Blinkly

blinkly

The Internet has become an essential part of our lives today, ever since the pandemic happened, the immersion of the internet in our society has reached drastic levels. This makes sense as well because it was only thanks to the internet that our world was able to function somewhat during peak pandemic times through practices like work from home and online education. These were the two notable fields out of so many different sectors that the internet helped remain functional during lockdowns at a time when the world was prisoned in lockdowns. Looking at its use in so many sensitive tasks, the internet was granted the status of an essential service, and governments all around the world including the US government stepped in to provide financial aid to users so they could remain connected to their internet services. The most prominent example of this was the EBB or Emergency Broadband Benefit Program. Similarly, service providers like Xfinity, Spectrum, and among others also stepped in to assist users in these dire economic conditions to stay online by providing discounts and promotions during this period. Cox internet Even though we are looking at the end of the pandemic in the near future, still the trends that we picked up during this time are here to stay. An example of such is the hybrid work model, which many companies are adopting, where employees are required to work on-site on certain days, while they can do work from home for the rest of the work week. But with the increase in the immersion of the internet and the surge in the number of users, the privacy factor also becomes a reason for concern. Today we are going to discuss an aspect of user’s privacy that most internet goers often ignore, and that’s the cookies. In the past couple of years, you might have often seen pop-ups on numerous websites that ask you to accept or reject the storage of cookies and their privacy policy agreement. Most users don’t pay much heed to that message and often accept that option in order to quickly access that particular website, here’s how it can be dangerous for your privacy on the web. What are Cookies? But first, in order to understand why accepting cookies from every random website may be a bad idea, we first need to understand what cookies are in the first place. The name might confuse some but don’t worry, these aren’t those freshly baked treats that we all like to enjoy with a cup of coffee. In terms of the web, cookies are small data files that are stored in your browser by the particular website that you visit. These are multipurpose files that are used to identify you as a particular user, and store data like login credentials, images, usage patterns, and shopping preferences. Basically, these are the vital components that a website uses in order to customize the web experience according to every individual user as per their behavior and usage of that said website. But in order to provide that personalized experience sometimes these cookies also hold users' sensitive data like IP address, physical location, and contact information, as well as dynamically track how often you visited that website and what you did on it. Sharing this type of information might make a user uncomfortable, and that’s where the privacy problem lies. Should you Always Accept Cookies? Cookies were always an integral part of the internet culture and websites always use cookies for various purposes including tracking users’ internet movements. However, it was only after the implementation of GDPR (General Data Protection Regulation) legislation by the European Union in 2018 that cookie consent from the user became mandatory. Before it, websites used cookies however they want, but now every global website has a pop-up for users when visiting it for the first time, this message asks users if they accept all cookies and the privacy policy of a website. Unfortunately, many users just click accept without even reading the message let alone analyzing the privacy policy of a website. That’s incredibly dangerous because accepting cookies gives a website all the rights to use the user’s personal data without any legal complications, as the user has themselves allowed those websites to basically use their data. But the question here is, should you always accept cookies? , this was the whole point of GDPR, to give users control over how their personal information will be used. And if any website bars you from accessing it if you say no to the cookie consent, then it’s probably not worth visiting in the first place. No, a user may not need to accept cookies in order to access a website Protective Measures Taken Against Tracking of Users This consent is important especially if you are visiting a rather shady or unknown website since you don’t want to give any random person on the other side of the screen all of your personal information. However, if a trusted website asks you for permission and you’re fully satisfied with their terms and conditions you can definitely go ahead and give your consent to store cookies in order to make the web experience better. Now countless protection measures are being placed by giants of the tech industry like Apple and Google to protect their users from being tracked across the web. Apple has launched enhanced security and privacy features in their iOS 14 that give users the control to stop websites and apps from tracking them. This move made many companies like Facebook angry with Apple as Facebook’s entire business model worked on selling ads to users based on their usage history. On the other hand companies like Google has also stopped the support of third-party cookies in their Chrome browser starting from 2022. This has effectively made users of Google Chrome and Apple iPhones secure from third-party tracking through the use of cookies. Final Thoughts Cookies are not entirely bad and can make your web experience a pleasant one, however, how websites use them and the kind of information they collect about a user is still a point of concern for many. But today companies like Google and Apple are fighting against these practices but until or unless there isn’t any substantial and safe alternative to cookies, it’s the job of the user to stay vigilant and careful while giving access to any random website to store cookies. As cookies are not just tools to make your internet experience better but can quickly become a weapon to invade your privacy, that too with your own consent.

Here's Why You Should Never Blindly Accept Cookies from Every Website

Businesses want to make sales. No doubt about that. However, they often end up spamming random people with their sales copy. Often even people who have nothing to do with their products or services. It feels spammy and it is. It doesn’t make sales. Businesses should follow a structure when sending out content and making sales pitches. They should use a sales funnel, and I’m going to go into this article as to how to structure one. The main idea is that companies shouldn’t target every single person they can reach. They should instead assert their prospect’s and deliver content accordingly. position within their sales funnel The funnel is made of 4 levels, which are separated in the middle by the “wall of trust”. The levels are the following: : These are the people who aren’t aware of your products and/or services. It’s very unlikely that they will pay loads of money to your brand just because you have something to sell to them. They don’t know anything about your brand or what makes you a better choice than your competitors. What you should do with them is . Use attention-grabbing headlines and relatable images or some sales copy that pinpoints their exact situations and feelings. The key word here is Just like when you meet someone new and what keeps you two engaged are your common interests and/or passions. …or even common problems. If they get the idea that you are knowledgeable of their business (their issues, roadblocks, questions and desires) then they will hear you out. Afterwards, they aren’t strangers anymore. They become Strangers pique their interest relatable. your audience. They are the people whose eyes and ears you managed to win over. What now? NOW… you need to win over their And when you do, you will have them on your side, believing in your message and in your vision. . How do you do that? By giving them as much value as possible. Yes, give them free stuff! Be it blog posts, tips, tutorials, PDF guides… anything that may be useful to them. The result of this is that they will start seeing you as being resourceful and as being a guide. Once they make they make the decision to trust you, they become your subscribers. Audience: trust. Believing in your brand Finally, you can pitch your products to them. Bear in mind that trust can be lost far easier that it has been won. So keep giving out gifts (aka free stuff) just like you normally would, if they weren’t subscribed; make sure your products are polished and presentable, and please don’t be too pushy and overwhelming with your sales pitches. Instead, keep sales pitches related to their needs/interests. Otherwise you could come off as spammy (and lose their trust). Subscribers: The people who went all the way through the funnel and bought your product. These are actually from your sales funnel and should be treated accordingly. Nurture them with exclusive content and offers. Make them feel special. Make it worthwhile to be your client. Word of mouth and referral programs are most of the time more effective than sales pitches. Clients: the most important people A lot of salespeople make the mistake of putting strangers first and seek to make gathering attention a first priority. It should be the other way around! Clients are your most important assets because they trust you and they will stick to your brand. You already won them over. Now make them stay and offer them more! Conclusion Using a sales funnel just like the one described above can prove useful when segmenting sales copy, posts, ads, promotional material, sales pitches, etc. Personalisation is a term in marketing, which means using information about prospects to target their needs and desires in their own context, so that they understand that you have a grip on their own situations and you know what you are talking about. You are proving this to them, and they choose you and your services because of your competency. Bear this diagram in mind, and use it whenever you get the chance so that you don’t put in too much effort in strangers who don’t want it and run out of energy/time/resources to nurture your clients who are counting on you. Also published here .

