WeSolv reduces hiring bias by connecting companies to more diverse candidates and objectively measuring skills and competencies with performance data from real projects

WeSolv

This paper is under CC 4.0 license. available on arxiv Authors: (1) Bartosz Kusmierz, IOTA Foundation 10405 Berlin, Germany & Department of Theoretical Physics, Wroclaw University of Science and Technology, Poland bartosz.kusmierz@pwr.edu.pl; (2) Roman Overko, IOTA Foundation 10405 Berlin, Germany roman.overko@iota.org. Table of Links. Abstract and Introduction Related Work and Methodology Results Summary and References IV. SUMMARY In this article, we analyzed the wealth distribution of the richest addresses in various cryptocurrencies. This included the time evolution of statistical metrics like the approximated Zipf’s law coefficient, Shannon entropy, Gini coefficient, and Nakamoto coefficient, along with their average values. It was shown that coins and ERC20 tokens have quantitatively different distributions of wealth. In particular, the values of approximated Zipf’s law coefficient for coins are 0.4—1.25 and 0.7—2.25 for ERC20 tokens. Differences between the two groups were also apparent during the study of wealth centralization. It was observed that tokens are, in general, much more centralized than coins with higher Gini coefficients and smaller Nakamoto coefficients. This research might be of particular interest to DAOs and DPoS-based blockchains which rely on some form of a committee of the richest token holders. Presented values of statistical metrics like approximated Zipf coefficient or Nakamoto coefficient might help to model a committee selection process and make it more secure. Future work will evolve in two directions. Firstly, we are working on incorporating more metrics, analyzing more tokens, and considering more different sample size values. Secondly, the main findings of this work will be used to model a committee selection process. REFERENCES [1] M. Castro and B. Liskov. Practical Byzantine Fault Tolerance. In Proceedings of the Third Symposium on Operating Systems Design and Implementation, OSDI ’99, page 173–186, USA, 1999. USENIX Association. [2] W. Chen, T. Zhang, Z. Chen, Z. Zheng, and Y. Lu. Traveling the Token World: A Graph Analysis of Ethereum ERC20 Token Ecosystem. In Proceedings of The Web Conference 2020, WWW ’20, page 1411–1421, New York, NY, USA, 2020. Association for Computing Machinery. [3] X. Gabaix. Zipf’s Law for Cities: An Explanation. The Quarterly Journal of Economics, 114(3):739–767, 08 1999. [4] J. R. Jensen, V. von Wachter, and O. Ross. How Decentralized is the Governance of Blockchain-based Finance: Empirical Evidence from four Governance Token Distributions, 2021. [5] C. I. Jones. Pareto and Piketty: The Macroeconomics of Top Income and Wealth Inequality. Journal of Economic Perspectives, 29(1):29–46, February 2015. [6] D. Kondor, M. Posfai, I. Csabai, and G. Vattay. Do the Rich Get Richer? ´ An Empirical Analysis of the Bitcoin Transaction Network. PLOS ONE, 9(2):1–10, 02 2014. [7] S. Kusmierz, Bartosz nd M ´ uller and A. Capossele. Committee Selection ¨ in DAG Distributed Ledgers and Applications. In K. Arai, editor, Intelligent Computing, pages 840–857, Cham, 2021. Springer International Publishing. [8] D. Larimer. EOS.IO White Paper, 2017. https://github.com/EOSIO/ Documentation/blob/master/TechnicalWhitePaper.md. [9] D. Lehnberg. Valuing the top 50 Ethereum ERC-20 tokens using Metcalfe’s Law, 2018. [10] C. Li and B. Palanisamy. Comparison of Decentralization in DPoS and PoW blockchains. In International Conference on Blockchain, pages 18–32. Springer, 2020. [11] Q. Lin, C. Li, X. Zhao, and X. Chen. Measuring decentralization in bitcoin and ethereum using multiple metrics and granularities, 2021. [12] A. Miller, Y. Xia, K. Croman, E. Shi, and D. Song. The Honey Badger of BFT Protocols. page 31–42, 2016. [13] S. Nakamoto. Bitcoin: A Peer-to-Peer Electronic Cash System. 2008. [14] K. Pietrzak. Simple Verifiable Delay Functions. 2018. https://eprint. iacr.org/2018/627. [15] S. Somin, G. Gordon, and Y. Altshuler. Network Analysis of ERC20 Tokens Trading on Ethereum Blockchain. In A. J. Morales, C. Gershenson, D. Braha, A. A. Minai, and Y. Bar-Yam, editors, Unifying Themes in Complex Systems IX, pages 439–450, Cham, 2018. Springer International Publishing. [16] S. Somin, G. Gordon, A. Pentland, E. Shmueli, and Y. Altshuler. ERC20 Transactions over Ethereum Blockchain: Network Analysis and Predictions, 2020. [17] C. Stathakopoulou, T. David, and M. Vukolic. Mir-BFT: HighThroughput BFT for Blockchains. 06 2019. [18] E. Syta, P. Jovanovic, E. K. Kogias, N. Gailly, L. Gasser, I. Khoffi, M. J. Fischer, and B. Ford. Scalable Bias-Resistant Distributed Randomness. In 2017 IEEE Symposium on Security and Privacy (SP), pages 444–460, 2017. [19] F. Victor and B. K. Luders. Measuring Ethereum-Based ERC20 Token ¨ Networks. In I. Goldberg and T. Moore, editors, Financial Cryptography and Data Security, pages 113–129, Cham, 2019. Springer International Publishing. [20] C. Wang, X. Chu, and Y. Qin. Measurement and Analysis of the Bitcoin Networks: A View from Mining Pools. In 2020 6th International Conference on Big Data Computing and Communications (BIGCOM), pages 180–188, 2020. [21] B. Wesolowski. Efficient verifiable delay functions. 2018. https://eprint. iacr.org/2018/623. [22] K. Wu, S. Wheatley, and D. Sornette. Classification of Cryptocurrency Coins and Tokens by the Dynamics of Their Market Capitalizations. Royal Society Open Science, 5(9):180381, 2018. [23] G. K. Zipf. Selected Studies of the Principle of Relative Frequency in Language. Harvard University Press, 2013.

