Doyensec was founded in 2017 by John Villamil and Luca Carettoni and they are its only stakeholders

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COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 24 of 42. 22. The 12 March 2008 Kleiman emails 415. There are two different versions of the 12 March 2008 email from Dr Wright to David Kleiman. The first was the Third Pleaded Example in COPA’s Particulars of Claim, contained in {ID_001318}. The second is {ID_000465}, produced in Dr Wright’s original disclosure. Both are alleged by COPA to be forgeries. They differ only in the domain from which the email was supposedly sent and in part of Dr Wright’s explanations. 416. Understandably, COPA pleaded the Third Pleaded Example because it was one of the few publicly available documents when COPA commenced this claim. Although the Third Pleaded Example (i.e. {ID_001318}) comes first procedurally, {ID_000465} appears to be the original email and was the focus of the expert analysis. 417. I explain each version first, and then set out the detail concerning ID_000465. (a) (Particulars of Claim at [28] – [29] {A/2/10}) The Third Pleaded Example ID_001318 418. In the Kleiman litigation in the United States involving Dr Wright (which is still ongoing), it is alleged that Dr Wright stole a substantial amount of Bitcoin and related intellectual property assets from a company named W&K Info Defence Research, LLC, after the death of its founder, David Kleiman, by forging documents (the “Kleiman Litigation”). In those proceedings Wright asserts that he sent an email to David Kleiman on 12 March 2008 which shows him to be the originator of the idea of Bitcoin. This version is the copy forwarded to Ira Kleiman (brother of David Kleiman) on 6 March 2014, when Dr Wright was seeking to persuade the Kleiman family to accept his claim to have created the Bitcoin system in collaboration with David Kleiman {ID_001318}. The email he claims to have forwarded is as follows: (b) COPA’s reason for alleging forgery. 419. The domain used by the sender of this email is ‘information-defense.com’, which was not created until 23 January 2009. For that reason, COPA allege that the email could not have been sent in 2008 as claimed from the “craig.wright@information-defense.com” email address and is a forgery. (c) Dr Wright’s Explanations 420. In his Defence, Dr Wright claimed that he sent an email to Mr Kleiman on 12 March 2008 and that “the body of the email [cited in COPA’s Particulars of Claim] is the same as the email which Dr Wright sent on 12 March 2008”. However, he claimed that the header was different and that he believed “that the difference has arisen as a result of the original email being moved from one exchange server to another”: Defence [50] {A/3/17}. In his fourth statement (from para. 93), he gave a supposed technical explanation for the email address having changed in this email as the result of mailboxes being moved from server to server: {E/4/31}. 421. In Appendices B and C to Wright11, Dr Wright changed his account entirely. He claimed that he had written an email to Mr Kleiman on 12 March 2008 which contained the first two paragraphs of the email above, but not the third paragraph. He accordingly claimed that all versions of the email in disclosure (all of which include the third paragraph) are fake. He said that the document had been faked by his enemies for deployment against him. See: {CSW/2/44}; {CSW/3/7}. 422. Dr Wright’s final explanation was an assertion that the authentic email is on a QNAP server, to which he does not have access. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.

This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 24 of 42. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 24 of 42. HackerNoon’s Legal PDF Series HackerNoon’s Legal PDF Series here here 22. The 12 March 2008 Kleiman emails 415. There are two different versions of the 12 March 2008 email from Dr Wright to David Kleiman. The first was the Third Pleaded Example in COPA’s Particulars of Claim, contained in {ID_001318}. The second is {ID_000465}, produced in Dr Wright’s original disclosure. Both are alleged by COPA to be forgeries. They differ only in the domain from which the email was supposedly sent and in part of Dr Wright’s explanations. 416. Understandably, COPA pleaded the Third Pleaded Example because it was one of the few publicly available documents when COPA commenced this claim. Although the Third Pleaded Example (i.e. {ID_001318}) comes first procedurally, {ID_000465} appears to be the original email and was the focus of the expert analysis. 417. I explain each version first, and then set out the detail concerning ID_000465. (a) (Particulars of Claim at [28] – [29] {A/2/10}) The Third Pleaded Example ID_001318 (a) (Particulars of Claim at [28] – [29] {A/2/10}) The Third Pleaded Example ID_001318 418. In the Kleiman litigation in the United States involving Dr Wright (which is still ongoing), it is alleged that Dr Wright stole a substantial amount of Bitcoin and related intellectual property assets from a company named W&K Info Defence Research, LLC, after the death of its founder, David Kleiman, by forging documents (the “Kleiman Litigation”). In those proceedings Wright asserts that he sent an email to David Kleiman on 12 March 2008 which shows him to be the originator of the idea of Bitcoin. This version is the copy forwarded to Ira Kleiman (brother of David Kleiman) on 6 March 2014, when Dr Wright was seeking to persuade the Kleiman family to accept his claim to have created the Bitcoin system in collaboration with David Kleiman {ID_001318}. The email he claims to have forwarded is as follows: (b) COPA’s reason for alleging forgery. (b) COPA’s reason for alleging forgery. 419. The domain used by the sender of this email is ‘information-defense.com’, which was not created until 23 January 2009. For that reason, COPA allege that the email could not have been sent in 2008 as claimed from the “craig.wright@information-defense.com” email address and is a forgery. (c) Dr Wright’s Explanations (c) Dr Wright’s Explanations 420. In his Defence, Dr Wright claimed that he sent an email to Mr Kleiman on 12 March 2008 and that “the body of the email [cited in COPA’s Particulars of Claim] is the same as the email which Dr Wright sent on 12 March 2008”. However, he claimed that the header was different and that he believed “that the difference has arisen as a result of the original email being moved from one exchange server to another”: Defence [50] {A/3/17}. In his fourth statement (from para. 93), he gave a supposed technical explanation for the email address having changed in this email as the result of mailboxes being moved from server to server: {E/4/31}. 421. In Appendices B and C to Wright11, Dr Wright changed his account entirely. He claimed that he had written an email to Mr Kleiman on 12 March 2008 which contained the first two paragraphs of the email above, but not the third paragraph. He accordingly claimed that all versions of the email in disclosure (all of which include the third paragraph) are fake. He said that the document had been faked by his enemies for deployment against him. See: {CSW/2/44}; {CSW/3/7}. 422. Dr Wright’s final explanation was an assertion that the authentic email is on a QNAP server, to which he does not have access. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. judiciary.uk judiciary.uk

Two Emails, One Controversy: Unravelling Craig Wright’s Correspondence with David Kleiman 

Film Trailer Generation via Task Decomposition: Conclusions and References

টাস্ক ডিকম্পোজিশনের মাধ্যমে ফিল্ম ট্রেলার জেনারেশন: উপসংহার এবং রেফারেন্স

Filmtrailer-Generierung durch Aufgabenzerlegung: Schlussfolgerungen und Referenzen

Generación de avances de películas mediante descomposición de tareas: conclusiones y referencias

Génération de bandes-annonces de films via la décomposition de tâches : conclusions et références

टास्क डिकंपोज़िशन के माध्यम से फ़िल्म ट्रेलर निर्माण: निष्कर्ष और संदर्भ

タスク分解による映画予告編生成: 結論と参考文献

작업 분해를 통한 영화 예고편 생성: 결론 및 참고 자료

Geração de trailer de filme por meio de decomposição de tarefas: conclusões e referências

Генерация трейлеров к фильму посредством декомпозиции задач: выводы и ссылки

Görev Ayrıştırma Yoluyla Film Fragmanı Oluşturma: Sonuçlar ve Referanslar

Tạo đoạn giới thiệu phim thông qua phân tách nhiệm vụ: Kết luận và tài liệu tham khảo

通过任务分解生成电影预告片：结论与参考文献

The Corner Pub.