Stop Blindly Targeting, Start Creating Smarter Content for the Funnel

I was playing around with the good old command in an container today. I wanted to perform a little load test for another service so I experimented with different package sizes ( ), which caused me quite a bit of MTU-related frustration (but that’s a different story). ping alpine Docker ping -s <size in byte> Upon googling about pings from docker, I stumbled across . this forum post I will let you read the post yourself instead of copying everything in here, but the essence is: . ping packets may return to you even if you send the pings to a non-existing host And it’s true. I took a random IP and made sure it’s not actually ping-able from my desktop. $ ping 11.12.13.14 -c 5PING 11.12.13.14 (11.12.13.14): 56 data bytesRequest timeout for icmp_seq 0Request timeout for icmp_seq 1Request timeout for icmp_seq 2Request timeout for icmp_seq 3^C--- 11.12.13.14 ping statistics ---5 packets transmitted, 0 packets received, 100.0% packet loss Then ran it from inside a Docker container. $ docker run alpine ping 11.12.13.14 -c 5PING 11.12.13.14 (11.12.13.14): 56 data bytes64 bytes from 11.12.13.14: seq=0 ttl=37 time=0.447 ms64 bytes from 11.12.13.14: seq=1 ttl=37 time=0.469 ms64 bytes from 11.12.13.14: seq=2 ttl=37 time=0.524 ms64 bytes from 11.12.13.14: seq=3 ttl=37 time=0.476 ms64 bytes from 11.12.13.14: seq=4 ttl=37 time=0.461 ms --- 11.12.13.14 ping statistics ---5 packets transmitted, 5 packets received, 0% packet lossround-trip min/avg/max = 0.447/0.475/0.524 ms This is bad news, because it means you can’t really trust anymore. You might have a health-check that tests connectivity to one of your production services through pings and keeps reporting green even though the service is down. ping To make matters worse, it doesn’t happen for all fake IPs and the forum post was created over a year ago. The good news is, this only happens for Docker on (maybe Windows as well), but not Linux. MAC Screenshot of the “ghost ping”.

