What is Milady Maker?

What is Milady Maker?

Milady Maker is a collection of 10,000 generative profile picture NFTs with a neochibi aesthetic inspired by street style tribes of Y2K Japan, according tomiladymaker.net. The Milady brand supports a thriving ecosystem of derivative projects from its fan community, such as the Redacted Remilio Babies. Each Milady NFT generates a variety of accessories, and their overall stylishness is rated with a drip score that helps tie value to aesthetics over pure trait scarcity, as per mythofcapitalism.com. The Milady style is inspired by the subcultural fashion tribes of Y2K Japan, and they are highly sought-after on Crypto Twitter, with a floor price of 1.95 ETH, according to mythofcapitalism.com.

Miladys are NFTs, which are financial instruments with their own discrete value, as per coindesk.com. The cheapest Miladys will cost you about $2,000 before fees, though they’ve historically sold for much more. The art for these 10,000 profile pictures was designed by a Remilia member called “Milady Sonora,” or “Milady Sonoro,” according to coindesk.com. The mastermind behind the project (and the self-identified “CEO” of Remilia) appears to be “Charlotte Fang,” also known as Charlie or Charlemagne, according to coindesk.com.

Miladys are rated with a drip score from "SS" tier down to "Normal," according to coindesk.com. Miladys with rarer items (clothes, accessories, skin colors) are assigned a higher drip score. Some have criticized the decision to use “SS,” rather than just “S,” given the shared initials with the Nazi paramilitary organization, though Fang has denied any such connection, as per coindesk.com.

Milady Maker NFTs are a collection of 10,000 generative profile picture NFTs featuring a neo-chibi aesthetic inspired by various street-style tribes, according to datawallet.com. Milady Maker's NFT project was officially introduced on March 13th, 2021, and since their release, Miladys have become a popular component in viral memes, serving as meme images or reaction pictures throughout Crypto Twitter, according to datawallet.com. Popular influencers like Su Zhu, Ansem (@blknoiz06), and others currently use the NFT as their PFP, as per datawallet.com.

Miladys are sold as themed profile pictures that people can use as Twitter avatars, which is a fairly retro concept, harking back to the days of userboxes of early internet personalization into modern social media platforms, with a syncretic design citing Wikipedia user pages, forum signatures, and operating system themes and text content, according to fastcompany.com. Milady NFTs are not just an aesthetic but also a lifestyle and community with various lifestyle experiences such as Milady VRtube, Milady Raves, Miladycore Bootlegs, and Milady Minecraft Metaverse, according to mythofcapitalism.com.

In summary, Milady Maker is a collection of 10,000 generative profile picture N

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What Pentagon documents were recently leaked?

What Pentagon documents were recently leaked?

Based on the provided sources, it is clear that a trove of highly classified Pentagon documents was recently leaked online. The documents contained sensitive national security secrets, including assessments of potential vulnerabilities in Ukraine's air defense capabilities, highly classified assessments of whether some US allies, including Israel and South Korea, are willing to provide military assistance to Ukraine, and other military intelligence. The Justice Department has launched a criminal investigation into the leak, and the Pentagon is also examining how the leak impacts US national security[0][3][4].

The leaked documents were posted on social media platforms and appear to have been transcribed from classified intelligence documents that the anonymous user, going by the moniker "OG," indicated he had brought home from his job on a "military base." The leaker was described in a Washington Post report as a lonely young man and gun enthusiast who was part of a chatroom of about two dozen people on Discord, a social media platform popular with video gamers, that shared a love of guns and military gear. The leaked materials appear to be photos of documents that were crumpled up and wrinkled, as though they had been folded and stuffed in a pocket. While many officials tend to take classified information home, the fact that the paper was folded up "tells me this person did not have that authority" [1][3].

There are many open questions surrounding the leak, including whether all of the documents that have circulated on various social media platforms are authentic as officials scramble to investigate. Many of the documents appear to be intelligence briefing materials on a wide range of subjects, dating back to around early March. They bear a variety of classification marks, from indications that the document was intended for the Five Eyes network involving only the US, Canada, the UK, Australia, and New Zealand to "NOFORN" markings barring sharing abroad and representing a higher level of sensitivity. Some of the documents have been doctored, and it is still unclear if more documents could be released or the motivation for leaking them. It is also unclear who is responsible for the intelligence breach [0][4].