By Anton Trukhanov Among all the big technology trends popular today, Artificial Intelligence (AI) is probably the most universally welcomed and praised in the business world. The majority of businesses across industries recognize the enormous potential of this technology and are willing to adopt it as soon as possible. What are the biggest obstacles to AI implementation? Even though companies and decision-makers of the business world believe in AI’s big future and the power of this technology, many of them are not able to start implementing AI just yet. Costs and the lack of high-qualified talent are the main reasons. According to a by Forbes, 83% of companies say AI is a strategic priority for them today yet, there is not enough data science talent. And 40% of businesses by Deloitte said that the AI technologies and expertise are too expensive for them to implement at this point. report surveyed A widening AI talent gap is probably the biggest challenge for businesses wanting to utilize AI technologies in 2021. As companies are rushing to join the new high-tech innovations race, the demand for all tech specialists has increased with the supply unable to catch up. And in the AI field, this is especially the case. According to a by MMC Ventures, the demand for AI talent has more than doubled in 2018-2019 and continues to increase, with 60% of all AI specialists being absorbed by companies in the technology and financial services sectors. The pool of AI talent, however, remains small. report As AI demands advanced competencies in mathematics, statistics, and programming; the entry threshold level for this profession is really high, and educational institutions aren’t able to produce capable and qualified graduates fast enough to satisfy the business demand. Why is low-code the gateway to AI for businesses? And this is where low-code development platforms such as LANSA come in. Due to the above-mentioned challenges, in the last several years, companies were forced to jump on using low-code solutions as a way to build tools and applications that would allow them to utilize the power of AI and other latest tech innovations, quickly and without extensive financial resources. Low-code platforms turned out to be exactly the solution businesses needed, especially in the time of a significant economic and social turbulence such as the COVID-19 epidemic, when factors like flexibility, rapid customization, and scalability play an increasingly important role. According to a by Brandessence Market Research (indicating LANSA as one of the key market players), the volume of the global low-code development platform market was $12.85 bln in 2020, and it is expected to reach $65.15 bln by 2027 with a CAGR of 26.1% over the forecast period. report And the need to integrate AI and ML technologies into their daily operations is one of the biggest drivers of the rapidly increasing adoption of low-code platforms by companies all around the globe. What are the advantages of low-code AI solutions? Visual low-code and no-code platforms make AI technologies instantly accessible even to small and medium-sized companies. They allow businesses to build various kinds of custom applications and integrate different elements of AI and machine learning (ML) into them through third-party APIs. To understand why low-code platforms are undeniably the future of enterprise AI solutions development, let’s look at some of the core advantages of low-code AI compared to the traditional way of developing AI-focused solutions. 1. Much lower costs Low-code platforms basically enable companies to create fully-functional AI-powered solutions without the need to hire expensive AI experts, software developers, and other technical specialists. With low-code platforms, the cost of implementation of AI into daily business operations is much lower. 2. High accessibility Low-code makes AI solutions a lot more accessible on a number of levels, not just the financial. Visual and simple drag-and-drop solutions are more accessible to non-technical people and programming beginners, making AI less complex and thus, less intimidating for them. 3. High usability The simple user-friendly interface of low-code platforms also allows more people within an organization to collaborate on the creation and implementation of a custom AI-powered solution and contribute their perspective to the project, which often results in a more functional and effective final product. 4. Quick development and deployment Another major advantage of low-code platforms is speed. As they make the whole process of development and deployment of enterprise apps with AI a lot faster, this allows organizations to be more flexible and experiment with different versions or approaches to a problem. Which, again, results in higher quality in the end. 5. Great scalability Accessing sophisticated AI through third-party APIs also makes your solutions extremely scalable at no additional cost as the server power will automatically increase or decrease according to the current load and needs. 6. Gateway to other tech innovations. Finally, low-code platforms are probably the best way for companies, SMEs especially, to start utilizing other high-tech innovations as well, including Big data, IoT (Internet of Things), robotics, embedded systems, and so on. Low-code allows businesses to build solutions combining these and other technologies in a way that would serve their specific needs and requirements. How to create powerful AI apps with Visual LANSA? As you may know, is a powerful development environment pushing the boundaries of what we have traditionally considered as low-code. LANSA allows organizations to build anything from a basic application to handle tasks or send feedback to complex supply chain apps. When it comes to AI, multiple powerful AI solutions can be integrated into apps and tools made in Lansa’s low-code builder by using third-party REST APIs. Visual LANSA Let us give you a few examples of such APIs that can be easily integrated into LANSA solutions. Google Cloud Natural Language API Able to inspect the given text and identify the prevailing emotional opinion. Google Cloud Vision API Can detect and extract information about entities in an image, across a broad group of categories. Image Similarity API Compares two images and returns a value that tells you how visually similar they are. Super Resolution API Uses machine learning to clarify, sharpen, and upscale the photo without losing its content and defining characteristics. Fast Style Transfer API Applies artistic style to an image quickly by pre-training on specific style examples. DenseCap API Captions an image by labeling every object the model detects is present within the image. Content Moderation API Analyzes images and videos to detect the presence of adult content, hate symbols, guns, and offensive words. Image Colorization API Allows to colorizing black and white images or videos using the image colorization API. And these are just several examples of available third-party APIs that allow you to integrate powerful AI capabilities into your tools created in LANSA. Also published on https://lansa.com/blog/app-development/the-future-of-ai-is-low-code/.