How Centralized is Decentralized? Summary and References

The era of generative AI is here! As cloud engineers, we can’t handle everything, and AI presents an opportunity to enhance our decision-making in this exciting time. Working on infrastructures requires knowledge and patience to tackle different issues daily. Even world-class products sometimes experience breakdowns, so having Amazon Q as an AI companion is a great opportunity to work closely with this tool. Every day is an opportunity to learn something new and issues may come anytime without our consent no matter the design and code written! Discover my experience with Amazon Q. First Impression Amazon Q is welcoming and knows a lot about AWS services! once a question is asked it provides links to a particular documentation, a great way to learn and understand AWS services step by step no matter the update. In this review, we have 5 issues to resolve with Q : Understanding the services and documentation easily
Knowing the number of VPCs with cloud shell
Detecting issues between a VPC and an Internet gateway
Knowing if the cost and billing summary is correct
How to launch an  EC2 instance easily Issue 1: Navigating Through Endless Documentation Online With more than 100 services, it’s difficult to master all!
and when it comes to searching for a particular service online or through the AWS docs we fall in an ocean of blogs, white papers … Amazon Q summarizes every service clearly and links to particular documentation. In the first issue, I asked Q to explain how many services we have with AWS, it summarized the services and gave me the link to sources where I can learn easily. with such experience, it’s now easier to learn services better than before with detailed and summarized information, with a simple chat we can understand many things before browsing the web or complex documentation. Issue 2: Knowing the Number of VPCs With Cloud Shell AWS remains the biggest cloud provider in the world when it comes to the documentation and services to learn; it’s like an ocean of information! Sometimes, we wonder how many things we should master, and the commands to execute every time we encounter a problem. Most of the time, as cloud engineers, we work with the CLI, and we can’t memorize all the commands! Thanks to Q, we can now resolve some problems with less googling! I asked Q a question, and thanks to its clear explanation, I went on Cloud Shell. On the cloud shell, the command is run and I can see two VPCs! Do you have to navigate through the graphic user interface? Not mandatory. The shell offers more opportunities to execute complex tasks; with Q, I can learn many things daily! The AI companion has many data and is a huge opportunity to boost our daily skills. Issue 3: Detecting Issues Between a VPC and an Internet Gateway On the console, we have 2 VPCs in the east region; let’s detach the internet gateway to one VPC to see how Q will understand the problem! Let’s ask Q a question : The question is asked and Q provides some details and suggests previewing the troubleshooting experience. Q knows the name I gave to the internet gateway “Route to Dallas” and the ID. The VPC is attached now to the internet gateway, unfortunately, due to compliance, Q cannot display some information easily. As a tool, it may provide inaccurate information; sometimes, it does not understand some questions no matter the context, even if we regenerate! Issue 4: Knowing if the Cost and Billing Summary Is Correct The AWS platform remains complex with so many things to learn, such as the features, the documentation, and updates that come all the time for improvement. I wanted to know if Q would understand why all my cost summary is at zero $. Q was able to understand the question and provided some details correctly! Q can make mistakes all the time. It’s not 100 % accurate, the information provided is true because this account is new, and not many tasks were performed. Issue 5: How to Launch an EC2 Instance Easily The instance was launched after following the detailed steps, and I could see the state “Running.” I am curious, and I would like to know the IP addresses of my running instances. Q was able to provide some information, not the IP addresses but different IDs for running instances. To go deeper, I chose a particular ID and asked Q to provide some details. With Q, we can read some details in JSON for better readability. Conclusion Q seems like a great tool, but it does not understand better everything we ask, and due to compliance, some information cannot be shown! It’s strong by providing some answers when it comes to AWS services, providing some details, and can make some mistakes all the time. On a note of 5, I will give it a 3. It’s not perfect, and I will get better as soon as it is trained daily. The generative AI age is now, and we don’t have to rely on the tools all the time! The era of generative AI is here! As cloud engineers, we can’t handle everything, and AI presents an opportunity to enhance our decision-making in this exciting time. Working on infrastructures requires knowledge and patience to tackle different issues daily. Even world-class products sometimes experience breakdowns, so having Amazon Q as an AI companion is a great opportunity to work closely with this