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OneSignal

onesignal

Happy to write here. In this blog, let us see how to send push notification using , a push notification service. We are going to build a simple react native app, just to receive notifications. Onesignal Firebase setup For using one signal service, we need two things. Google server key Sender Id To create the above two, we have to create a project in . Head over to Firebase console.firebase.google.com Create a project in it. Once it is done, move to the settings page. Click on the gear icon near the Project overview then move to cloud messaging. You will probably see a screen as shown below. We can see the Server key and Sender ID from the setting page. We are now done with the Firebase setup. One signal setup Now, let’s move to the one signal setup. Head over to onesignal.com Create an account in onesignal.com. Once you are done creating an account, you will need to add the app that you are going to create, to one signal. The following steps are to be followed One signal will prompt for Name of the app and the platform that we are going to use. Give a name to your app and select Android on the platform. Next, we will be configuring the platform. We will have to provide the Google server key and Sender Id in the next window. Next, we will have to select the SDK. In our case, we have to select React Native in it. Now we are done with the One Signal set-up. React Native Setup Now let’s see how to set up one signal in our code. Before you start, please ensure that you have already installed React Native and NPM in your system. If not install them to proceed. To create a new project, move to a desired directory/location and open Terminal. Type in the below command react- WhateverProject native init This will create a new react native project. Once it is done, install the one signal npm package npm react- -onesignal install native --save Next, open the project in any editor. Open the Manifest file, which you can find in android/app/src/main/AndroidManifest.xml In the manifest file, we will have to add the following to the activity tag. launchMode= android: "singleTop" Refer to the below screenshot if you have any doubts. Save the file. Next, open the build.gradle file located in android/app/build.gradle In that add the One signal app id and Google sender id in the default config, like shown below manifestPlaceholders = [onesignal_app_id: , : ] "your-one-signal-app-id" onesignal_google_project_number "google-sender-id" That’s it the setup for React native. Now, let’s run the app. Open two terminal tabs and in the first tab, type the following react-native start This will start the server in your machine. In the other terminal tab, type in adb devices This will list the connected android devices/emulators in your machine. It is advised to use physical devices for this case, as in most cases, notification is not received in emulators. Now if you see your device id in the terminal, type in react-native run android Once the app runs in your device, you are done with the setup. Lets push notification Now, to ensure one signal is working fine, we have to go to the one signal dashboard. Go to the tab in onesignal.com. If you see the count 1 in subscribed users, congratulations! you have successfully configured one signal. Audience Now, let’s send some push notification to your device. Head over to the message tab and create a new push notification. Refer below screenshot for sending a push notification. Now you can see notification received in your device. By default, if you tap on the notification, it will take you to the home page of the respective application. We can also give an external link for the notification. Now we are done with the push notification. Bonus A question may arise to you on how to programmatically send push notification. That is with the react-native code. Here is the solution. Create a new .js file and add the following code in your project. { headers = { : , : ,
  }; endpoint = ; params = { : , : headers, : .stringify({ : , : [ { field: , : , : , : id,
        },
      ], : { : }, : { : data}, : , }),
  };
  fetch(endpoint, params).then( .log(res));
} export ( ) function sendNotification data, id let 'Content-Type' 'application/json; charset=utf-8' Authorization "Basic '<<Your one signal rest api key>>'" let 'https://onesignal.com/api/v1/notifications' let method 'POST' headers body JSON app_id '<<Your one signal app id>>' filters // Will send notification only to specific device // Optional 'tag' key 'Id' relation '=' value headings en 'Your Heading' contents en url 'https://something.any' // optional => res console Now we can send notification by just importing this function and calling it like this. sendNotification ( , ) "This notification is from RN code" 1 Now the notification will be sent to user with the particular tag, id===1. This is how we can send push notification in React native through One signal. I encourage you to explore more about this in one signal documentation so that you can learn more about creating different segments in it. Thank you!