AI for Businesses: The Future is Low-Code

Couple of months back, I was listening to Eric Evan’s pod cast on Software radio where he mentioned that actor model is a great fit for modeling aggregate root in domain driven , I was like what, wait? ain’t OO languages the only way to model concepts like aggregate, sagas, domain service, repositories or bounded Contexts in Domain driven design ? engineering design After hours of googling and days of reading and weeks of asking questions on forums, I realized that modeling DDD concepts is more explicit and easy in Elixir because of the language constructs it provides. For example using GenServer for aggregate root, OTP application for each Bounded Context, putting all of these Bounded Context together as an umbrella App. What is an Aggregate Root ? Essentially aggregate is pattern in domain driven design, Aggregate is a cluster of your domain objects which can be treated as a singe unit. Let’s say, you are writing a blog engine and you have identified some domain models like Blog Post Comment Like A Blog contains multiple Posts, a post contains multiple Comments and Likes, while defining relationship between domain models, it is a good practice to define relationship in such a way that object graph is acyclic, that is, there are no cycles. for example a blog might have posts but post should not contain reference to blog, similarly blog might contain comments but comment should not contain reference to post. Blog, Post, Comment and Likes can be considered as a collection of objects which defines a aggregate in our application. Next thing is to identify a model among these models and promote it to be root (called aggregate root) and make sure that all the operations/access on these models are happening through the aggregate root. In our case we can promote Blog model to be aggregate root, and make sure that we only access Post, like Blog.Post or Comments like Blog.Post.Comment, this approach guarantees that our models are being modified only via our aggregate roots and we are not breaking any invariant. DDD and Phoenix 1.3 If you have read about latest changes in Phoenix framework (1.3), you would realize that it forces developer to think about their models upfront and it encourages the concepts like Bounded Context and aggregate, wherein user need to specify context (more in line with Bounded context in DDD) to access resource (there are no models anymore). For example Blog, Post, Comment and Likes can be grouped together and accessed by using a Blog Context object. For detailed information take a look at this by Chris McCord at ElixirConf 2017. keynote Phoenix framework is awesome, but it is not my application ( look if not convinced), I like to think of Phoenix as only a web interface for my application and should only be responsible for exposing endpoints to interact with my application with minimal business logic. Following this approach means that I am going to move out my business logic from Phoenix app and put it somewhere else, to achieve this I will use Umbrella project and cut out our Bounded Context in our application into different OTP application within single Umbrella project. here Aggregate root as Elixir Process Let’s consider an application where we are storing and retrieving user information such as name, age, gender and educational details, so we have following models. NOTE: If you want to look the the entire repository, you can find it here . BasicInfo module containing name, age and gender fields. defmodule Bhaduli.User.BasicInfo dodefstruct [name: nil, age: nil, gender: nil] def new(basic_info) do%Bhaduli.User.BasicInfo{name: basic_info.name, age: basic_info.age, gender: basic_info.gender}endend EducationalDetails module containing graduation, senior_secondary and intermediate fields defmodule Bhaduli.User.EducationalDetails dodefstruct graduation: nil, senior_secondary: nil, intermediate: nil def new(educational_details) dostruct(Bhaduli.User.EducationalDetails, educational_details)endend User module which contains user_id, basicInfo and EducationalDetails model. defmodule Bhaduli.User do alias Bhaduli.User.{BasicInfo, EducationalDetails} defstruct [user_id: nil, basic_info: %Bhaduli.User.BasicInfo{}, educational_details: %Bhaduli.User.EducationalDetails{}] ... User Module as Aggregate root I have got three models, basicInfo, educationalDetails and User. I am going to make User as my aggregate root and expose methods to save and retrieve EducationalDetails and BasicInfo as shown below. defmodule Bhaduli.User do alias Bhaduli.User.{BasicInfo, EducationalDetails} defstruct [user_id: nil, basic_info: %Bhaduli.User.BasicInfo{}, educational_details: %Bhaduli.User.EducationalDetails{}] def update(id, %BasicInfo{} = basic_info) doend def update(id, %EducationalDetails{} = educational_details) doend def get_basic_info(id) doend def get_educational_details(id) doend def get(id) doend So we have got basic skeleton for User aggregate, but still we haven’t really done anything interesting. Like I said earlier I want to model my aggregate root as a long running elixir process and way to do that is to use GenServer. But before we do that I want to explain how entire flow should work. Each user will be an Elixir process, for example If a new user is created with user_id “Erin” than we would create a new process with user_id “Erin”. If we try to get user (user_id “Mustang”) for the first time , we will fetch it from database and spawn a new process with given user_id. Now we will have a total of two user processes one each for “Erin” and “Mustang”. If an existing user (user_id “Envy”) is updated and a process for that user has not been created yet than we are going to spawn new process and load its state from Database and update it. Now we will have three processes each for “Erin”, “Mustang” and “Envy”. We will keep one process for each user and keep it loaded in memory. If user process is already running we will read from it and return otherwise we go to database and spawn a new process. We will add a supervisor for user process so that every time a process crashes we create a new one and rehydrate it from database. Process Registry Even though I have outlined overall flow of application, there are few things that needs to be addressed first. Whenever a new process is created, we get back a PID. Since we are creating one process for each user, soon we will have thousands of processes . Anyone who want to get user data will have to know PID of each user process in order to interact with it. Having a supervisor for user process makes our application fault tolerant but also adds a complexity because every time a process crashes supervisor is going to create a new one and this new process will have a different PID than the one that had crashed. Though we can hand-roll our process registration mechanism,but it will soon get hard to track all of these processes as we intend to create thousands of them. On top of it, since supervisor is going to create a new process for every crashed one, propagating new process Id to the client would add more complexity. Luckily we have a module in Elixir called Registry, which allows us to map PID to any given string/atom, so that we don’t have to deal with cryptic PIDs. In our case we want to client to be able to interact with our application only by using user_id, so we would be mapping PID to user_id. Registry can be started with , once registry is started we need some kind of mechanism which will map our user_id to the PID of spawned process and way to do that is to implement a function that returns a tuple, based on a value we provide. Registry.start_link/3 :via Our function, along with the Registry module, will allows us to spawn a process for a given and make subsequent calls on the process using the same id, rather than a PID. via_tuple user_id Our :via_tuple function def via_tuple(user_id) do{:via, Registry, {:user_process_registry, user_id}}end GenServer provides the plumbing to make use of this tuple and automatically handle the registration of our name ( ) to the actual process. :via user_id we will use this via_tuple function while spawning a new user process(User module) #User.ex def start_link(name) doGenServer.start_link(__MODULE__, %Bhaduli.User{user_id: name}, name: via_tuple(name))end Aggregate Root as GenServer In order to make User aggregate an Elixir process, I am going to use GenServer behavior in User module. In our main application file, start the registry. Here we are starting the registry by giving it a unique name as `user_process_registry` and supervising it. defmodule Bhaduli douse Application def start(_type, _args) doimport Supervisor.Spec, warn: false children = [ supervisor(Bhaduli.UserSupervisor, [])]opts = [strategy: :one_for_one, name: Bhaduli.Supervisor]Supervisor.start_link(children, opts)endend supervisor(Registry, [:unique, :user_process_registry]), User Supervisor, we are defining a Supervisor for user module by giving it a simple_one_for_one strategy and module name. defmodule Bhaduli.UserSupervisor douse Supervisor def start_link doSupervisor.start_link(__MODULE__,nil, name: :user_sup)end def init(_) dochildren = [worker(Bhaduli.User, [])]supervise(children, strategy: :simple_one_for_one)endend Now, we will use GenServer behaviour in User module and alias BasicInfo and Educational Details. defmodule Bhaduli.User do use GenServer alias Bhaduli.User.{BasicInfo, EducationalDetails} ... Next we will define a User struct to contain user_id, basic_info and educational_details. defmodule Bhaduli.User do use GenServer alias Bhaduli.User.{BasicInfo, EducationalDetails} :user_process_registry @registry defstruct [user_id: nil, basic_info: %Bhaduli.User.BasicInfo{}, educational_details: %Bhaduli.User.EducationalDetails{}] Let’s write a start_link/3 method in User module which will be called by User supervisor while starting User process. Here we are passing three arguments to start_link function __MODULE__, this is the module name. User struct as initial argument. Third parameter in alias for the spawned process, which allows us to access this process with given alias, we are using function because GenServer provides the plumbing to make use of this tuple and automatically handle the registration of our alias ( ) to the actual process. via_tuple :via user_id #User.ex def start_link(name) doGenServer.start_link(__MODULE__, %Bhaduli.User{user_id: name}, name: via_tuple(name))end Now comes the interesting part, we are defining a callback which will be called when GenServer is started. It is this method which will be responsible for loading the User state from database. init #User.ex def init(%Bhaduli.User{} = user) doGenServer.cast(self(), {:populate_user}){:ok, user}end Inside we are calling function, this callback goes to and fetches the data from database, If a user is found it is returned otherwise and error is returned. init handle_callback UserRepository #User.ex def handle_cast({:populate_user}, user) docase Bhaduli.UserRepository.get(user.user_id) do{:error, _} ->   {:noreply, user}{:ok, user} ->   {:noreply, user}endend It is important to note that GenServer won’t start until method returns. for this reason we are using method instead of because can load data asynchronously and init method don’t have to wait for it to finish. init handle_cast handle_call handle_cast Once we have done all this plumbing we can get the PID of any process for a given user_id from Registry as shown below. [{pid, _}] = Registry.lookup( , id) @registry Getting basic_info for a given user_id def get_basic_info(id) do[{pid, _}] = Registry.lookup( , id)GenServer.call(pid, {:basic_info})end @registry We are using for getting basic_info as we are going to wait for reply. Interesting thing to note here is that we are not making database call (Guess why ?) handle_call def handle_call({:basic_info}, pid, state) do{:reply, state.basic_info, state}end Similarly for educational_details def get_educational_details(id) do[{pid, _}] = Registry.lookup( , id)GenServer.call(pid, {:educational_details})end @registry def handle_call({:educational_details}, pid, state) do{:reply, state.educational_details, state}end For updating basic_info and educational details we are going to use as we don’t need to wait for response from GenServer callback. handle_cast def update(id, %BasicInfo{} = basic_info) do[{pid, _}] = Registry.lookup( , id)GenServer.cast(pid, {:update_basic_info, basic_info})end @registry def update(id, %EducationalDetails{} = educational_details) do[{pid, _}] = Registry.lookup( , id)GenServer.cast(pid, {:update_educational_details, educational_details})end @registry #Callbacks def handle_cast({:update_basic_info, basic_info}, user) douser = %Bhaduli.User{user | basic_info: basic_info}{:noreply, user}end def handle_cast({:update_educational_details, educational_details}, user) douser = %Bhaduli.User{user | educational_details: educational_details}{:noreply, user}end Putting it all together. #User.exdefmodule Bhaduli.User douse GenServeralias Bhaduli.User.{BasicInfo, EducationalDetails} :user_process_registrydefstruct [user_id: nil, basic_info: %Bhaduli.User.BasicInfo{}, educational_details: %Bhaduli.User.EducationalDetails{}] @registry def start_link(name) doGenServer.start_link(__MODULE__, %Bhaduli.User{user_id: name}, name: via_tuple(name))end def init(%Bhaduli.User{} = user) doGenServer.cast(self(), {:populate_user}){:ok, user}end def update(id, %BasicInfo{} = basic_info) do[{pid, _}] = Registry.lookup( , id)GenServer.cast(pid, {:update_basic_info, basic_info})end @registry def update(id, %EducationalDetails{} = educational_details) do[{pid, _}] = Registry.lookup( , id)GenServer.cast(pid, {:update_educational_details, educational_details})end @registry def get_basic_info(id) do[{pid, _}] = Registry.lookup( , id)GenServer.call(pid, {:basic_info})end @registry def get_educational_details(id) do[{pid, _}] = Registry.lookup( , id)GenServer.call(pid, {:educational_details})end @registry def get(id) docase Registry.lookup( , id) do[{pid, _}] -> Registry.lookup( , id)GenServer.call(pid, {})[]      -> {:error, :not_found}end @registry @registry end def handle_cast({:update_basic_info, basic_info}, user) douser = %Bhaduli.User{user | basic_info: basic_info}{:noreply, user}end def handle_cast({:update_educational_details, educational_details}, user) douser = %Bhaduli.User{user | educational_details: educational_details}{:noreply, user}end def handle_cast({:populate_user}, user) docase Bhaduli.UserRepository.get(user.user_id) do{:error, _} ->   {:noreply, user}{:ok, user} ->   {:noreply, user}enddef handle_cast({:update_basic_info, basic_info}, user) douser = %Bhaduli.User{user | basic_info: basic_info}{:noreply, user}end def handle_cast({:update_educational_details, educational_details}, user) douser = %Bhaduli.User{user | educational_details: educational_details}{:noreply, user}end end def handle_call({:basic_info}, pid, state) do{:reply, state.basic_info, state}end def handle_call({:educational_details}, pid, state) do{:reply, state.educational_details, state}end def handle_call({}, pid, state) do{:reply, state, state}end def via_tuple(user_id) do{:via, Registry, {:user_process_registry, user_id}}end end Code for this can be found . This application is part of an Umbrella project and in next post I’ll show how this application is used from a Phoenix application. here About Author About Author : Naveen Negi is software engineer based out of Wolfsburg Germany. He believes in software craftsmanship. He is also an avid blogger, reader, and a polyglot developer. In his free time he tries to explore different spiritual paradigm and the one which entices him most is Samakhya school of philosophy. LinkedIn profile: http://linkedin.com/in/nav301186