tool. Every day is an opportunity to learn something new and issues may come anytime without our consent no matter the design and code written! Discover my experience with Amazon Q. First Impression Amazon Q is welcoming and knows a lot about AWS services! once a question is asked it provides links to a particular documentation, a great way to learn and understand AWS services step by step no matter the update. In this review, we have 5 issues to resolve with Q : : Understanding the services and documentation easily Knowing the number of VPCs with cloud shell Detecting issues between a VPC and an Internet gateway Knowing if the cost and billing summary is correct How to launch an  EC2 instance easily Understanding the services and documentation easily Understanding the services and documentation easily Understanding the services and documentation easily Knowing the number of VPCs with cloud shell Knowing the number of VPCs with cloud shell Knowing the number of VPCs with cloud shell Detecting issues between a VPC and an Internet gateway Detecting issues between a VPC and an Internet gateway Detecting issues between a VPC and an Internet gateway Knowing if the cost and billing summary is correct Knowing if the cost and billing summary is correct Knowing if the cost and billing summary is correct How to launch an  EC2 instance easily How to launch an  EC2 instance easily How to launch an  EC2 instance easily Issue 1: Navigating Through Endless Documentation Online Issue 1: With more than 100 services, it’s difficult to master all!
and when it comes to searching for a particular service online or through the AWS docs we fall in an ocean of blogs, white papers … Amazon Q summarizes every service clearly and links to particular documentation. In the first issue, I asked Q to explain how many services we have with AWS, it summarized the services and gave me the link to sources where I can learn easily. with such experience, it’s now easier to learn services better than before with detailed and summarized information, with a simple chat we can understand many things before browsing the web or complex documentation. Issue 2: Knowing the Number of VPCs With Cloud Shell Issue 2: Knowing the Number of VPCs With Cloud Shell AWS remains the biggest cloud provider in the world when it comes to the documentation and services to learn; it’s like an ocean of information! Sometimes, we wonder how many things we should master, and the commands to execute every time we encounter a problem. Most of the time, as cloud engineers, we work with the CLI, and we can’t memorize all the commands! Thanks to Q, we can now resolve some problems with less googling! I asked Q a question, and thanks to its clear explanation, I went on Cloud Shell. On the cloud shell, the command is run and I can see two VPCs! Do you have to navigate through the graphic user interface? Not mandatory. The shell offers more opportunities to execute complex tasks; with Q, I can learn many things daily! The AI companion has many data and is a huge opportunity to boost our daily skills. Issue 3: Detecting Issues Between a VPC and an Internet Gateway Issue 3: Detecting Issues Between a VPC and an Internet Gateway On the console, we have 2 VPCs in the east region; let’s detach the internet gateway to one VPC to see how Q will understand the problem! Let’s ask Q a question : : The question is asked and Q provides some details and suggests previewing the troubleshooting experience. Q knows the name I gave to the internet gateway “Route to Dallas” and the ID. The VPC is attached now to the internet gateway, unfortunately, due to compliance, Q cannot display some information easily. As a tool, it may provide inaccurate information; sometimes, it does not understand some questions no matter the context, even if we regenerate! Issue 4: Knowing if the Cost and Billing Summary Is Correct Issue 4: The AWS platform remains complex with so many things to learn, such as the features, the documentation, and updates that come all the time for improvement. I wanted to know if Q would understand why all my cost summary is at zero $. Q was able to understand the question and provided some details correctly! Q can make mistakes all the time. It’s not 100 % accurate, the information provided is true because this account is new, and not many tasks were performed. Issue 5: How to Launch an EC2 Instance Easily The instance was launched after following the detailed steps, and I could see the state “ Running. ” Running. Running. I am curious, and I would like to know the IP addresses of my running instances. Q was able to provide some information, not the IP addresses but different IDs for running instances. To go deeper, I chose a particular ID and asked Q to provide some details. With Q, we can read some details in JSON for better readability. Conclusion Q seems like a great tool, but it does not understand better everything we ask, and due to compliance, some information cannot be shown! It’s strong by providing some answers when it comes to AWS services, providing some details, and can make some mistakes all the time. On a note of 5, I will give it a 3. It’s not perfect, and I will get better as soon as it is trained daily. The generative AI age is now, and we don’t have to rely on the tools all the time!

