Direktiv

direktiv

How are you, hacker?
🪐What's happening in tech this week:
The Noonification by HackerNoon  has got you covered with fresh content from our top 5 stories of the day, every day at noon your local time! Set email preference here. Your AI-Powered Kitchen Assistant: Building a Recipe Genius With GPT3.5-turbo and Agenta By @abram [ 1 Min read ]
In this article, Ill detail how I developed an LLM app utilizing GPT-3.5 turbo LLM to assess available ingredients and recommend recipes. Read More. 100 Days of AI, Day 11: Becoming a Finetuning Wizard in Gen AI Applications By @sindamnataraj [ 4 Min read ]
Fine tuning allows us to overcome the limitation of base models that ChatGPT or LLama rely on to provide a response. Heres how to fine tune your base model! Read More. What Skills Will Help You Become a Good Development Team Leader? By @aleksandrguzenko [ 15 Min read ]
In this article explained of how I became a team lead, what skills you can’t do without in this position, and how to improve your team management skills Read More. Mastering Type-Safe JSON Serialization in TypeScript By @nodge [ 11 Min read ]
How to utilize TypeScript for type-safe serialization and deserialization of data in JSON format. Read More. How to Use the Angular CDK Trap Focus Directive By @briantreese [ 4 Min read ]
In this post, I’ll first help you understand what a focus trap is,  then why it’s important, and finally, I’ll show how to quickly and easily add the concept  Read More.
🧑‍💻 What happened in your world this week?It's been said that writing can help consolidate technical knowledge, establish credibility, and contribute to emerging community standards. Feeling stuck? We got you covered ⬇️⬇️⬇️
ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME
We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.See you on Planet Internet! With love,
The HackerNoon Team ✌️ How are you, hacker?
🪐 What's happening in tech this week: The Noonification by HackerNoon has got you covered with fresh content from our top 5 stories of the day, every day at noon your local time! Set email preference here . What's happening in tech this week: The Noonification by HackerNoon here Your AI-Powered Kitchen Assistant: Building a Recipe Genius With GPT3.5-turbo and Agenta Your AI-Powered Kitchen Assistant: Building a Recipe Genius With GPT3.5-turbo and Agenta Your AI-Powered Kitchen Assistant: Building a Recipe Genius With GPT3.5-turbo and Agenta By @abram [ 1 Min read ]
In this article, Ill detail how I developed an LLM app utilizing GPT-3.5 turbo LLM to assess available ingredients and recommend recipes. Read More. @abram Read More. 100 Days of AI, Day 11: Becoming a Finetuning Wizard in Gen AI Applications 100 Days of AI, Day 11: Becoming a Finetuning Wizard in Gen AI Applications 100 Days of AI, Day 11: Becoming a Finetuning Wizard in Gen AI Applications By @sindamnataraj [ 4 Min read ]
Fine tuning allows us to overcome the limitation of base models that ChatGPT or LLama rely on to provide a response. Heres how to fine tune your base model! Read More. @sindamnataraj Read More. What Skills Will Help You Become a Good Development Team Leader? What Skills Will Help You Become a Good Development Team Leader? What Skills Will Help You Become a Good Development Team Leader? By @aleksandrguzenko [ 15 Min read ]
In this article explained of how I became a team lead, what skills you can’t do without in this position, and how to improve your team management skills Read More. @aleksandrguzenko Read More. Mastering Type-Safe JSON Serialization in TypeScript Mastering Type-Safe JSON Serialization in TypeScript Mastering Type-Safe JSON Serialization in TypeScript By @nodge [ 11 Min read ]
How to utilize TypeScript for type-safe serialization and deserialization of data in JSON format. Read More. @nodge Read More. How to Use the Angular CDK Trap Focus Directive How to Use the Angular CDK Trap Focus Directive How to Use the Angular CDK Trap Focus Directive By @briantreese [ 4 Min read ]
In this post, I’ll first help you understand what a focus trap is,  then why it’s important, and finally, I’ll show how to quickly and easily add the concept Read More. 🧑‍💻 What happened in your world this week?It's been said that writing can help consolidate technical knowledge , establish credibility , and contribute to emerging community standards . Feeling stuck? We got you covered ⬇️⬇️⬇️ ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.See you on Planet Internet! With love,
The HackerNoon Team ✌️ @briantreese Read More. writing can help consolidate technical knowledge establish credibility and contribute to emerging community standards ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME

