We now know the name of Travis Barker and Kourtney Kardashian’s first child together. The Blink-182 rocker shared the name during a recent conversation on the “One Life One Chance with Toby Morse” podcast. When asked about making more music with the ...

Travis Barker reveals new baby’s name and due date

Pop-punk rockers Blink-182 will return to Indianapolis next summer for a headlining show at Gainbridge Fieldhouse. The One More Time Tour, named for the band's latest album released on Friday, will hit the Fieldhouse on Aug. 2. Pierce the Veil will open ...

Blink-182 announces summer Indianapolis show as part of One More Time Tour

The baby of Travis Barker and Kourtney Kardashian is imminent, and the Blink-182 singer appears to have confirmed the name and due date of the new arrival. Speaking on the One Life One Chance podcast,

Travis Barker confirms baby name and due date on new podcast

Things can change in the blink of an eye. And when it comes to Josh McDaniels and the Las Vegas Raiders, that blink happened late last night. While most of the football world was resting from a long day of NFL trade deadline stories,

Who Is Antonio Pierce? Interim Head Coach Set To Replace Josh McDaniels for the Raiders

Travis Barker and Kourtney Kardashian could be parents by the time you read this. The Blink-182 drummer and the reality TV star are expecting their first child together any minute now and in a new podcast interview on One Life One Chance with Toby Morse ...

Travis Barker Confirmed the Hard-Hitting Name of the Baby Boy He’s Expecting With Kourtney Kardashian

Kourtney Kardashian and Travis Barker appear to have decided on the name of their first child together.

Kourtney Kardashian and Travis Barker's baby name revealed before birth

Kourtney Kardashian and Travis Barker's baby name announced before birth

On the 'One Life One Chance' podcast, Travis Barker revealed the name of his and Kourtney Kardashian’s soon-to-be-arriving baby boy. The Blink-182 drummer confirmed that their son will be named

Travis Barker Reveals His And Kourtney Kardashian’s Baby Name

We wonder, though, exactly when, since Oct. 7 has it been independently determined that Israel has intentionally pursued civilians or that the notion that it does, is not anything but yet another exam

No Time To Blink, Mr. President, Only Hamas’s Unconditional Surrender Will Do

Tom DeLonge had an amazing October. The first thing most everyone knows about Tom DeLonge is that he founded Blink-182. If people know a second thing about DeLonge, it’s probably that he’s fascinated with extraterrestrials, dedicating his life to the ...

Blink-182’s Tom DeLonge Made A Throwback Amblin Movie About Aliens, Conspiracies, And Southern California, And His Fans Will Love It

Blink

blink

The ability to catch a blink of an eye, detect the pupil, and even track its movements may seem like fancy tasks, but they have numerous practical applications. Let’s explore a handful of use cases that become possible when we have the knowledge and capability to accomplish these tasks: : By analyzing eye movements and the direction of gaze, we can develop applications for driver drowsiness detection or even enhance human-computer interaction experiences. Eye Gaze Detection : Tracking the pupil can provide valuable insights for the early detection of neurological disorders as changes in pupil size or responsiveness can indicate certain conditions. Early Neurological Disorders Detection : With the ability to accurately track the pupil, we can develop systems that allow users to control the mouse cursor or interact with digital interfaces using eye movements. Eye Mouse Control/Cursor : The field of human-computer interaction can greatly benefit from eye-tracking technology. By understanding where a person is looking, we can create more intuitive and immersive user interfaces. Human-Computer Interaction These are just a few examples of the exciting possibilities that arise from mastering the techniques of eye blink detection, pupil detection, and tracking using OpenCV. Let’s dive deeper into these topics and unlock the potential of computer vision in various domains. Coming back to the topic, we’ll follow the divide and conquer strategy. First, we’ll see how eye pupils can be detected, then we’ll see how to catch the blink of an eye. : In this blog, it’s important to note that the sample video and images used are obtained from medical devices rather than directly from a camera. ( )Fig 1 (shown below) illustrates this distinction, which might appear unsettling to some individuals. NOTE Courtesy However, it is crucial to understand that data collection from medical devices often involves such procedures Image from both the left and right eye is concatenated to make a single image. The human eye is a complex organ consisting of the iris, sclera, and pupil. The pupil, located in the black region of a grayscale image, plays a vital role in regulating light intake. Detection of Pupil Ever wondered what makes the eye truly captivating? It’s the pupil, that enchanting black center in a grayscale image. To automatically detect it, we can employ a clever technique called thresholding. By converting the frame to grayscale and setting a threshold, we can reveal its mesmerizing presence Thresholding an image means taking a grayscale image (a grayscale image has pixel values ranging from 0 to 255, 0 being the darkest and 255 being the lightest) and thresholding all the pixels whose values are less than the threshold to 0 and all the values which are greater than the threshold to 1. To process the image, I applied a simple thresholding technique, resulting in the generation of a binary thresholded image. This technique involves setting a basic condition to classify pixels as either black or white, based on their intensity values. # 1 if pixel_value > threshold else 0 | threhsold = 30
_​, ​threshold_img = cv2.threshold(gray_f​, ​threshold​, ​255​, ​cv2.THRESH_BINARY) By converting the frame to grayscale and setting a threshold, we reveal its mesmerizing presence. Now, we can locate the pupil using Contour Detection Contours can be thought of as curves that connect adjacent points along a boundary, sharing the same color or intensity. These contours play a crucial role in identifying and capturing all the intricate points that comprise the boundary. ORContours are nothing but continuous points corresponding to a boundary (generally). In the binary thrashed image, , mark it with a color, and we just located the pupil. This simple yet effective method yields excellent results in identifying the pupil. only the pupil is visible contours, _ = cv2.findContours(threshold_img, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
# drawing contours
cv.drawContours(img, contours, ​-1​, (​0​,​255​,​0​), ​3​) Have you ever wondered why contours can take on any shape? It’s because they simply represent boundaries. However, when it comes to detecting the pupil contour, we need to ensure accuracy. The key? Remembering that the pupil is circular. Let’s employ a clever technique by drawing the minimum enclosing circle. Minimum enclosing circle is a circle of the minimum area enclosing a 2D point set. approx = cv2.approxPolyDP(contour, ​3​, ​True​)
center, radius = cv2.minEnclosingCircle(approx) The pupil has been detected and marked with a red circle. While this blog post doesn’t cover pupil tracking in detail, it’s worth considering the ease with which we can accomplish it. Think about how we can now proceed to track the pupil. Challenges: Discovering Problems and Their Solutions A significant challenge arises when we rely on a fixed threshold to achieve a precise binary-thresholded image. This approach heavily depends on finding the right threshold value. However, I’ve devised a solution using a simple blob detection algorithm that autonomously determines the threshold. If you’re interested, I’d be delighted to share the implementation details of this innovative approach. Contour detection poses a challenge as it may detect additional contours and draw their circles, if not handled carefully. To address this, we only draw circles whose perimeters closely match the contour’s perimeter. This approach ensures promising results, although it involves a trade-off and may not achieve perfect accuracy. Catching the Blink of an Eye Blink detection may sound challenging, but there’s no need to carry around complex neural networks everywhere you go. Surprisingly, achieving blink detection can be accomplished effortlessly with classical computer vision techniques and a touch of mathematical magic. There are two distinct approaches for blink detection, depending on the input image: Whole Face Image: If the input image encompasses the entire face, including f , a specific approach can be employed for blink detection. acial landmarks Eye Image: On the other hand, if the input image focuses solely on the eye without any facial landmarks, an alternative approach can be utilized for blink detection. Both methods offer their own unique advantages and can be tailored to suit different scenarios. Let’s explore these approaches further. With Facial Landmarks If the whole image of the face is available, then blink detection is very straightforward. You only need facial landmarks which you can easily get from the dlib library. Detecting a blink involves analyzing the ratio between horizontal and vertical landmarks. When the user blinks, there is a momentary fluctuation or minima in this ratio. Congratulations, you’ve successfully identified a blink! Kudos to your detection skills! Without Facial Landmarks When dealing with data from devices, the traditional approach for blink detection falls short, requiring an alternative method. To illustrate this, take a look at the image below, showcasing a commonly used healthcare device. In scenarios where data is solely captured from specialized eye-recording devices, limited to the eyes without the whole face, detecting a blink might seem challenging at first. However, with the right approach, it can be achieved with simplicity. In these recordings, an intriguing pattern emerges. The act of blinking, a subtle yet recurring event, can be observed and validated by analyzing the structured similarity index (SSIM). This index provides insights into the similarity between two images, unveiling the hidden patterns within. SSIM returned a score that represents how similar both images are.The range of score is between 0 to 1 and the greater the score the similar the images are. The Experiment By calculating the Structural Similarity Index (SSIM) between consecutive frames, I plotted the results and made an intriguing discovery. Take a look at the pattern below, and see if you can determine the exact time frame when the person blinked. Things to note from the above image Whenever the person blinks an eye, there is a sudden drop in the index. The Problem? What will happen when by, some means, there will be noise in the SSIM technique that will eventually lead to false positives? See fig 2 Upgrading the Blink Detection Noise can disrupt accurate blink detection, resulting in false positives. The presence of two eyes raises questions: should we treat them individually? For enhanced blink detection, analyzing the left and right eye separately proves beneficial. A blink is registered when the SSIM index drops in both eyes simultaneously. A blink will only be detected when there will be a sudden drop in the structured similarity index of both of the left and right eye. If a drop occurs in only one of the eyes, then ignore it, it is noise. Logic Building | Hit and Trial The algorithm’s logic for detecting blinks involves examining a window of 12 frames (a hyperparameter). If at least 6 frames within the window score below 0.85, a blink is detected. After the window length of 12, both the window_counter and blink_counter are reset. This approach leverages the SSIM index, ensuring that the algorithm considers low index values for both the left and right eye. By doing so, it mitigates the influence of noise from one eye on the other, reducing the risk of false positives. Blink detection is a nuanced process that involves several heuristics to enhance accuracy. Factors like video quality, eye movement variations, and even manual errors can influence the results, making it crucial to consider these variables during analysis. Final Result https://giphy.com/embed/M9yf0i7qmxsspZqGUd?embedable=true Thanks for reading! If you found this story compelling and informative, I would greatly appreciate your support and engagement. I'm eager to discuss any specific topic of your interest. Furthermore, you can find the code for this project on my , allowing you to explore and experiment further. GitHub repository Let's continue the conversation and dive deeper into the world of technology and innovation! About Me Aditya is a Data Scientist and a software developer! Writes clean Python code and embraces the art of good programming. Enjoys diving into mind-bending series and movies and indulging in the joy of reading. He writes about stuff that he experiments with and explore! Follow him on , , and LinkedIn Github Twitter