Don’t blindly trust the Docker ping

Authors:
(1) Athanasios Angelakis, Amsterdam University Medical Center, University of Amsterdam - Data Science Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands
(2) Andrey Rass, Den Haag, Netherlands. Table of Links Abstract and 1 Introduction
2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture
2.1 Data Augmentation Robustness Scouting
2.2 The Specifics Of Data Affect Augmentation-Induced Bias
2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias
2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias
3 Conclusion and Limitations, and References
Appendices A-L Abstract Data augmentation (DA) enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly. Our study extends this inquiry, examining DA’s class-specific bias across various datasets, including those distinct from ImageNet, through random cropping. We evaluated this phenomenon with ResNet50, EfficientNetV2S, and SWIN ViT, discovering that while residual models showed similar bias effects, Vision Transformers exhibited greater robustness or altered dynamics. This suggests a nuanced approach to model selection, emphasizing bias mitigation. We also refined a ”data augmentation robustness scouting” method to manage DA-induced biases more efficiently, reducing computational demands significantly (training 112 models instead of 1860; a reduction of factor 16.2) while still capturing essential bias trends. 1 Introduction Machine learning is generally defined as aiming at systems to solve, or ”learn” a particular task or set of tasks (e.g. regression, classification, machine translation, anomaly detection - (Goodfellow, Bengio, and Courville 2016a) based on some training data, allowing computers to assign labels or predict future outcomes without manual programming. A typical case involves a training dataset that is finite, and the system’s parameters are optimized using a technique such as gradient descent, using some performance measure (e.g. “accuracy”) for evaluation during optimization and on a holdout “test” set (LeCun et al. 1998; Bishop and Nasrabadi 2006; Shalev-Shwartz and Ben-David 2014; Goodfellow, Bengio, and Courville 2016a). In particular, computer vision tasks (e.g. image classification) are contemporarily accomplished via deep learning methods (Feng et al. 2019) such as convolutional neural networks (CNNs), which have continuously been held in high regard as the go-to approach to such problems (LeCun, Bengio et al. 1995; LeCun, Kavukcuoglu, and Farabet 2010; Voulodimos et al. 2018), owing to their ability to extract features from data with known grid-like topology, such as images (Goodfellow, Bengio, and Courville 2016b). Like any other machine learning systems, CNNs can suffer from overfitting - a performance gap between training and test data samples, which represents inability to generalize the learned task to unseen data (Goodfellow, Bengio, and Courville 2016c). To combat this, various regularizaton methods have been developed for use during optimization (Tikhonov 1943; Tihonov 1963). Particular (though not limited - (Ko et al. 2015)) to image-based tasks is the use of data augmentation - applying certain transformations, such as random cropping, stretching and color jitter, to training data during training iterations. Such techniques are near-ubiquitous in computer vision tasks due to their effectiveness as a regularization measure (Shorten and Khoshgoftaar 2019). However, recent research by Balestriero, Bottou, and LeCun (2022) suggests that, despite data augmentation being such a prevalent method of improving model performance, it may actually prove a risk to blindly turn to this technique regardless of dataset and approach. This appears to be a part of a larger phenomenon concerning a tendency of parametrized regularization measures to sacrifice performance on certain classes in favor of overall model accuracy. However, it is also caused in large part by the fact that different image transformations seem to possess varying levels of label preservation (Cui, Goel, and Kingsbury 2015; Taylor and Nitschke 2018) dependent on the class of image data, and, as such, can have a severe class-specific negative impact on the model performance by virtue of incurring label loss if applied too aggressively. This impact can also be so deep as to even impact downstream performance in the case of transfer learning tasks. It may be a natural reaction, as such, to caution against using data augmentation as a regularization technique to avoid this. However, this can prove to be difficult in practice, as the overall performance boost this technique provides is undeniable, and it is currently used ubiquitously, with few alternatives. In addition, as alluded to before, the paper showed that other regularization methods, such as weight decay, can also be subject to this phenomenon. It is also important to note that Balestriero, Bottou, and LeCun (2022) claims that the phenomenon is model-agnostic for popular CNNs based on residual blocks such as ResNet (He et al. 2015), DenseNet (Huang, Liu, and Weinberger 2016), and others, but does not make any claims in regards to whether the phenomenon is data-agnostic, or how it would manifest in image classification networks that belong to architectures founded in considerably different principles, such as Vision Transformers (Dosovitskiy et al. 2020b; Liu et al. 2021) that use patch-based image processing and self-attention mechanisms to extract features from images. Our work follows a course of further investigation. To formulate our primary research question, we follow the tenets of the data-centric AI movement championed by Andrew Ng, which seeks to systematically engineer the data used in training AI systems, and places special focus on accounting for imperfections in real world data. In data augmentation, the discipline outlines issues such as domain gaps, data bias and noise. Following this school of thought leads us to question if class-specific bias from data augmentations affect datasets different from Imagenet, in a different way. In particular, we seek to test whether this phenomenon can be observed on datasets that differ in nature to various degrees from Imagenet (Deng et al. 2009), which was used in Balestriero’s 2022 paper, and to what extent. To supplement this line of validation, another, secondary research question emerged from a seemingly minor detail in the original study. Concretely, we investigate if the addition of Random Horizontal Flipping have an effect on how the class-specific bias phenomenon manifests. While not our core focus, we also seek to confirm how model-agnostic this phenomenon is on these new datasets. For this, it is first necessary to test with a model that has common features with ResNet50, which was the baseline for many experiments in (Balestriero, Bottou, and LeCun 2022). We selected EfficientNet, first described in Tan and Le (2019), which is a family of models that also utilizes residual blocks, but was designed via a neural architecture search (Elsken, Metzen, and Hutter 2019) using a new scaling method that uniformly scales all dimensions of model depth/width/resolution using a simple yet highly effective compound coefficient. To that end, we question if class-specific bias from data augmentations on the same dataset would affect a different Residual CNN architecture in the same manner as it would a ResNet. Finally, it is also worth investigating the effects of a vastly different architecture, as mentioned earlier. For these purposes, we have selected to use a SWIN Transformer, which is a relatively small patch-based vision transformer that uses a novel shifted windowing technique for more efficient computation of the self-attention mechanism that is inherent to Transformer-type models (Liu et al. 2021). Finally, we consider if class-specific bias from data augmentations on the same dataset would affect a Vision Transformer model in the same manner as it would a ResNet. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Athanasios Angelakis, Amsterdam University Medical Center, University of Amsterdam - Data Science Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands (2) Andrey Rass, Den Haag, Netherlands. Authors: Authors: (1) Athanasios Angelakis, Amsterdam University Medical Center, University of Amsterdam - Data Science Center, Amsterdam Public Health Research Institute, Amsterdam, Netherlands (2) Andrey Rass, Den Haag, Netherlands. Table of Links Abstract and 1 Introduction 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture 2.1 Data Augmentation Robustness Scouting 2.2 The Specifics Of Data Affect Augmentation-Induced Bias 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias 3 Conclusion and Limitations, and References Appendices A-L Abstract and 1 Introduction Abstract and 1 Introduction 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture 2 The Effect Of Data Augmentation-Induced Class-Specific Bias Is Influenced By Data, Regularization and Architecture 2.1 Data Augmentation Robustness Scouting 2.1 Data Augmentation Robustness Scouting 2.2 The Specifics Of Data Affect Augmentation-Induced Bias 2.2 The Specifics Of Data Affect Augmentation-Induced Bias 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias 2.3 Adding Random Horizontal Flipping Contributes To Augmentation-Induced Bias 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias 2.4 Alternative Architectures Have Variable Effect On Augmentation-Induced Bias 3 Conclusion and Limitations, and References 3 Conclusion and Limitations, and References Appendices A-L Appendices A-L Abstract Data augmentation (DA) enhances model generalization in computer vision but may introduce biases, impacting class accuracy unevenly. Our study extends this inquiry, examining DA’s class-specific bias across various datasets, including those distinct from ImageNet, through random cropping. We evaluated this phenomenon with ResNet50, EfficientNetV2S, and SWIN ViT, discovering that while residual models showed similar bias effects, Vision Transformers exhibited greater robustness or altered dynamics. This suggests a nuanced approach to model selection, emphasizing bias mitigation. We also refined a ”data augmentation robustness scouting” method to manage DA-induced biases more efficiently, reducing computational demands significantly (training 112 models instead of 1860; a reduction of factor 16.2) while still capturing essential bias trends. 1 Introduction Machine learning is generally defined as aiming at systems to solve, or ”learn” a particular task or set of tasks (e.g. regression, classification, machine translation, anomaly detection - (Goodfellow, Bengio, and Courville 2016a) based on some training data, allowing computers to assign labels or predict future outcomes without manual programming. A typical case involves a training dataset that is finite, and the system’s parameters are optimized using a technique such as gradient descent, using some performance measure (e.g. “accuracy”) for evaluation during optimization and on a holdout “test” set (LeCun et al. 1998; Bishop and Nasrabadi 2006; Shalev-Shwartz and Ben-David 2014; Goodfellow, Bengio, and Courville 2016a). In particular, computer vision tasks (e.g. image classification) are contemporarily accomplished via deep learning methods (Feng et al. 2019) such as convolutional neural networks (CNNs), which have continuously been held in high regard as the go-to approach to such problems (LeCun, Bengio et al. 1995; LeCun, Kavukcuoglu, and Farabet 2010; Voulodimos et al. 2018), owing to their ability to extract features from data with known grid-like topology, such as images (Goodfellow, Bengio, and Courville 2016b). Like any other machine learning systems, CNNs can suffer from overfitting - a performance gap between training and test data samples, which represents inability to generalize the learned task to unseen data (Goodfellow, Bengio, and Courville 2016c). To combat this, various regularizaton methods have been developed for use during optimization (Tikhonov 1943; Tihonov 1963). Particular (though not limited - (Ko et al. 2015)) to image-based tasks is the use of data augmentation - applying certain transformations, such as random cropping, stretching and color jitter, to training data during training iterations. Such techniques are near-ubiquitous in computer vision tasks due to their effectiveness as a regularization measure (Shorten and Khoshgoftaar 2019). However, recent research by Balestriero, Bottou, and LeCun (2022) suggests that, despite data augmentation being such a prevalent method of improving model performance, it may actually prove a risk to blindly turn to this technique regardless of dataset and approach. This appears to be a part of a larger phenomenon concerning a tendency of parametrized regularization measures to sacrifice performance on certain classes in favor of overall model accuracy. However, it is also caused in large part by the fact that different image transformations seem to possess varying levels of label preservation (Cui, Goel, and Kingsbury 2015; Taylor and Nitschke 2018) dependent on the class of image data, and, as such, can have a severe class-specific negative impact on the model performance by virtue of incurring label loss if applied too aggressively. This impact can also be so deep as to even impact downstream performance in the case of transfer learning tasks. It may be a natural reaction, as such, to caution against using data augmentation as a regularization technique to avoid this. However, this can prove to be difficult in practice, as the overall performance boost this technique provides is undeniable, and it is currently used ubiquitously, with few alternatives. In addition, as alluded to before, the paper showed that other regularization methods, such as weight decay, can also be subject to this phenomenon. It is also important to note that Balestriero, Bottou, and LeCun (2022) claims that the phenomenon is model-agnostic for popular CNNs based on residual blocks such as ResNet (He et al. 2015), DenseNet (Huang, Liu, and Weinberger 2016), and others, but does not make any claims in regards to whether the phenomenon is data-agnostic, or how it would manifest in image classification networks that belong to architectures founded in considerably different principles, such as Vision Transformers (Dosovitskiy et al. 2020b; Liu et al. 2021) that use patch-based image processing and self-attention mechanisms to extract features from images. Our work follows a course of further investigation. To formulate our primary research question, we follow the tenets of the data-centric AI movement championed by Andrew Ng, which seeks to systematically engineer the data used in training AI systems, and places special focus on accounting for imperfections in real world data. In data augmentation, the discipline outlines issues such as domain gaps, data bias and noise. Following this school of thought leads us to question if class-specific bias from data augmentations affect datasets different from Imagenet, in a different way. In particular, we seek to test whether this phenomenon can be observed on datasets that differ in nature to various degrees from Imagenet (Deng et al. 2009), which was used in Balestriero’s 2022 paper, and to what extent. To supplement this line of validation, another, secondary research question emerged from a seemingly minor detail in the original study. Concretely, we investigate if the addition of Random Horizontal Flipping have an effect on how the class-specific bias phenomenon manifests. While not our core focus, we also seek to confirm how model-agnostic this phenomenon is on these new datasets. For this, it is first necessary to test with a model that has common features with ResNet50, which was the baseline for many experiments in (Balestriero, Bottou, and LeCun 2022). We selected EfficientNet, first described in Tan and Le (2019), which is a family of models that also utilizes residual blocks, but was designed via a neural architecture search (Elsken, Metzen, and Hutter 2019) using a new scaling method that uniformly scales all dimensions of model depth/width/resolution using a simple yet highly effective compound coefficient. To that end, we question if class-specific bias from data augmentations on the same dataset would affect a different Residual CNN architecture in the same manner as it would a ResNet. Finally, it is also worth investigating the effects of a vastly different architecture, as mentioned earlier. For these purposes, we have selected to use a SWIN Transformer, which is a relatively small patch-based vision transformer that uses a novel shifted windowing technique for more efficient computation of the self-attention mechanism that is inherent to Transformer-type models (Liu et al. 2021). Finally, we consider if class-specific bias from data augmentations on the same dataset would affect a Vision Transformer model in the same manner as it would a ResNet. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