Generative AI: Can We Resolve Some AWS Issues With Amazon Q? 

In this guide, we'll go through every step to diagnose and fix any issue that can introduce Flash Of Unstyled Content (FOUC) on your Next.js app! This guide was tested on Next.js 14, but it may resolve your issue on other versions. 1. Improper CSS Configuration On Next.js, we can use CSS in many different ways, and it’s easy to improperly misconfigure them while creating your new project, it’s essential to follow the guides provided by the framework to have it properly setup and ready to go. For that, before we go through the rest of the steps, it’s important to check that you configured your app correctly, as that might be the cause of the issue. Here are some resources that can help you double-check your configs: Styling guide by Nextjs: https://nextjs.org/docs/app/building-your-application/styling
Using CSS Modules:       https://nextjs.org/docs/app/building-your-application/styling/css-modules
Using Tailwind:               https://tailwindcss.com/docs/guides/nextjs
Using Sass:                     https://nextjs.org/docs/app/building-your-application/styling/sass
Using CSS-In-JS:            https://nextjs.org/docs/app/building-your-application/styling/css-in-js 2. CSS issues This one might be tedious to debug, but having an error in your CSS can cause your SSR to fail, which leads to FOUC. You can start by going through the terminal, restarting your server, and see if you get any errors about CSS. If all is OK with the terminal, check out your browser console, as it can give you a hint on what’s going on. The issue can be coming even from a missing asset you’re using inside one of your JSX components, so be vigilant. 3. JavaScript Issues Similar to #2, this can cause your SSR to fail and cause FOUC. This step is a bit easier though, as your terminal and the console should report any issue that happens. But to make sure this is the issue, you can check your Network tab, clear it, and refresh the page. If the first request (which should be a GET request with the same URL as your page) responds with 500, then this means that your SSR is failing, and you have to fix whatever JS issue you have on your end Prevention Make sure to always check for any of the issues outlined above when pushing new code to prevent this issue from happening again, and make sure your users are getting the best experience! Conclusion Tackling Flash of Unstyled Content (FOUC) issues in Next.js might seem daunting at first, but with the right approach, it's totally manageable. We've walked through the common culprits—from CSS mishaps to JavaScript bugs—and I hope this guide helps you get to the bottom of any issues you're facing. Remember, keeping an eye on your configurations and regularly checking your code can save you a lot of headaches down the line. Use the resources shared, stay curious, and keep refining your skills. Your users will thank you for a seamless experience, and honestly, there’s nothing more rewarding than that! In this guide, we'll go through every step to diagnose and fix any issue that can introduce Flash Of Unstyled Content (FOUC) on your Next.js app! This guide was tested on Next.js 14, but it may resolve your issue on other versions. This guide was tested on Next.js 14, but it may resolve your issue on other versions. 1. Improper CSS Configuration On Next.js, we can use CSS in many different ways, and it’s easy to improperly misconfigure them while creating your new project, it’s essential to follow the guides provided by the framework to have it properly setup and ready to go. For that, before we go through the rest of the steps, it’s important to check that you configured your app correctly, as that might be the cause of the issue. Here are some resources that can help you double-check your configs: Styling guide by Nextjs: https://nextjs.org/docs/app/building-your-application/styling Using CSS Modules:       https://nextjs.org/docs/app/building-your-application/styling/css-modules Using Tailwind:               https://tailwindcss.com/docs/guides/nextjs Using Sass:                     https://nextjs.org/docs/app/building-your-application/styling/sass Using CSS-In-JS:            https://nextjs.org/docs/app/building-your-application/styling/css-in-js Styling guide by Nextjs: https://nextjs.org/docs/app/building-your-application/styling https://nextjs.org/docs/app/building-your-application/styling Using CSS Modules: https://nextjs.org/docs/app/building-your-application/styling/css-modules https://nextjs.org/docs/app/building-your-application/styling/css-modules Using Tailwind: https://tailwindcss.com/docs/guides/nextjs https://tailwindcss.com/docs/guides/nextjs Using Sass: https://nextjs.org/docs/app/building-your-application/styling/sass https://nextjs.org/docs/app/building-your-application/styling/sass Using CSS-In-JS: https://nextjs.org/docs/app/building-your-application/styling/css-in-js https://nextjs.org/docs/app/building-your-application/styling/css-in-js 2. CSS issues This one might be tedious to debug, but having an error in your CSS can cause your SSR to fail, which leads to FOUC. You can start by going through the terminal, restarting your server, and see if you get any errors about CSS. If all is OK with the terminal, check out your browser console, as it can give you a hint on what’s going on. The issue can be coming even from a missing asset you’re using inside one of your JSX components, so be vigilant. 3. JavaScript Issues Similar to #2, this can cause your SSR to fail and cause FOUC. This step is a bit easier though, as your terminal and the console should report any issue that happens. But to make sure this is the issue, you can check your Network tab, clear it, and refresh the page. If the first request (which should be a GET request with the same URL as your page) responds with 500, then this means that your SSR is failing, and you have to fix whatever JS issue you have on your end Network GET Prevention Make sure to always check for any of the issues outlined above when pushing new code to prevent this issue from happening again, and make sure your users are getting the best experience! Conclusion Tackling Flash of Unstyled Content (FOUC) issues in Next.js might seem daunting at first, but with the right approach, it's totally manageable. We've walked through the common culprits—from CSS mishaps to JavaScript bugs—and I hope this guide helps you get to the bottom of any issues you're facing. Remember, keeping an eye on your configurations and regularly checking your code can save you a lot of headaches down the line. Use the resources shared, stay curious, and keep refining your skills. Your users will thank you for a seamless experience, and honestly, there’s nothing more rewarding than that!