Send Push Notifications via OneSignal by Building a React Native app [A How-To Guide]

Authors:
(1) Shih-Tang Su, University of Michigan, Ann Arbor (shihtang@umich.edu);
(2) Vijay G. Subramanian, University of Michigan, Ann Arbor and (vgsubram@umich.edu);
(3) Grant Schoenebeck, University of Michigan, Ann Arbor (schoeneb@umich.edu). Table of Links Abstract and 1. Introduction 2. Problem Formulation 2.1 Model of Binary-Outcome Experiments in Two-Phase Trials 3 Binary-outcome Experiments in Two-phase Trials and 3.1 Experiments with screenings 3.2 Assumptions and induced strategies 3.3 Constraints given by phase-II experiments 3.4 Persuasion ratio and the optimal signaling structure 3.5 Comparison with classical Bayesian persuasion strategies 4 Binary-outcome Experiments in Multi-phase trials and 4.1 Model of binary-outcome experiments in multi-phase trials 4.2 Determined versus sender-designed experiments 4.3 Multi-phase model and classical Bayesian persuasion and References 3.5 Comparison with classical Bayesian persuasion strategies Given the optimal signaling strategy derived in Lemma 5, one natural followup question is the quantification of the sender’s utility improvement obtained by adopting the optimal signaling strategy in comparison to using strategies structurally similar to the optimal strategies in classical Bayesian persuasion for a binary state of the world. Owing the page limit, we directly define a class of strategies structurally similar to the classical Bayesian persuasion strategy below and provide the justification in our online version [23]. Definition 4. With binary states of the world, a (binary-state) Bayesian persuasion (BBP) strategy is a strategy that “mixes two possible states in one signal and reveals the true state on the other signal”. This paper is available on arxiv under CC 4.0 license. Authors: (1) Shih-Tang Su, University of Michigan, Ann Arbor (shihtang@umich.edu); (2) Vijay G. Subramanian, University of Michigan, Ann Arbor and (vgsubram@umich.edu); (3) Grant Schoenebeck, University of Michigan, Ann Arbor (schoeneb@umich.edu). Authors: Authors: (1) Shih-Tang Su, University of Michigan, Ann Arbor (shihtang@umich.edu); (2) Vijay G. Subramanian, University of Michigan, Ann Arbor and (vgsubram@umich.edu); (3) Grant Schoenebeck, University of Michigan, Ann Arbor (schoeneb@umich.edu). Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2. Problem Formulation 2. Problem Formulation 2.1 Model of Binary-Outcome Experiments in Two-Phase Trials 2.1 Model of Binary-Outcome Experiments in Two-Phase Trials 3 Binary-outcome Experiments in Two-phase Trials and 3.1 Experiments with screenings 3 Binary-outcome Experiments in Two-phase Trials and 3.1 Experiments with screenings 3.2 Assumptions and induced strategies 3.2 Assumptions and induced strategies 3.3 Constraints given by phase-II experiments 3.3 Constraints given by phase-II experiments 3.4 Persuasion ratio and the optimal signaling structure 3.4 Persuasion ratio and the optimal signaling structure 3.5 Comparison with classical Bayesian persuasion strategies 3.5 Comparison with classical Bayesian persuasion strategies 4 Binary-outcome Experiments in Multi-phase trials and 4.1 Model of binary-outcome experiments in multi-phase trials 4 Binary-outcome Experiments in Multi-phase trials and 4.1 Model of binary-outcome experiments in multi-phase trials 4.2 Determined versus sender-designed experiments 4.2 Determined versus sender-designed experiments 4.3 Multi-phase model and classical Bayesian persuasion and References 4.3 Multi-phase model and classical Bayesian persuasion and References 3.5 Comparison with classical Bayesian persuasion strategies Given the optimal signaling strategy derived in Lemma 5, one natural followup question is the quantification of the sender’s utility improvement obtained by adopting the optimal signaling strategy in comparison to using strategies structurally similar to the optimal strategies in classical Bayesian persuasion for a binary state of the world. Owing the page limit, we directly define a class of strategies structurally similar to the classical Bayesian persuasion strategy below and provide the justification in our online version [23]. Definition 4. With binary states of the world, a (binary-state) Bayesian persuasion (BBP) strategy is a strategy that “mixes two possible states in one signal and reveals the true state on the other signal”. Definition 4. With binary states of the world, a (binary-state) Bayesian persuasion (BBP) strategy is a strategy that “mixes two possible states in one signal and reveals the true state on the other signal”. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