Elixir: Domain Driven Design with Actor Model

U.S. Supreme Court Justice Clarence Thomas and His Powerful Billionaire Friends

মার্কিন সুপ্রিম কোর্টের বিচারপতি ক্লারেন্স থমাস এবং তার শক্তিশালী বিলিয়নেয়ার বন্ধুরা

Clarence Thomas, Richter am Obersten Gerichtshof der USA, und seine mächtigen milliardenschweren Freunde

El juez de la Corte Suprema de Estados Unidos, Clarence Thomas, y sus poderosos amigos multimillonarios

Clarence Thomas, juge de la Cour suprême des États-Unis, et ses puissants amis milliardaires

अमेरिकी सुप्रीम कोर्ट के न्यायाधीश क्लेरेंस थॉमस और उनके शक्तिशाली अरबपति मित्र

米国最高裁判所判事クラレンス・トーマスと彼の強力な億万長者の友人たち

미국 대법원 판사 클라렌스 토마스(Clarence Thomas)와 그의 막강한 억만장자 친구들

O juiz da Suprema Corte dos EUA, Clarence Thomas, e seus poderosos amigos bilionários

Судья Верховного суда США Кларенс Томас и его влиятельные друзья-миллиардеры

ABD Yüksek Mahkemesi Yargıcı Clarence Thomas ve Güçlü Milyarder Arkadaşları

Thẩm phán Tòa án Tối cao Hoa Kỳ Clarence Thomas và những người bạn tỷ phú đầy quyền lực của ông