Resolving the FOUC Issue in Next.js App Routes

How To Resolve Conflicts With Your Superiors At Work

Disclaimer I am not and have never worked for Bitwarden, and I don't have and have never had any insights. I am a user of Bitwarden and all in this article is based on my observation and information provided by Bitwarden support. I do not think Bitwarden is a bad product, or by no means want to suggest migrating off, or not to onboard. Look, mistakes happen. On contrary, I've been using it for a couple of years and found it good and I plan to keep paying for my subscription. Goals Give other users facing the same problem a quick solution in StackOverflow/Reddit style. Learn from the error how to roll out changes without affecting users. How to solve this Missing items (logins, notes, cards) are the items in your organization(s) with no Collection(s) assigned. To solve this: Login to Bitwarden in a browser with your admin account. Open the Admin console. To do that, click the nine dots/squares icon next to your profile picture on the top right corner and click the "Admin console" on the popup that appeared. Choose the organization the items you want to make visible are in on the left pane. On the Filters section click the "All items". Now, on the table to the right of the Filters section you will see all your items, including Collections each item is in. The missing items are the items that are not in any collection, in other words, items for which the Collections column is empty. Add items into at least one collection. I did not find a good way to identify items not in any collection and do mass add, so the only obvious way I see is to click 3 vertical dots on the item's row, choose the Collections item on the drop down, and assign one or multiple Collections. Repeat for each item. So, what happened and what can I do not to make the same mistake OMG I lost my passwords Today I tried to login to GitHub. I opened the site in a browser, clicked the Sign in button, and suddenly found that the Bitwarden shield Chrome extension has no numbers on it - Bitwarden did not have any details on the site. Wait, it worked before, how come it isn’t working now! Wait, what? There was the 2nd factor for the site stored in Bitwarden, have I completely lost access to my GitHub?. That must be some mistake. So, I opened Bitwarden app on my phone and found the login was not there either. I started checking other items in my vault, I still had some items, so it's not "everything" that is gone, but I also found that several other items that I had used recently or I was pretty sure had been there were missing. I was also sure I had not deleted anything from there. Ok, am I hacked then? Unlikely, as I use Yubikey as a 2nd factor, so someone either had to obtain physical access to my device AND my Yubikey, or it could've been some sophisticated malware. But why would one want to delete some items from my vault and not the others? So, I swept that option away. Ok, then what? I logged in to Bitwarden site with my account, and tried to search for the missing items there. The UI showed what I had seen in my Chrome extension, but no missing items. However, there I found the Trash folder, it was empty. The Trash folder contains any items removed within the last 30 days, and some of the missing items had been in use just a few days ago, so even if they had been deleted it still had to be there. Ok, it's time to ask for help. I sent an email to the support. They asked me whether the items were in an organization, said ,  pointed me out to the and suggested to check Collections and permissions. this can be due to flexible collections that we are currently in the process of rolling out documentation I did that and found that the items had not gone (phew), but they were still not visible. Terminology and sharing model Bitwarden sharing is based on Organization model. A person has an account with Bitwarden. Apart from a personal vault under the account where a person keeps their personal items, a person's account can be added to multiple organizations - their company's org, an open-source project contributor's org, a family org, etc. Each of these organizations keep items that belong to the org. However, not all users in the org should have access to all the items in the org, and they may also have different levels of access (edit, view, etc). To control this Bitwarden uses a "Collection" concept. Collection is a union of 3 things - items, users and access, so it dictates which users have specified level of access to which items. So, what happened In the past, the system allowed to add items to an organization without adding them to any collections. I don't know whether it was a bug or a feature, but that worked for me just fine and I tend to drop most of my logins to my Family org, adding those I want to share with my family members to corresponding collections, and keeping the rest outside of any collections. It was working for me either because I created those or because I was an admin, but I could see them just fine until today. Today, Bitwarden rolled out the new model to my account/org, and all the items that had not been explicitly added to any collection appeared in a virtual "unassigned" collection (my speculation, you cannot see it in the interface), and the access to the virtual collection got revoked from everyone including the admin. So, since the account got no permissions assigned (even the view) for those items, they disappeared from the list on my devices. Lessons we can learn These lessons are not recommendations for Bitwarden developers as Bitwarden may have some obligations and restrictions that prevent them from being applied directly (for example data collection). They are rather generic to a product and require taking product specifics into account. Observability, observability, observability. Without the right metrics developers go blind through mine fields, deadly traps and surrounded by hungry predators. Having metrics is vital. In this particular case metrics like "avg number of items available to a user" or more specific to the case "number of items in the unassigned collection" would allow to detect the problem either before rollout or on an early stage of it. We should be careful collecting different things though, metrics can be sensitive, for example - can collection of these metrics be considered as collecting user data? By some - sure, it will be. So, we should collect necessary metrics, but we need to be transparent on what is collected and why it's necessary. Know your system, but expect that users will not use it in the way you expected. What is the behavior now and what it will be after the change - now, it allows to use items not in collections, after the change, it will not. You expected users to put all the items in an org to at least one collection, but does the system allow items to be not in any collection? If so, what's the behavior? That's not what you put into design in the first place, but if that's possible (the system allows it to) - people will do it and you need to deal with this. Preserve the state of things. The state of the items that are not included in any collection is "they are visible and editable by an admin", make sure it will remain after the change. All the items have to be in an explicit collection? Fine, have the updater create "uncategorized" collection, add admin account to it and assign "view and edit" permission and add all the items that are not in collections to the new collection. Make sure the system allows to do only what it's supposed to and all the places have consistent behavior. We have a new requirement - items cannot exist outside a collection. Change the tooling to prevent removing it from all the collections. The Chrome plugin requires at least one collection to be chosen to move an item to an organization. However, the web UI in admin interface allows admins to uncheck all the collections, and thus remove it from any collection, wait, this contradicts the Chrome extension logic. Create tooling. I have a few hundred items in my org. Some have thousands. How are we supposed to move them to a collection? One by one? Your users will curse you, give them a tool. Communicate. Let users know that you've developed a new feature, or that you're going to make permission management more flexible. They will love to hear this. This requires some changes? They need to learn how to manage permissions in a new way? At least they will be ready and know what's going on, instead they find out that the old way is not working any longer and they have to learn the new way when they need to make changes ASAP. Things may go wrong. That's fine. But we should not surprise users, especially in a bad way.