A Data-centric Approach to Class-specific Bias in Image Data Augmentation: Abstract and Intro

There is productivity. Then there is AI-powered productivity. I like to call these the “10X Makers”.  While that may be an overused phrase, bear with me. A 10X Maker is the perfect marriage between those who naturally innovate and the AI tools on offer that allow them to create groundbreaking solutions at an unprecedented pace. These individuals - strapped to the rocketship of AI - are the ones who will reshape industries and drive global transformation. You probably already have these in your team. They have visionary thinking, adaptability, and a strong bias for action. They are rarely satisfied with small improvements. Instead, they aim for huge leaps forward. With AI tools at their disposal, they can accelerate every phase of the innovation process from ideation to launch. The game changer is this - AI innovation is becoming more accessible. No-code and low-code AI platforms are democratizing the field, allowing team members with great ideas to become AI innovators, regardless of their technical backgrounds. There are plenty of examples. The AI-powered chatbots are providing constant mental health support. There are AI systems that detect strokes faster than human doctors. It goes beyond technological showcases, and instead transforms entire sectors and improves lives. Sustaining innovation in your team To keep this innovation momentum going, you need to reconsider your approach to team development. It's not just about hiring coders; you need to encourage critical thinking, creativity, and a strong understanding of AI ethics within your team. Look towards educational institutions and partner with them to source the best talent. Use online platforms to find the innovators that aren’t on your radar but are already innovating. But that's just the start. Build a supportive ecosystem within your organization. This might include internal incubators for AI projects, allocating resources for AI ventures, and establishing mentorship programs that understand the specific challenges of AI innovation. Your goal should be to create an environment where AI talent can thrive and drive your company forward. And, please don't just throw money at the problem. Too often I see money being pumped into technology rather than talent. It’s not sustainable. Just look at the recent sell-off in the stock market, fuelled by the mounting skepticism over how much “big tech” can benefit from its vast spending on AI. The benefit comes via the talent that can leverage the tech. Not just the tech itself. Create a culture that embraces risk-taking and isn't afraid of failure. For every AI breakthrough, there might be a dozen that fall short. But that's how innovation works. Build resilience into your support systems, ensuring that you can bounce back from setbacks and keep pushing the boundaries. The future of innovation in your organization Make no mistake, the 10X Makers in your team are going to keep pushing the envelope of what's possible with AI. The best of the best won't just optimize existing processes; they'll create entirely new ones. There is, of course, a catch. With great power comes great responsibility. As your innovators push boundaries, ensure they're not leaving ethics behind. Issues like bias, privacy, and transparency need to be baked into your AI innovation process. It's not just about creating incredible tech - it's about ensuring that tech doesn't backfire on your company. It's up to you to create the right environment for them to thrive. If you are a CTO or a dev lead, it's time for some honest reflection. This isn't a call to blindly jump on the AI bandwagon. Instead, it’s a nudge to think about how AI can enhance your team's capabilities and drive innovation. Are you creating an environment where AI can be leveraged responsibly? Are you balancing the excitement of new AI tools with the ethics, privacy, and long-term impact? It should be less about that quick win, and rather about strategic integration and continuous learning. Consider how you can gradually introduce AI tools to augment your developers' skills, not replace them. Think about the training and support your team might need to become comfortable with AI-assisted development. And, most importantly, keep the dialogue open about the challenges and opportunities AI presents. There is productivity. Then there is AI-powered productivity. I like to call these the “10X Makers”.  While that may be an overused phrase, bear with me. A 10X Maker is the perfect marriage between those who naturally innovate and the AI tools on offer that allow them to create groundbreaking solutions at an unprecedented pace. These individuals - strapped to the rocketship of AI - are the ones who will reshape industries and drive global transformation. You probably already have these in your team. They have visionary thinking, adaptability, and a strong bias for action. They are rarely satisfied with small improvements. Instead, they aim for huge leaps forward. With AI tools at their disposal, they can accelerate every phase of the innovation process from ideation to launch. The game changer is this - AI innovation is becoming more accessible. No-code and low-code AI platforms are democratizing the field, allowing team members with great ideas to become AI innovators, regardless of their technical backgrounds. There are plenty of examples. The AI-powered chatbots are providing constant mental health support. There are AI systems that detect strokes faster than human doctors. It goes beyond technological showcases, and instead transforms entire sectors and improves lives. Sustaining innovation in your team Sustaining innovation in your team To keep this innovation momentum going, you need to reconsider your approach to team development. It's not just about hiring coders; you need to encourage critical thinking, creativity, and a strong understanding of AI ethics within your team. ethics Look towards educational institutions and partner with them to source the best talent. Use online platforms to find the innovators that aren’t on your radar but are already innovating. But that's just the start. Build a supportive ecosystem within your organization. This might include internal incubators for AI projects, allocating resources for AI ventures, and establishing mentorship programs that understand the specific challenges of AI innovation. Your goal should be to create an environment where AI talent can thrive and drive your company forward. And, please don't just throw money at the problem. Too often I see money being pumped into technology rather than talent. It’s not sustainable. Just look at the recent sell-off in the stock market, fuelled by the mounting skepticism over how much “big tech” can benefit from its vast spending on AI . The benefit comes via the talent that can leverage the tech. Not just the tech itself. fuelled by the mounting skepticism over how much “big tech” can benefit from its vast spending on AI fuelled by the mounting skepticism over how much “big tech” can benefit from its vast spending on AI Create a culture that embraces risk-taking and isn't afraid of failure. For every AI breakthrough, there might be a dozen that fall short. But that's how innovation works. Build resilience into your support systems, ensuring that you can bounce back from setbacks and keep pushing the boundaries. The future of innovation in your organization The future of innovation in your organization Make no mistake, the 10X Makers in your team are going to keep pushing the envelope of what's possible with AI. The best of the best won't just optimize existing processes; they'll create entirely new ones. There is, of course, a catch. With great power comes great responsibility. As your innovators push boundaries, ensure they're not leaving ethics behind. Issues like bias, privacy, and transparency need to be baked into your AI innovation process. It's not just about creating incredible tech - it's about ensuring that tech doesn't backfire on your company. It's up to you to create the right environment for them to thrive. If you are a CTO or a dev lead, it's time for some honest reflection. This isn't a call to blindly jump on the AI bandwagon. Instead, it’s a nudge to think about how AI can enhance your team's capabilities and drive innovation. Are you creating an environment where AI can be leveraged responsibly? Are you balancing the excitement of new AI tools with the ethics, privacy, and long-term impact? It should be less about that quick win, and rather about strategic integration and continuous learning. Consider how you can gradually introduce AI tools to augment your developers' skills, not replace them. Think about the training and support your team might need to become comfortable with AI-assisted development. And, most importantly, keep the dialogue open about the challenges and opportunities AI presents.