Insights Through Vision: Tracking Eyes Using OpenCV for Blink Detection

Today, we’re announcing , the first ever generative AI for automating and IT operations workflows. This innovative tool makes it possible for any security operator to generate workflows using a simple prompt. Blink Copilot security no-code When Blink was founded in 2021, we knew security teams needed a better way to automate internal workflows. Low-code platforms had already transformed business operations for CRM and marketing, and it was only a matter of time before low-code solutions enabled security teams to automate their own workflows across their stacks. Recent advancements in large language models (LLM) and generative AI have supercharged our mission, finally making true no-code automation possible. That’s why today, we’re excited to announce , the first ever generative AI for security workflow automation. Blink Copilot Blink Copilot enables security teams to generate any simple or complex workflow, instantly. Generative AI makes it possible to build workflows without writing code or needing to be an expert in target applications. Critical Need for Security Automation According to the there are over 3.4 million open security jobs and too few skilled security operators to fill them. Meanwhile, cybersecurity breaches in 2022 were With such high stakes and a massive skills gap, security automation is necessary for organizations to defend against the massive volume of attacks enterprises face on a daily basis. 2022 (ISC)² Cybersecurity Workforce Study, more costly than ever before. Blink Copilot empowers teams of any size to build any no-code, low-code, or code security automation using generative AI. With Blink Copilot, security and IT operators of all skill levels can now leverage AI to increase productivity and deliver workflows to help protect their organizations. Blink Copilot unlocks unparalleled automation capabilities for security teams. Security automation projects that once took months are now built in seconds. Automate Security Beyond the SOC Blink Copilot empowers security teams to automate any operations workflow, effortlessly. Security practitioners regardless of skill set can automate any operational task or security workflow using a simple text prompt. Blink Copilot can help security teams automate workflows for: SOC & Incident Response Cloud Security IT & SaaS Security Identity & Access Management Governance, Risk & Compliance Using Blink Copilot, security teams can finally automate workflows beyond the SOC. Blink customers get out-of-the-box automations for popular security use cases and powerful automation capabilities backed by the robust Blink platform. Our internal team at Blink Ops has already generated over 7,000 workflow automations. Now with Blink Copilot, we’re empowering any team to build no-code, low-code, or code security workflow automations using generative AI. Powerful No-Code Security Automation Blink Copilot is backed by the most powerful no-code security automation platform ever built. Key features of the Blink platform include: The first ever generative AI for automating security and IT operations workflows Blink Copilot: Drag-and-drop user interface that makes it easy for security teams to customize workflow automations Low-Code Editor: 7,000+ security workflow automations built by the Blink community Automation Library: Shift-left service requests by making automated workflows available in a self-service portal or interactive Slack/Microsoft Teams app Self-Service Portal: Blink was designed for enterprise security teams, offering robust platform features like secure workspaces, on-prem runners, and multi-tenant support for MSPs and MSSPs. Blink is committed to upholding the highest grade of industry security standards, including SOC 2 Type 2, GDPR, ISO 27001 compliance. Blink can also help teams leverage no-code automation to achieve compliance across their own organizations. Learn more about the Blink platform and technical capabilities in the Blink documentation. Get Started with Blink Copilot Blink Copilot completely transforms how security teams will automate simple and complex workflows. Security teams can take advantage of Blink Copilot today to shift-left internal workflows. Along with more than 7,000 out-of-the-box automations for common security use cases and powerful automation capabilities backed by the robust Blink platform, enterprise security teams can finally achieve operational excellence across their entire stack. For more information on Blink Copilot, visit the Blink Ops website.

