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Checkr

checkr

In quickly becoming one of the preeminent companies in the background check space, (YC S14) has experienced a steady and amazing amount of growth. With that rise in volume has come the demand for better insight into the operation of running background checks and, in turn, the need for reliable and scalable data processing and storage. Checkr Thus, in 2017, we started a significant push to building out our data infrastructure. What that meant depended on the demand we received both internally and externally, which we identified as the following: 1. Providing customer-facing analytics Many enterprise customers care deeply about the efficiency and accuracy of their background checks. Since a number of these customers operate at a high volume (some via extensive use of the ), questions like… Checkr API “What’s the average county record turnaround time for our applicants?” “What are the most common reasons for motor vehicle reports to not clear?” “What’s the daily background check volume by type?”… come up frequently as customer feedback. 2. Guiding product decisions Since the early days at Checkr, we’ve been fortunate to find great product/market fit, a position that’s served us well. As an example, our has been consistently remarked as simple to set up and comprehensive in its results. However, as our applicant base has grown, there has come a more pressing need to monitor applicant behavior, and to do so with ease. Access to disparate sources of data, be it application logs or events in a report lifecycle, offers an opportunity to construct a more holistic picture into the system’s health and inner workings. motor vehicle record check 3. Facilitating eng development During engineering development, we frequently need to query our production data to fetch and verify records. Nonetheless, querying against our transactional stores has historically been slow, as well as stressful on and potentially destabilizing to our production environment. 4. Enabling data scientists and machine learning engineers Beyond the above, looking forward, a key differentiator between Checkr and other background check services is our keen focus on automation, something we’ve managed to accomplish in part by building custom machine learning models for tasks such as name matching. Reducing burden for development of machine learning models — and as a result, the downstream operational burden — can assist in easing even the most mundane of tasks, such as data extraction. In this blog post, you’ll see an overview of how we established a stable data infrastructure to fuel our growth in the matter of a few months. We hope our insights can be useful for companies facing similar challenges at scale. In the beginning, we implemented some quick and dirty to enable infrequent querying of data. Our solution was by no means scalable, but it was sufficient to answer some key business questions. tools Here at Checkr, we rely heavily on MySQL ( ) and MongoDB ( ). We use MySQL to store volatile and structured data including users, reports and package, while leveraging MongoDB to store largish or more static content such as post-processed provider results or webhook logs. For simplicity, we won’t go into detail on some of the less frequently used storage at Checkr. Amazon RDS Atlas To fulfill the need to query production data, we created a temporary solution, as shown in the graph above. For our MySQL cluster, we used replication and allowed clients to open direct connections to our replica set. Since MongoDB is a schemaless key-value store, we built a streaming service on top of by Stripe, which tailed MongoDB’s oplog and stored the resulting data — mapped by a schema defined per Mongo collection — in a PostgreSQL instance. From there, we allowed clients to open direct connections to our PostgreSQL instance. This simple architecture served us well at the time for ad-hoc data queries. However, using this approach, there were some fairly significant and key disadvantages: MoSQL Joining across stores was difficult to impossible. Since some data resided in MySQL and other data resided in MongoDB, complex reports often required a federated query, something that wasn’t easily manageable given the above architecture. . Infrequently, we ran into MoSQL stability issues, and had to kick off processes to “catch up” and/or “restart” replication. If anything ever changed in an upstream collection, we also needed to update schemas defined in our streaming service. As many fields were in JSON, parsing for analysis was also complicated. Lastly, with extensive use of embedded documents in MongoDB, seemingly simple queries were slow and difficult to author. MoSQL, mo’ like mo’ maintenance To no surprise, building complex logic to support analytics quickly grew out of control. To address the above challenges, it became clear that 2017 would be the year we’d double down on our data infrastructure investment. In the course of a few months, we established more robust and stable data infrastructure. To complete the entire picture, we needed: federated querying engine schema solution, reliable data storage workflow engine data analysis tools As a rapidly growing startup, we had limited engineering resources, so, in our design decisions, we generally favored well-adopted and pre-existing solutions, be it a popular OSS project or enterprise solution. In being an early adopter of a microservices architecture, it made this transition easier than originally anticipated. As we’d already invested in simple and fast introduction of new services into our ecosystem, we had the flexibility to adeptly select different components, choosing what we determined to be the best tool for each respective job. Checkr data infrastructure in mid 2017 For the foundational layer of our data infrastructure, we required a tool to “stitch” all our production data together. The solution could have been a consolidated database or a federated querying engine with access to various sources of production data. Given the nature of our initial needs (i.e. analytical data could be latent), we agreed to first take the traditional ETL approach and focus on creating stable batch snapshots of our data. In the end, we chose as our query engine to provide federated access to numerous data sources, as we saw several advantages: Presto Presto is extremely fast, not to mention in use at large scale at Facebook, Airbnb, Netflix, and more. Given the size of our data (hundreds of gigabytes), we knew Presto could handle our use at all levels. Presto is not a database, which, at minimum, saves us the trouble of maintaining yet another database. Presto has extensive support for different connectors, covering MySQL, MongoDB, and more. Presto has a renowned community and is actively maintained. That said, we did experience some incompatibilities with connectors. As one example, at one point, we needed to add SSL support for the MongoDB connector. Shortly thereafter, for the MySQL connector, we found that we had to introduce the ability to parse certain data types as varchar. Even with our hiccups though, setting up Presto was as straightforward as it gets, with the speed and reliability far trumping any challenges posed in getting our cluster up and running. And one of the main reasons for that being the case was through the use of our beloved deployment tool . As it stands today, our cluster has 20 nodes with 2 CPU cores and 24G of memory, with each node running in a in Kubernetes. Sounds complicated, but Codeflow made all of that easily to define and manage. Codeflow Docker container You can see our Presto deployment setup from below: Codeflow Presto deployment page on Codeflow We intentionally skipped some other popular data infrastructure solutions, such as Hadoop or Redshift. Hadoop requires a high level of maintenance, and as we currently aren’t concerned about operating at petabyte scale, it was a non-starter. We also elected Presto over Redshift because a) we’d identified it as a potentially expensive solution for our use-case and