AI Is Creating the Next Generation of Innovators. Here’s How To Find, and Nurture, Your “10X Makers”

TikTok and Diagnosing Mental Illness: Would You Please Kindly Shut Up About ADHD

TikTok এবং মানসিক অসুস্থতা নির্ণয়: আপনি কি দয়া করে ADHD সম্পর্কে চুপ করবেন

TikTok und die Diagnose psychischer Erkrankungen: Würden Sie bitte den Mund halten, wenn es um ADHS geht?

TikTok y el diagnóstico de enfermedades mentales: ¿podría por favor callarse sobre el TDAH?

TikTok et le diagnostic de la maladie mentale : pourriez-vous, s'il vous plaît, vous taire à propos du TDAH

TikTok और मानसिक बीमारी का निदान: क्या आप कृपया ADHD के बारे में चुप रहेंगे?

TikTokと精神疾患の診断：ADHDについては黙っててください

TikTok 및 정신 질환 진단: ADHD에 대해 조용히 말씀해 주시겠어요?

TikTok e diagnóstico de doenças mentais: você poderia, por favor, calar a boca sobre o TDAH

TikTok и диагностика психических заболеваний: не могли бы вы, пожалуйста, помолчать о СДВГ?

TikTok ve Akıl Hastalıklarının Teşhisi: Lütfen DEHB Konusunda Çenenizi Kapatır Mısınız?

TikTok và chẩn đoán bệnh tâm thần: Bạn vui lòng im lặng về ADHD

TikTok 与精神疾病诊断：请你闭嘴，不要再谈论多动症了

How to Improve Your Blog Posts by Proofreading With ChatGPT

As I prepare to take my terrifying leap into hackademia, I've been freelancing a lot more lately—both as a way to freshen up some of my more rusty skills (Directing leaves little room for slinging code or technical writing), and as a way to offset some of the lost income (teachers are criminally underpaid in this country, so please hire me). In one of the projects I'm working on—a custom job board for a company in the energy and infrastructure industry—I recently had a need to add a "copy to" feature, allowing hiring managers to copy an application to another listing without modifying or removing the original record. It's not a straight copy, though. Most of the values remain unchanged, but the status of the new application itself needs to be reset, and the relationship to the new Job obviously changes as well. "Good Enough" Isn't Always Good Enough Now, this is hardly the most complicated thing in the world. Early in my career, I would have just grabbed each of the values I needed from the record, and re-created it from scratch right there. As an example, let's say we needed to retrieve a job application by an $id: $application = Application::findOrFail($id); The old-school way I would have solved this would be to create a new record, referencing each of the original $application values, like so: Application::create([
	'name' => $application->name,
	'email' => $application->email,
	// ...
]); You get the idea. This gets the job done, but is challenging for a few reasons, the most important one is the fragility of the implementation. What if we add new columns to the applications table? Or we updated the implementation of the Application model itself? This "brute-force" method means that we then have to both remember we implemented our copy function like this, and then update it to reflect reality. It works, but it's not sustainable. Don't Be Too Clever Later on in my career, I probably would have grabbed an array of the record values and then removed the elements I didn't need with an unset() or array_diff_key() or some other method (there's no one right way to do things, and I don't feel like thinking too hard about how I would have handled this in the past) and created the new record from that. It would have probably looked something like this: $values = array_diff_key($application->getAttributes(), [
				'id' => true,
				'job_id' => true,
				'status' => true,
			]); This works, and is mostly Vanilla PHP, but is almost a little too clever. You have to know that any key set to true in the second parameter of array_diff_key gets removed from the original array. It's a nuance that requires documentation, or a comment, and lowers readability for more junior engineers. A less clever (less clever is a good thing), yet more readable solution would instead probably look something like this: $values = $application->getAttributes();