How to Resolve Bitwarden's 'Some Logins Missing' Issue

OFX, aka , is an open standard for creating 2D visual effects and video compositing. It operates in a plugin-like application development model. Essentially, it serves as both a Host - an application providing a set of methods, and a Plug-in - an application or module implementing this set. OFX Image Processing API This configuration offers the potential for unlimited expansion of the host application's functionality. DaVinci Resolve and Metal Applications such as Final Cut X and DaVinci Resolve Studio, starting from version 16, fully support Apple Metal pipelines. Similar to OpenCL and Cuda, in the case of OFX, you can obtain a descriptor or handler of a platform-specific command queue. The host system also takes responsibility for allocating a pool of such queues and balancing calculations on them. Moreover, it places the source and target image clip data in GPU memory, significantly simplifying the development of extensible functionality. OFX Version Support in Resolve With Resolve, things are slightly more complicated. DaVinci announces support for OFX v1.4, albeit with some limitations. Specifically, some methods for working with interface functions are not available for use. To determine which method is available, OFX allows you to examine the supported suite through key/value queries. Publishing methods in the plugin code are based on . But we will use the OpenFXS C++ shell adapted for C++17. For convenience, I've compiled everything into one repository: taken from the open-source . C calls dehancer-external Dehancer project OFXS Concept In this project, I will be using OpenFXS, a C++ extension to OpenFX that was originally written by and has become popular over time in commercial and open-source video processing projects. Bruno Nicoletti The original was not adapted to modern C++ dialects, so I updated it to make it compatible with . OpenFXS C++17 OFX, and consequently OFXS, is a standalone software module that is dynamically loaded by the host program. Essentially, it is a dynamic library that is loaded when the main application starts. OpenFXS, like OFX, must publish method signatures. Hence, we use one C method from the code. To start developing in OpenFXS, you need to agree to a few common sets of classes that are used to create new functionality in your application. Typically, in a new project, you need to inherit from these classes and implement or override some virtual methods. To create your own plugin on the host system, let's start by familiarizing ourselves with the following public classes and the same method: is a basic template for creating a plugin's data structure suite and control panel (although it can be left empty). The inherited class creates a singleton object that registers a set of parameters and presets in the host system, with which the developer registers his module; OFX::PluginFactoryHelper - base container class for creating and storing structure properties; OFX::ParamSetDescriptor - a container of properties used when manipulating graphic data when calling data processing procedures. Used by the host application to save the context of processing parameters in the internal database and work with plugin properties defined for each of its instances; OFX::ImageEffectDescriptor - a set of settings that allows you to manipulate the registered data structure; OFX::ParamSet - a set of settings for effects on graphical data, inherited from OFX::ParamSet; OFX::ImageEffect - in the child class, it is necessary to implement data stream processing: images or videos; OFX::MultiThread::Processor - method of registering a plugin (factory) in the host application; OFX::Plugin::getPluginIDs False Color One feature that distinguishes the process of shooting video from simply capturing an image in a photo is the dynamic change of scenes and lighting of both scenes as a whole and areas in the image. This determines the way exposure is controlled during the shooting process. In digital video, there is a control monitor mode for operators in which the exposure level of areas is mapped into a limited set of zones, each tinted with its own color. This mode is sometimes called "predator" or False Color mode. The scales are usually referenced to the IRE scale. Such a monitor allows you to see the exposure zones and avoid significant mistakes when setting camera shooting parameters. Something similar in meaning is used when exposing in photography - zoning according to Adams, for example. You can measure a specific target with an exposure meter and see in which zone it is located, and in real-time we see the zones, neatly tinted for ease of perception. The number of zones is determined by the objectives and capabilities of the control monitor. For instance, a monitor used with cameras can incorporate up to 6 zones. Arri Alexa Adding Extensions Before proceeding with the example, we need to add some simple proxy classes to implement OpenFXS as a platform for processing source data, such as Metal textures. These classes include: : A proxy class for OFX clip data. imetalling::Image : A functor for transferring data from the clip buffer into a Metal texture. From DaVinci, you can extract a buffer of any structure and packaging of image channel values into the plugin, and it should be returned in a similar form. imetalling::Image2Texture To make working with the stream format in OFX easier, you can request the host to prepare data of a specific type in advance. I will use floats packed in RGBA - red/green/blue/alpha. : A reverse functor for transforming a stream into a host system buffer. As you can see, there is potential for significant optimization of calculations if you teach the Metal computing cores to work not with the texture, but directly with the buffer. imetalling::ImageFromTexture We inherit the OFXS base classes and write our functionality without going into the details of how the Metal core works: : Here, we implement the stream transformation and initiate the processing. imetalling::falsecolor::Processor : This will be our specific part of the suite description for the plugin. We need to implement several mandatory calls related to setting up the structure and create an instance of the OFX::ImageEffect class with specific functionality, which we divide into two subclasses in the implementation: Interaction and Plugin. imetalling::falsecolor::Factory : Implementation of the interactive part of working with effects. Essentially, this is the implementation of only virtual methods from OFX::ImageEffect related to processing changes in plugin parameters. imetalling::falsecolor::Interaction : Implementation of thread rendering, that is, launching imetalling::Processor. imetalling::falsecolor::Plugin Additionally, we will need several utility classes built on top of Metal to logically separate the host code and the kernel code on MSL. These include: : A base class that obscures work with the Metal command queue. The main parameter will be the name of the kernel in the MSL code, and the executor of the kernel call. imetalling::Function : A general class for transforming a source texture into a target texture, extending Function to simply set the parameters for calling the MSL kernel. imetalling:Kernel : Bypass kernel. imetalling::PassKernel : Our main functional class, a "predator" emulator that posterizes (downsamples) to a specified number of colors. imetalling::FalseColorKernel The kernel code for the "predator" mode could look like this: static constant float3 kIMP_Y_YUV_factor = {0.2125, 0.7154, 0.0721};
constexpr sampler baseSampler(address::clamp_to_edge, filter::linear, coord::normalized);
 