b) we didn’t want to maintain and update our schema (covered in the next section). MongoDB is great in that it provides a schemaless solution for easy storage of diverse document types, while also scaling horizontally “out-of-the-box” and at relatively low cost. Nonetheless, it also presents challenges for querying and performing data analysis, both obstacles when data warehousing. As a storage schema is the heart of any ETL process, to extract data successfully, we needed a process for inferring a target schema from our MongoDB data. After some research, we landed on implementing a MongoDB schema analyzer, built atop an open-source tool, . With the analyzer, we could regularly update collection schemas used by the PrestoDB connector with little to no maintenance cost. Variety With a way to neatly map all fields to a target table schema, we had a method for extracting data from MongoDB. We then needed a means to load data into our data warehouse of choice. For this part of the process, we chose and its metastore, in part because it’s natively supported by Presto. Given that Hive can be deployed as a standalone service without any Hadoop infrastructure, using the Hive connector, we were able to efficiently extract data from MySQL and MongoDB by just writing Presto queries, and perform some simple data transforms as needed before outputting processed data into our data warehouse. Hive Checkr is a cloud-based company, so to no surprise, a lot of our data is stored in the cloud, specifically on AWS. With that, we wanted a cost effective way to store large chunks of data for data analysis and was the obvious choice. While many traditional data warehouse solutions use distributed file systems such as HDFS, our Presto + Hive approach integrated particularly well with S3, because S3 As Presto handles all data manipulation in memory, there is limited “temporary storage” on disk needed. Since we configured a large memory footprint for our Presto cluster, disk IO to S3 is limited as a large amount of data is cached. It’s worth noting that there are also limitations with this approach, as the Presto cluster needs to have enough memory to hold all data in-memory for any given query. While there are some possible optimizations available — including splitting up data into separate queries -, those approaches can be tedious to implement. By keeping data on S3, we achieved high reliability and were also able to keep multiple snapshots with relatively low cost. There are many existing scheduling engine solutions out there, including and . We decided to choose because it has an active community and is well maintained. There are many great features that come shipped with Airflow, the following are a few that we like in particular: Pinball Luigi Airflow Presto connector. It’s easy to setup connection to Presto. Since we have already consolidated all data storages with Presto + Hive, connection with Presto means we can query any data with Airflow. Easy to create operators. Airflow uses operators to complete different type of tasks, and it’s easy to write your own. For example, we implemented a Presto query operator with custom condition checks and retention rules. All presto query tasks can then reuse the operator with just a few lines of code. Monitoring and alerting. With Airflow, it’s easy to create workflow notifications and alerts with email, slack or pagerduty. Airflow can be integrated well with existing engineering services in your organization. Airflow also has OAuth support. We will have another blog post with details about how we use Airflow to create workflows here at Checkr, so stay tuned! With all the ETL pieces in place, we need to open up our dataset for data scientists and analysts, so we can extract values out of it. For basic querying needs, we provided support for SQL clients to connect to Presto. There is no built in OAuth support for Presto, so we had to create our own LDAP service to enable authentication for Presto. It’s good to enable authentication to Presto not only for security reasons, but also for monitoring usage and adoption ratio within the organization. For more advanced data users, we also created a notebook service. We used S3 as the file system with the open source . This made it easier to share analysis within the organization as we store all notebooks within the same bucket. The plugin also loads the directories in a passive way, so the notebook file explorer is fairly responsive even though all files are on S3. We also created code repo sync workflows to load Github repos onto S3 periodically. This makes it easier for notebooks to reuse logic from other repos. These workflows are controlled by Airflow and run on schedule. With Jupyter, data scientists can access any repo and any dataset we have through their browsers, which makes it a powerful analysis tool. Jupyter Jupyter S3 content manager As mentioned in the earlier section, one motivation for building this infrastructure was to provide analytics to our customers. We’re lucky enough to be in a world with many mature analytics providers. We picked as our analytics provider, primarily for the following reasons: Chartio Easy to setup. Has drag and drop dashboard building. Good embedding support. Fit naturally in our customer-facing web application. Ability to build custom dashboard for every customer. Compatible with Presto. The ease of setup saved us a significant amount of development time. We got a lot of positive feedback from our customers and were able to iterate quickly as our dashboards were highly configurable. One thing worth mentioning is the fact that we power customer-facing analytics directly with Presto. This is an unconventional approach and might not be a working solution for everyone. A more traditional approach is usually to use NoSQL to precompute possible permutations to achieve O(1) lookup, or to use a relational database with aggressive caching and limit segmentation and filtering functionality. We can use Presto directly because we generate semi-precomputed datasets and we have most data loaded in memory before a request. We make sure the data tables powering analytics only contain the columns necessary, and since currently our analytics customers are employers, we do not need to prepare analytics for hundreds of millions of users. Checkr customer facing analytics We’ve also focused on improving user event logging on our site. In addition to logging with MongoDB, we’re adding more extensive user logging, sending those events to to share with services like for funnel analysis. In exploring internal BI, we also connected with and others. We won’t go into too much detail on these integrations in this blog post. Segment Amplitude Looker And there you have it, the complete picture of Checkr’s data infrastructure as of today: Full Checkr data infrastructure with external integrations In the last few months, Checkr data infrastructure has come a long way and, already, we’re starting to see the benefits from our labor. Now we have daily dumps of our production data in S3, fast querying with Presto, powerful analysis tools including Jupyter and Looker, and application funnel analysis. We were able to complete all major components shown above in a relatively short amount of time thanks to the rapidly growing open source community and the microservices architecture we adopted early on. Additionally, our development progress has been greatly expedited by our open source deployment framework (much more to come about that later!). Codeflow If you too are interested in building data infrastructure at a rapidly growing startup — come join us in the next part of our journey! We’re hiring! , and Acknowledgements: Checkr data infrastructure was built in collaboration with Kurt Ruppel , Stewart Park , Grace Lee , Kevin Woods and the rest of the customer report and money team. Kurt Ruppel Michelle Berry gave valuable edits to this post. Additionally, many engineers and product managers across the company provided feedback. Huge appreciation to everyone involved in the project! Originally published on Checkr blog

