that I received from google):

18 November 2020:

Dear Developer,

Protecting users and their data is a fundamental aspect of the work we do on Chrome. Last year we announced a set of policies to protect users and their data by requiring that extensions request the narrowest possible permissions, and we required more extensions to post privacy policies and handle user data securely.

Today, we are announcing policy changes that build upon those protections by:

• Limiting what extension developers can do with the data they collect.
• Requiring developers to certify their data use practices.

Starting January 2021, each extension’s detail page in the Chrome Web Store will show the data collected by the extension, in clear and easy to understand language.

Data privacy policy update

We’re introducing an additional policy focused on limiting usage of user data collected through a Chrome extension. More specifically:

• Reiterating that the sale of user data is never allowed. Google does not sell user data and extension developers may not do this either.
• The use or transfer of user data should be for the primary benefit of the user and in accordance with the stated purpose of the extension.
• The use or transfer of user data cannot be used for creditworthiness or any form of lending qualification.

The Chrome Web Store will also help users understand an extension’s privacy practices directly on the Chrome Web store listing.

On each extension detail page, the data collected by the extension will be displayed in a standardized manner. Additionally, whether a developer has certified their compliance with the limited use policy will also be displayed.

Developer-provided privacy disclosures

To publish or update an extension, developers must provide data usage disclosures directly from the developer dashboard. These disclosures include:

• The nature of the data being collected from users.
• The developer’s certification that they comply with the new policy on limited use.

The content of the form is grouped by category to make it simpler for developers, and maps exactly to the disclosures that will be displayed to Chrome users. Most of this information should be consistent with the existing privacy policies that developers have provided to the Chrome Web Store.

Implementation timeline

Data disclosures collection will be made available to developers today and will be displayed on the Chrome Web Store listing starting January 18, 2021.

Starting in March 2021, the Chrome Web Store team will reach out to developers with a warning to complete the disclosure requirement. Inaction after 30 days of the warning will result in the suspension of affected items and the deactivation of the existing user base.

See the limited use policy FAQ and the corresponding blog post for additional detail. Thank you for your cooperation, and for your participation in the Chrome extension ecosystem!

• The Google Chrome Web Store team

5 February 2021:

"Dear Developer, Last year, we announced the rollout of Manifest V3 support for Chrome extensions alongside Chrome 88. These updates to the extension platform make the extension experience safer, more privacy-preserving, and more performant for Chrome users. One of the key changes for V3 extensions is the disallowing of remotely hosted code. Now that you can submit to the Chrome Web Store, we’ve updated our Developer Program Policies to reflect these new guidelines. Please refer to our Developer Program Policies for more details on these updates. Thank you for your cooperation and for your participation in the Chrome extension ecosystem!"

31 March 2021

"We regret to inform you that the item has been removed from the Chrome web store. Details are shown below."

"We did not receive an update from you regarding the Google Chrome item before the end of the warning period specified in our previous email. Because the item still does not meet our policy requirements mentioned in the previous email, it was removed from the Chrome web store. "

I'd like to know how to do 2 execution plans: "traditional" execution plan joins (A with B) and then C. The "new" plan joins (A with B) then (A with C) and then joins the result of those joins so there would be 3 joins. How would I code the traditional and new plan in Oracle SQLPlus given the code below? I also need to measure the time complexity of both methods to show that the new plan takes less time, which I believe I just do with set timer on; The joins can be on whatever attributes work. Same with select statements. I made a artist, b album, c track, and d played.

Here's the database:

I have a data frame data1 with cleaned strings of text matched to their ids

In my project, I have presently a legacy authentication that works in such a way:

1. There is a client (standalone) that talks to an API service which is a custom application running in a container.
2. A cloud Identity provider (IdP) is used that supports OAuth with PKCE. It provides its token via usual login process when the user goes to a login page and then gets redirected to a callback.
3. API service acts as a recipient for the callback. It, therefore, obtains Identity Provider token-1 and stores it in a cache. Based on this it returns to the client a modified token-2 "computed" from token-1 but different.
4. Once the client needs to make a REST call, it decorates it with the token-2 JWT token. The call goes to the API service that matches it to token-1 which can be then validated against IdP.

I have a need to get rid of the API service with cloud-native mechanisms. I assume that AWS HTTP API gateway can be integrated with IdP directly using its JWT Authorizer capability. Regretfully I can not affect the current legacy flow that must remain functional.

However, I would like to insert a Lambda between the JWT Authorizer and client endpoints which would be doing the exchange of client-facing tokens to IdP tokens (doing what API service was doing). Would that be possible and how I can approach this?

I am trying to replace the null values in a column based on categorical value of another column.But the == operator is making me regret all the big decision in my life. I have 8523 rows and 12 columns in Train set, of which 7 are categorical and 5 are numerical.

Columns are 'Item_Identifier', 'Item_Weight', 'Item_Fat_Content', 'Item_Visibility', 'Item_Type', 'Item_MRP', 'Outlet_Identifier', 'Outlet_Establishment_Year', 'Outlet_Size', 'Outlet_Location_Type', 'Outlet_Type', 'Item_Outlet_Sales'

I want to fill the NaN values(float dtype) in the 'Item_Weight' column based on the categorical value of 'Outlet_Location_Type'. I have a dictionary(city_type_mean) with the categorical values as keys and the corresponding values to be replaced as values. I used the following code

C++11 introduced std::begin(std::valarray&) as well as std::end(std::valarray&).

C++17 introduced std::data() which works with std::vector, std::array, C-style arrays, etc. But why wasn't an overloaded std::data() introduced for std::valarray?

std::valarray is specified to have contiguous storage, which can be accessed by taking the address of a[0] (see Notes).

std::data(std::valarray& a) could have simply been defined to return &(a[0]). Why hasn't this been done? Is it an oversight?

My motivation is that I'm working on a general-purpose serialization library. When it receives contiguous binary number arrays from a source (such as CBOR), it detects if the destination container has an overloaded data(container) function, a container.resize(n) member function, as well as an appropriate value_type (matching primitive number type). The existence of all three makes it possible to efficiently memcpy() the source data directly into the destination container. It would make my life simpler if there was a std::data(std::valarray&) overload. The lack of it isn't a showstopper, but it does make the code more messy.

ADDENDUM: The reason why I want to detect a data function is that it tells me that the destination container is contiguous. If it's contiguous, then I can do an efficient byte copy (via std::memcpy or std::copy doesn't really matter). If it's not contiguous, then I have to unpack each unaligned source array number one at a time and append it to the destination container using push_back, emplace, etc depending on the container type.

ADDENDUM 2: I've decided to use an adaptor and traits approach instead of detecting the presence of a data function. This will make it easier to support non-standard or user-defined container types. My question about why there is no std::data(std::valarray& a) still stands.

ADDENDUM 3: I should have clarified that I need to do this hackery for CBOR typed arrays, which can only be numbers. Furthermore, the numbers in the source buffer are not aligned to element boundaries. I'm aware that the binary data may need endian swapping, and that copying bytes to a floating point type may trigger weird NaN behavior if not treated carefully.

I now regret mentioning my motivation, and should have let the std::data(std::valarray& a) question stand on its own. What a trainwreck this question has become, haha. :-)