How Optimal Signaling Outperforms Classical Bayesian Strategies in Multi-Phase Trials

Writing and Selling Unit Tests: When You Need Them and When You Don't

Single-Threaded Node.js is a Myth  

HDR or SDR? A Study of Scaled and Compressed Videos: Objective Assessment

For most founders, landing Fortune 500 customers like GrubHub, Warby Parker & RBC Royal Bank is the definition of success. Imagine, then, telling these customers that you’re pivoting from your successful product to focus on something new. But that’s exactly what did — deprecating and launching . Jonathan Taplytics DevCycle Why pivot? Taplytics is a broad A/B testing platform for marketing teams. While DevCycle is a feature flagging tool built for developers. Taplytics actually has feature flagging, but DevCycle is much more focused and plans to compete directly with incumbents like by building a better developer experience (more on how later). LaunchDarkly But with Taplytics, they built so many features, and every customer was using them in a different way that it became hard for such a small team to maintain. Meanwhile, feature flagging is a much narrower problem where most customers will have similar use cases. In addition, Taplytics required lots of face-to-face time selling to executives, while DevCycle is more of a bottoms-up, self-serve sales process. Finally, we didn’t cover it in the interview, but feature flagging is a huge market. LaunchDarkly was in 2021. valued at $3 billion How did they make the pivot? Jonathan shared some interesting tidbits on what they did well. — they were able to bring many of their customers on the journey with them. There was two-way trust, and they struck me as a very transparent team. Have good relationships with your big customers For feature flagging, they shared that DevCycle would be a direct (better) replacement. For Taplytic’s other features, they communicated clearly and shared alternatives (such as ) while also continuing to maintain the Taplytics platform for those customers who are still in the process of migrating over. Ensure continuity for your customers — OneSignal With this approach, Jonathan shared that they were able to bring almost all their key customers with them. What does developer experience mean? I have a healthy skepticism of founders claiming they will win with developer experience. In , I don’t think I’ve ever heard someone say developer experience wasn’t crucial, but the reality is that very few companies create great developer experiences. 60+ DevTools interviews So when Jonathan shared that he would beat the incumbents by building a better developer experience, I asked him what he really meant by developer experience. Your focus is on building a great core product that delights more than frustrates. As an example, they spent months on a custom testing system to ensure the consistency and quality of their server-side SDK. The team even jokes about having a landing page that just says, “DevCycle, well tested.” It’s not about building the flashiest and most marketable features. Iterating on core features can be boring when compared to shipping new things. So, they have a specific team focused on their core features. It’s about continually iterating on the core features. — DevCycle tries to make every piece of software that their clients install open source. With CNCF, they are leading a project called — a vendor-agnostic interface for feature flagging. Open-source & standardise interfaces OpenFeature Overall, this was a very brave decision to make, and only time will tell if it was the right call. However, I do believe that their commitment to quality and focus is credible, given the tough decision they made. P.S. I’m a lazy writer, so I condensed this write-up to only be about Jonathan. Even though the podcast conversation also included the equally great & . You can hear them in the . Andrew Brad full episode here Also published . here