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QA Checks for Big Datasets With Deequ & Statistical Methods

QA Kontroleer vir groot datastelle met Deequ en statistiese metodes

QA ትላልቅ የውሂብ ስብስቦችን በDeeku እና በስታቲስቲክስ ዘዴዎች ይፈትሻል

فحص ضمان الجودة لمجموعات البيانات الضخمة باستخدام Deequ والأساليب الإحصائية

QA ukax Jach’a Datos ukanakat Deequ & Métodos Estadísticos ukamp uñakipi

QA Deequ və Statistik Metodlarla Böyük Məlumat Kütlələrini yoxlayır

Праверка якасці вялікіх набораў даных з дапамогай Deequ і статыстычных метадаў

QA проверки за големи набори от данни с Deequ и статистически методи

QA provjerava velike skupove podataka pomoću Deequ i statističkih metoda

Comprovacions de control de qualitat per a grans conjunts de dades amb Deequ i mètodes estadístics

Kontrola kvality pro velké soubory dat pomocí Deequ a statistických metod

QA-tjek for store datasæt med Deequ og statistiske metoder

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QA مجموعه داده های بزرگ را با روش های Deequ و آماری بررسی می کند

Laadunvarmistus tarkistaa suuria tietojoukkoja Deequ- ja tilastomenetelmillä

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בדיקת QA עבור מערכי נתונים גדולים עם Deequ ושיטות סטטיסטיות

Provjere kvalitete za velike skupove podataka s Deequom i statističkim metodama

QA chèk pou Gwo Datasets Avèk Deequ ak Metòd Estatistik

A minőségbiztosítás nagy adathalmazokat ellenőrzi Deequ és statisztikai módszerekkel

QA-ն ստուգում է տվյալների մեծ հավաքածուները Deequ և վիճակագրական մեթոդներով

Pemeriksaan QA untuk Kumpulan Data Besar dengan Deequ & Metode Statistik

Controlli QA per grandi set di dati con metodi Deequ e statistici

QA ამოწმებს დიდი მონაცემთა ნაკრების Deequ და სტატისტიკური მეთოდებით

QA Deequ және статистикалық әдістермен үлкен деректер жиынын тексереді

ពិនិត្យ QA សម្រាប់សំណុំទិន្នន័យធំជាមួយនឹងវិធីសាស្រ្ត Deequ និងស្ថិតិ

QA Deequ жана статистикалык методдор менен чоң маалымат топтомдорун текшерет

QA Vérifier ba Big Datasets Na Deequ & Méthodes Statistiques

QA ກວດສອບຊຸດຂໍ້ມູນໃຫຍ່ດ້ວຍ Deequ & ວິທີການສະຖິຕິ

QA tikrina, ar nėra didelių duomenų rinkinių, naudojant „Deequ“ ir statistinius metodus

QA pārbauda lielas datu kopas, izmantojot Deequ un statistikas metodes

Manara-maso ny angona lehibe miaraka amin'ny Deequ & Statistical Methods ny QA

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QA-controles voor grote datasets met Deequ en statistische methoden

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Kontrole QA dla dużych zestawów danych z Deequ i metodami statystycznymi

QA د ډیکو او احصایوي میتودونو سره د لوی ډیټاسیټونو لپاره چک کوي

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QA Deequ සහ සංඛ්‍යානමය ක්‍රම සමඟ විශාල දත්ත කට්ටල සඳහා පරීක්ෂා කරයි

Kontrola kvality kontroluje veľké súbory údajov pomocou metód Deequ a štatistických metód

QA Inoongorora Mahombe Dataset ane Deequ & Statistical Methods

QA waxay ku hubisaa Xog-ururinta Weyn ee Deequ & Hababka Tirakoobka

Kontrollet e QA për grupe të mëdha të të dhënave me Deequ & Metodat Statistikore

КА провере за велике скупове података помоћу Дееку и статистичких метода

QA-kontroller för stora datamängder med Deequ & Statistical Methods

QA Hukagua Seti Kubwa za Data Na Mbinu za Deequ & Takwimu

டீக் மற்றும் புள்ளியியல் முறைகளுடன் பெரிய தரவுத்தொகுப்புகளுக்கான QA சோதனைகள்

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QA Deequ we statistika usullary bilen uly maglumat bazalaryny barlaýar

Sinusuri ng QA ang Malaking Dataset na May Deequ at Mga Paraan ng Istatistika

QA Yi Kambela Ti Datasets Letikulu Hi Deequ & Maendlelo Ya Tinhlayo

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QA Deequ va statistik usullar bilan katta ma'lumotlar to'plamini tekshiradi