The leak poses a significant national security threat and is embarrassing to the US. Officials are still trying to wrap their heads around the full scale of the leak, and if it's as serious as recent reporting suggests, then "the impact to relations with foreign partners may be significant," according to retired US Army Brig. Gen. Kevin Ryan, a former defense attaché to Russia. The FBI arrested an airman in the National Guard linked to a closed gamer chat where the documents were posted at his family home in Massachusetts. Ex-military and intelligence officials say the leak is one of the most significant intelligence leaks in decades that exposes how the US spies on allies and adversaries alike [1][4].

There are steps that the Department of Defense is taking to address the leak and prevent future leaks of this magnitude. The Department of Defense is reviewing how this type of information is distributed and to whom. It is examining how it distributes highly classified information and whether it needs to change who receives it on a daily basis. The Army Criminal Investigation Division is also assisting the DoD in their investigation of the leak. "Way too many people have access to very sensitive information," a senior US official noted, and there have been stern notes from the Defense Department's White House Liaison Office to be more careful about social media and keeping track of classified material [0][3].

In conclusion, a trove of highly classified Pentagon documents was recently leaked online, posing a significant national security threat and embarrassing the US. The leaked documents contained sensitive national security secrets, including assessments of potential vulnerabilities in Ukraine's air defense capabilities and highly classified assessments of whether some US allies, including Israel and South Korea, are willing to provide military assistance to Ukraine. The Justice Department has launched a criminal investigation into the leak, and the Pentagon is also examining how the leak impacts US national security. The leak is one of the most significant intelligence leaks in decades that exposes how the US spies on allies and adversaries alike, and there are steps that the Department of Defense is taking to address the leak and prevent future leaks of this magnitude.

What is Bluesky?

In social media, what is bluesky?

Bluesky is a social media initiative created by a team of software developers led by Twitter's former CEO Jack Dorsey[0]. Bluesky is an independent entity that aims to develop a decentralized social network protocol. This means that different social networks could interact with each other through an open standard, despite having separate systems of curation and moderation [0]. The company has developed the AT Protocol, which allows users to control their accounts and decide which network can access them. AT Protocol also has a transparent algorithm and high performance for fast loading at large scales [3].

Bluesky aims to build a social internet that allows people to freely interact and create content without a single intermediary. Rather than relying on the complicated structure of sites like Twitter or Instagram, Bluesky instead aims to give creators independence from platforms, developers the freedom to build, and users a choice in their experience. The AI Protocol is Bluesky's solution to achieve this goal. If successful, it will give creators independence from platforms, developers the freedom to build, and users a choice in their experience [2].

Bluesky is not a rival to Twitter but a spin-off created and funded by Twitter in the hope of one day adopting its technology. It is an open and decentralized form of social media that is not controlled by any single company [4]. The company's vision is for an open social media ecosystem that gives developers more opportunities to build and develop applications. Meanwhile, users will have control over their experiences, data, and services of choice [3].

Bluesky has been in private beta since late last year, and potential users can currently sign up to the waitlist to join on Bluesky's website [1]. The company has announced its first server, AT Protocol, which allows users to see the content they want across multiple social networks [0]. Bluesky Social, the social media app that runs on the protocol, has been added to the Apple App Store, giving us a peek at the social media app's familiar-looking interface. Screenshots on the listing appear almost identical to Twitter in many respects, right down to the round profile pictures, following and follower count, and bolded account names followed by grey handles [1].

While Bluesky's App Store listing is public for anyone to see, actually creating an account and using the app still requires an invitation [1]. Bluesky's design looks as though it may be one of the closest to replicate that Twitter experience we all know and love/hate [1]. An early 2023 review for the fresh social media app describes it as "a functional, if still rather bare-bones, Twitter-like experience" [2].

Bluesky reflects much of what Elon Musk has in mind for Twitter. It's open-sourced and puts content moderation in the hands of those on the platform. If Musk fumbles the ball in his Twitter takeover, users looking for Musk's vision may be motivated to move to AT Protocol [0]. The question is whether Musk is really interested in Bluesky's vision of liberation from corporate control – especially now that he controls the purse strings [4].