Pivoting for Success: Jonathan's Startup Journey from Taplytics to DevCycle

Internet of Vehicles, or IoV, is the product of the marriage between the automotive industry and IoT. IoV data is expected to get larger and larger, especially with electric vehicles being the new growth engine of the auto market. The question is: Is your data platform ready for that? This post shows you what an OLAP solution for IoV looks like. What is special about IoV data? The idea of IoV is intuitive: to create a network so vehicles can share information with each other or with urban infrastructure. What‘s often under-explained is the network within each vehicle itself. On each car, there is something called Controller Area Network (CAN) that works as the communication center for the electronic control systems. For a car traveling on the road, the CAN is the guarantee of its safety and functionality, because it is responsible for: : The CAN is the pulse of the vehicle system. For example, sensors send the temperature, pressure, or position they detect to the CAN; controllers issue commands (like adjusting the valve or the drive motor) to the executor via the CAN. Vehicle system monitoring : Via the CAN, sensors send the speed, steering angle, and brake status to the controllers, which make timely adjustments to the car to ensure safety. Real-time feedback : The CAN allows for data exchange (such as status and commands) between various devices, so the whole system can be more performant and efficient. Data sharing and coordination : The CAN keeps an eye on devices and components in the system. It recognizes, configures, and monitors the devices for maintenance and troubleshooting. Network management and troubleshooting With the CAN being that busy, you can imagine the data size that is traveling through the CAN every day. In the case of this post, we are talking about a car manufacturer who connects 4 million cars together and has to process 100 billion pieces of CAN data every day. IoV data processing To turn this huge data size into valuable information that guides product development, production, and sales is the juicy part. Like most data analytic workloads, this comes down to data writing and computation, which are also where challenges exist: : Sensors are everywhere in a car: doors, seats, brake lights... Plus, many sensors collect more than one signal. The 4 million cars add up to a data throughput of millions of TPS, which means dozens of terabytes every day. With increasing car sales, that number is still growing. Data writing at scale : This is perhaps the best manifestation of "time is life". Car manufacturers collect the real-time data from their vehicles to identify potential malfunctions, and fix them before any damage happens. Real-time analysis : It's hard to talk about huge data size without mentioning its costs. Low cost makes big data processing sustainable. Low-cost computation and storage From Apache Hive to Apache Doris: a transition to real-time analysis Like Rome, a real-time data processing platform is not built in a day. The car manufacturer used to rely on the combination of a batch analytic engine (Apache Hive) and some streaming frameworks and engines (Apache Flink, Apache Kafka) to gain near real-time data analysis performance. They didn't realize they needed real-time that bad until real-time was a problem. Near Real-Time Data Analysis Platform This is what used to work for them: Data from the CAN and vehicle sensors are uploaded via 4G network to the cloud gateway, which writes the data into Kafka. Then, Flink processes this data and forwards it to Hive. Going through several data warehousing layers in Hive, the aggregated data is exported to MySQL. At the end, Hive and provide data to the application layer for data analysis, dashboarding, etc. MySQL Since Hive is primarily designed for batch processing rather than real-time analytics, you can tell the mismatch of it in this use case. : With such a huge data size, the data ingestion time from Flink into Hive was noticeably long. In addition, Hive only supports data updating at the granularity of partitions, which is not enough for some cases. Data writing : The Hive-based analytic solution delivers high query latency, which is a multi-factor issue. Firstly, Hive was slower than expected when