美国最高法院法官克拉伦斯·托马斯和他有权势的亿万富翁朋友

Image by: Sébastien Thibault Data brokers’ millions of dollars in lobbying spending in 2020 rivaled that of some Big Tech firms By Alfred Ng and Maddy Varner The data brokers who’ve made fortunes from collecting and sharing millions of people’s personal information tend to fly under the radar. Names like LiveRamp or RELX might not be familiar to most Americans, but they’re making themselves known on Capitol Hill. Collectively, data broker spending on lobbying in 2020 rivaled the spending of individual Big Tech firms like Facebook and Google. The Markup searched lobbying disclosures in the U.S. Senate’s database and the watchdog Center for Responsive Politics’ tool for the names of companies that registered as data brokers in or . Those states are the only two that require companies to annually disclose that they collect, sell, or share people’s personal information without having a direct relationship to them. Lobbying Disclosure Act OpenSecrets Vermont California All in all, we found 25 companies whose combined spending on federal lobbying totaled $29 million in 2020. Many of the top spenders were not pure data brokers but companies that nonetheless have massive data operations. Oracle, which has spent the past decade acquiring companies that collect data, spent the most by far, with disclosure documents showing $9,570,000 spent on federal lobbying. For comparison, of the Big Tech firms with heavy lobbying presences, Facebook spent $19,680,000, Amazon $18,725,000, and Google $8,850,000 in the same period, according to the Center for Responsive Politics. Public Citizen, a consumer advocacy group, found that Big Tech spent . $108 million collectively on lobbying in 2020 Oracle has its own data collection arm but has also built its portfolio by buying up companies like , Compendium, and Crosswise. The companies, which were acquired in 2012, 2013, and 2016, respectively, take data from a variety of sources. DataRaker gets data from millions of smart meters and sensors for utilities companies, while Compendium delivers targeted ads. Crosswise allows Oracle to track people across devices, claiming to process data from billions of devices every month. DataRaker Oracle also acquired , in 2014, which connected offline purchases to online profiles. Additionally, Oracle combines datasets from more than 75 other data brokers, which it calls “the world’s largest collection of third-party data.” Datalogix Our report comes as the data broker industry is not only growing but also facing scrutiny for the first time. California recently passed a statewide privacy law that establishes an agency focused on regulating data privacy issues. Virginia and Maine have also passed regulations to protect people’s online information. California’s and Virginia’s privacy laws hit at the core of what data brokers do by requiring companies to delete data collected about people upon request and allowing people to prevent their personal information from being used for targeted advertising. Maine’s privacy law prevents internet service providers from sharing personal information with data brokers until people give “express, affirmative consent.” And the past year also saw concerns raised about how well these brokers protect their massive troves of data—Oracle, for instance, after security researchers found were left exposed on a server. suffered a data breach billions of records from its BlueKai data collection The Markup contacted all 25 companies for comment on their lobbying activities. Several companies, like Inmar Intelligence and LiveRamp denied being data brokers, though they had self-identified as such to California and/or Vermont regulators. “Inmar Intelligence does not generally consider itself a data broker though one of our entities, Inmar-OIQ, LLC, is registered as such in a couple of states,” Holly Pavlika, a corporate marketing senior vice president for Inmar Intelligence, said. The industry itself is hard to define—many companies, including tech giants, make money off of personal data, though the technical details of how they use that data vary. So The Markup relied on companies that self-reported to and as members of the industry. The list, 480 companies long, shows just how pervasive it has become for companies to traffic in personal information. Vermont California The list includes businesses that are primarily data brokers, like CoreLogic, which claims to have collected data on 99 percent of property and homeowners in the U.S. and spent $215,000 on lobbying in 2020; and Acxiom, which spent $360,000 on lobbying in 2020 for issues related to data security and privacy. Acxiom’s InfoBase boasts of datasets on more than 250 million people and collects information including location data, purchases, interests, life events, and behaviors, according to its marketing material. Our list also includes credit monitoring services like TransUnion, Equifax, and Experian, which each spent about $1.4 million lobbying in 2020 on issues related to credit score reform, like the of 2020, the Credit Access and Inclusion Act of 2019, and the of 2019. TransUnion also owns subsidiaries like Callcredit, Iovation, and Signal, which has been used for collecting and profiling users for gambling apps, . Protecting Your Credit Score Act Clarity in Credit Score Formation Act according to The New York Times Notably, our tally also does not include lobbying by trade associations that data brokers are a part of, such as the Interactive Advertising Bureau and the Digital Advertising Alliance. Of the data brokers who spent the most on lobbying, Oracle far outspent its peers Oracle, the biggest spender, used lobbyists to advocate on issues like annual defense and intelligence budgets and “competition and antitrust in the mobile telecommunications and digital advertising industries,” according to . public records The company didn’t respond to requests for comment. The second largest spender was Accenture—a technology and consulting company that boasts a marketing and analytics branch called . According to its , the company can combine datasets from sources including people’s purchase history and location to help its clients build customer profiles for advertising. Accenture Interactive marketing materials In 2020, the company spent $3,250,000 lobbying on issues like artificial intelligence, the , and COVID-19 contact tracing, according to public records. Digital Dollar When The Markup reached out to Accenture, the company pointed us to two statements CEO Julie Sweet made on data privacy legislation in 2018. The statements called for a federal privacy law that would preempt state laws. PricewaterhouseCoopers, a major accounting firm, spent $2,820,000 lobbying the federal government in 2020, third most among registered data brokers. The total amount 25 data brokers spent on federal lobbying in 2020. $29M PWC collects data from advertising networks and data analytics partners including personal information like names and addresses, which it uses to help clients personalize advertising campaigns, . according to the company’s privacy statement Spending went to monitoring privacy legislation and accounting and auditing issues. PWC didn’t respond to a request for comment. Other big spenders included Deloitte, which was awarded a $106 million contract to build a development environment for the Joint Artificial Intelligence Center this summer. The company spent $2,400,000 on federal lobbying in 2020, including on bills like the and the . Department of Defense Artificial Intelligence in Government Act Artificial Intelligence Initiative Act Deloitte is an auditing and advisory company but provides services like , which uses information including people’s hobbies, interests, and financial data to generate a health risk prediction score and help life insurance companies figure out how likely people are to buy their products. PredictRisk provided by data brokers And RELX, which spent $2,375,000 on federal lobbying in 2020, including on data privacy bills like the , the , and the ; and on bills related to data privacy and COVID-19, including the and the . Data Accountability and Trust Act Information Transparency & Personal Data Control Act Data Broker Accountability and Transparency Act Covid-19 Consumer Data Protection Act Exposure Notification Privacy Act RELX is a major data broker that owns companies like LexisNexis and ThreatMetrix, which has customers in law enforcement, insurance, and financial services. ThreatMetrix alone boasts tracking on 4.5 billion devices, from RELX in 2018. according to a statement Deloitte and RELX didn’t respond to a request for comment. In addition, many other data brokers spent lower amounts on lobbying efforts An Industry Increasingly Under Fire Some of the bills targeted by lobbyists would have regulated the data broker industry, though disclosures do not specify whether they lobbied for or against the bills. The Data Accountability and Trust Act looked to establish security standards and require postbreach audits for data brokers and also prohibit collecting information under false pretenses. The Information Transparency & Personal Data Control Act would have required data brokers to get consent to collect sensitive data and go through an annual privacy audit. The Data Broker Accountability and Transparency Act of 2020 wanted to mandate opt-outs from data brokers and for the FTC to create a national list of data brokers. While lobbying records don’t always list specific bills, filings show that RELX paid lobbyists to address all three bills. Deloitte records show that some of its lobbying efforts went toward addressing the Data Accountability and Trust Act. Both COVID-19 data privacy bills looked to provide stronger controls over data related to contact tracing. None of the bills named in this story passed Congress, except for the National Defense Authorization Act, which was enacted. Some of the companies that showed up in lobbying records faced other sorts of pressure during 2020. For instance, Apple and Google from their app stores last December following a series of reports from on how the location data broker was providing information to contractors who passed that information to the U.S. military. X-Mode spent $30,000 on lobbying in the last quarter of 2020, during the height of the public pressure. banned X-Mode’s trackers Motherboard Both and , another location data broker, which spent $160,000 on lobbying, are facing scrutiny from Congress over their location data sales. Sen. Ron Wyden, a Democrat from Oregon who signed on to , called the data broker industry “out of control,” in a statement to The Markup. X-Mode Venntel a letter calling for an investigation on Venntel “Americans are learning more every day about the secretive and shady data broker industry and they’re demanding new laws to protect our privacy,” Wyden said in the email to The Markup. “It’s no surprise data brokers are trying every avenue they can think of to ward off the common sense protections Americans desperately need.” -Sen. Ron Wyden X-Mode and Venntel didn’t respond to a request for comment. Some companies said they were lobbying to have a voice on legislation, including potential federal laws on privacy. “With increased momentum, attention and legislative activity in multiple states, we support a federal data privacy law that can rebalance the system and set standards that rebuild trust with the people providing the data—consumers,” Christine Travis, a senior communications director for LiveRamp, said in an email. “A federal approach to data privacy and security is better than a patchwork of state laws for all stakeholders.” When somebody shows up on the lobbying records, it just tells me they see a real threat to their business model. -Hayley Tsukayama, Electronic Frontier Foundation LiveRamp was the top lobbying spender among companies whose for advertising purposes. The company, which connects people’s activity across the web and from offline purchases for advertisers, spent $630,000 on lobbying in 2020 on issues such as the and Privacy Shield. primary focus is collecting data Augmenting Compatibility and Competition by Enabling Service Switching (ACCESS) Act Privacy Shield was a framework for transferring data between the European Union and the U.S. but was declared . invalid last July for failing to meet the EU’s privacy regulations Travis denied that LiveRamp is a data broker despite being listed on , insisting instead that the company is a “data connectivity platform.” California’s data broker registry Experts said scrutiny and oversight is fairly new to the data broker industry. “When somebody shows up on the lobbying records, or in meetings or in trade associations, it just tells me they’ve probably recently woken up to this issue and they see a real threat to their business model by privacy regulations,” Hayley Tsukayama, a legislative activist for the Electronic Frontier Foundation, said. Others, however, said Congress is not doing nearly enough to regulate the unwieldy industry. “What is widely understood now in Congress and among independent agencies like the FTC are the ways in which large tech companies like Facebook and Google are extracting data from consumers and using that to monopolize markets like the advertising industry,” Jane Chung, a Big Tech accountability advocate at the consumer advocacy group Public Citizen, said. “What a lot of people don’t understand is how data brokers fit into that ecosystem.” Originally published as " “ with the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license The Little-Known Data Broker Industry Is Spending Big Bucks LobbyingCongress

Who are the Data Brokers Spending Big Bucks to Lobby Congress?

A Comprehensive Guide To The Next Generation Of Crypto Funding | (V)ICO | IEO | DAICO | ETO | STO |