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Fake Websites, Fake Companies, Fake Everything: What Can We Do to Protect Ourselves? Malware protection should act the same way, as a set of shields that protect the entire browser from any type of malware, not just traditional threats. 20. What Are the Most Common Malware Attacks on Macs? Apple started making computers in 1976, long before they became common in most households in the developed world. 21. How RAT Mutants, in Python, Steal Data and Evade Detection Even though malicious Python packages are found every day by our security researchers, a new type of malware we call RAT mutants is catching our attention. 22. 7 Ways to Prevent and Mitigate Malware Attacks Knowing how to prevent malware attacks or mitigate those that are already on your devices is crucial. Here are seven effective tips you need to adopt to secure 23. 5 Life-Saving Tips About Cyber Security Introduction: 24. What Organizations Should Learn From the Colonial Pipeline Breach The Colonial Pipeline Breach has shown us that ransomware attacks are not going away anytime soon and having been increasing due to the COVID-19 pandemic. 25. What Are The Cybersecurity Risks Associated With Personal Devices? Let's look at some of the most significant risks posed by your personal digital devices, and what we can do to boost your security. 26. The Disruption of Emotet and What we Know About it Emotet, which was once a Banking Trojan and became a full-blown botnet was the most successful and prevalent malware of 2020 by a long way. 27. How to Make a Malicious USB Device and Have Some Harmless Fun Make an inexpensive BadUSB from an old USB drive and learn about Windows shortcut files and how malicious actors are using .lnk files to spread malware. 28. True Hackers and the Monsters we Invent Common misconceptions about hacking that makes you vulnerable and how to avoid them. 29. Avoid Becoming a Victim to These Cybersecurity Threats in 2022 With increased hacking activity focused on vulnerable businesses and at-home employees, cybersecurity has become a major business priority. 30. Ransomware Is Getting "User Friendly" To Victims Cybersecurity risk regarding ransomware and how it is evolving with cryptocurrency. 31. The Rise and Fall of Dridex: What were the Most Prevalent Malwares of Last Spring With Cybersecurity constantly evolving, each month has its own malware that poses the greatest threat. These are the malware threats that were most concerning. 32. You'll Probably Be Exposed To These 5 Cybersecurity Threats In 2021 How to prepare yourself to face the Cybersecurity Threats in the year ahead? Check out the top 5 of the most expected attacks in 2021. 33. 7 Sneaky Ways Hackers Are Using Machine Learning to Steal Your Data Machine learning is famous for its ability to analyze large data sets and identify patterns. It is basically a subset of artificial intelligence. Machine learning uses algorithms that leverages previous data-sets and statistical analysis to make assumptions and pass on judgments about behavior.
The best part, software or computers powered by machine learning algorithms can perform functions that they have not been programmed to perform. 34. Types of Cross-Site Scripting(XSS) Attacks An article about how each Cross-Site Scripting Attack behaves in order to understand how to defend our systems on the web against them 35. What Vulnerabilities FreakOut Takes Advantage of for Creating a Botnet Check Point Research encountered several attacks that exploited multiple vulnerabilities, including some that were only recently published, to inject OS command 36. The Growing Importance of Cybersecurity Awareness The protection of your computer is becoming very important as more and more hackers learn how to make smarter invasions that would not be noticed at first. 37. Fraudulent Ads Exploit COVID-19 Fears on Disney's ESPN.Com Scam advertisements across major media outlets originate from DoubleClick and Xandr/AppNexus 38. PlugX & PortScan Chinese Malware Surfacing in Non-Tech Companies PlugX & PortScan Chinese Malware Surfacing in Non-tech Companies - shows Motivation Was Financial. 39. Steganography: How Hackers Hide Malware in Images Image steganography is a technique that attackers use to hide a secret piece of text, malware, or code inside of an image. This technique is difficult to detect 40. 7 Deadly Digital Sins: Understanding the Main Types of Malware Malware is everywhere these days. You've probably been a victim at some stage, and you may well not even know it. From all-too-visible ransomware attacks to botnets and adware, it's a complex picture, and new variants are emerging all the time. 41. How the Top Result In Google Could Be a Scam Website Jailbreak Checkrain Checkra1n Security Scam Malware 42. How Getting Hacked Led me to a Job in CyberSecurity ReThink Cybersecurity - Cyber Wellness  the New Healthly Livign Trend 43. Why smartphone hacking is a real threat & how to handle it Even if you aren't very tech-savvy, you may have heard about vulnerabilities in both Android and iOS platforms making the news. Despite these headlines, smartphone hacking is a real challenge many of us still don't take seriously. 44. CloudEye Turns Out To Be A Front For Hackers spreading GuLoader Conservative estimates put company’s monthly income at a minimum $500,000. 45. ☢️ Dissecting DEFENSOR - An Android Malware That Affects Your Banking Apps Android malware apps are nothing new, but this one is of particular interest in how it implements no such functionality that can be readily detected by security products. The apps named DEFENSOR ID and Defensor Digital rely mainly on Android's Accessibility Service to conduct malicious activities, and go undetected. 46. Juice Jacking: Is it a Real Threat? When a USB outlet or charger cable already attached to an adapter is modified to supply power and gather data, this is known as juice jacking. 47. Reversing a NodeJS Malware and Finding its Author Reverse engineering a NodeJS Discord malware and exposing the authors by finding the webhook they used. 48. The Essential Guide to Email Security: Threats, Costs, and Strategies We all use email on a regular basis, but we aren’t always cognizant of the email security standards we use. If a hacker gains access to your account, or manages to fool you into downloading an attachment with malware via email, it could have devastating consequences for your business. 49. How a Wormable Android Malware Used to Spread Using WhatsApp Auto-Replies Android Malware has started spreading by creating auto-replies to messages in WhatsApp. It's hidden cleverly in dubious apps that are surprisingly good. 50. How to Secure Your Crypto Wallet There are several reports of threats and attacks on crypto wallets every day. You must be on the lookout to keep your investment safe and secure from hackers. 51. Analyzing the Source Code of Popular Desktop Apps for NodeJS Malware. Part2 Following my last article about a Discord malware, we now take a deeper look inside the inner workings of the PirateStealer malware. 52. How To Prevent Trending Ransomware Attacks What is ransomware? Why is it so dangerous? Who can be affected? How to get rid of it…or maybe to prevent it? 53. LocalBitcoins Review: I'm Not the Only One Who's Been Scammed This isn’t a review, and — as much as I wish it was — it’s not a promotion  either. It’s a firsthand account… a testament to the continual negative  impact of frauds in the crypto space. I wrote this article to make the  community aware that LocalBitcoins doesn’t consider to accept moral  responsibility when users get scammed on the platform. 54. What if Malware isn’t Slowing Down Your Games? Most experienced gamers realize that many different kinds of malware can slow down their games. 55. How to Ensure Privacy and Security on Your Smartphones All you need to know about the security loopholes in your android device and how to protect your mobile phones from privacy invasions and security breaches. 56. An Introduction to Malware Analysis Malicious software (Malware) are either programs or files that are intended to cause harm to a system and thereby its user. 57. Lessons Learned From SolarWinds: Defensive Strategy Against Novel Malware SolarWinds, a leading provider of IT monitoring and management solutions for enterprises, disclosed that it had fallen prey to a widespread supply chain attack 58. The Story Behind The Most Popular Android Malware Distributor on Dark Web Now more than ever, we rely on our smartphones to keep in touch with our work, our families and the world around us. There are over 3.5 billion smartphone users 59. Hunting Exploits by looking for the Author’s fingerprints: Graphology of an Exploit When analyzing a complicated attack against one of our customers, we noticed a very small 64-bit executable that was executed by the malware. Thank you for checking out the 59 most read stories about Malware on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

59 Stories To Learn About Malware

Previous Parts: Clean Code: Functions and Methods in TypeScript [Part 1] Clean Code: Naming and Code Composition in TypeScript [Part 2] Table of contents Classes a. Naming b. Encapsulation c. Classes instead of prototypes d. Method chaining e. Access modifier static Objects a. Fields b. Accessors Getters/setters Closures 1 Closures 2 Classes . a Naming When you create a class you already set the context for the class in its name. It will help us to avoid unnecessary context for class methods. // Bad
class UserRoleServive {
  getUserRole() {}
  checkUserRole() {}
}

// Better
class UserRoleServive {
  getRole() {}
  checkRole() {}
} . b Encapsulation // Bad
class MakeUserProfile {
  constructor() {
    this.email = null;
    this.phone = null;
    /..logic../
  }
}

const userProfile = new MakeUserProfile();
userProfile.email = 'any@gmail.com'; In this example, fields are opened for modification: we can delete fields, or assign not valid values. It is not safe. userProfile The better way is to encapsulate some class fields and use to get value and to set value. As setter is a method we can make some checking if value is valid before assignment. getters setters // Better
class MakeUserProfile {
  
  private email = null;
  private phone = null;

  getEmail(): string {
    return this.email;
  }
  
  setEmail(emailVal: string): void {
    // check validity before assignment
    if (typeof emailVal === 'string' && isNotUsedBefore(emailVal)) {
        this.email = emailVal;
    }
  }
}

const userProfile = new MakeUserProfile();
userProfile.setEmail('any@gmail.com'); c. Classes instead of prototypes syntax is more concise and easier to understand. class // bad
function UserProfile(name) {
  this.name = name;
}

UserProfile.prototype.checkName = function() {
  /..logic../
};