Meet Blink Copilot: A Generative AI for Security Workflows

Conoce a Blink Copilot: una IA generativa para flujos de trabajo de seguridad

Découvrez Blink Copilot : une IA générative pour les workflows de sécurité

मीट ब्लिंक सह-पायलट: सुरक्षा वर्कफ़्लो के लिए एक जनरेटिव एआई

Blink Copilot のご紹介: セキュリティ ワークフローのための生成 AI

Conheça o Blink Copilot: uma IA generativa para fluxos de trabalho de segurança

Gặp gỡ Blink Copilot: Trí tuệ nhân tạo sáng tạo cho quy trình làm việc bảo mật

认识 Blink Copilot：用于安全工作流程的生成式 AI

Alibaba completes testing and optimization for a revolutionary distributed open-source computing framework in just one year. This article is part of Alibaba’s Flink series . As demands on network equipment and infrastructures grow, there is an ever-increasing need for powerful solutions that can still operate when certain components are down. As one such solution, distributed computing frameworks effectively overcome potential network downtime by spreading loads over the entire network. Blink is a distributed open-source computing framework for data stream processing and batch processing, supporting the real-time processing of big data for thousands of services in the Alibaba Group. To ensure its reliability, the Blink test team was set up in 2017 to establish a complete Blink test system from scratch and guarantee the quality of Blink. In this article, we look at the fundamentals of open-source computing frameworks before exploring the Blink test platform in detail. Open-source Computing Frameworks Apache Flink is a widely used distributed open-source computing framework for data stream processing and batch processing. In 2016, using Flink as a foundation, Alibaba got to work in creating the Blink framework. In 2017, Alibaba integrated its entire stream computing products and decided to build a world-leading real-time computing engine based on Blink. For the 2017 11.11 Global Shopping Festival, Blink supported more than 20 business units and groups by running thousands of real-time computing jobs simultaneously, with processed logs per second reaching an astonishing 470 million. Therefore, the assurance of Blink’s reliability and stability has become extremely important. To this end, the Blink test team has established a comprehensive testing system, progressing from code quality to continuous integration and pre-testing in just over one year and greatly improving Blink’s quality. Blink Test Platform The test team has tailored the Blink test platform to optimize its quality, as shown in the following figure: As outlined above, the Blink test platform consists of three test phases. First, in code quality verification, the static code scan, unit test, and minicluster-based test are performed. Second, in integration testing, the functional test, performance test, and destructive stability test are performed. Finally, in the pre-test, the simulation test is performed by simulating the user’s jobs, and the final version compatibility test is performed before the release. The platform selects part of the test collection to be included in the precommit verification to detect problems in the code as early as possible. Meanwhile, large-scale functional, performance, and stability tests are usually used as a collection of dailybuilds. In addition, the Blink test platform has established a relatively complete quality measurement system. Except for statistical analysis and change analysis of code coverage, the system can generate test reports with one click and horizontally compare the quality of different versions. Code quality verification phase The code quality verification phase is the basis for the entire Blink quality assurance. It mainly includes unit testing, code style scanning, as well as the standalone minicluster-based integration test. Only when the Blink code passes all these three tests is it allowed to be submitted to the project git, as shown in the following figure: Functional Test The Blink functional test framework uses Defender, is derived from pytest and supports the features of the Blink SQL test and the introduction of third-party plugins. Moreover, it can be used for end-to-end accurate testing on a scenario in a test cluster (as shown in the figure below). It supports two trigger modes: IDE and Jenkins; and three dispatch modes for case running: yarn_job, yarn_session, and local. After the execution, it displays the results in the form of web pages or emails, and performs persistence on the results. Its advantages include: 1. Case unified dispatch and refined management At present, Blink has more than 4,000 cases in 12 scenes on the Defender, which can perform dailyrun at a fixed time every day. If a problem occurs in the case of a certain category, the case can be executed separately, and details can be displayed on the page. 2. Three running modes meeting the test requirements of different scenarios Among these, the yarn_session mode is more suitable for the scenario where sql case exists in a module, as it can greatly reduce the interaction time with Yarn. 3. Flexible case configuration It can not only support the system configuration, but also separately configure the required resources (slot, memory, and so on) of each case set or other configurations of the cluster. 4. A case can support both batch and stream types of processing 5. Flexible extension of the client type Integration and extension of existing data storage and services are possible. Multi-type data store read and write services, start of yarn_session, Blink job interaction, and so on are now supported. Performance Test As a real-time big data processing engine, Blink puts very stringent requirements on the data processing capability per unit time and the real-time performance of data processing. Therefore, performance testing is a very important part of the entire Blink test and is one of the core standards for measuring whether a new version of Blink can be released. Blink’s performance test mainly includes operator performance testing, SQL performance testing, and runtime performance testing: Operator performance testing Operator refers to an atomic operation that constitutes the semantics of SQL, such as Sum, Aggregate, and so on, which can no longer be split. The operator performance test is mainly used to monitor the performance changes of a single operator throughout the development process to ensure the optimization and improvement of local processing. Currently, Operator’s testing is divided into the performance test of a single operator and performance test of a combination of operators. The Operator test feeds back performance changes in the way of Daily Run. SQL performance testing This is mainly used to monitor the performance changes of a single SQL during the version development. TPCH and TPCDS are the industry SQL standard performance test sets and have 22 and 103 test cases, respectively. The test platform introduces it into the Blink performance test to more fully measure Blink’s performance changes. Runtime performance testing This is mainly to ensure that the performance at the operational level has no regression. It mainly includes end-to-end performance testing and module performance testing. The end-to-end performance test firstly figures out the test scenario and pays attention to the job processing time of each scenario under a specified data volume. The module performance test mainly includes network layer performance testing, dispatching layer performance testing, failover performance testing, and so on. It pays more attention to the job processing time in a specific scenario. The future plan of performance testing is to integrate the E2E performance test, module level performance test, and parameter adjustment to make them better able to help develop the root cause of the performance problem and view the parameter tuning effect (as shown below). Stability Test For distributed systems that support high concurrency, multi-node, and complex physical environments, it