unset($values['id'], $values['job_id', $values['status']); Again, it relies on more Vanilla PHP, but makes the implementation more clear. We're getting the attributes of the original application, and explicitly unsetting what we don't need. Know Thy Toolbelt For a long time, I've felt that it's important to embrace the underlying toolset of the framework you are working under, both for a "best practices" standpoint and an onboarding standpoint (you can argue all you want with your coworkers about the "right" way to do things, but basing your standards off of the ones encouraged by your tools gives you a higher foundation to start from. In the case of Laravel, there is a built-in method for copying an Eloquent model, leaving you with a new instance that you can work with. It's called replicate() , and it does the job of automatically excluding the primary key, lets you define what other attributes you'd like to not copy over, and creates a new model instance that you can interact with (instead of having to create one yourself). See below: $application->replicate(['job_id', 'status'])
			->fill([
				'job_id' => $job_id
			])
			->save(); As you can see, the solution is readable (we are literally replicating the $application object without the job_id and status attributes, filling in a new job_id, and saving the record). It's a one-liner without all of the maintainability concerns that one-liners often come with. Pilot to Copilot One of the biggest challenges of getting spun up on a new language or framework is those sticky "unknown unknowns." You can be a great developer with many years of experience, but that doesn't mean you natively understand the dialect of a technology you've never used before. In the past, research and mentorship were often the only way to approach this. The best way to know is to make mistakes and be told how to improve. But, thanks to modern tools like GitHub Copilot and ChatGPT, you can ask the questions you want to ask and let that kick off your research, rather than blindly groping around the documentation or hoping someone asked a question on a forum one day that solves your problem. A simple Is this the best way accomplish this? goes a long way. For myself, replicate() wasn't something I knew or had needed to use in the past. It was a suggestion by Github Copilot Chat because I knew how to accomplish the task, but I needed to riff off of someone to make sure I was doing it in a way that felt "good" to me. As I prepare to take my terrifying leap into hackademia , I've been freelancing a lot more lately—both as a way to freshen up some of my more rusty skills (Directing leaves little room for slinging code or technical writing), and as a way to offset some of the lost income (teachers are criminally underpaid in this country, so please hire me ). hackademia hire me In one of the projects I'm working on—a custom job board for a company in the energy and infrastructure industry—I recently had a need to add a "copy to" feature, allowing hiring managers to copy an application to another listing without modifying or removing the original record. It's not a straight copy, though. Most of the values remain unchanged, but the status of the new application itself needs to be reset, and the relationship to the new Job obviously changes as well. Most "Good Enough" Isn't Always Good Enough Now, this is hardly the most complicated thing in the world. Early in my career, I would have just grabbed each of the values I needed from the record, and re-created it from scratch right there. As an example, let's say we needed to retrieve a job application by an $id : $id $application = Application::findOrFail($id); $application = Application::findOrFail($id); The old-school way I would have solved this would be to create a new record, referencing each of the original $application values, like so: $application Application::create([
	'name' => $application->name,
	'email' => $application->email,
	// ...
]); Application::create([
	'name' => $application->name,
	'email' => $application->email,
	// ...
]); You get the idea. This gets the job done, but is challenging for a few reasons, the most important one is the fragility of the implementation. What if we add new columns to the applications table? Or we updated the implementation of the Application model itself? This "brute-force" method means that we then have to both remember we implemented our copy function like this, and then update it to reflect reality. applications Application remember It works, but it's not sustainable. Don't Be Too Clever Later on in my career, I probably would have grabbed an array of the record values and then removed the elements I didn't need with an unset() or array_diff_key() or some other method (there's no one right way to do things, and I don't feel like thinking too hard about how I would have handled this in the past) and created the new record from that. It would have probably looked something like this: unset() array_diff_key() something $values = array_diff_key($application->getAttributes(), [
				'id' => true,
				'job_id' => true,
				'status' => true,
			]); $values = array_diff_key($application->getAttributes(), [
				'id' => true,
				'job_id' => true,
				'status' => true,
			]); This works, and is mostly Vanilla PHP, but is almost a little too clever. You have to know that any key set to true in the second parameter of array_diff_key gets removed from the original array. It's a nuance that requires documentation, or a comment, and lowers readability for more junior engineers. mostly too true array_diff_key A less clever (less clever is a good thing), yet more readable solution would instead probably look something like this: $values = $application->getAttributes();

unset($values['id'], $values['job_id', $values['status']); $values = $application->getAttributes();

unset($values['id'], $values['job_id', $values['status']); Again, it relies on more Vanilla PHP, but makes the implementation more clear. We're getting the attributes of the original application, and explicitly unsetting what we don't need. Know Thy Toolbelt For a long time, I've felt that it's important to embrace the underlying toolset of the framework you are working under, both for a "best practices" standpoint and an onboarding standpoint (you can argue all you want with your coworkers about the "right" way to do things, but basing your standards off of the ones encouraged by your tools gives you a higher foundation to start from. In the case of Laravel, there is a built-in method for copying an Eloquent model, leaving you with a new instance that you can work with. It's called replicate() , and it does the job of automatically excluding the primary key, lets you define what other attributes you'd like to not copy over, and creates a new model instance that you can interact with (instead of having to create one yourself). replicate() See below: $application->replicate(['job_id', 'status'])
			->fill([
				'job_id' => $job_id
			])
			->save(); $application->replicate(['job_id', 'status'])
			->fill([
				'job_id' => $job_id
			])
			->save(); As you can see, the solution is readable (we are literally replicating the $application object without the job_id and status attributes, filling in a new job_id , and saving the record). It's a one-liner without all of the maintainability concerns that one-liners often come with. $application job_id status job_id Pilot to Copilot One of the biggest challenges of getting spun up on a new language or framework is those sticky "unknown unknowns." You can be a great developer with many years of experience, but that doesn't mean you natively understand the dialect of a technology you've never used before. In the past, research and mentorship were often the only way to approach this. The best way to know is to make mistakes and be told how to improve. But, thanks to modern tools like GitHub Copilot and ChatGPT , you can ask the questions you want to ask and let that kick off your research, rather than blindly groping around the documentation or hoping someone asked a question on a forum one day that solves your problem. GitHub Copilot ChatGPT A simple Is this the best way accomplish this? goes a long way. Is this the best way accomplish this? For myself, replicate() wasn't something I knew or had needed to use in the past. It was a suggestion by Github Copilot Chat because I knew how to accomplish the task, but I needed to riff off of someone to make sure I was doing it in a way that felt "good" to me. replicate() how