// good
class UserProfile {
  constructor(name) {
     this.name = name;
  }

  checkName(): boolean {
    /..logic../
} d. Method chaining This pattern is very useful and you can see it in many libraries such as and It allows your code to be expressive, and less verbose. For that reason, I say, use method chaining and take a look at how clean your code will be. In your class functions, simply return at the end of every function, and you can chain further class methods onto it. jQuery Lodash. this // Bad
class User {
  constructor(name: string, email: string, phone: string) {
    this.name = name;
    this.email = email;
    this.phone = phone;
  }

  setCountry(country: string): void {
    this.country = country;
  }

  setAddress(address: string): void {
    this.address = address;
  }

  setId(id: string): void {
    this.id = id;
  }

  save(): Observable<void> {
    /..logic../
  }
}

const user = new User("Tom", "888@gmail.com", "888");
user.setCountry("France");
user.setId("100");
user.save(); // Better
class User {
  constructor(name: string, email: string, phone: string) {
    this.name = name;
    this.email = email;
    this.phone = phone;
  }

  setCountry(country: string): IUser {
    this.country = country;
    return this;
  }

  setAddress(address: string): IUser {
    this.address = address;
    return this;
  }

  setId(id: string): IUser {
    this.id = id;
    return this;
  }

  save(): Observable<void> {
    /..logic../
  }
}

const user = new User("Tom", "888@gmail.com", "888");
user.setCountry("France").setId("100").save(); e. Access modifier static Class methods should employ the use of , or they should be transformed into static methods unless an external library or framework necessitates the utilization of particular non-static methods. If a method is designated as an instance method, it should imply that it functions differently based on the properties of the object it is called on. this // Bad
class User {
  notify(): void {
    console.log('User Loaded');
  }

// Better - static methods aren't expected to use this
class User {
  static notify(): void {
    console.log('User Loaded');
  }
}

User.notify(); Objects a. Fields Use bracket notation when accessing properties with a variable. [] const user = {
  name: 'Tom',
  age: 28,
};

function getUserProp(prop) {
  return user[prop];
}

const age = getUserProp('age'); b. Accessors In this case, we will take a look at very similar to Class encapsulation logic. If object fields are not defended from the wrong assignment or deletion our code becomes unsafe.  Lets take a look at different approaches. : Encapsulate object fields with getters and setters // Bad
const user = {
  email: null,
  phoe: null,
}

// assign unexpected value
user.email = [{}];

// delete field
delete user.email; In this example, we can freely assign the wrong value to fields or delete fields. user The solution will be to use getters and setters for fields. As setters are functions we also can make additional checks before assignment: // Better
const user = {
  
  get email(): string {
    return this._email;
  },

  set email(value: string) {
    if (typeof value !== 'string') {
     return;
    }
    this._email = value;
  }
}; Use closures when a function builds an object Sometimes we prefer to create function that built objects. In this case, we also need to defend it from unexpected behavior. Let’s take a look at bad example. // Bad
function makeUserProfile(): UserProfile {
  /..logic../

  return {
    email: null,
    phone: null,
    // ...
  };
}

const userProfile = makeUserProfile();

// Let's assign unxpected value
userProfile.email = new Error();

// And delete this field
delete userProfile.email; Here we also open fields and give the ability to break our logic. userProfile Let’s improve with closures: // Better
function makeUserProfile(): UserProfile {
  // make the field private
  let email = null;

  // getter for email field
  function getEmail(): string {
    return email;
  }

  // setter for email field
  function setEmail(emailVal: string): void {
    // check validity before assignment
    if (emailVal && isNotUsedBefore(emailVal)) {
        email = emailVal;
    }
  }

  return {
    getEmail,
    setEmail,
  };
}

const userProfile = makeUserProfile();
userProfile.setEmail('any@gmail.com'); Another way to make closures // Bad
const UserProfile = function(name) {
  this.name = name;
};

UserProfile.prototype.getName = function getName() {
  return this.name;
};

const userProfile = new UserProfile("Any user");
console.log(`User name: ${userProfile .getName()}`); // Any user: Any user
delete userProfile.name;
console.log(`User name: ${userProfile .getName()}`); // User name: undefined In this example, we also make field public. name Let’s improve with closures to prevent deletion: // Better
function makeUserProfile(name: string) {
  return {
    getName(): string {
      return name;
    }
  };
}

const userProfile = new UserProfile("Any user");
console.log(`User name: ${userProfile.getName()}`); // User name: Any user
delete employee.name;
console.log(`User name: ${userProfile.getName()}`); // User name: Any user Conclusion In this article we considered code smells in Classes and Objects. In the next article, we will take a look at some problems in architecture and SOLID principles which will help to improve your code.

Clean Code: Classes and Objects in TypeScript [Part 3]

CAPTCHAs have long been the first line of defense against spam and automated bots on the internet. While there are many types of CAPTCHAs available, the slider CAPTCHA has gained popularity due to its intuitive nature, however, this type of captcha is most applicable on mobile devices, where a swipe represents an intuitive action. In this post, we'll walk through the process of creating a custom slider CAPTCHA service using and without complex checking of full behavior, like duration for solving captcha and checking if users make movement linear (from my experience, bots make linear). Node.js Redis Introduction A "slider captcha" refers to a type of CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) that requires users to drag or slide an element to a particular position on a scale or along a track to verify that they are human. The primary idea behind CAPTCHAs is to prevent bots from automatically submitting forms, scraping websites, or conducting other potentially harmful actions. In a slider captcha, the user might be presented with a simple puzzle, such as sliding a piece to complete an image or moving a slider to a specified position. The action required to solve this type of CAPTCHA typically involves nuanced human coordination, which is difficult for bots to replicate accurately. Here's a general way it might work: The user is presented with a visual element (like a puzzle piece or slider) and a target area or track. The user needs to drag or slide that element to the correct position. When the element is in the correct position, the system should validate the user's action, and if the result is correct, it allows the user to proceed.. This kind of CAPTCHA is designed to be more user-friendly than traditional text-based CAPTCHAs, which often require users to decipher distorted characters. However, the efficacy of slider CAPTCHAs can vary, and sophisticated bots can defeat some. As with any other security measure, evaluating its effectiveness and adapting to emerging threats is essential. Prerequisites For this project, were selected: node.js redis npm package manage (or yarn) I chose JavaScript language because in my opinion is an almost ideal tool for prototyping, and in the future, this example can easily be rewritten to suit your programming language. To create a slider captcha service with Node.js, you'll need to follow these steps: Create a new project mkdir slider-captcha-service
cd slider-captcha-service
npm init -y Prepare project yarn add typescript ts-node @tsconfig/node20 --dev And create tsconfig.json {
  "extends": "@tsconfig/node20/tsconfig.json",
  "compilerOptions": {
    "outDir": "dist"
  },
  "include": ["src"],
  "exclude": ["node_modules"]
} I nstall the Required Packages yarn add express redis uuid jimp : Framework for creating the web server. express : Node.js Redis client. redis : For generating unique identifiers for each CAPTCHA instance. uuid And types yarn add @types/express @types/uuid --dev Create skeleton create src/index.ts import express from 'express';
import {createClient} from 'redis';

const app = express();
const client = createClient();

client.on('error', (err) => {
    console.error('Redis error:', err);
});

const PORT = 3000;

client.connect().then(() => {
    app.listen(PORT, () => {
        console.log(`Server is running on port ${PORT}`);
    });
}) Generate images The main component of the captcha slider is two pictures that need to be superimposed on each other. Main background image The place where there should be a puzzle Puzzle Puzzle may be every form and size that you prefer: import jimp from "jimp";

const FRAME_WIDTH = 100;
const FRAME_HEIGHT = 100;
const BORDER = 2;

export async function getImages(positionX: number, positionY: number): Promise<{ back: string, puzzle: string }> {
    const startX = 147 + positionX * 1.5;
    const startY = 70 + positionY * 2;