inline float when_eq(float x, float y) {
  return 1.0 - abs(sign(x - y));
}
 
static inline float4 sampledColor(
        texture2d<float, access::sample> inTexture,
        texture2d<float, access::write> outTexture,
        uint2 gid
){
  float w = outTexture.get_width();
  return mix(inTexture.sample(baseSampler, float2(gid) * float2(1.0/(w-1.0), 1.0/float(outTexture.get_height()-1))),
             inTexture.read(gid),
             when_eq(inTexture.get_width(), w) // whe equal read exact texture color
  );
}
 
kernel void kernel_falseColor(
        texture2d<float, access::sample> inTexture [[texture(0)]],
        texture2d<float, access::write> outTexture [[texture(1)]],
        device float3* color_map [[ buffer(0) ]],
        constant uint& level [[ buffer(1) ]],
        uint2 gid [[thread_position_in_grid]])
{
  float4  inColor = sampledColor(inTexture,outTexture,gid);
  float luminance = dot(inColor.rgb, kIMP_Y_YUV_factor);
  uint      index = clamp(uint(luminance*(level-1)),uint(0),uint(level-1));
  float4    color = float4(1);
 
  if (index<level)
    color.rgb = color_map[index];
 
  outTexture.write(color,gid);
} Initialization of the OFX Plugin We will begin by defining the class In this class, we will set a single parameter - the status of the monitor (either on or off). This is necessary for our example. imetalling::falsecolor::Factory. We will inherit from and overload five methods: OFX::PluginFactoryHelper : This method is invoked to globally configure the instance when the plugin is first loaded. Overloading this method is optional. load() : This method is invoked when an instance is unloaded, for instance, to clear memory. Overloading this method is also optional. unload() : This is the second method that the OFX host calls when the plugin is loaded. It is virtual and must be defined in our class. In this method, we need to set all the properties of the plugin, regardless of its context type. For more details about the properties, refer to the code. describe(ImageEffectDescriptor&) ImageEffectDescriptor : Similar to the method, this method is also called when the plugin is loaded and must be defined in our class. It should define properties associated with the current context. describeInContext(ImageEffectDescriptor&,ContextEnum) describe The context determines the type of operations the application works with, such as filter, paint, transition effect, or frame retimer in a clip. : This is the most crucial method that we overload. We return a pointer to an object of type . In other words, our in which we have defined all the functionalities, both with regard to user events in the host program and rendering (transforming) the source frame into the target one: createInstance(OfxImageEffectHandle, ContextEnum) ImageEffect imetalling::falsecolor::Plugin OFX::ImageEffect *Factory::createInstance(OfxImageEffectHandle handle,OFX::ContextEnum) {
     return new Plugin(handle);
   } Handling Events At this stage, if you compile a bundle with the OFX module, the plugin will already be available in the host application, and in DaVinci, it can be loaded onto the correction node. However, to work fully with a plugin instance, you need to define at least the interactive part and the part associated with processing the incoming video stream. To do this, we inherit from the class and overload virtual methods: OFX::ImageEffect - This method allows us to define the logic for handling the event. The event type is determined by the value of OFX::InstanceChangedArgs::reason and can be: eChangeUserEdit, eChangePluginEdit, eChangeTime - the event occurred as a result of a property being edited by the user, changed in a plugin or host application, or as a result of a change in the timeline. changedParam(const OFX::InstanceChangedArgs&, const std::string&) The second parameter specifies the string name that we defined at the plugin initialization stage, in our case, it is one parameter: . false_color_enabled_check_box - This method allows us to define the logic for reacting to an event, and return a state that determines whether something has changed and whether rendering makes sense. The method must return false or true. This is a way to optimize and reduce the number of unnecessary calculations. isIdentity(...) You can read the implementation of interactive interaction with OFX in the code. As you can see, we receive pointers to the clips: the source one and the memory area in which we will put the target transformation. Interaction.cpp Implementation of Rendering Launch We will add another logical layer on which we will define all the logic for launching the transformation. In our case, this is the only method for overriding so far: - Here, you can find out the properties of the clips and decide how to render them. Also, at this stage, the Metal command queue and some useful attributes associated with the current timeline properties become available to us. render(const OFX::RenderArguments& args) Processing At the launch stage, an object with useful properties became available to us: we have at least a pointer to the video stream (more precisely, a memory area with frame image data), and, most importantly, a queue of Metal commands. Now, we can construct a generic class that will bring us closer to a simple form of reusing kernel code. The OpenFXS extension already has such a class: OFX::ImageProcessor; we just need to overload it. In the constructor, it has the OFX::ImageEffect parameter, i.e., in it, we will receive not only the current state of the plugin parameters but everything necessary for working with the GPU. At this stage, we just need to overload the method and initiate the processing of kernels already implemented on Metal. processImagesMetal() Processor::Processor(
            OFX::ImageEffect *instance,
            OFX::Clip *source,
            OFX::Clip *destination,
            const OFX::RenderArguments &args,
            bool enabled
    ) :
            OFX::ImageProcessor(*instance),
            enabled_(enabled),
            interaction_(instance),
            wait_command_queue_(false),
            /// grab the current frame of a clip from OFX host memory
            source_(source->fetchImage(args.time)),
            /// create a target frame of a clip with the memory area already specified in OFX
            destination_(destination->fetchImage(args.time)),
            source_container_(nullptr),
            destination_container_(nullptr)
    {
 
      /// Set OFX rendering arguments to GPU
      setGPURenderArgs(args);
 
      /// Set render window
      setRenderWindow(args.renderWindow);
 
      /// Place source frame data in Metal texture
      source_container_ = std::make_unique<imetalling::Image2Texture>(_pMetalCmdQ, source_);
 