Duplicating a Database Record in Laravel

It's absolutely infuriating how cybercriminals get away with their heinous actions with little to no consequences! We’re living in a digital age where everything—from our most personal information to critical national security data—is stored online, and yet these cyber scum are free to exploit these vulnerabilities without fear. It’s like our legal and regulatory frameworks are stuck in the Dark Ages, hopelessly out of touch with the sophisticated and constantly evolving threats posed by these digital marauders. The primary danger of cybercrime is the horrifyingly low rate at which guilty criminals are caught or prosecuted. This pitifully low conviction rate leaves governments and businesses exposed to relentless, targeted attacks, creating a breeding ground for digital chaos. A measly 0.3% of all reported cybercrime complaints actually result in enforcement and prosecution. That's a pathetic 3 out of 1,000 malicious cyber incidents where the perpetrators are arrested and brought to justice. This enormous enforcement gap gives these malicious actors the boldness to carry out their nefarious activities without fear of being caught, prosecuted, or punished. To make matters worse, a significant percentage of cybercrime victims don't even bother reporting the cases, meaning the real enforcement rate might be less than a laughable 0.05%. It's also crucial to highlight that cybercrime has skyrocketed in recent years, as more and more individuals dive into this vice for the lucrative rewards. Recent research shows that some cybercriminals rake in up to $2 million annually, while others earn between $40,000 and $1 million each year. The attackers spend as much time meticulously planning and designing their malicious activities as they do devising ways to remain hidden and under the radar. The dark web serves as their perfect playground, a shadowy marketplace where they can freely engage in various cybercrime activities, further compounding the difficulty of apprehending them. I’d like to point out,  the sheer gall of these cybercriminals is beyond infuriating. They hide in the shadows, comfortably ensconced behind their screens, wreaking havoc on individuals, businesses, and governments alike. And when they do get caught? What do they face? A laughable slap on the wrist! A short prison sentence or a measly fine that does nothing to deter them. These so-called "penalties" are a joke, a disgrace considering the monumental damage these tech-savvy crooks inflict. They can easily vanish into the digital abyss, only to resurface and start their dirty work all over again. And don’t get me started on the international aspect of cybercrime. These digital parasites can operate from anywhere in the world, exploiting legal loopholes and hiding in countries that refuse to cooperate with international law enforcement. The lack of global coordination is a glaring weakness that these criminals exploit with glee. Why should they fear any repercussions when they can simply jump from one safe haven to another, always staying one step ahead of the authorities? It’s a pathetic game of cat and mouse, and the mice are winning! Then there’s the infuriating issue of underreporting. Businesses and individuals alike are often too scared to report cybercrimes, fearing damage to their reputation or believing, rightly, that the authorities are useless. This only emboldens the cybercriminals further, creating an environment where they can operate with impunity. It’s an outrage that the cumulative damage of cybercrime goes largely unacknowledged because victims are silenced by fear and helplessness. The scale of the threat is astronomical, yet it’s treated with a casual indifference that’s maddening. The financial toll is staggering. Cybercrime drains billions from the global economy every single year. Companies are forced to pay ransoms, invest heavily in cybersecurity, and deal with the fallout of data breaches, all while consumers bear the brunt through identity theft and fraud. Yet, despite these catastrophic financial implications, we allocate a pittance to combat cybercrime. We need more funding, more resources, and more political will to tackle this menace head-on. The current state of affairs is a travesty, an absolute dereliction of duty by those in power. The Human Cost The human cost is equally maddening. The victims of cybercrime suffer immensely, experiencing stress, anxiety, and a profound sense of violation. These aren’t just abstract attacks on systems; they’re brutal assaults on real people. Whether it’s an individual whose identity has been stolen or a business owner whose life’s work is being held hostage, the emotional and psychological toll is immense. And what do the cybercriminals face? Virtually nothing. It’s a gross injustice, a slap in the face to every victim. Our legal system’s response to cybercrime is pitifully slow and outdated. Laws lag behind technological advancements, and by the time we catch up, cybercriminals have already found new ways to exploit weaknesses. We need agile, forward-thinking policies that can anticipate future cyber threats and adapt quickly. This requires not just legal reform but a complete overhaul of our cultural approach to cybersecurity. Pandora's Box - Opened By Us It's downright maddening that we humans have opened Pandora's box with our insatiable drive for digital advancement, only to watch in horror as cybercriminals run rampant without facing real consequences. We created this monster, and now we're helplessly watching it tear through our lives, our businesses, and our governments, all while these digital parasites cackle from the dark corners of the internet. It’s beyond infuriating that we’ve unleashed this chaos upon ourselves and yet seem utterly incapable of reining it in. Our relentless quest for connectivity and convenience has left us exposed. We’ve put everything online—our bank accounts, our personal information, our deepest secrets—like lambs to the slaughter. We’ve built this intricate web of technology, only to be ensnared by it ourselves. It’s our own damn fault that cybercriminals are having a field day, exploiting every vulnerability we’ve created in our rush to digitize every aspect of our lives. And the worst part? We’re the ones who suffer, while the criminals laugh to their offshore accounts. Think about the absurdity of it: we’ve invented all these incredible technologies, but we’ve also handed cybercriminals the keys to our kingdoms. We’ve armed them with the tools they need to wreak havoc, and now they’re doing it with impunity. It’s like we’ve built a fortress but forgot to lock the doors, and now we’re surprised that the thieves are helping themselves to everything inside. The sheer incompetence is staggering. We’ve also created a perfect breeding ground for these criminals by being so ridiculously dependent on digital systems. We can’t go a day without our phones, our emails, our online banking. We’ve woven technology so tightly into the fabric of our lives that we’re left vulnerable at every turn. And yet, despite this dependency, we’ve done a pathetic job of protecting ourselves. It’s like we’ve built a house of cards and are now shocked that the slightest breeze is bringing it all down. Where Is The Accountability? And where is the accountability? Where are the harsh penalties, the deterrents that should be striking fear into the hearts of these cybercriminals? Nowhere to be found. Instead, we have a system that’s slow, outdated, and completely outmatched by the speed and sophistication of cybercrime. We need a radical overhaul, not just a few tweaks here and there. We need laws that are as fast and ruthless as the criminals they’re meant to catch. Until we get serious about this, we’re just spinning our wheels while the criminals race ahead. We’ve also been incredibly naive, trusting blindly in the security of our digital systems. We’ve bought into the illusion that technology is infallible, that our data is safe. Well, guess what? It’s not. Cybercriminals are constantly evolving, finding new ways to exploit our weaknesses, and we’re always playing catch-up. It’s a losing game, and we’re losing badly. Cybercriminals should be terrified of the repercussions, not laughing at the pathetic state of our current defenses. Until we tackle these issues with the urgency and seriousness they deserve, we’ll continue to live in a world where cybercriminals run rampant, causing untold damage with impunity. And that is utterly unacceptable! It’s us humans who opened Pandora’s box, and now we’re reaping the whirlwind. We’ve created the technology, we’ve built the systems, and we’ve made ourselves dependent on them, only to see it all turned against us by cybercriminals who face little to no consequences. It’s a colossal failure on our part, a stunning display of hubris and incompetence. We need to wake up, get serious about cybersecurity, and start holding these criminals accountable with real, severe consequences. Otherwise, we’re just going to keep watching as the chaos we unleashed continues to destroy everything we hold dear. It's absolutely infuriating