    // generate back image
    const back = await jimp.read("server/assets/puzzle.png");
    const backShadow = new jimp(FRAME_WIDTH, FRAME_HEIGHT, 'rgba(0,0,0,0.5)');
    back.composite(backShadow, startX - FRAME_WIDTH / 2, startY - FRAME_HEIGHT / 2);

    // generate puzzle image
    const puzzlePattern = await jimp.read("server/assets/puzzle.png");
    puzzlePattern.crop(startX + BORDER - FRAME_WIDTH / 2, startY + BORDER - FRAME_HEIGHT / 2, FRAME_WIDTH - BORDER * 2, FRAME_HEIGHT - BORDER * 2);

    const puzzle = new jimp(100, 400, 'transparent')
    const puzzleBack = new jimp(FRAME_WIDTH, FRAME_HEIGHT, 'rgba(0,0,0,0.5)');

    puzzle.composite(puzzleBack, 0, startY - FRAME_WIDTH / 2);
    puzzle.composite(puzzlePattern, 0 + BORDER, startY + BORDER - FRAME_HEIGHT / 2);

    return {
        back: await back.getBase64Async(jimp.AUTO),
        puzzle: await puzzle.getBase64Async(jimp.AUTO)
    }
} Three constants are defined: , , and , which determines the size and border dimensions of the puzzle piece. Function takes positionX and positionY coordinates for the captcha. FRAME_WIDTH FRAME_HEIGHT BORDER getImages Back image - background image for our captcha Puzzle image - image for the puzzle. to make it easier to work with positioning on the Y-axis, we make the height of the puzzle equal to the height of the back Create CAPTCHA Generator Logic The basic principle of operation of such types of captcha is to guess a certain number on our backend and let the user guess it using a hint (in this case, by combining two pictures) app.get('/captcha', async (req, res) => {
    // Generate a random position for the slider between 0 to 255 for x position
    const positionX = Math.floor(Math.random() * 255) + 1;
    // Generate a random position for the slider between 0 to 130 for y position
    const positionY = Math.floor(Math.random() * 130) + 1;
    const captchaId = uuidv4();

    // Store the position in Redis with an expiry time of 5 minutes
    await client.set(captchaId, positionX, {'EX': 300});
    
    const {back, puzzle} = await getImages(positionX, positionY);

    res.json({
        captchaId,
        back,
        puzzle,
        prompt: 'Drag the slider to the correct position.'
    });
}); The range for positionX is generated in the range between 0 and 255 because it can be easily converted to bytes and after in base64. CAPTCHA Validation Logic After the user combined two pictures, we need to check his result. Right now, it's a simple comparison of two numbers in the specific range from guessed numbers. We use a range of numbers because it matches a specific number. Also, to prevent bruteforce, we need to remove the current captcha Id, because otherwise user can create many requests, and one of them will be correct. Otherwise, the user has only one attempt to solve a captcha, and if it fails to request a new captcha. app.post('/captcha', async (req, res) => {
    const {captchaId} = req.body;

    try {
        const actualPosition = await client.get(captchaId);

        if (!actualPosition) {
            return res.status(400).json({message: 'Invalid or expired CAPTCHA'})
        }

        await client.del(captchaId);

        const values = Buffer.from(req.body.value, 'base64');

        if (Math.abs(values[values.length - 1] - parseInt(actualPosition, 10)) <= 10) {
            return res.json({success: true});
        } else {
            return res.json({success: false});
        }

    } catch (e) {
        return res.status(500).json({message: 'Internal Server Error'});
    }
}); Frontend part This block will be described rather superficially, leaving more freedom in choosing tools for implementation (for example, react or web components, or whatever you like best) We will make it in Vanila javascript At first, markup for our slider: document.querySelector<HTMLDivElement>('#app')!.innerHTML = `
    <div id="captcha-container">
        <div id="slider-bar">
            <img src=""  alt="back"/>
            <div id="slider"><img src="" alt="slider" /></div>
            <div id="slider-status"></div>
        </div>
        
        <div id="slider-navigation">
            <input type="range" min="0" max="500" value="0" class="slider">
        </div>
    </div>
` Define basic variables for the slider. const slider = document.querySelector('#slider') as HTMLElement;
const sliderBar = document.querySelector('#slider-bar') as HTMLElement;
const sliderStatus = document.querySelector('#slider-status') as HTMLElement;
const input = document.querySelector('#slider-navigation input') as HTMLInputElement; Check the user's answer and show our answer for his solution async function check(captchaId: string, value: string) {
    const checkResp = await fetch('http://localhost:3000/captcha', {
        method: 'POST',
        headers: {
            'Accept': 'application/json',
            'Content-Type': 'application/json'
        },
        body: JSON.stringify({
            captchaId,
            value,
        })
    });

    const resp = await checkResp.json();

    if (resp.success) {
        sliderStatus.innerText = 'Success';
        sliderStatus.style.backgroundColor = '#0080003d';
        sliderStatus.style.opacity = '1';
    } else {
        sliderStatus.innerText = 'Failed';
        sliderStatus.style.backgroundColor = '#ff00003d';
        sliderStatus.style.opacity = '1';
    }
} On moving range input, we interpolate the puzzle image horizontally let path = [0];

input.addEventListener('input', (_) => {
    const value = parseInt(input.value, 10);
    const pos = Math.round((value - 97.5) / 1.5);
    if (pos !== path[path.length - 1]) {
        path.push(pos);
    }
    slider.style.left = value + 'px';
}); After all content is loaded, we request a new capture, place all required images, and await input changes to check the results document.addEventListener('DOMContentLoaded', async () => {
    const {captchaId, back, puzzle} = await fetch('http://localhost:3000/captcha').then(res => res.json());

    let path = [0];

    sliderBar.querySelector('img')!.src = back;
    slider.querySelector('img')!.src = puzzle;
    input.disabled = false;

    input.addEventListener('change', async (_) => {
        input.disabled = true;
        const value = parseInt(input.value, 10);
        path.push(Math.round((value - 97.5) / 1.5));
        await check(captchaId, arrayBufferToBase64(path));
        path = []
    })
}); Little bit css :root {
  font-family: Inter, system-ui, Avenir, Helvetica, Arial, sans-serif;
  line-height: 1.5;
  font-weight: 400;

  color-scheme: light dark;
  color: rgba(255, 255, 255, 0.87);
  background-color: #242424;

  font-synthesis: none;
  text-rendering: optimizeLegibility;
  -webkit-font-smoothing: antialiased;
  -moz-osx-font-smoothing: grayscale;
  -webkit-text-size-adjust: 100%;