How Robert Mercer financed the transition from targeting demographics to exploiting psychographics, and why the 70-year-old presidential candidate with the fun hair and Twitter Tourette’s had the dominant cyber game. Expect more of the same on the road to 2020. OCEAN’s 2016 If you follow the world of finance, and particularly investment, you’re no doubt familiar with Renaissance Technologies’ Medallion portfolio, the most profitable hedge fund in history. Open to employees only, its returns are unmatched: an of 71.8% during the 20 years between 1994 and 2014; included in that average was a massive 98% return for 2008 alone, in the wake of the global financial crisis and the same year that the S&P dropped 38%. Founded by former NSA cryptanalyst and all-around math genius Jim Simons in 1982, it was the first company to deploy mathematical statistical modeling and quantitive analysis applications as the primary way to predict the stock market. Simons would later be joined by Robert Mercer, a computational finance wizard fresh out of IBM. The company is extremely secretive — their website homepage consists of — and its PhDs hold doctorates in math, physics, and computer science. Statisticians and scientists are preferred employees, prototypical Wall Street-types are met with disdain, and the company has made a ton of money embracing big data analysis decades before the current explosion of integrating AI with fintech began to take hold. average one sentence and two addresses For Simons, who stepped down as CEO of Renaissance Technologies in 2009 (he was followed by Mercer, who served as co-CEO alongside Peter Brown until late 2017), a chunk of that money is spent on many philanthropic endeavors via the and . When he has contributed to politicians, they’ve been primarily Democratic. Robert Mercer has different, somewhat quirkier appetites when it comes to distributing his wealth, the most notable being the person more responsible than any other single human for Donald Trump’s victory over Hillary Clinton in the 2016 election. (Though an argument can be made that that distinction should go to Clinton herself.) Mercer’s quirky side is worth examining, but with respect to installing our 45th president, Mercer relied on a three-prong strategy: Simons Foundation Math for America Pour millions into Cambridge Analytica, where Steve Bannon was a vice-president. Employ the power of data analytics. Pour millions into Breitbart News, where Steve Bannon served as executive chairman. Exploit the power of social media. Pour millions into the campaign to elect Trump, where Steve Bannon served as head of that campaign, and later as chief strategist to President Trump. Happily lurking in plain sight orchestrating the Trump bombast, everyone got to see Bannon as the bulldog face of this covert agenda cloaked in revolutionary rhetoric, with all the dark lord bravado actually masking Bannon’s essential job: that of being the best girl Friday Robert Mercer would ever hire. Eventually, Bannon’s toxicity would define his expendability quotient after the main objective had been realized, but before being shown the door(s), his staunch dedication to implement Mercer’s bidding in getting Trump elected was pure, blind loyalty to the mission, and required one more moving part. Enter Facebook. When Mark Zuckerberg testified before Congress in earlier this month, he was well-prepared to point out how Facebook was assured all the compromising data in the Kogan / Cambridge Analytic data breach had been neutralized. When he found out that Facebook was essentially lied to, he immediately leapt into action and waited two years before doing anything about it. Nevertheless, he did come across as reasonably earnest to a room full of people who were clearly doing their level best to understand this visitor from foreign lands. The whistleblower on the other side of this equation, Christopher Wylie, in a slightly different light: tools he helped build to harvest and exploit the data profiles of tens of millions of American citizens at the direction of Steve Bannon and Cambridge Analytica were known to Facebook by late 2015. Wylie was sent a letter in August of 2016 requesting he immediately delete any of the material obtained. He didn’t. Facebook didn’t follow up. Bannon then became Trump’s campaign manager, which effectively meant Cambridge Analytica became Trump’s campaign manager. recounts the series of events What do you mean, “trying to”? While it’s easy to be cynical about an extremely wealthy (and cynical) hedge fund manager deciding he wants to be a political puppet master later in life, let’s not give the extremely wealthy coder who swapped out the hoodie for a suit while visiting D.C. a pass just yet. A leaked document from Facebook Australia detailed one down under research project it would have preferred kept quiet: the company’s use of algorithms to the sentiments of children and young adults in real time, allowing marketeers to capitalize accordingly along the emotional spectrum of youthful experiences, and not just day to day, but minute by minute. This was research done in 2017, long after Facebook pulled the Aleksandr Kogan app, and on over 6 million people, some barely teenagers. pinpoint and then exploit But the clinical trial perhaps most ominous in the Facebook catalog of sentiment metrics analysis is its attempt at driving those sentiments toward predetermined objectives and observing the results. In 2012, Facebook and Cornell University studied almost 700,000 FB users for a week to gauge responses when one group had positive content removed from their feeds while the another group had negative content removed from their feeds, and both were measured against a control. The objective — to observe if “emotional contagion” was indeed a thing, and would indeed spread, like anything viral — achieved its desired, and unsurprising, result: sad shit makes you sad, and happy shit makes you happy. What was significant was that those leading the experiment neither told the participants what was happening nor obtained their consent to participate in a research study specifically engineered to affect their emotional state. Amazingly, the results were in the Proceedings of the National Academy of Sciences before for such an wanton breach of trust. published everyone involved had to duck and cover With the Australian case from last year, a wholly unsurprising twist: Facebook claims their internal data about your public data is not for public consumption. This aligns well with points congressman Ben Lujan (D-NM) made to Mark Zuckerberg before his allotted four minutes ran out during congressional testimony: first, he queried Zuckerberg on why a that Facebook was made aware of in 2013 didn’t get resolved until a week before Zuck’s 2018 arrival in Washington; second, he sought clarity on how many average data points Facebook collects on “shadow profiles,” or people that don’t use Facebook. (FB users, the congressman claims, have an average of 29,000.) Finally, Rep. Lujan asked for an explanation as to why a non-Facebook user who wants to continue being a non-Facebook user, has never signed a consent form with Facebook, and simply wishes to not have their identity tracked and contained within Facebook’s data analytics engines is instructed to to turn off those permissions. It was one of the more , with Zuckerberg’s silent stares speaking volumes. major data scraping vulnerability sign into their Facebook page informative exchanges of the day He’s totally checking you out It’s important to keep perspective here: Facebook, as Mark Zuckerberg explained to Senator Orrin Hatch through a muffled, I-can’t-believe-these-are-the-dudes-in-charge-of-oversight giggle, makes money through advertising. So they use targeted data to sell things, and they’re going to let others use targeted data to sell things, and they’re going to apologize for it once they get caught doing something unethical. It’s become a facet of their business model. When Zuckerberg was asked by congressman John Sarbanes (D-MD) about the number of Facebook-approved ads for the two presidential candidates in the 2016 election, he didn’t have the numbers handy, but Rep. Sarbanes did: roughly 66,000 ads approved for Clinton, and 5.9 million approved for Trump. Which brings us back to Cambridge Analytica. of course of course of course There has been a lot of press covering Cambridge Analytica, it’s successful use of psychometric profiling and personality-centric, micro-targeted advertising at the behest of the Trump campaign, and the fact that in three states easily carried by Obama in 2012, Pennsylvania, Michigan, and Wisconsin, Trump won each state’s electoral votes by . This is the number that should resonate when considering the power of an effectively deployed AI system: yes, the schematic for a personality quiz developed by two doctoral candidates was hijacked by Aleksandr Kogan, a professor doing academic-that-was-actually-commercial research sold to Cambridge Analytica, and the resulting data harvest of over 80 million Facebook users has got the company some well-deserved scrutiny; however, Trump won those three aforementioned states with less than . . This percentage is smaller than 1/1000th of the number of FB profiles exposed, and Cambridge Analytica obviously had access to more than Facebook intel when using data analytics to divide the US populace into 32 personality types. But Facebook users were targeted on a massive scale, and not just by an . It took a highly — and artificially — intelligent operation to correlate quantitative, qualitative, and non-linear data-driven insights to position the Trump campaign heavily in Michigan and Wisconsin during the final weeks of the 2016 election. Trump took Michigan by 0.2%. This will look less random as time allows emotional humans to catch up to what machine-learning frameworks can already comprehend. less than one percentage point 80,000 votes Total infamous troll farm in St Petersburg If the data science behind such an operation is extremely intricate, the layout for this optimization loop would present as the most rudimentary sketch ever: a circle of speech bubbles tied together describe Bannon / Breitbart > Bannon / Cambridge Analytica > Facebook > Target Demo > Bannon / Breibart > Bannon / Cambridge Analytica > Facebook > Target Demo, etc., repeating endlessly and cyclically until desired feedback has been actualized. The first three have direct, connective tissue to the bubble with Robert Mercer’s name in it; the fourth, Target Demo, generally has no idea who Mercer is but does identify with having perceptions validated and reinforced, even if the shaping of those perceptions goes unnoticed. Mark Lombardi How to Shag an Election Robert Mercer funded Cambridge Analytica to influence U.S. elections the way he saw fit. Operating under the umbrella of the SCL Group, a British strategic communications firm, Cambridge Analytica was set forth “to address the vacuum in the US Republican political market.” It’s behavioral modification by way of digital engagement, according to its recently unemployed founder and , Alexander Nix. And the personal ROI for Mercer has surely mirrored any financial ones he’s received from Renaissance’s Medallion Fund over the years. hot mic celebrity It’s important to note, however, that Mercer initially deployed Cambridge Analytica to work its magic for Ted Cruz, his first choice in the 2016 election. When Trump won the primary, Mercer capitalized on that, and certainly not because Trump is also an introverted, socially awkward, gifted data scientist and successful billionaire (Trump is none of those things); rather, it’s because Robert Mercer wanted to buy the largest number of shares — controlling stock — in the American presidency, which he appears to have done. As the secretive king maker who is reportedly cripplingly shy in social situations, Robert Mercer would probably not appreciate his personal data being mined and available for purchase, yet he appears highly motivated to do just that to others, as it gels with his world view in general: , and if he earns thousands of times what a schoolteacher earns, he is therefore that much more valuable than a thousand teachers. It is blatantly obvious that Mercer cares not a whit about Trump supporters or their values, other than as fodder to suit his interests. Unlike Facebook / Cambridge Analytica publicity that has erupted in recent weeks, has unearthed another funnel of neoconservative dark money that infiltrated the waning days of the 2016 election with fear-mongering advertising: a 501(c)(4) named Secure America Now. Most Americans didn’t see the ads dripping with near-comedic xenophobia, but a few in swing states did, which was the intent. Facebook and Google coordinated to get those ads where they were directed, and helping underwrite it all was a cool $2 million from Robert Mercer. people are worth what they earn opensecrets.org Using Citizens United to unite citizens In Mercer’s eyes, those on welfare are regarded as having negative value, but not cats, because they provide humans pleasure. And it only gets more uncomfortable from there: Mercer considers the Civil Rights Act of 1964 a major mistake, thinks white racism no longer exists, and considers climate change a hoax. He also really, really, likes guns, and that includes with them. His other hobbies include a $2.7 million dollar toy train set in his basement and financing a massive stockpile of urine in the mountains of Oregon. (Yep. The latter, it should be noted, has been with the help of chemist Arthur Robinson, founder of the , who conducts research on the 14,000-and-counting specimens when not rejecting global warming or promoting intelligent design. Unknown if any pee pee vials have labels denoting surnames of Russian call girls, or — as — what influence this may have had on naming the 2016 Mercer-funded Super PAC to get Trump elected: “Make America Number 1.”) really playing dress up Oregon Institute of Science and Medicine The Intercept was quick to notice The bottom line for Robert Mercer is the bottom line: he and everyone at Renaissance made a lot of money, and continue to do so. And they are a creative lot, to be sure: Renaissance has succeeded (so far) in not paying $6.8 billion in taxes the IRS claims it owes for tens of millions of trades made in the 2000s by classifying them as long-term, and therefore lower-taxed, it purchased through banks and held for at least a year. Renaissance appeared before the Senate in 2014, defending its position, and the banks named no longer engage in the practice of selling basket options to the firm. The IRS is steadfastly demanding the billions in back taxes be paid. It may not require a room full of quants or an artificial neural network to predict with reasonable accuracy how the Trump administration will view Robert Mercer’s plight with the taxman. “basket options” To conclude on a less-than-apocalyptic note regarding artificial intelligence and the power of social networks, when assessing all the gloom and doom a manipulative, power-hungry individual like Robert Mercer can wield deploying AI systems across any and all media, it’s important to recall how it was also just one person, a retired lawyer living in Hawaii, who thought “I should do something,” on November 9, 2016, and did. She used Facebook’s platform to instigate one of the finest examples of American democracy and global protest ever recorded: the . Mercer and his ilk will never have the proper currency to purchase that away. 2017 Women’s March