handling large tables with 1 billion rows. Secondly, within Hive, data is extracted from one layer to another by the execution of Spark SQL, which could take a while. Thirdly, as Hive needs to work with MySQL to serve all needs from the application side, data transfer between Hive and MySQL also adds to the query latency. Data analysis Real-Time Data Analysis Platform This is what happens when they add a real-time analytic engine to the picture: Compared to the old Hive-based platform, this new one is more efficient in three ways: : Data ingestion into Apache Doris is quick and easy, without complicated configurations and the introduction of extra components. It supports a variety of data ingestion methods. For example, in this case, data is written from Kafka into Doris via , and from Hive into Doris via . Data writing Stream Load Broker Load : To showcase the query speed of Apache Doris by example, it can return a 10-million-row result set within seconds in a cross-table join query. Also, it can work as a unified query gateway with its quick access to external data (Hive, MySQL, Iceberg, etc.), so analysts don't have to juggle between multiple components. Data analysis : Apache Doris provides the Z-Standard algorithm that can bring a 3~5 times higher data compression ratio. That's how it helps reduce costs in data computation and storage. Moreover, the compression can be done solely in Doris so it won't consume resources from Flink. Computation and storage costs A good real-time analytic solution not only stresses data processing speed, it also considers along your data pipeline and smoothens every step of it. Here are two examples: 1. The arrangement of CAN data In , CAN data was arranged by the dimension of CAN ID. However, for the sake of data analysis, analysts had to compare signals from various vehicles, which meant to concatenate data of different CAN ID into a flat table and align it by timestamp. From that flat table, they could derive different tables for different analytic purposes. Such transformation was implemented using Spark SQL, which was time-consuming in the old Hive-based architecture, and the SQL statements are high-maintenance. Moreover, the data was updated by batch daily, which meant they could only get data from a day ago. Kafka In Apache Doris, all they need is to build the tables with the , specify VIN (Vehicle Identification Number) and timestamp as the Aggregate Key, and define other data fields by . With Doris, they don't have to take care of the SQL statements or the flat table, but are able to extract real-time insights from real-time data. Aggregate Key model REPLACE_IF_NOT_NULL 2. DTC data query Of all CAN data, DTC (Diagnostic Trouble Code) deserves high attention and separate storage, because it tells you what's wrong with a car. Each day, the manufacturer receives around 1 billion pieces of DTC. To capture life-saving information from the DTC, data engineers need to relate the DTC data to a DTC configuration table in MySQL. What they used to do was to write the DTC data into Kafka every day, process it in Flink, and store the results in Hive. In this way, the DTC data and the DTC configuration table were stored in two different components. That caused a dilemma: a 1-billion-row DTC table was hard to write into MySQL, while querying from Hive was slow. As the DTC configuration table was also constantly updated, engineers could only import a version of it into Hive on a regular basis. That meant they didn't always get to relate the DTC data to the latest DTC configurations. As is mentioned, Apache Doris can work as a unified query gateway. This is supported by its feature. They import their DTC data from Hive into Doris, and then they create a MySQL Catalog in Doris to map to the DTC configuration table in MySQL. When all this is done, they can simply join the two tables within Doris and get real-time query response. Multi-Catalog Conclusion This is an actual real-time analytic solution for IoV. It is designed for data at really large scale, and it is now supporting a car manufacturer who receives 10 billion rows of new data every day in improving driving safety and experience. Building a data platform to suit your use case is not easy, I hope this post helps you in building your own analytic solution. Also published here.