The Noonification: How to Use the Angular CDK Trap Focus Directive (2/26/2024)

A focus trap is a very important accessibility feature within a web application. In this post, I’ll first help you understand what it is, then why it’s important, and finally, I’ll show how to quickly and easily add the concept within your Angular apps where needed. And, it’s pretty easy thanks to the Angular CDK. Alright, let’s get to it. https://youtu.be/JKHAjYJ5T2o?embedable=true Accessibility can be a little difficult to understand or even think about if you’re someone who can see, hear, and use the web easily with different devices, so it can also be easy to build things that don’t work well for those who can’t. What Is a Focus Trap? A focus trap is a feature that helps make experiences better for these folks. It’s a way of managing user focus within a given container. Take, for example, a modal. If a user opens a modal without managing focus, when they start navigating via the keyboard by using the tab key, items behind the modal continue to receive focus. This is weird and not what the user would expect, right? We need to programmatically focus them within the modal. But this isn’t enough; if we stop here, as we tab through the modal, eventually, we’ll tab through all of the focusable items, and then, we’ll be back to focusing items within the original document behind the modal again. What we need to do instead is trap the focus so that when the user focuses on the last item in the modal, the focus will be placed back on the first focusable element in the modal on the next tab. Ok, now that we know what a focus trap is, we are probably beginning to realize that there’s going to be some effort to create this functionality, right? Well, not within Angular. With the CDK, this is pretty easy using the cdkTrapFocus directive from the A11yModule. The Angular cdkTrapFocus Directive Let’s look at an example. Here, we have a web app for the Vans shoe brand. Up at the top, we have a sign-up button. When we click it, and then tab around, we can see focus is moving around the items in the background. Not what we want, right? Ok, let’s add the cdkTrapFocus. First thing we need to do in order to use it is, we need to import the A11yModule. sign-up-modal.component.ts import { A11yModule } from '@angular/cdk/a11y';