In conclusion, Bluesky is an independent entity that aims to develop a decentralized social network protocol. It is not a rival to Twitter but a spin-off created and funded by Twitter in the hope of one day adopting its technology. Bluesky's vision is to build an open social media ecosystem that gives developers more opportunities to build and develop applications while providing users control over their experiences, data, and services of choice. The company has developed the AT Protocol, which allows users to see the content they want across multiple social networks. The protocol is Bluesky's solution to give creators independence from platforms, developers the freedom to build, and users a choice in their experience. Bluesky Social, the social media app that runs on the protocol, has been added to the Apple App Store, and it looks like it may be one of the closest to replicate that Twitter experience we all know and love/hate.

Python -m flag

python -m

The-m flag in Python is used to execute a module's content as the __main__ module directly from the command line. When the -m flag is followed by a module name, the Python interpreter will locate the module and execute its content. appdividend.com

The -m flag was added to Python 2.4.1 to provide an alternative means of identifying the Python module to execute from the command line. Initially, -m only worked with top-level module names, meaning modules that could be found directly on sys.path without any intervening package modules. However, with the completion of PEP 338, -m was extended to support module name representations beyond the top level, such as http.server. stackoverflow.com

The -m flag can be used to execute Python code from the command line via a module name, rather than a file name. It can also be used to add a directory to sys.path for use in import resolution, and to execute Python code that contains relative imports from the command line. stackoverflow.com

There are two notable use cases for the -m flag:

1. To execute modules from the command line for which one may not know their filename. This use case takes advantage of the fact that the Python interpreter knows how to convert module names to file names. This is particularly advantageous when one wants to run standard library modules or third-party modules from the command line. For example, one can execute the http.server module from the command line using python -m http.server. stackoverflow.com

2. To execute a local package containing absolute or relative imports without needing to install it. This use case is detailed in PEP 338 and leverages the fact that the current working directory is added to sys.path rather than the module's directory. This use case is very similar to using pip install -e . to install a package in develop/edit mode. stackoverflow.com

When executing a module via the -m flag, sys.path is modified to include the current directory, __name__ is set to '__main__', __package__ is set to the immediate parent package in the module name, and __init__.py is evaluated for all packages (including its own for package modules). __main__.py is evaluated for package modules, and the code is evaluated for code modules. stackoverflow.com

One major shortcoming of the -m flag is that it can only execute modules written in Python (i.e., *.py). If -m is used to execute a C compiled code module, the following error will be produced: "No code object available for ". stackoverflow.com

Linting

Linting

Linting is the process of analyzing code to flag potential errors, bugs, and stylistic issues. This is done by running a program that analyzes the code to identify potential issues, and is a basic static code analyzer[1]. The term "lint" was originally used to describe a program that identified problematic and non-portable constructs in C language source code [0]. The term "lint" is now used generically to describe tools that flag suspicious usage in software written in any computer language [0]. Linting is important for reducing errors and improving the overall quality of your code. Using lint tools can help you accelerate development and reduce costs by finding errors earlier [2].

There are several reasons why linting is important in software development. Firstly, linting can help catch static code errors early on. When you run a linter on your code, it will identify issues you should fix before running the code. As such, linting is one of the testing methods every developer should know [4]. Secondly, linting helps improve the quality of your code. By ensuring consistency in your code and checking potential issues and errors early on, linting helps improve the quality of your code. Linters can also suggest best practices while doing certain things. For instance, unused variables have no use in your code [4]. Thirdly, linting helps enforce a specific coding style, so code reviews are about the implemented changes and less about style or code formatting conventions [4]. Finally, security linters can help identify potential security issues in your code [4].

There are several linting tools available depending on the programming language you are using. Some examples of available linters include RuboCop, ESLint, JSLint, Gosec, Prettier, CSSLint, PyLint, and StandardJS [4]. For instance, ESLint is a popular linter for JavaScript. To install ESLint, you can run yarn add eslint -D and add it to your package.json as a script [1].

Linters can be used to catch various kinds of issues in your code, such as programming errors, bugs, and stylistic errors. For instance, if you declare a constant twice in your JavaScript code, your javascript engine would throw an error. However, with the proper linter settings and watch configuration, instead of getting caught later as an error when the code runs, you’ll immediately get an error through your linter running in the background. This can save tons of time having to hunt down a pesky bug that’s not always obvious [1].

Linting may not be effective for all programming languages. For example, while using lint software is effective for ensuring consistent coding style and resolving basic coding errors in interpreted languages like Python and JavaScript, it might not be enough for compiled languages such as C and C++ which are more complex and may require more advanced code analysis [2].