Driving Sentiment Metrics with AI

It feels like it’s been months, rather than years, since the tech community last banded together in support of net neutrality, and achieved an unprecedented victory over broadband providers. And yet, once again, we are forced to fight for a more free, more fair , one where internet providers can’t use their positions in the market to crush competing services we use every day. internet Under the Trump Administration, net neutrality is once more under threat, and both incumbent ISPs and chairman Ajit Pai himself are arguing that net neutrality laws represent burdensome regulation, and stifle free market competition. I’ve even seen misinformed consumers fighting for net neutrality concede this, particularly libertarians and others concerned about big government, as if fighting for net neutrality contradicted one of their core tenets. FCC That simply is not true, though. We just have to delve into the murky, messy history of the broadband industry to see it. Origin of the telecom industry The broadband industry, as we know it today, was born through the Cable Communications Policy Act of 1984, otherwise known as the 1984 Cable Act. The act was passed with the intention of clarifying the relationship between municipalities and cable providers, with the goal of gradually improving cable expansion throughout the continental United States. In essence, the act allowed regional cable providers the right to invest in local cable infrastructure, in exchange for becoming the area’s sole provider. At which point, municipal and other providers could no longer compete. Over time, these local providers began to consolidate with neighboring local providers, growing ever-larger as they merged, until at last municipalities found themselves negotiating with billion-dollar companies, and losing. Although Congress later passed additional legislation intended to correct some of the issues stemming from the 1984 Cable Act, by this point local providers had merged into some of the same broadband companies now leading the fight against net neutrality. Cable companies grew to become some of the most powerful telecom and media companies of our time, and faced barely any competition on the way. Protectionism and competition today Despite its crucial role in the research, development, and advancement of the internet, in terms of internet speed or cost. We would expect that in a healthy competitive environment, market forces would drive prices down and broadband access up. the U.S. is nowhere near the best country Unfortunately, there is no healthy competitive environment for broadband in this country. Instead, when we consider the average download speed (in Mbps) that $50 per month will buy you around the world, American cities fare quite terribly. Cost aside, the U.S. continues to lag behind other developed countries in average connection speed. Attempts to compete with the broadband monopolies have mostly come from municipal governments and energy cooperatives wishing to provide better internet access, . particularly in rural areas where major telecom providers aren’t willing to go These attempts have faced severe opposition from incumbent ISPs, however, who have actively — and successfully — lobbied to in several states. Not surprisingly, the most active lobbyists for the telecom industry have been some of net neutrality’s biggest opponents, and they have spent tens of millions on these efforts: block competition from municipal governments Courtesy of OpenSecrets Make no mistake: on rare occasion, when a competitor successfully enter a market dominated by one of these incumbents, some real magic happens. Incumbents not only increase internet speeds dramatically, but also lower their prices. Kansas City and Charlotte, North Carolina when Google announced plans to install Google Fiber in those markets. does recently experienced this effect firsthand Competition favors the customer, forcing incumbents to differentiate and offer better options at lower prices. This is a battle most ISPs are not prepared for, one they have never particularly had to worry about. What does this all mean for net neutrality? It means that ISPs have never depended on the free market. They were not born out of the free market, they did not grow to their current size through the free market, and they have not maintained their customers or their market share through the free market. If the market for internet access in the U.S. was truly free and competitive — if for broadband — then perhaps the market could incentivize the sort of behavior mandated by net neutrality. 48% of the country didn’t have only one choice That’s just not the case, and the cost of infrastructure is an extraordinary challenge. Incumbent ISPs remain unwilling to expand to many rural areas, and even Google has had trouble setting up Fiber in many metropolitan areas. Recall that when cable companies were originally tasked with setting up infrastructure, they at least had temporary exclusivity to look forward to. Today? All you have to look forward to is severe competition, and possibly opposition from lobbyists. It is no longer economically viable to start an ISP as we know it. We can’t go back in time and simply deny cable providers the government-mandated monopolies they built their successes on. What we can do is recognize the unique circumstances that gave birth to some of the most hated companies in America, acknowledge the role that history has played in their success, and start treating them more like the utility companies they always should have been. today In short: Cable companies have never played by the rules of the free market; they should not get to reap its benefits. Marcello Miranda is a tech fanatic, and frequently writes on startups. Follow him on Medium and Twitter , and if you enjoyed this content, leave a few claps to help spread the word!