How Big Data in IoV Analytics Prevents Accidents

RAILWAY SIGNALLING.

Backlinks, or links from one website to another, have served as a crucial ranking signal since the ascent of search engines in the late 90s. While they are no longer the only signal used by Google to determine how to rank search results, they continue to remain important. However, the problem with using backlinks as a ranking factor is that the system can easily be manipulated. By throwing money around, it's easy to acquire links even from some of the most authoritative websites in the world. The art of manipulating the search engine (termed ‘optimization’ to not sound mischievous) is an . Top dollar not only influences the search results but also creates an unlevel playing field for smaller businesses and content creators. $80 billion industry While Google is and claims that they are than they were in the past, several case studies available on the internet would prove that buying backlinks from big websites can give you top rankings if you tick some of the other boxes. A conspiracy theorist could also argue that Google is purposely keeping link-building alive to compel small businesses into spending money on Google Ads. progressively de-emphasizing the importance of backlinks a lot less important now The rise of Large Language Modelling (LLM) and the amount of user data in Google’s possession should be enough for it to retire this ranking signal for good. Backlinks are a relic of the past, and it's time to rank content based on  merit and relevance to the search intent of the user. History of Backlinks as a Ranking Factor: A 25-Year-Old Tech That Doesn’t Die! In 1996, Sergey Brin and Larry Page, founders of Google, developed the idea of ‘PageRank’. The core principle of PageRank was fairly simple. It would determine a website’s authority based on how other websites link to it. This approach was a departure from the then-prevalent practice of —a method susceptible to manipulation through keyword stuffing. ranking websites based on keyword presence PageRank would shape the future of search engines and establish Google’s monopoly in the market that we see today. This search engine monopoly has . only started to be challenged recently after Microsoft got involved with OpenAI During Google’s initial run, the more backlinks a website had, the better it ranked on the Search Engine Results Pages (SERPs). Over time, the algorithm evolved, placing greater emphasis on link quality and relevance. Google's algorithmic updates also began accounting for factors like site experience, user behavior, content freshness, and how a piece of content “fits within the whole context of the web”. Yet, even after all of these developments, backlinks continue to remain a critical signal for ranking search results, 25 years later. While the web has evolved drastically, this one idea, that search results should be ranked with respect to how other websites cite them, has continued to have major influence. The Problem with Backlinks Despite Google repeatedly claiming that backlinks are not as important as they used to be, there are hundreds of examples that prove otherwise. Here are some of them: Brian Dean, founder of Backlinko, . (Published in 2022) increased traffic by 110% using link building Ahrefs ranked a . (Published in 2020) ‘Stats’ page at #1 by building backlinks Chris from Sirlinksalot ranked a keyword with backlinks alone to make . (Published in 2022) traffic jump from 160 to 4000 a month An from SEO chatter. aggregated list of case studies Wait, these studies are from  SEO companies. Isn’t it like__ __ to show sugar is not bad for you? Coca Cola sponsoring scientific research Yes, but most people in digital marketing are aware of the fact that backlinks are still being bought and sold in 2023, and they indeed have an impact. Good content and user experience remain essential, but backlinks play a crucial role in achieving higher rankings. Every website that has good gets thousands of requests for guest posts and link inserts every month. Even the biggest companies you know for backlinks. Often, these firms mask their intent by purchasing guest post slots, not primarily for traffic but for securing those precious . Some large entities even engage in direct link exchanges, being seemingly immune to penalties. domain authority PAY do-follow links Google has fostered a culture where you either pay your way into the SERPs or pay them for advertising. Simply creating good content is not sufficient. Consequently, many aiming for organic search traffic resort to these tactics as failing to do so could lead to someone else taking their place. It's a bit like . It's a common practice, but nobody openly admits to it. For actors, it's because it's illegal, and for businesses buying backlinks, it's because it violates Google's Terms of Services. I am not blaming anyone for being involved in these practices. steroid use in Hollywood Can Websites Be Efficiently Ranked Without Backlinks? Tech has improved significantly since backlinks were first used as a ranking signal. And, as of now, they are just one signal among many but they continue to be crucial. With the rapid advancements in AI technology, coupled with the vast user data that Google amasses, there's a valid argument for eliminating reliance on backlinks. The potential lies in using AI to assess content quality, align search results with user intent, and incorporate user engagement metrics for refined search results. If you submit an article to ChatGPT and ask it to critique your article and assess its relevance for a specific keyword, the tool will do a very good job at highlighting the strengths, weaknesses, and areas for improvement. Granted, ChatGPT is prone to but Google with all the technological backing should be able to refine this process. hallucinations Over time, the potential of AI to not only scrutinize content but also compare all the search results with each other and find the best matches to address the intent