is difficult to avoid physical node anomalies such as disk fullness and network latency. As a highly available distributed system, Blink must ensure the stable operation of the system and the normal output of data under abnormal conditions. By taking on “the best way to avoid failure is to continually fail” approach, the team simulated all possible exceptions during the Blink task run to thereby verify the stability of Blink. Abnormal scenes were divided into two categories: · The first is related to the running environment, including machine restart, network exception, disk exception, CPU exception, and so on. These exceptions are mainly simulated by shell command. · The second is related to Blink jobs, including RPC message timeout, task exception, heart beat message timeout, and so on, and are mainly implemented through the integrations of byteman software. In the stability test, those pre-defined abnormal scenarios will be randomly selected and combined for testing. The stability test is set to an iterative loop. Each round of iterations involves releasing the abnormal scene, submitting the task, waiting for the job recovery, and verifying. The verification is mainly to check whether the checkpoint, container, and slot resources meet the expectations. If the check fails, the alarm will be issued. Once the verification succeeds and passes approval, the next iteration starts and the stability of the task is verified under long-term operation. The stability test architecture is divided into four layers (as shown in the figure below): · The component layer mainly includes testing Blink jobs, monkeys, and the dumper. · The action layer includes job start, status check, output check, and so on. · The execution layer includes services, abnormal scene operations, and so on, and the abnormal scene operates based on SSH to the specific machine. · The top layer is the UI. Pre-test The Blink pre-test phase mainly tests complex business logic and large data volumes by cloning the real tasks and data online. Therefore, the Blink pre-test is a supplement to the code quality verification and integration test, and is the last guard before the release of a Blink version. The Blink pre-test is divided into the simulation testing and compatibility testing. Simulation testing The simulation test further measures the basic test indicators such as Blink’s functionality, performance, and stability, and compares the developing version with the current online version. As a result, the simulation test can identify various problems related to features, performance degradation, and stability at an earlier stage, improving the quality of the online version. The simulation test is divided into three stages: environment cloning, environment adaptation, and test run. Environment cloning Environment cloning is the basis for implementing the entire simulation testing, including the selection and cloning of online tasks and the sampling of test data (as shown below). Blink’s online tasks are numerous and are scattered across many different projects. Although each online task has its own internal business logic, different tasks can be classified according to their main processing logic, for example with a task set based on Agg operations or a task set based on Sum operations, and so on. Therefore, the Blink simulation test needs to distinguish online tasks and select the most representative ones. The test data set of the simulation test is a sample of the input data of the current online task, which differs only in data scale, and can be dynamically adjusted according to the test requirements, thereby achieving accurate measurement of the test target. Environment Adaptation Environment adaptation is the initialization phase in the simulation test process, and is mainly to modify the test cases to make it run normally. The process consists of two main steps: Changing the test data input source and test result output address and updating the resource configuration of the task. Test Run Test run is the actual execution module in the simulation test process, including the test case run and result feedback. Blink simulation testing includes the functional test, performance test, and stability test. Different test modules have different metrics and feedback methods. The test results of these test modules, together with the results of the code quality check and integration test, form the result set of the Blink test (as shown below). The performance test and the functional test use simulation tasks and sample data as input to compare and analyze the execution and output of tasks on different execution engines. The performance test focuses on performance metrics such as resource utilization and throughput of different execution engines during execution; while the functional test compares the final results of the execution. It should be noted that in the functional test, the running result of the online version is assumed to be true; that is, when the execution result of the online version is different from the execution result of the developing version, the execution of the developing version is considered wrong, and the error in the developing version needs to be repaired. The stability test focuses on the stability of simulation tasks under the conditions of the online cloning environment, large data volume and long-time running. It uses the Blink developing version as the only execution engine, and measures stability metrics such as resource utilization, throughput, and failover during execution. Compatibility Test The Blink compatibility test is mainly used to discover compatibility issues between new and old Blink versions. At present, compatibility testing is mainly divided into two stages: static checking and dynamic execution checking. Static Check Static check is mainly used to analyze differences in the generated execution plan of the online task under different execution engines and includes two aspects: · The correctness of the execution plan generated by the new execution engine and the length of generation time. · The compatibility of plans generated by new and old execution engines, respectively. The static check can be considered a failure if the new execution engine cannot generate an execution plan correctly, or if the generation of the execution plan takes longer time than expected, which indicates that there is an exception or defect in the new version of Blink. When the new version can generate the execution plan correctly, if the execution plan generated by the new-version execution engine is not compatible with the one generated by the old version then the comparison result needs to be fed back to the developer to determine whether the change of the execution plan is expectable; if the execution plans are compatible or the change is expectable, then the runtime test can be directly run for the new version. Dynamic Runtime Test The Blink dynamic runtime test uses the functional test module in the simulation test to run the task, and is the last round of testing before upgrading the new version of Blink. If the task can be started normally and the test result is in line with expectations, the task is considered to be capable of automatic upgrade; otherwise, manual intervention is required for manual upgrading. Outlook With just over a year of hard work, the overall quality of Blink has been greatly improved, and the testing methods and tools are becoming increasingly mature. As the Blink user base grows, demand from Blink service developers for quality assurance of their service tasks is increasing dramatically. Moving forward, the Blink test team aims to provide more quality assurances and development tools to further improve Blink developers’ engineering efficiency. ( Original article by Zhao Li赵丽) This article is part of Alibaba’s Flink series . Alibaba Tech First hand and in-depth information about Alibaba’s latest technology → Facebook: . Twitter: . “Alibaba Tech” “AlibabaTech”