@Component({
    selector: 'app-sign-up-modal',
    ...
    imports: [A11yModule]
})
export class SignUpModalComponent {
} For those of you who are unfamiliar with this acronym, Ally is an abbreviation for the word accessibility. It comes from the concept that the word begins with an A, ends with a Y, and there are eleven characters in between. The Angular CDK has several accessibility features within its accessibility module, and the cdkTrapFocus directive is one of them. So, once we have it imported, we can now use it within the template. We just need to add it to an element that wraps our form. sign-up-modal.component.html <div cdkTrapFocus>
    <app-sign-up-form (formSubmitted)="modalClose.emit()"></app-sign-up-form>
    ...
</div> Providing a Focus on Initialization With the cdkTrapFocusAutoCapture @Input Good news, the cdkTrapFocus directive has an auto-focus feature built right in. We can use the cdkTrapFocusAutoCapture @Input. This input will automatically focus the first focusable item within the directive when it’s initialized. It defaults to false, so we need to provide a value of true. <div cdkTrapFocus [cdkTrapFocusAutoCapture]="true">
    <app-sign-up-form (formSubmitted)="modalClose.emit()"></app-sign-up-form>
    ...
</div> Now, when we open the modal, the name field is properly focused when it’s opened. And, as we tab through, when we get to the close button, we’ll cycle back to the name field. Also, if we hit shift + tab to navigate backward, we’ll go in reverse order all without leaving the modal. So, not only is it super helpful for all users really, but it’s super easy to implement too. One last thing to note, the trap focus directive keeps track of the last focused element before its initialization, so when we close the modal, the focus is returned to the button that opened it. Nice, we just saved a ton of work and made our modal more accessible. Want to See It in Action? Check out the demo code and examples of these techniques in the stackblitz example below. If you have any questions or thoughts, don’t hesitate to leave a comment. https://stackblitz.com/edit/stackblitz-starters-2bznzs?embed=1&file=src%2Fsign-up-modal%2Fsign-up-modal.component.html&embedable=true A focus trap is a very important accessibility feature within a web application. In this post, I’ll first help you understand what it is, then why it’s important, and finally, I’ll show how to quickly and easily add the concept within your Angular apps where needed. And, it’s pretty easy thanks to the Angular CDK. Alright, let’s get to it. https://youtu.be/JKHAjYJ5T2o?embedable=true https://youtu.be/JKHAjYJ5T2o?embedable=true Accessibility can be a little difficult to understand or even think about if you’re someone who can see, hear, and use the web easily with different devices, so it can also be easy to build things that don’t work well for those who can’t. What Is a Focus Trap? A focus trap is a feature that helps make experiences better for these folks. It’s a way of managing user focus within a given container. Take, for example, a modal. If a user opens a modal without managing focus, when they start navigating via the keyboard by using the tab key, items behind the modal continue to receive focus. This is weird and not what the user would expect, right? We need to programmatically focus them within the modal. But this isn’t enough; if we stop here, as we tab through the modal, eventually, we’ll tab through all of the focusable items, and then, we’ll be back to focusing items within the original document behind the modal again. What we need to do instead is trap the focus so that when the user focuses on the last item in the modal, the focus will be placed back on the first focusable element in the modal on the next tab. Ok, now that we know what a focus trap is, we are probably beginning to realize that there’s going to be some effort to create this functionality, right? Well, not within Angular. With the CDK, this is pretty easy using the cdkTrapFocus directive from the A11yModule . cdkTrapFocus A11yModule The Angular cdkTrapFocus Directive cdkTrapFocus Let’s look at an example. Here, we have a web app for the Vans shoe brand. Up at the top, we have a sign-up button. When we click it, and then tab around, we can see focus is moving around the items in the background. Not what we want, right? Ok, let’s add the cdkTrapFocus . First thing we need to do in order to use it is, we need to import the A11yModule . cdkTrapFocus A11yModule sign-up-modal.component.ts import { A11yModule } from '@angular/cdk/a11y';

@Component({
    selector: 'app-sign-up-modal',
    ...
    imports: [A11yModule]
})
export class SignUpModalComponent {
} import { A11yModule } from '@angular/cdk/a11y';

@Component({
    selector: 'app-sign-up-modal',
    ...
    imports: [A11yModule]
})
export class SignUpModalComponent {
} For those of you who are unfamiliar with this acronym, Ally is an abbreviation for the word accessibility. It comes from the concept that the word begins with an A, ends with a Y, and there are eleven characters in between. The Angular CDK has several accessibility features within its accessibility module, and the cdkTrapFocus directive is one of them. cdkTrapFocus So, once we have it imported, we can now use it within the template. We just need to add it to an element that wraps our form. sign-up-modal.component.html <div cdkTrapFocus>
    <app-sign-up-form (formSubmitted)="modalClose.emit()"></app-sign-up-form>
    ...
</div> <div cdkTrapFocus>
    <app-sign-up-form (formSubmitted)="modalClose.emit()"></app-sign-up-form>
    ...
</div> Providing a Focus on Initialization With the cdkTrapFocusAutoCapture @Input cdkTrapFocusAutoCapture @Input Good news, the cdkTrapFocus directive has an auto-focus feature built right in. We can use the cdkTrapFocusAutoCapture @Input . This input will automatically focus the first focusable item within the directive when it’s initialized. It defaults to false, so we need to provide a value of true. cdkTrapFocus cdkTrapFocusAutoCapture @Input <div cdkTrapFocus [cdkTrapFocusAutoCapture]="true">
    <app-sign-up-form (formSubmitted)="modalClose.emit()"></app-sign-up-form>
    ...
</div> <div cdkTrapFocus [cdkTrapFocusAutoCapture]="true">
    <app-sign-up-form (formSubmitted)="modalClose.emit()"></app-sign-up-form>
    ...
</div> Now, when we open the modal, the name field is properly focused when it’s opened. And, as we tab through, when we get to the close button, we’ll cycle back to the name field. Also, if we hit shift + tab to navigate backward, we’ll go in reverse order all without leaving the modal. shift tab So, not only is it super helpful for all users really, but it’s super easy to implement too. One last thing to note, the trap focus directive keeps track of the last focused element before its initialization, so when we close the modal, the focus is returned to the button that opened it. Nice, we just saved a ton of work and made our modal more accessible. Want to See It in Action? Check out the demo code and examples of these techniques in the stackblitz example below. If you have any questions or thoughts, don’t hesitate to leave a comment. https://stackblitz.com/edit/stackblitz-starters-2bznzs?embed=1&file=src%2Fsign-up-modal%2Fsign-up-modal.component.html&embedable=true https://stackblitz.com/edit/stackblitz-starters-2bznzs?embed=1&file=src%2Fsign-up-modal%2Fsign-up-modal.component.html&embedable=true