how cybercriminals get away with their heinous actions with little to no consequences! We’re living in a digital age where everything—from our most personal information to critical national security data—is stored online, and yet these cyber scum are free to exploit these vulnerabilities without fear. It’s like our legal and regulatory frameworks are stuck in the Dark Ages, hopelessly out of touch with the sophisticated and constantly evolving threats posed by these digital marauders. The primary danger of cybercrime is the horrifyingly low rate at which guilty criminals are caught or prosecuted. This pitifully low conviction rate leaves governments and businesses exposed to relentless, targeted attacks, creating a breeding ground for digital chaos. cybercrime A measly 0.3% of all reported cybercrime complaints actually result in enforcement and prosecution. That's a pathetic 3 out of 1,000 malicious cyber incidents where the perpetrators are arrested and brought to justice. This enormous enforcement gap gives these malicious actors the boldness to carry out their nefarious activities without fear of being caught, prosecuted, or punished. To make matters worse, a significant percentage of cybercrime victims don't even bother reporting the cases, meaning the real enforcement rate might be less than a laughable 0.05%. It's also crucial to highlight that cybercrime has skyrocketed in recent years, as more and more individuals dive into this vice for the lucrative rewards. Recent research shows that some cybercriminals rake in up to $2 million annually, while others earn between $40,000 and $1 million each year. The attackers spend as much time meticulously planning and designing their malicious activities as they do devising ways to remain hidden and under the radar. The dark web serves as their perfect playground, a shadowy marketplace where they can freely engage in various cybercrime activities, further compounding the difficulty of apprehending them. I’d like to point out,  the sheer gall of these cybercriminals is beyond infuriating. They hide in the shadows, comfortably ensconced behind their screens, wreaking havoc on individuals, businesses, and governments alike. And when they do get caught? What do they face? A laughable slap on the wrist! A short prison sentence or a measly fine that does nothing to deter them. These so-called "penalties" are a joke, a disgrace considering the monumental damage these tech-savvy crooks inflict. They can easily vanish into the digital abyss, only to resurface and start their dirty work all over again. And don’t get me started on the international aspect of cybercrime. These digital parasites can operate from anywhere in the world, exploiting legal loopholes and hiding in countries that refuse to cooperate with international law enforcement. The lack of global coordination is a glaring weakness that these criminals exploit with glee. Why should they fear any repercussions when they can simply jump from one safe haven to another, always staying one step ahead of the authorities? It’s a pathetic game of cat and mouse, and the mice are winning! Then there’s the infuriating issue of underreporting. Businesses and individuals alike are often too scared to report cybercrimes, fearing damage to their reputation or believing, rightly, that the authorities are useless. This only emboldens the cybercriminals further, creating an environment where they can operate with impunity. It’s an outrage that the cumulative damage of cybercrime goes largely unacknowledged because victims are silenced by fear and helplessness. The scale of the threat is astronomical, yet it’s treated with a casual indifference that’s maddening. The financial toll is staggering. Cybercrime drains billions from the global economy every single year. Companies are forced to pay ransoms, invest heavily in cybersecurity, and deal with the fallout of data breaches, all while consumers bear the brunt through identity theft and fraud. Yet, despite these catastrophic financial implications, we allocate a pittance to combat cybercrime. We need more funding, more resources, and more political will to tackle this menace head-on. The current state of affairs is a travesty, an absolute dereliction of duty by those in power. The Human Cost The human cost is equally maddening. The victims of cybercrime suffer immensely, experiencing stress, anxiety, and a profound sense of violation. These aren’t just abstract attacks on systems; they’re brutal assaults on real people. Whether it’s an individual whose identity has been stolen or a business owner whose life’s work is being held hostage, the emotional and psychological toll is immense. And what do the cybercriminals face? Virtually nothing. It’s a gross injustice, a slap in the face to every victim. Our legal system’s response to cybercrime is pitifully slow and outdated. Laws lag behind technological advancements, and by the time we catch up, cybercriminals have already found new ways to exploit weaknesses. We need agile, forward-thinking policies that can anticipate future cyber threats and adapt quickly. This requires not just legal reform but a complete overhaul of our cultural approach to cybersecurity. cyber threats Pandora's Box - Opened By Us It's downright maddening that we humans have opened Pandora's box with our insatiable drive for digital advancement, only to watch in horror as cybercriminals run rampant without facing real consequences. We created this monster, and now we're helplessly watching it tear through our lives, our businesses, and our governments, all while these digital parasites cackle from the dark corners of the internet. It’s beyond infuriating that we’ve unleashed this chaos upon ourselves and yet seem utterly incapable of reining it in. Our relentless quest for connectivity and convenience has left us exposed. We’ve put everything online—our bank accounts, our personal information, our deepest secrets—like lambs to the slaughter. We’ve built this intricate web of technology, only to be ensnared by it ourselves. It’s our own damn fault that cybercriminals are having a field day, exploiting every vulnerability we’ve created in our rush to digitize every aspect of our lives. And the worst part? We’re the ones who suffer, while the criminals laugh to their offshore accounts. Think about the absurdity of it: we’ve invented all these incredible technologies, but we’ve also handed cybercriminals the keys to our kingdoms. We’ve armed them with the tools they need to wreak havoc, and now they’re doing it with impunity. It’s like we’ve built a fortress but forgot to lock the doors, and now we’re surprised that the thieves are helping themselves to everything inside. The sheer incompetence is staggering. We’ve also created a perfect breeding ground for these criminals by being so ridiculously dependent on digital systems. We can’t go a day without our phones, our emails, our online banking. We’ve woven technology so tightly into the fabric of our lives that we’re left vulnerable at every turn. And yet, despite this dependency, we’ve done a pathetic job of protecting ourselves. It’s like we’ve built a house of cards and are now shocked that the slightest breeze is bringing it all down. Where Is The Accountability? And where is the accountability? Where are the harsh penalties, the deterrents that should be striking fear into the hearts of these cybercriminals? Nowhere to be found. Instead, we have a system that’s slow, outdated, and completely outmatched by the speed and sophistication of cybercrime. We need a radical overhaul, not just a few tweaks here and there. We need laws that are as fast and ruthless as the criminals they’re meant to catch. Until we get serious about this, we’re just spinning our wheels while the criminals race ahead. We’ve also been incredibly naive, trusting blindly in the security of our digital systems. We’ve bought into the illusion that technology is infallible, that our data is safe. Well, guess what? It’s not. Cybercriminals are constantly evolving, finding new ways to exploit our weaknesses, and we’re always playing catch-up. It’s a losing game, and we’re losing badly. Cybercriminals should be terrified of the repercussions, not laughing at the pathetic state of our current defenses. Until we tackle these issues with the urgency and seriousness they deserve, we’ll continue to live in a world where cybercriminals run rampant, causing untold damage with impunity. And that is utterly unacceptable! It’s us humans who opened Pandora’s box, and now we’re reaping the whirlwind. We’ve created the technology, we’ve built the systems, and we’ve made ourselves dependent on them, only to see it all turned against us by cybercriminals who face little to no consequences. It’s a colossal failure on our part, a stunning display of hubris and incompetence. We need to wake up, get serious about cybersecurity, and start holding these criminals accountable with real, severe consequences. Otherwise, we’re just going to keep watching as the chaos we unleashed continues to destroy everything we hold dear.

Stories, News & Mentions about BLINKLY

Blinkly’s Stories on HackerNoon