From Code Quality to Integration: Optimizing Alibaba’s Blink Testing Framework

If you weren’t aware, there is a new paradigm for web development on the horizon. Under development are various web browser specifications under the banner of “web components”. Web components aim to standardize what most mainstream JavaScript frameworks are already doing — provide a set of tools allowing the developer to create new HTML element-like classes implementing custom application logic. These components can potentially expose APIs through HTML properties, just like real elements, and be reused in many applications, finally solving the problem of code re-use on the front-end. If you also weren’t aware, this process has been very painful and slow, with disagreements on standards, clunky APIs, and poor performance slowing adoption. One of the top Google results for a “web components” search, for me anyway, is this post titled . The Google Polymer framework attempts to put a band-aid on this by providing an idealized version of the web components API, and shoehorning it to work in all modern browsers. They have had some success, but even their main developer evangelist admits that . The Broken Promise of Web Components the broken promise guy kind of has a point So where does that leave us front-end developers or dabblers? I have sung the praises of Polymer in the past and I still think it offers the ideal web development experience, although it may not be the right solution for most new web applications. To really get a feel for where the underlying technology is at, we should implement a web component in VanillaJS. The tag was a beloved design element in the late 90s and early 2000s. The native tag, available in most browsers, would cause whatever was enclosed in it to hide and show itself on an interval, so that the user would notice it. For example, one might add a promotion to one’s online store that flashed “SALE” in bright red serif text. This noble tag has been so maligned that it has fallen far out of fashion, even causing the normally stoic Mozilla Developer Network to publish perhaps . Like Dr. Frankenstein, we aim to raise it from the dead, consequences be damned. <blink> the most hostile documentation page ever To create a custom element in ES6, we will first create a class that extends the built-in class. Then, we can register it with the browser using the API. HTMLElement customElements class XBlink extends HTMLElement { } customElements.define('x-blink', XBlink); Why ? Well, custom elements must have a dash in their name, so their names do not collide with built-in elements that have no dashes. As a result, most custom elements have some kind of prefix tacked on so they can still use simple one-word tag names. The prefix is also commonly used for non-standard HTTP headers to distinguish them. Here is where we wonder aloud why adoption of this standard has been slow. x-blink x- Our element in this form does and shows nothing. In order for it to display its children, we must attach a slot. The slot API lets us define where content placed inside our element should be displayed in our internal or “shadow” DOM tree. The blink tag has no structure of its own, so the internal DOM will only contain what we put in the slot. To add it we first have to attach a shadow DOM, which we declare as open for some reason, and create the slot element. class XBlink extends HTMLElement { constructor() {super();this.attachShadow({ mode: 'open' }).appendChild(document.createElement('slot'));}} customElements.define('x-blink', XBlink); Now, if we include this script in our page and use the tag, we should see the text displayed normally. <x-blink>I'll blink if I like</x-blink> Now to actually make it blink. Custom elements have reserved method names for “lifecycle callbacks”, simply meaning the developer can write functions to be run at certain key moments, mostly when the element is created or destroyed. When our element is created, we want to dispatch an interval function which will show and hide the contents of the tag. To avoid having the tag re-size itself when hiding, we will modify the opacity property to accomplish this. connectedCallback() { this.style.opacity = '1';this.interval = setInterval(() => {this.style.opacity = this.style.opacity === '1' ? '0' : '1'}, 500);} disconnectedCallback() {clearInterval(this.interval);} Now, our tag is blinking! We have brought back an old friend from the early days of the web. However, we are not using the full power of web components yet. For our component to be re-usable, it should expose an API so that it can be configured by a user without having to be re-written. The blink speed of 500ms could be a exposed in an HTML property, then the user could set it to blink faster or slower, depending on their UX requirements. To do this, we can simply call from inside of our , just as we would with an object instance of , which we inherited from. Let’s set a default of 500ms if we don’t find that attribute, but use it if we do. HTML attributes are always strings, so we will attempt to parse it, not because we have to, but to avoid . this.getAttribute connectedCallback HTMLElement silly type coercion errors connectedCallback() {this.speed = parseInt(this.getAttribute('speed'));this.speed = isNaN(this.speed) ? 500 : this.speed;this.style.opacity = '1'; this.interval = setInterval(() => {this.style.opacity = this.style.opacity === '1' ? '0' : '1'}, this.speed);} And that’s it! We’ve created a custom element, configurable through attributes, based entirely on the emerging browser standard. You can see why a framework like Polymer would want to add a templating syntax and data binding engine on top of this in order to actually build web applications. Are web components the end-all, be-all solution for re-usable browser code? Who knows. Right now they look about as good as the blink tag we resurrected, but beauty is in the eye of the beholder. If you’d like to use to make your own web page beautiful, the full example code is available on . x-blink CodePen

Web Components from Scratch — Bringing Back the Blink Tag

(BMC) is a Lebanese company that delivered on demand car cleaning services in Beirut; a very promising premise. Two short years after its launch, Blink My Car announced it will cease its operations. Blink My Car startup Blink My Car was doing great. Delivering a great service, building a really nice brand, attracting the attention of local and regional investors, all while also doing a favor to our planet, saving its water. It is definitely sad seeing a promising Lebanese startup reach a dead-end. But it is also a great opportunity to learn something from their story. PS. I have no information on the real reasons for closure. The below are merely some observations and some learning opportunities for myself and for fellow entreprenuers. Here are eight lessons we can learn from this startup’s story: **Big investments do not guarantee success.**Many entrepreneurs think of investments as the light at the end of the tunnel. It’s all they need to make it! Most entreprenuers attribute their failure to the lack of investment. , BMC’s seed round got them $1.2M, an amount most Lebanese entreprenuers can only dream of. A number that you, as a Lebanese entrepreneur, would think could take you to the moon and back. You will later realize, some challenges are tougher than the money you have. Only 6 months ago UPDATE : Through insider information the following was clarified. While BMC raised $1.2M, the startup did not burn through the whole amount before closure. Only ~$200K were used before the company decided that the smart decision is to cease operations. Bonus lesson : Respect your investors, and do not burn through money just because you have it! **Celebrities and ads don’t make a successful business.**BMC did a great job exposing their brand to their target market. Their first video ad went viral. The concept was great. Everyone loved them. This didn’t seem to translate to customers though. A common mistake among many startups is believing that advertising gets them traction; some throw a lot of advertising money on their problems. Doesn’t work. business UPDATE : Through insider information the following was clarified. The advertising campaign referred here was bootstrapped and pulled-off at nearly zero cost using founder resources, connections, and skills. Bonus lesson 2 : Build a network of valuable connections, and think of creative ways to generate impactful campaigns at the lowest cost possible. **Eco-friendly? Who cares?**BMC claims to save 250 liters of water per wash. Awesome! But in a real world, Who cares? Customers don’t generally care if your product is Eco-friendly or socially responsible. People will buy your product because it’s good for THEM not because it’s good for the environment. Being socially or environmentally responsible should be the icing on the top, it will never work as the batter of the cake. **Changing people’s habits isn’t easy.**Whether it is the way you use public transportation, or the way you order food, or even buy your groceries, all businesses that tried to change these habits had to do a lot of aggressive awareness campaigns and work on creative “growth hacking” strategies to convince potential customers to adopt their services. You might be used to washing your car on the weekend on your way to your village, or maybe asking your building’s janitor to take it over to the gas station, or simply ignoring it until you forget what color lies under the dust. Whatever your habit is, changing it is tough. A business trying to change people’s habits is not an easy business. **Great customer service goes a long way.**Blink My Car had loyal fans! Their customer service was on par with international standards. I even once got a personalized email from the CEO after a small complaint I made. Whatever your product is, it is not worth much without great customer service. Some tech giants such as Amazon, eBay, and Zappos were built on that premise alone. Nothing is perfect. This is what allows for competitive businesses that cater for different needs and priorities. BMC wanted to cater for those who value their time, hence paying a premium for a quick convenient service at your doorstep. Customers might compromise one thing, not everything. Service businesses are built on three factors, Speed/Convenience, Price, and Quality. And just like your favorite superhero, or video game character, you can’t get it all, and that is fine. However, with BMC, I found that all three were compromised. I tried Blink My Car once. So the below is based on a single personal experience. Needless to say, BMC services were priced at a premium. 2 to 3 times the average market rate for washing your car. But, what is that premium for? Price — From the minute I placed my order until I was handed back my keys, I had to wait a total of 2 hours. Any one who has taken their car to a car wash know that it takes much less than this, even on a busy day. Convenience, their major value proposition, was totally lacking in my case. Speed?— — The fact that BMC is a mobile service, they are naturally very limited with their tools. They used a handheld vacuum cleaner which is not nearly as effective as the real thing. They used chemical sprays instead of water jets, which also limited how clean the car can get, and only one person worked on your car, which limited speed and attention to detail. You get the idea. It wasn’t as clean as the real thing. Quality? I would have been willing to compromise one of the above, probably two. I’m fine with a super fast, not so clean service, or a super clean not so fast service. But paying a premium for a slow, sub-par cleaning service? Didn’t make much sense. Disclaimer: This was a one time incident which might not represent their general practices. **You can’t solve your problem by trying to solve 10 others.**At one point, Blink My Car started introducing other services, and some times irrelevant services, extremely prematurely. Instead of trying to solve the problem of “not enough people using our car cleaning service”, BMC introduced a motor-bike cleaning service. Now having to market for two segments, this only might have dug them deeper into their hole. BMC also introduced some awesome, yet very irrelevant, services, from taking your car for the annual inspection (dreading the fact that I have to do that this week) to paying your annual registration fees. It clearly is no more about blinking your car. Trying to solve more problems, and targeting more markets prematurely will only dilute your business development and marketing efforts. **Know when to say goodbye.**Admitting defeat is a tough man’s job. It takes balls to say goodbye. Blink My Car did it so gracefully while still being the great brand we knew. Don’t keep dragging your business when you know it’s over.