  --slider-width: 600px;
  --slider-height: 400px;

  --slider-bar-width: 600px;
  --slider-thumb-width: 25px;
  --slider-thumb-height: 25px;
  --slider-thumb-color: #04AA6D;
}

body {
  margin: 0;
  display: flex;
  place-items: center;
  min-width: 320px;
  min-height: 100vh;
}

#app {
  max-width: 1280px;
  margin: 0 auto;
  padding: 2rem;
  text-align: center;
}

#slider-bar {
  display: flex;
  justify-content: center;
  align-items: center;
  margin-bottom: 1rem;
  width: 600px;
  height: 400px;
  position: relative;

  img {
    width: 100%;
    height: 100%;
    z-index: 1;
  }
}

#slider {
  width: 100px;
  height: 400px;
  position: absolute;
  top: 0;
  left: 0;
  overflow: hidden;
  z-index: 2;
}

#slider-status {
  position: absolute;
  top: 0;
  left: 0;
  right: 0;
  bottom: 0;
  display: flex;
  justify-content: center;
  align-items: center;
  font-size: 2rem;
  font-weight: 700;
  color: #fff;
  opacity: 0;
  transition: opacity 0.2s ease-in-out;
  z-index: 3;
}

#slider-navigation {
  width: 600px;

  input {
    -webkit-appearance: none;
    appearance: none;
    width: 100%;
    height: 25px;
    background: #d3d3d3;
    outline: none;
    opacity: 0.7;
    -webkit-transition: .2s;
    transition: opacity .2s;

    &:hover {
      opacity: 1;
    }

    &::-webkit-slider-thumb {
      -webkit-appearance: none;
      appearance: none;
      width: var(--slider-thumb-width);
      height: var(--slider-thumb-height);
      background: var(--slider-thumb-color);
      cursor: pointer;
    }

    &::-moz-range-thumb {
      width: var(--slider-thumb-width);
      height: var(--slider-thumb-height);
      background: var(--slider-thumb-color);
      cursor: pointer;
    }
  }
} Advice block : Bots usually fill out forms much faster than humans. Monitoring how quickly form fields are filled out can help in differentiating bots from humans. Time Analysis : Restrict the number of CAPTCHA attempts allowed from a single IP in a given time period to deter brute-forcing. Limit Attempts : Randomly change the layout, style, and type of CAPTCHA to keep it unpredictable. Add Randomness : Constantly monitor attempts to solve or bypass your CAPTCHA. Any patterns or trends could indicate weaknesses in the system that you can address. Monitor and Adapt