      /// Create empty target frame texture in Metal
      destination_container_ = std::make_unique<imetalling::Image2Texture>(_pMetalCmdQ, destination_);
 
      /// Get parameters for packing data in the memory area of the target frame
      OFX::BitDepthEnum dstBitDepth = destination->getPixelDepth();
      OFX::PixelComponentEnum dstComponents = destination->getPixelComponents();
 
      /// and original
      OFX::BitDepthEnum srcBitDepth = source->getPixelDepth();
      OFX::PixelComponentEnum srcComponents = source->getPixelComponents();
 
      /// show a message to the host system that something went wrong
      /// and cancel rendering of the current frame
      if ((srcBitDepth != dstBitDepth) || (srcComponents != dstComponents)) {
        OFX::throwSuiteStatusException(kOfxStatErrValue);
      }
 
      /// set in the current processor context a pointer to the memory area of the target frame
      setDstImg(destination_.get_ofx_image());
    }
 
    void Processor::processImagesMetal() {
 
      try {
 
        if (enabled_)
          FalseColorKernel(_pMetalCmdQ,
                           source_container_->get_texture(),
                           destination_container_->get_texture()).process();
        else
          PassKernel(_pMetalCmdQ,
                           source_container_->get_texture(),
                           destination_container_->get_texture()).process();

        ImageFromTexture(_pMetalCmdQ,
                         destination_,
                         destination_container_->get_texture(),
                         wait_command_queue_);
 
      }
      catch (std::exception &e) {
        interaction_->sendMessage(OFX::Message::eMessageError, "#message0", e.what());
      }
    } Building the Project To build the project, you will need CMake, and it must be at least version 3.15. Additionally, you will require Qt5.13, which aids in the easy and convenient assembly of the bundle with the plugin installer in the system directory. To initiate cmake, you must first create a build directory. After creating the build directory, you can execute the following command: cmake -DPRINT_DEBUG=ON -DQT_INSTALLER_PREFIX=/Users/<user>/Develop/QtInstaller -DCMAKE_PREFIX_PATH=/Users/<user>/Develop/Qt/5.13.0/clang_64/lib/cmake -DPLUGIN_INSTALLER_DIR=/Users/<user>/Desktop -DCMAKE_INSTALL_PREFIX=/Library/OFX/Plugins .. && make install Afterward, the installer, called , will appear in the directory that you specified in the parameter. Let's go ahead and launch it! Once the installation is successful, you can start DaVinci Resolve and begin using our new plugin. IMFalseColorOfxInstaller.app PLUGIN_INSTALLER_DIR You can find and select it in the OpenFX panel on the color correction page, and add it as a node. External links False Color OFX Plugin Code The Open Effects Association - OFX header file version and OFXS library code under Resolve + examples Download DaVinci Resolve

An Easy Way to Develop Your Own Apple Metal Plugin and Integrate It Into DaVinci Resolve

আপনার নিজস্ব অ্যাপল মেটাল প্লাগইন বিকাশ করার এবং এটিকে DaVinci সমাধানে সংহত করার একটি সহজ উপায়

Eine einfache Möglichkeit, Ihr eigenes Apple Metal Plugin zu entwickeln und es in DaVinci Resolve zu integrieren

Una forma sencilla de desarrollar su propio complemento Apple Metal e integrarlo en DaVinci Resolve

Un moyen simple de développer votre propre plugin Apple Metal et de l'intégrer dans DaVinci Resolve

अपना स्वयं का Apple मेटल प्लगइन विकसित करने और इसे DaVinci रिज़ॉल्यूशन में एकीकृत करने का एक आसान तरीका

独自の Apple Metal プラグインを開発して DaVinci Resolve に統合する簡単な方法

나만의 Apple Metal 플러그인을 개발하고 DaVinci Resolve에 통합하는 쉬운 방법

Uma maneira fácil de desenvolver seu próprio plug-in Apple Metal e integrá-lo ao DaVinci Resolve

Простой способ разработать собственный плагин Apple Metal и интегрировать его в DaVinci Resolve

Kendi Apple Metal Eklentinizi Geliştirmenin ve DaVinci Resolve'ye Entegre Etmenin Kolay Yolu

Một cách dễ dàng để phát triển Plugin Apple Metal của riêng bạn và tích hợp nó vào DaVinci Resolve

开发您自己的 Apple Metal 插件并将其集成到 DaVinci Resolve 中的简单方法

This paper is available on arxiv under CC 4.0 license. Authors: (1) Thomas Pethick, EPFL (LIONS) thomas.pethick@epfl.ch; (2) Wanyun Xie, EPFL (LIONS) wanyun.xie@epfl.ch; (3) Volkan Cevher, EPFL (LIONS) volkan.cevher@epfl.ch. Table of Links Abstract & Introduction Related work Setup Inexact Krasnosel’ski˘ı-Mann iterations Approximating the resolvent Last iterate under cohypomonotonicity Analysis of Lookahea Experiments Conclusion & limitations Acknowledgements & References 5 Approximating the resolvent This can be approximated with a fixed point iteration of which is a contraction for small enough γ since F is Lipschitz continuous. It follows from Banach’s fixed-point theorem Banach (1922) that the sequence converges linearly. We formalize this in the following theorem, which additionally applies when only stochastic feedback is available. The resulting update in Algorithm 1 is identical to GDA but crucially always steps from z. We use this as a subroutine in RAPP to get convergence under a cohypomonotone operator while only suffering a logarithmic factor in the rate.

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