behind a search term. The search engine already takes hundreds of signals into consideration for ranking content and a focus on identifying quality content should be at the forefront. A glance at today's search results reveals a pattern: many articles seem like rehashed versions of others. Barely a few websites create inspired, high-value content. Most just "spray and pray," creating content as if they were growing paddy. Many writers tasked with creating content for SEO simply read other articles and create a cocktail of all the information they gather to provide you with a 'brand new' article. This isn't necessarily the fault of content creators or the websites they work for. When websites need to continuously churn out content and update their existing content as frequently as possible while also paying for backlinks to maintain their rankings, this is to be expected. Web search desperately needs an alternate method for analyzing the value of content. By fusing the capabilities of LLMs in content assessment with other crucial ranking signals, such as aggregated website interaction metrics, search engines can severely reduce backlink dependency. Google tracks our data as much as they can. No reason why they can’t actually use it to improve our search experiences. The Biggest Challenge: Determining Authority While analyzing content and user interactions seems to be a foolproof plan, I have to admit that there’s one major hiccup in this process. If a website’s backlink profile is not taken into consideration, how does the search engine determine the authority and the legitimacy of the website? Let’s take an example. Imagine two websites, Website A and Website B, both publishing articles on the same keyword. Both articles are equally exceptional, crafted by talented writers. However, Website A is a reputable business known for its credibility, while Website B is disseminating misleading information as part of a propaganda campaign. In this scenario, how does a search engine like Google ascertain brand authority without analyzing backlink profiles? Even if other brand authority signals are considered, this conundrum raises important questions about the fair representation of websites in search results. Relying solely on indicators of brand authority risks monopolizing SERPs in favor of well-established brands while smaller businesses are compelled to invest heavily in advertising to compete. So, what’s the solution? I don’t know. Perhaps this is something greater minds need to solve. It can also be argued that the website that's running propaganda can simply skip the hurdles by creating adverts and get the same results as long as they fill Google’s pockets. So, even if you are able to correctly determine brand authority, businesses with nefarious purposes will always continue to thrive. In any case, while the solution may not be immediately apparent, it's worth considering various perspectives and potential approaches to strike the right balance. Why Doesn’t Google Solve The Backlink Problem? For a new business looking to dive into content marketing or a website simply wanting to share information, the barrier to entry is extremely high. SERPs are dominated by authoritative websites with years in the market, and in most cases for good reasons, as I mentioned earlier. To establish themselves, new entrants find themselves funneling vast resources into content generation, backlink acquisition, and other SEO practices. It can take months for their content to gain visibility. To compound the challenge, maintaining rankings often requires consistent content updates and backlink building (not always, but in many cases). Neglecting either can result in a substantial drop in rankings. So, what's the alternative? It's quite straightforward. Pay Google to display your content as advertisements. Google's annual advertising revenue reached a staggering 224.47 billion U.S. dollars in 2022. Maybe the problem of backlinks is indeed difficult to solve, as determining authority without using backlinks is complex, or perhaps... puts on the tinfoil hat… Google doesn't want to change the status quo as doing so would jeopardize their ad revenue. You be the judge! Summing Up The practice of using backlinks to determine website and page authority for ranking content dates back to the inception of Google. In fact, it played a pivotal role in establishing Google as the premier search engine. However, the $80 billion SEO industry reveals that financial resources often dictate who can secure good positions on the SERPs. While Google has claimed that backlinks are losing importance and will continue to do so, recent case studies and the sheer number of backlink requests that webmasters receive from some of the most authoritative websites on the internet prove otherwise. Paying for backlinks or publishing articles on other websites with the sole intention of acquiring backlinks is against Google's Terms of Service, but it remains a common practice that brands feel compelled to engage in to maintain their positions on the SERPs. Moreover, if you're willing to pay enough, you can acquire links from some of the biggest websites on the internet. This creates an unfair disadvantage for smaller businesses and content creators who are at a significant disadvantage when competing with larger brands. While completely eradicating the importance of backlinks is a formidable challenge as they play a pivotal role in measuring website authority to uphold the quality and reliability of search results, Google needs to find a way to ensure that quality content truly rules the web. Recent advancements in AI and the wealth of data at Google's disposal should provide the means to address this issue. This effort would not only enhance the user experience but also enable small businesses and content creators to gain more visibility. This is undoubtedly a complex problem that Google should be addressing, unless they believe that doing so would cannibalize their ad revenue.