Create Slider Or Puzzle Captcha Service with Node.js

Let's learn about via these 47 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Macos /Learn Repo 1. Homebrew - Explained in Simple English So, there are tonnes of jargon, commands, processes that we follow blindly after surfing from the internet without knowing the actual reasons and under the hood processes, that just remain in our short term memory and get purged eventually, thus we don't feel comfortable using or even about discussing in a group about them. 2. How to Make your Rails App Work with the Mac M1 Chip [SOLVED] Get your rails app running from terminal using the following steps on MacBook M1 Chip. 3. How to Install MySQL 8 on macOS Using Homebrew A step-by-step guide 📙 on how to install, configure and run MySQL 8 server on macOS using Homebrew 🍺 4. How to Run MacOS on VirtualBox in Less Than 30 mins How to run macOS in VirtualBox on Linux and Windows? 5. How to Enable Autocomplete (and AI) in your Terminal Here's how I enhance my terminal with autocomplete and Artificial Intelligence 6. DBngin: Instant Local Server for MySQL, PostgreSQL, and Redis Docker might be an easy solution as an all-in-one tool for setting up a Local Development Environment. But on the other hand, it eats up a huge portion of your system resources to get up and running, and we hate it when our computer becomes lagging and clumsy. [7. 7 Reasons Why You Should Switch To Linux](https://hackernoon.com/7-reasons-why-you-should-switch-to-linux-rp6f3wd6) Your current computer or laptop shows the same performance no longer? This is absolutely no reason to choose new hardware and invest money in buying a cut edge device. We know a much more attractive solution. It is Linux! 8. Installing Virtual Box ON Mac OS Let's say that just like myself you are in a situation where all you have is a Mac Book Pro, and you have no idea when you will be getting access to a windows system. You want to run Virtual Box, but it turns out that you come across a few issues before you get up and running. In this article we are gonna cover the main issues that arise when installing Virtual Box  on a Mac OS. 9. How to Play Xbox Game Pass Games on Mac There are two ways to enjoy Xbox Game Pass on Mac: cloud gaming and Parallels Desktop. Each option has its pros, cons, and technical considerations. 10. Hellfred or: How I Learned To Automate macOS and Become Hellishly Productive Hellfred apps are built on top of Hammerspoon so you can automate tasks and boost productivity by programming shortcuts into your daily workflows. 11. 5 Mac Apps with the Largest Service Files Merely removing apps on Mac OS X is not a hard thing to do: drag & drop or just delete to the Trash. But sometimes after such removal on your Mac, there could be left system files, caches, logs, and other debris – called app leftovers. That is why it is better to use special third-party app uninstallers for removing programs from your Mac with all their service files. 12. Is Ruby Installed On A Mac? How To Check On macOS Catalina and Big Sur Ruby comes installed on macOS Catalina and Big Sur. Learn how to check for the system Ruby on your Mac. 13. Why Programmers Love Macbooks? MacOS was first released in 1984, which makes it even older than Windows. At first, it came to light as a graphical user interface, but in 2005 its design and structure were changed to Intel x86 based architecture. Apple’s computers are great pieces of machinery – that’s beyond debate. But why are they so popular among programmers? 14. Leaving Linux for Mac After 15 Years I’m sorry Linux. It’s not me, it’s you. The “year of the Linux desktop” never came to fruition. It was always next year, year after, some year. Now, it’s almost a running joke. 15. Why TextMate 2 is My Favorite Code Editor for Mac (Part 3) Comparing the project search and the open quickly feature. 16. Making Apache Superset into a macOS App Superset puts most heavy work onto the database side, I was thinking is there may be some possibility to have a Superset.app 17. Using a Script to Send Desktop Notifications from Your Linux Terminal Send visual feedback and reminders from scripts, using notify-send and at commands on Linux.  Also how to get MacOS to do it as well, through some jury-rigging. 18. Virtualization and Emulation on MacOS Want to run Windows, Linux, or any another operating system on your Mac? Here are your options! 19. How to Play any Video Format on Your Mac Ever come across a situation when your video file won't play on MAC in QuickTime or any other media player due to an unsupported codec?
Hope this helps you. 20. How to Set Up Notarization on macOS with Electron Builder and Electron Notarize Learn how to set up notarization on macOS for Electron apps built with Electron Builder and work around a bizarre issue I’ve experienced with Apple's ANS. 21. Will Ubuntu 20.04 Steal Windows Users t’s that time of year again, Ubuntu 20.04 is now available. Will this be the release that steals away more Windows users? 22. macOS Ventura's New Privacy and Security Features 23. 6 Things You Need to Make Your Dev Job Fun Again It's easy to sit down and try to get straight to work, but it's important to have the right tools, extensions, and themes to make you the most productive. 24. Cross Compiling Rust on MacOS to Run as a Unikernel Most programs on Linux are dynamically linked. So when you are creating a unikernel with OPS out of a linux application OPS goes out and finds all the libraries it's dynamically linked to and throws it onto the disk image. This works well if you are on linux - 99% of everything on linux is dynamically linked. 25. Why TextMate 2 is My Favorite Code Editor for Mac (Part 1) The best code editor for Mac is TextMate 2. In this article, I will explain my reasons for this by comparing it to other well-known editors. 26. How to Stop Giving Away Money to AWS by Running KubeCF with Kind on MacOS I've reviewed how to deploy KubeCF on EKS, which gives you a nice, stable deployment of KubeCF, for cost. Now let's run KubeCF on your Mac for free(ish)! 27. Setting Up Apache and PHP from Homebrew on macOS Learn how to set up a local development environment with Apache and PHP installed from Homebrew running natively on macOS. 28. Deskreen Introduction: Turn Any Device With Browser Into a Secondary Screen I created Deskreen. It turns any device with a web browser into a secondary screen for your computer 29. MacOS Window Manager Reviews: Moom vs. Magnet vs. Rectangle On macOS, the window manager is pretty basic. There are some tools to improve this on macOS. The two I have tried are Moom, Magnet and Rectangle. 30. How To Extend Your Terminal Functionality with zsh and Prezto For some time now I've been using zsh as my default Unix shell, which is an extended Bourne shell with many improvements, including some features from bash, ksh and tcsh. One of my favorite shells is fish shell. It's easy to set-up and many of the features that I use comes pre-installed, as it is not POSIX supported it has a much readable (fish) syntax for scripting. 31. 7 Reasons Your Mac Is Slower Than Mine. And How to Fix It Do you open your Mac and it takes a forever to wake up? Or are app on your Mac is slower than a Sloth? Do heavy apps crash frequently on your Mac making you lose loads of work? 32. A Linux, Mac and UNIX Tutorial Of The Top 6 Cool Terminal Commands For Beginners In this tutorial, you'll learn the most common and useful commands that you can run in almost any UNIX-like environment, including Linux and Mac OS X. 33. 8 Best EPUB Readers for Windows and macOS It is a program that uses for reading books. It is built for Mac and Windows. With an EPUB reader, users can convert EPUB files to pdf documents instantly. 34. How the Top Result In Google Could Be a Scam Website Jailbreak Checkrain Checkra1n Security Scam Malware 35. MacDroid: Synchronize Android with Mac Easily MacDroid allows you to move music files, videos, and contacts from your Android device to your Mac. 36. 7 Mac Trackpad Gestures To Help You Become A Superuser The first time I got my hands on a MacBook, I was 13. I used to do everything to explore macOS. That was when I accidentally noticed that doing certain gestures on the trackpad triggers some actions. Upon some exploring, I found out a list of Mac trackpad gestures and ever since I’ve been using them extensively. They’re super useful. 37. How to Boot macOS Recovery Mode on an Apple Silicone Mac in 5 Steps The ways to enter into a special recovery boot system in both INTEL and M1 Mac are different. The recovery mode helps the mac user to troubleshoot different errors. On Intel Macs, the method is really simple and easy, you just need to hit the CMD + R during startup to enter into recovery mode. 38. How I Migrated Applications and Data to a New Mac Without Time Machine Is it possible to migrate between macs using nifty command line tools? Well yes, kind of. Should you do it? Well that is up to you :) 39. The Powerdir  MacOS Bug Could Allow Attackers to Snoop On You The vulnerability allows malicious apps to bypass privacy preferences. Precisely, it could allow an attacker to bypass the operating system's Transparency, Cons 40. Creating a Crypto Terminal for macOS: Lessons Learned From Expanding Our Product Ecosystem How we decided to build the best-ever crypto trading terminal for macOS to make technical analysis and scalping easier 41. Why TextMate 2 is My Favorite Code Editor for Mac (Part 2) Comparing file type detection, color scheme, code highlighting and markdown support 42. How To Run An ETH 2.0 Beacon Node Using The Lighthouse MacOS Client This article will guide you through running ETH2.0 Beacon Node with Lighthouse client on MacOS if you have barely to 0 experiences with Terminal command lines and you want to spin off Lighthouse Beacon node and join the validator pool on ETH2.0 Beacon chain with Lighthouse client. This guide includes spinning off Nethermind Goerli node. 43. Display Hierarchical Data as List with Search Feature Recently I faced an interesting task to display hierarchical data as a list for the iOS platform. 44. A Curious Case of Mach-O Executable This is going to be a low-level article, but I guess you already knew that since you landed here, right? 45. 8 Mac Hacks You Can Use to Stop Wasting Your Machine's Time Photo by Bram Naus on Unsplash 46. YSK about [Unix tools] This Slack discussion, featuring myself in some sleep-deprived rambling, occurred in hackernoon's official #protips channel, and has been [barely] edited for readability. 47. Top 25 Employee Monitoring and Time Tracking Apps for Windows/MAC Many companies today install various tracking apps on their employees’ computers and check if their team members are productive during the working day. Such an extreme approach to monitoring what the staff is doing is due to the lack of employees’ efficiency at the workplace and to the employer’s desire to prevent data leakage and other unwanted actions. Before we go through the list of the most popular and effective programs to monitor employees’ activities at the workplace, let’s take a look at the most efficient ways to find out if your company is monitoring you. Thank you for checking out the 47 most read stories about Macos on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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