8+ Lessons to be learned from Blink My Car’s Failure.

The Internet Has Spiraled Out of Control: Crimes, Scams, Cyberbullying, Theft & Deception 

Turn Your Dumb Solar Inverter Into a Smart One With This Home Assistant Hack

Imagine a chalkboard stretching into the void, equations flaring neon against a galactic swirl. That’s where my head’s at — I’m a guy obsessed with math, physics, and the messy brilliance of human thought. Lately, I’ve been tangled in a question that’s less puzzle and more explosion: Who should do the jobs — AI or humans? Let’s get real. AI’s a beast— silicon synapses outracing light around a black hole. It’s crunching data, steering Cybertrucks, and scripting Zero Day plots while we’re still rubbing sleep from our eyes. I’ve coded it, watched it shred integrals faster than I can brew coffee. The numbers don’t lie: Goldman Sachs says AI could swipe 300 million jobs by 2030. That’s not a glitch; that’s a seismic reboot. But humans? We’re the wild card no code can cage. I’ve seen a kid’s grin crack a fraction, a stranger rewrite their life after one of my books. AI can’t feel that jolt, can’t doodle a galaxy from a gut spark. We’re the ones who ask “why?” — not just “how?”. Call it soul, chaos, or the thing that keeps me up scribbling theories — it’s ours alone. So, who pulls off this cosmic heist? The sci-fi geek in me sees AI as the ultimate gear. It’s the engine — humming, precise, churning out what we dream up. I’ve leaned on it to solve equations that’d break my pencil, to map ideas too big for one brain. It’s not stealing jobs; it’s amplifying them — freeing us to chase the infinite, not just the inbox. Yet the philosopher in me — haunted by late-night debates — pushes back. Jobs aren’t just tasks; they’re meaning. Strip that away, and what’s left? A trillion transistors can’t match the coffee-stained notebook where my first theory sparked. Words shape worlds, sure, but humans mean them. No orange-a-day mood boost for a bot — biology says we’re wired different. Here’s my take: it’s not AI or humans — it’s a galactic tango. AI grinds the gears; humans chart the stars. Imagine bots eating the grunt work — data entry, assembly lines — while we wrestle cancer, rethink ethics, or just dream weirder. I’ve seen it work: machines crank, but humans dare to ask, “What’s next?” Don’t get me wrong — the heist’s real. AI’s not just eyeing warehouses; it’s creeping into creative desks. PwC says 20% of those gigs could vanish to generative models by 2035. I’ve coded enough to know it’s possible, studied enough physics to see it’s coming. But extinction’s not fate — it’s evolution. Steam engines didn’t erase us; they launched railroads. AI’s our next rail — terrifying if we snooze, electric if we steer. So, HackerNoon’s restless crew, I’m tossing this at you: AI or humans — who should hold the keys? Bots grinding perfection, or us weaving the flaws that make life kick? Drop your take below — I’m not here to hoard answers; I’m here to light the fuse. This isn’t just about jobs; it’s cosmic. The universe is watching — don’t blink. Imagine a chalkboard stretching into the void, equations flaring neon against a galactic swirl. That’s where my head’s at — I’m a guy obsessed with math, physics, and the messy brilliance of human thought. Lately, I’ve been tangled in a question that’s less puzzle and more explosion: Who should do the jobs — AI or humans? Who should do the jobs — AI or humans? Let’s get real. AI’s a beast— silicon synapses outracing light around a black hole. It’s crunching data, steering Cybertrucks, and scripting Zero Day plots while we’re still rubbing sleep from our eyes. I’ve coded it, watched it shred integrals faster than I can brew coffee. The numbers don’t lie: Goldman Sachs says AI could swipe 300 million jobs by 2030. That’s not a glitch; that’s a seismic reboot. Goldman Sachs says But humans? We’re the wild card no code can cage. I’ve seen a kid’s grin crack a fraction, a stranger rewrite their life after one of my books. AI can’t feel that jolt, can’t doodle a galaxy from a gut spark. We’re the ones who ask “ why?” — not just “ how?” . Call it soul, chaos, or the thing that keeps me up scribbling theories — it’s ours alone. why?” how?” So, who pulls off this cosmic heist? The sci-fi geek in me sees AI as the ultimate gear. It’s the engine — humming, precise, churning out what we dream up. I’ve leaned on it to solve equations that’d break my pencil, to map ideas too big for one brain. It’s not stealing jobs; it’s amplifying them — freeing us to chase the infinite, not just the inbox. Yet the philosopher in me — haunted by late-night debates — pushes back. Jobs aren’t just tasks; they’re meaning. Strip that away, and what’s left? A trillion transistors can’t match the coffee-stained notebook where my first theory sparked. Words shape worlds, sure, but humans mean them. No orange-a-day mood boost for a bot — biology says we’re wired different. mean Here’s my take: it’s not AI or humans — it’s a galactic tango. AI grinds the gears; humans chart the stars. Imagine bots eating the grunt work — data entry, assembly lines — while we wrestle cancer, rethink ethics, or just dream weirder. I’ve seen it work: machines crank, but humans dare to ask, “What’s next?” or Don’t get me wrong — the heist’s real. AI’s not just eyeing warehouses; it’s creeping into creative desks. PwC says 20% of those gigs could vanish to generative models by 2035. I’ve coded enough to know it’s possible, studied enough physics to see it’s coming. But extinction’s not fate — it’s evolution. Steam engines didn’t erase us; they launched railroads. AI’s our next rail — terrifying if we snooze, electric if we steer. PwC says So, HackerNoon’s restless crew, I’m tossing this at you: AI or humans — who should hold the keys? Bots grinding perfection, or us weaving the flaws that make life kick? Drop your take below — I’m not here to hoard answers; I’m here to light the fuse. This isn’t just about jobs; it’s cosmic. The universe is watching — don’t blink. AI or humans — who should hold the keys?