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Hacker to Security Pro! On the Shoulders of #InfoSec Giants

At work I use a Mac, at home I have a Lenovo notebook with installed. There's several reasons why people, and specially developers, adopt different platforms simultaneously and there's nothing wrong with that, in fact, we are just adopting the right tool for the right job… or budget. ArchLinux However, it can be annoying when you depend on productivity tools that are not available on all platforms. You also start to think twice before buying a license for a tool that won't be available on one of the platforms you adopt. Cross platform consistent user experience is hard, but we can find ways to solve this issue. This article will focus on how to improve the experience for one kind of tool: Keyboard triggered Productivity and Assistant tool The title above is my tentative to classify tools like , , and . Mac Alfred Ubuntu Unity Dash Gnome Shell overview others Screenshot of Alfred in action. The way they work is simple. After a global keyboard shortcut is hit by the user, they can type commands to: Launch applications Search desktop Open URLs and search specific sites Calculate something Manage clipboard Even trigger more complex workflows that can be customized It's a true Swiss-army tool that saves a lot of time, but given that most of them are not cross-platform because of their deep relationship with the operating system, it's really hard to find a consistent experience. If we had a tool that was as powerful as these ones, reachable from a simple keyboard shortcut and available on all these platforms would be nice, right? Right? Introducing… Terminal Nothing new here, just the plain and simple terminal you are used to have available in any operating system. Basically we just need to find a way to have the same experience as these keyboard triggered productivity and assistant tools. As I'll show below, it's totally possible to achieve something similar, sometimes even more powerful. Let's do it! Keyboard Shortcut First important thing is to have a terminal that pops up by hitting a simple keyboard shortcut, luckily this is a feature that can be easily found in the most popular Terminal apps. On Mac, I use support for Hotkey window and to trigger that terminal. iTerm2 CMD+space On Linux, I use with shortcut. Guake Super+space Usually I configure these terminals to hide when losing focus so you can have a better experience when executing a command that opens the browser, for example. You can also tweak the terminal fonts and color the way you want. Guake terminal screen on ArchLinux with a Timezone custom command I created So far so good, we have a quick way of opening a Terminal, now we need a good set of commands and packages that reproduces the nice behavior we get with the other tools. Unix Shell On both Mac and Linux I use and and this combination is perfect because already comes with a pretty good way of organizing the features I want to have on my productivity tool set. By creating a you keep all configurations in one place and can easily edit them to tweak existing or add new features. zsh oh-my-zsh oh-my-zsh plugin I have this file on my oh-my-zsh install (with a quite suggestive name): $ ~/.oh-my-zsh/custom/plugins/terminalfred/terminalfred.plugin.zsh All aliases and functions you add to that file will be available in your PATH when you open the terminal. In the next sections I'll show what I have on this file. If you are not patient, you can take a look on this . Gist What I also do is to keep that file on Dropbox and symlink to it from all machines I use. It's okay if you don't have any sensitive information in its content. I'm confident that you can find a similar solution no matter the Unix Shell you use because in the end, what you need is only . installed packages, aliases and functions Searching the web This is the most used feature and oh-my-zsh has a perfect plugin, called , that allows me to do this: [web-search](https://github.com/robbyrussell/oh-my-zsh/blob/master/plugins/web-search/web-search.plugin.zsh) $ ddg how to ride a bicycle   # search duckduckgo$ image beautiful bicycle     # search for images$ map bicycle store           # search on a map These commands will open a browser and do the search, more straightforward impossible. If you want to add your own custom searches, my approach to that was to copy that function in my custom plugin and add them manually: alias urban=’terminalfred urban’alias regex=’terminalfred regex’alias shell=’terminalfred shell’alias jsonb=’terminalfred jsonb’alias aur=’terminalfred aur’ function terminalfred() {emulate -L zsh define search engine URLS typeset -A urlsurls=(urban “ "regex “ "shell “ "jsonb “ "aur “ ") http://www.urbandictionary.com/define.php?term= https://regex101.com/library?orderBy=RELEVANCE&search= http://explainshell.com/explain?cmd= http://codebeautify.org/jsonviewer https://www.archlinux.org/packages/?q= if [[ -z “$urls[$1]” ]]; thenecho “Check available searches"return 1fiif [[ $# -gt 1 ]]; thenurl=”${urls[$1]}${(j:+:)@[2,-1]}”elseurl=”${(j://:)${(s:/:)urls[$1]}[1,2]}”fiopen_command “$url”} See a syntax colored version of this code . Quick calculator From time to time I need to do quick calculations, for that one I use : Calc — C-style arbitrary precision calculator $ calc 24 * 496$ calcC-style arbitrary precision calculator (version 2.12.5.0)Calc is open software. For license details type:  help copyright[Type "exit" to exit, or "help" for help.] ; 365/12~30.41666666666666666667; .*12365; exit Dictionary Few people know , a dictionary network protocol and its command line companion , a dictionary database server. After installing dictionaries, it's fairly simple to use and the result is formidable: DICT [dictd](http://linuxcommand.org/man_pages/dictd8.html) $ dict formidable3 definitions found From WordNet (r) 3.0 (2006) [wn]: formidableadj 1: extremely impressive in strength or excellence; "aformidable opponent"; "the challenge was formidable";"had a formidable array of compositions to his credit";"the formidable army of brains at the Prime Minister'sdisposal"2: inspiring fear; "the formidable prospect of major surgery";"a tougher and more redoubtable adversary than the heel-clicking, jackbooted fanatic"- G.H.Johnston; "somethingunnerving and prisonlike about high grey wall" [syn:{formidable}, {redoubtable}, {unnerving}]... To make it easy to use a few dictionaries and make the result more brief, I have the following aliases: $ alias | grep dictdefine='dict -d gcide'          # english definitionsdicts='dict -d moby-thesaurus'  # synonym dictionary Follow if you want to install it. these instructions Translations Being a Brazilian living in Berlin makes me a heavy user of translation services. is the best tool for that. Translate Shell $ trans -brief ‘Super fácil traduzir com essa ferramenta!’Super easy to translate with this tool! Of course having some alias to make commands shorter is always good: $ alias | grep transtre=’trans -b’                 # translate with brief outputtrep=’trans -b :pt’            # translate to Portuguesetres=’trans -shell -brief’     # open a REPL to translatetrev=’trans -v’                # view translation in terminal pager Task management I use as task manager and I have a simple board to track my daily TODOs, tasks that I should be focusing during the day. Another wanted featured was to be able to add tasks easily to that board. Trello Sample execution, now on Mac. Implementation is done using the . I could have installed a Trello command line tool, but I decided to keep it simple and I have the following: API alias tasks='curl -s -X GET -H "Cache-Control: no-cache" -H "Terminalfred" " ~/.secrets/trello.key.secret`&token=`cat ~/.secrets/trello.token.secret`" | jq ".cards[].name"' https://api.trello.com/1/lists/569623d6blablabla309c4d301?cards=open&card_fields=name&fields=cards&key=`cat function taskadd {str="$*"if [ -z "$str" ]; thenecho usage: $0 taskelsecurl -s -X POST -H "Cache-Control: no-cache" --data-urlencode "name=${str}" -H "Terminalfred" " ~/.secrets/trello.key.secret`&token=`cat ~/.secrets/trello.token.secret`" | jq ".url"fi} https://api.trello.com/1/cards/?idList=569623d660blablablad301&key=`cat You can see a syntax colored version here . In the code above I have one alias that basically do a request to fetch cards from a specific list on a board of mine. The result is in JSON format, then I use , a json command line processor to have a nice output like the snapshot shows above. The function to add a task is basically doing another request to create a card on the same list and as a result, I just add the link on the response. jq tool Given that I want to sync zsh plugin file on Dropbox, the code expects to fetch Trello API secrets from a specific folder in my machines with the right permissions. Be safe! Launching applications To mention one limitation of using exclusively the terminal, I didn't find a good way to replace GUI application launch and sometimes I still rely on Alfred, Gnome Dash or simply global shortcuts to open them. Even so, I have found one function in a that works if the GUI app command is available on the PATH: StackOverflow thread o() {if [ $# -gt 0 ] ; then# Expand if $1 is an aliasif [ $(alias | awk -F "[ =]" '{print $2}' | grep -x $1) > 0 ] ; thenset -- $(alias $1 | awk -F "['']" '{print $2}') "${@:2}"fi($@ &) &>/dev/nullelseecho "missing argument"fi} The main benefit is that you start the GUI app detached from terminal and avoiding flooding your shell. However, to be honest, I don't use that much. stdout Everything is possible If you achieved that point in the article I believe you get the point, we just scratched the surface of what can be done with a Terminal and basically there's no limit on what you can do to automate tasks and be more productive. You can find other tools I use, such as consulting timezones or finding files, in . my plugin file Hope the lives of cross platform users are improved a little bit with that approach. . As a last note, I purposely ignored Windows because I'm not an active user, but if you are able to have a similar setup using it let me know and I can update this article or link to yours. Leave a comment if you have any curious way of improving your productivity with Terminal

Cross platform productivity with Terminal

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