In conclusion, linting is an important process in software development that helps catch potential errors, bugs, and stylistic issues early on. Linters are available for various programming languages, and can be used to improve the quality of your code, enforce coding style, and even catch security issues. While linting may not be effective for all programming languages, it is an important tool for every developer to know.

Jailbreaking an iPhone

Jailbreaking an iPhone means removing its operating system's security limitations. Apple uses these limitations to protect its intellectual property and to prevent dangerous apps from being downloaded [4]. Jailbreaking is not illegal, but it does void the warranty and, in some cases, can make it easier to access apps for illegal purposes [4].

Jailbreaking gives users more freedom to customize their iPhone and access new apps that aren't approved by Apple. The Cydia package manager allows users to make lots of changes to their operating system [4].

There are tools available, such as Unc0ver, that allow you to jailbreak your iPhone on a Mac or a PC. The Unc0ver tool can jailbreak all iPhones that use iOS 11 up to iOS 14.8, including the iPhone 11 and the iPhone SE. However, it is only possible with the semi-tethered version of Unc0ver [2].

To jailbreak an iPhone using Unc0ver on a Mac, you need to follow these steps [2]:

1. Download Unc0ver and Cydia Impactor to your Mac.
2. Connect your iPhone to your Mac using a USB cable.
3. Open Cydia Impactor and drag the Unc0ver IPA file onto it.
4. Enter your Apple ID and password when prompted.
5. Trust the Unc0ver app on your iPhone.
6. Open Unc0ver and tap the blue Jailbreak button.
7. When the jailbreak is complete, tap OK on the confirmation message.
8. Reboot your iPhone by tapping OK on the message that appears.
9. Run the Jailbreak tool in Unc0ver a second time.
10. When your iPhone comes up, re-open Unc0ver and tap Jailbreak again.
11. When the jailbreak is complete this time, tap OK again, and wait for your iPhone to reboot again.

It's essential to keep in mind that jailbreaking an iPhone eliminates security measures that Apple has included for a reason. Jailbroken iPhones increase the responsibility of users many times over. If you manage that responsibility well, jailbreaking can provide an enhanced experience on your iPhone [4]. However, users of jailbroken iPhones have so much freedom in iOS that it’s easier than normal to accidentally download malware. Apps also have fewer restrictions due to the removal of security limitations which can lead to unintended consequences [4].

What is deep learning?

What is deep learning?

Deep learning is a subset of machine learning that involves the use of neural networks with three or more layers to simulate the human brain's behavior [0]. Deep learning is a modern variation of machine learning that is concerned with an unbounded number of layers of bounded size, which permits practical application and optimized implementation, while retaining theoretical universality under mild conditions. The word "deep" in deep learning refers to the use of multiple layers in the network [1]. Deep learning is a machine learning technique that teaches computers to learn by example [2].

Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is also the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers [2].

Deep learning helps to disentangle abstractions and pick out which features improve performance. For supervised learning tasks, deep learning methods eliminate feature engineering by translating the data into compact intermediate representations akin to principal components and derive layered structures that remove redundancy in representation. Deep learning algorithms can be applied to unsupervised learning tasks. This is an important benefit because unlabeled data is more abundant than labeled data. Examples of deep structures that can be trained in an unsupervised manner are deep belief networks [1].

Deep learning architectures, such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks, and transformers, have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance [1].

Deep learning is being successfully applied to financial fraud detection, tax evasion detection, and anti-money laundering [1]. Deep learning is also used in industries from automated driving to medical devices. For example, automotive researchers are using deep learning to automatically detect objects such as stop signs and traffic lights. In addition, deep learning is used to detect pedestrians, which helps decrease accidents. Cancer researchers are using deep learning to automatically detect cancer cells. Teams at UCLA built an advanced microscope that yields a high-dimensional data set used to train a deep learning application to accurately identify cancer cells [2].

Deep learning requires large amounts of labeled data and substantial computing power. High-performance GPUs have a parallel architecture that is efficient for deep learning. When combined with clusters or cloud computing, this enables development teams to reduce training time for a deep learning network from weeks to hours or less [2].

In summary, deep learning is a subset of machine learning that involves the use of neural networks with multiple layers to simulate the human brain's behavior. It achieves state-of-the-art accuracy in various fields and is being used in industries from automated driving to medical devices. Deep learning requires large amounts of labeled data and substantial computing power.