AI Works While We Sleep—But Who’s Dreaming Bigger?

On January 17, a new meme coin called $Trump was launched by the President-Elect. Within two days, its total market value peaked at over $14.5 billion - only to crash by two-thirds shortly after. Reuters reported that the entities behind the coin raked in close to $100 million in trading fees in under two weeks (according to estimates from three blockchain analysis firms). But while it was a windfall for some; hundreds of thousands of everyday traders lost money. Meanwhile, in late 2024 when Trump announced a Department of Government Efficiency - DOGE - Dogecoin surged to achieve a 150% increase post-election -  far outpacing Bitcoin’s 30% gain. Thanks to moments like these, meme coins are now firmly on the investment radar, but their erratic highs and often painful crashes make them hard to navigate at best, and downright frightening and risky at worst. Are they an exciting new asset class, or are they just a well-packaged scam? First things first, what are meme coins? Meme coins are cryptocurrencies that originate from internet culture, social media trends, or even okes. Unlike Bitcoin or Ethereum, which have clear technical foundations, meme coins thrive on hype, community sentiment, and celebrity endorsements. Their value is often volatile, dictated by online trends rather than fundamental utility. While they often start as parodies, meme coins can gain serious traction due to viral marketing and highly speculative trading. Sometimes, this traction is short-lived. Meme coins have gained mainstream visibility due to their cultural relevance and the potential they offer traders for outsized short-term gains. High-profile figures like Elon Musk have fueled meme coin rallies with a single tweet, driving huge speculative interest. While many traders are drawn to this possibility of overnight riches, the reality is that meme coins are high-risk assets with unpredictable price swings. Somewhat ironically, the SEC’s negative stance toward crypto has actually fuelled the rise of meme coins. While crypto assets that provide utility or financial returns face significant regulatory hurdles, or (worse) run the risk of ‘regulation by enforcement’, meme coins are openly and blatantly speculative and make no promises to holders, leaving them outside of the regulatory net. A high-risk game: meme coin volatility While meme coins can generate enormous gains, they are among the most volatile assets in crypto. Their prices are driven by community hype rather than fundamental value, meaning they can skyrocket or collapse within mere hours. For every story about a trader making 600% gains; there's another about someone losing everything. Fartcoin is one of the classic meme coins that went all in on absurdity. Launched as a joke (as many meme coins are), it promised absolutely nothing except ‘gassy gains’. Its community thrives on toilet humor and the idea that financial markets should be fun. Despite this ridiculous premise, Fartcoin saw some real traction (hitting 2.2B market cap) proving once again that in crypto, a good meme can be just as valuable as a good use case. Fartcoin followed the typical meme coin trajectory: early hype, a parabolic rise in value and then the inevitable crash when its whales cashed out. In line with this, Pump.fun is a meme coin platform that allows users to create and trade meme coins quickly and easily, often with little to no technical knowledge. The platform has gained popularity due to its streamlined process for launching tokens, which has led to a surge in speculative, short-lived coins. Beware the rug pull… Meme coins are notorious breeding grounds for what the industry calls pump-and-dump schemes. Influencers, celebrities and even viral internet personalities have launched their own coins - but this is often done at the expense of their fans. Take Haliey Welch, known online as the "Hawk Tuah" girl. After gaining internet infamy (you can read some of the details in Vanity Fair - they’re not for the fainthearted), Welch launched $HAWK which reached a market cap of nearly half a billion dollars within a single day. Hours later, however, the coin collapsed, wiping out investor funds and leading to nasty accusations of fraud. Some called it a textbook pump-and-dump scheme, where insiders hype up a coin, inflate its value and then sell off their holdings, leaving everyday traders with worthless tokens and sometimes devastating losses. This story isn’t just a one-off - meme coins routinely experience these catastrophic crashes and in February alone, several high-profile meme tokens plummeted, taking millions in investor funds with them. The most concerning amongst these was the $Libra cryptocurrency debacle - an ongoing political scandal that began on February 14 when the president of Argentina, Javier Milei, promoted a crypto project by the same name. The price of the meme coin spiked following Milei's promotion but then saw a severe price drop, leading to allegations of a rug pull scam (dubbed Cryptogate) and The Economist calling it the "first big scandal" of Milei's presidency. (These examples underscore why memecoins are also referred to by pejorative term ‘shi*coins’, or “a crypto with little to no value or no immediate, discernible purpose”. ) What does it all mean? Meme coin mania is often seen as a barometer of retail investor sentiment (the appetite of everyday investors to buy crypto) in the crypto market. When meme coin activity spikes, it often signals a growing appetite for risk. However, 2024 and early 2025 have introduced a new dynamic: institutional investors, via ETFs, have largely steered clear of meme coins. Analysts at Bitwise have pointed to a divergence of views which has seen institutions remain bullish on Bitcoin; but retail traders are pulling back, burned by previous meme coin implosions. Meme coin market dynamics For those still willing to take the plunge, understanding key market concepts before you risk it all is essential: Liquidity: Meme coins with low trading volumes can experience extreme price swings. Always check liquidity before investing.
Community sentiment: Meme coin prices are often dictated by social media hype. Platforms like Twitter and Telegram can provide insight into whether enthusiasm is genuine or artificially generated.
Tokenomics: Some meme coins have built-in mechanisms to drive scarcity, while others dilute their supply constantly. Understanding supply and demand is crucial.
Pump-and-dump risk: If a meme coin is being aggressively marketed with unrealistic promises, it’s likely a short-term hype play rather than a sustainable investment.