Dear Google, It is Time to Kill Backlinks!

প্রিয় Google, এটি ব্যাকলিংকগুলিকে হত্যা করার সময়!

Lieber Google, es ist Zeit, Backlinks zu töten!

Querido Google, ¡es hora de acabar con los vínculos de retroceso!

Cher Google, il est temps de tuer les backlinks !

प्रिय गूगल, अब बैकलिंक्स ख़त्म करने का समय आ गया है!

親愛なる Google の皆様、バックリンクを廃止する時が来ました!

친애하는 Google, 이제 백링크를 없앨 시간입니다!

Caro Google, é hora de eliminar backlinks!

Дорогой Google, пришло время уничтожить обратные ссылки!

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FTC v. Binance Court Filing, retrieved on March 27, 2023 is part of . You can jump to any part in this filing . This is part 22 of 31. HackerNoon’s Legal PDF Series here L. Examples of Market Participants Currently Trading on Binance and Binance’s Efforts to Help Them Evade Its Compliance Controls Trading Firm C 177. Trading Firm C, another quantitative trading firm that trades digital asset derivatives on Binance through automated trading strategies, is headquartered and incorporated in New York. Trading Firm C is majority owned by U.S. residents and the individual with the largest ownership share is also a U.S. citizen. Trading Firm C trades in numerous financial markets around the globe and is part of a corporate “umbrella” of commonly controlled affiliates and subsidiaries that has branch offices in London, Singapore, and Hong Kong, among other locations. 178. Zhao has communicated directly with Trading Firm C’s CEO, who has a New York phone number. In one Signal text chain, Trading Firm C’s CEO messaged to Zhao: Hello CZ. This is [first name of the CEO] from [Trading Firm C]. Firstly I wanted to reach out and thank you for meeting with me and my team several weeks ago. Secondly, I also kindly wanted to follow up with [you] regarding the day limit on Spot. I have been struggling internally with my portfolio management teams to get traction on increasing the day limit as we are looking to expand our trading as well as bring on a few more portfolio teams onto the platform. Please let me know when it would be a convenient time to discuss with you. Thank you. We had heard that we would be expecting an increase last Friday but that did not happen, and we are not sure how to interpret. Zhao replied within five minutes: “Looking into.” Zhao has also been a member of a Signal group chat titled “Binance/[Trading Firm C’s d/b/a],” that includes Trading Firm C’s New Yorkbased Chief Investment Officer. 179. Trading Firm C’s trading activity on Binance is conducted through automated trading strategies that use tailor made computer algorithms that take in market data and other trading information to generate orders to send to market to fulfil the strategies’ overall objectives. Development of Trading Firm C’s algorithms used for trading on Binance occurs in the United States and in other locations. 180. Trading Firm C conducts its trading activity on Binance through at least 15 independent trading teams, including teams based in and managed from New York. Trading Firm C’s trading teams are managed by “portfolio managers,” who are responsible for the overall management of their respective trading teams, including trading strategy development. Most, if not all, of the relevant portfolio managers are physically located in the United States and some are partners in Trading Firm C. 181. Trading Firm C’s trading activity, including its trading activity on Binance, relies on functionalities the development of which is supervised by three CTOs, two of whom are located in the United States and ultimately report to Trading Firm C’s New York-based CEO. 182. Trading Firm C and its commonly-owned corporate affiliate (a Delaware limited liability company) have been the real economic party to Trading Firm C’s trading activity on Binance at all times, regardless of whether it traded through accounts held by wholly-owned corporate subsidiaries or an off-shore nominee company. Trading Firm C’s net trading revenue derived from its trading activity on Binance is consolidated into the financial statements of Trading Firm C’s commonly-owned Delaware-incorporated affiliate and combined into Trading Firm C’s financial statements. 183. Initially, Trading Firm C traded on Binance through an account opened in the name of a Singapore-incorporated subsidiary. In approximately October 2021, Trading Firm C “migrated” its Binance trading activity to an account opened under the name of a corporate entity that is organized under the laws of the Cayman Islands. Binance confirmed that the “migration” would have “no impact on [Trading Firm C’s] trading.” Consistent with this representation, Binance ported over Trading Firm C’s VIP status including its reduced trading fees, as well as Trading Firm C’s low latency connection to Binance’s matching engines and ability to exceed Binance’s default order-messaging limits to the “new” account. 184. In connection with the opening of the “new” account, Trading Firm C informed Binance that it “structured” its Cayman Islands-incorporated nominee to roll up to a trust that owns certain “voting” shares but “does not hold any participating shares so all economic interest flows through [a holding company] and then to [Trading Firm C’s commonly-owned domestic affiliate] as the sole shareholder of the participating shares” so that Trading Firm C “retains all decision making power and economic interest” in its Binance account. Trading Firm C explained to Binance that the “main reason for using this structure is to comply with the Binance requirements” concerning “US beneficial ownership.” 185. Consistent with its understanding that the trading activity in the “new” account held by the Cayman nominee is attributable to Trading Firm C, Binance has communicated about that account with Trading Firm C personnel that use @[trading-firmc].com email addresses and through Telegram chat groups with titles that include “Binance” and “Trading Firm C.” 186. Trading Firm C continues to trade digital asset derivative products on Binance, including bitcoin perpetuals. 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 1:23-cv-01887 retrieved on September 4, 2023, from 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. docdroid.net

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