Early entry vs. longevity: Getting in early can be lucrative, but it’s also incredibly risky to try and ‘pick the top’. Some traders prefer holding well-established meme coins with strong communities rather than chasing the latest trend. If you’re considering diving into meme coins, it is wise to never invest more than you can afford to lose because much like gambling, fortunes can be made and lost in the blink of an eye. On January 17, a new meme coin called $Trump was launched by the President-Elect. Within two days, its total market value peaked at over $14.5 billion - only to crash by two-thirds shortly after. Reuters reported that the entities behind the coin raked in close to $100 million in trading fees in under two weeks (according to estimates from three blockchain analysis firms). But while it was a windfall for some; hundreds of thousands of everyday traders lost money. Meanwhile, in late 2024 when Trump announced a Department of Government Efficiency - DOGE - Dogecoin surged to achieve a 150% increase post-election -  far outpacing Bitcoin’s 30% gain. Reuters Reuters Thanks to moments like these, meme coins are now firmly on the investment radar, but their erratic highs and often painful crashes make them hard to navigate at best, and downright frightening and risky at worst. Are they an exciting new asset class, or are they just a well-packaged scam? meme coins First things first, what are meme coins? Meme coins are cryptocurrencies that originate from internet culture, social media trends, or even okes. Unlike Bitcoin or Ethereum, which have clear technical foundations, meme coins thrive on hype, community sentiment, and celebrity endorsements. Their value is often volatile, dictated by online trends rather than fundamental utility. While they often start as parodies, meme coins can gain serious traction due to viral marketing and highly speculative trading. Sometimes, this traction is short-lived. Meme coins have gained mainstream visibility due to their cultural relevance and the potential they offer traders for outsized short-term gains. High-profile figures like Elon Musk have fueled meme coin rallies with a single tweet, driving huge speculative interest. While many traders are drawn to this possibility of overnight riches, the reality is that meme coins are high-risk assets with unpredictable price swings. Somewhat ironically, the SEC’s negative stance toward crypto has actually fuelled the rise of meme coins. While crypto assets that provide utility or financial returns face significant regulatory hurdles, or (worse) run the risk of ‘regulation by enforcement’, meme coins are openly and blatantly speculative and make no promises to holders, leaving them outside of the regulatory net. A high-risk game: meme coin volatility While meme coins can generate enormous gains, they are among the most volatile assets in crypto. Their prices are driven by community hype rather than fundamental value, meaning they can skyrocket or collapse within mere hours. For every story about a trader making 600% gains; there's another about someone losing everything. Fartcoin is one of the classic meme coins that went all in on absurdity. Launched as a joke (as many meme coins are), it promised absolutely nothing except ‘gassy gains’. Its community thrives on toilet humor and the idea that financial markets should be fun. Despite this ridiculous premise, Fartcoin saw some real traction (hitting 2.2B market cap) proving once again that in crypto, a good meme can be just as valuable as a good use case. Fartcoin followed the typical meme coin trajectory: early hype, a parabolic rise in value and then the inevitable crash when its whales cashed out. In line with this, Pump.fun is a meme coin platform that allows users to create and trade meme coins quickly and easily, often with little to no technical knowledge. The platform has gained popularity due to its streamlined process for launching tokens, which has led to a surge in speculative, short-lived coins. Beware the rug pull… Meme coins are notorious breeding grounds for what the industry calls pump-and-dump schemes. Influencers, celebrities and even viral internet personalities have launched their own coins - but this is often done at the expense of their fans. Take Haliey Welch, known online as the "Hawk Tuah" girl. After gaining internet infamy (you can read some of the details in Vanity Fair - they’re not for the fainthearted), Welch launched $HAWK which reached a market cap of nearly half a billion dollars within a single day. Hours later, however, the coin collapsed, wiping out investor funds and leading to nasty accusations of fraud. Some called it a textbook pump-and-dump scheme, where insiders hype up a coin, inflate its value and then sell off their holdings, leaving everyday traders with worthless tokens and sometimes devastating losses. Vanity Fair Vanity Fair This story isn’t just a one-off - meme coins routinely experience these catastrophic crashes and in February alone, several high-profile meme tokens plummeted, taking millions in investor funds with them. several high-profile meme tokens plummeted, several high-profile meme tokens plummeted, The most concerning amongst these was the $Libra cryptocurrency debacle - an ongoing political scandal that began on February 14 when the president of Argentina, Javier Milei, promoted a crypto project by the same name. The price of the meme coin spiked following Milei's promotion but then saw a severe price drop, leading to allegations of a rug pull scam (dubbed Cryptogate) and The Economist calling it the "first big scandal" of Milei's presidency. (These examples underscore why memecoins are also referred to by pejorative term ‘shi*coins’, or “a crypto with little to no value or no immediate, discernible purpose”. ) What does it all mean? Meme coin mania is often seen as a barometer of retail investor sentiment (the appetite of everyday investors to buy crypto) in the crypto market. When meme coin activity spikes, it often signals a growing appetite for risk. However, 2024 and early 2025 have introduced a new dynamic: institutional investors, via ETFs, have largely steered clear of meme coins. Analysts at Bitwise have pointed to a divergence of views which has seen institutions remain bullish on Bitcoin; but retail traders are pulling back, burned by previous meme coin implosions. Meme coin market dynamics For those still willing to take the plunge, understanding key market concepts before you risk it all is essential: Liquidity: Meme coins with low trading volumes can experience extreme price swings. Always check liquidity before investing. Community sentiment: Meme coin prices are often dictated by social media hype. Platforms like Twitter and Telegram can provide insight into whether enthusiasm is genuine or artificially generated. Tokenomics: Some meme coins have built-in mechanisms to drive scarcity, while others dilute their supply constantly. Understanding supply and demand is crucial. Pump-and-dump risk: If a meme coin is being aggressively marketed with unrealistic promises, it’s likely a short-term hype play rather than a sustainable investment. Early entry vs. longevity: Getting in early can be lucrative, but it’s also incredibly risky to try and ‘pick the top’. Some traders prefer holding well-established meme coins with strong communities rather than chasing the latest trend. Liquidity: Meme coins with low trading volumes can experience extreme price swings. Always check liquidity before investing. Community sentiment: Meme coin prices are often dictated by social media hype. Platforms like Twitter and Telegram can provide insight into whether enthusiasm is genuine or artificially generated. Tokenomics: Some meme coins have built-in mechanisms to drive scarcity, while others dilute their supply constantly. Understanding supply and demand is crucial. Pump-and-dump risk: If a meme coin is being aggressively marketed with unrealistic promises, it’s likely a short-term hype play rather than a sustainable investment. Early entry vs. longevity: Getting in early can be lucrative, but it’s also incredibly risky to try and ‘pick the top’. Some traders prefer holding well-established meme coins with strong communities rather than chasing the latest trend. If you’re considering diving into meme coins, it is wise to never invest more than you can afford to lose because much like gambling, fortunes can be made and lost in the blink of an eye.

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