How to Use the Angular CDK Trap Focus Directive

কৌণিক সিডিকে ট্র্যাপ ফোকাস নির্দেশিকা কীভাবে ব্যবহার করবেন

So verwenden Sie die Angular CDK Trap Focus-Direktive

Cómo utilizar la directiva Angular CDK Trap Focus

Comment utiliser la directive de focus angulaire CDK Trap

एंगुलर सीडीके ट्रैप फोकस निर्देश का उपयोग कैसे करें

Angular CDK トラップ フォーカス ディレクティブの使用方法

Angular CDK Trap Focus 지시문을 사용하는 방법

Como usar a diretiva Angular CDK Trap Focus

Как использовать директиву Angular CDK Trap Focus

Açısal CDK Tuzak Odak Direktifi Nasıl Kullanılır

Cách sử dụng Chỉ thị tập trung vào bẫy CDK góc

如何使用 Angular CDK 陷阱焦点指令

Visualizing PostgreSQL Data With Angular To Analyze What Passengers Find Rude to Do During a Flight

Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes. Data science and user experience design have recently become critical to the success of companies' sophisticated internal digital tools for analyzing, processing information, and predicting what-if scenarios. Used by internal teams and operations staff, these tools aim to optimize processes, predict outcomes, and inform decision-making in increasingly complex systems. For those working in data science, a common approach is to view humans as “human-in-the-loop,” meaning users provide inputs to a system or validate outputs, but their role remains secondary to technology and algorithms. It is worth considering that the complexity and nuanced requirements of modeling tools have begun to require a shift to a more human-centered approach to make the tools accessible to a wider audience. This article examines human-centered design and the need for such a focus for those working on data science projects and suggests strategies to facilitate this transition. Determining the Difference To address human-related issues (emotional state, learning, outcome), the concept of “human-in-the-loop” (HITL) was introduced, which involves incorporating human knowledge into the modeling process. The HITL approach is often a common way of thinking among data science teams, where humans are seen as secondary players who help the machine function better or validate the results generated by algorithms. This interdisciplinary approach crosses computer science, cognitive science, and psychology and consists of three components: data preprocessing, data modeling, and process modification to improve performance. However, it is important to consider that the results and performance of ML models can be unpredictable, so it remains unclear which aspect of human-machine interaction yields the best learning results. People are thus viewed as add-ons to the system—sources of labeled data, corrections, or ideas that help fine-tune the models—without taking into account their broader needs, motivations, or limitations. Human involvement depends on the type of solution being implemented: strategic, tactical, or operational. In operational and tactical systems, the human role is usually minimal. In contrast, in strategic solutions, the human role is critical because these decisions require context, explainability, and domain expertise. They often rely on human interpretation to validate assumptions, assess risks, and align outcomes with broader goals. Human-centered design, on the other hand, places the user at the center of the design and development process by aligning technology with human needs, behaviors, motivation, and goals. Understanding the "why" behind user behaviors is a key component of human-centered design, which integrates user context and requirements thoroughly into all phases of product development. For data scientists, embracing human-centered design is more than just incorporating human input into systems; it also entails actively reshaping systems to meet the actual users’ needs and developing tools that empower rather than burden them. Why Human-Centered Design Matters In complex, high-stakes environments, inconsistent forecasts, confusing workflows, or overly complex interfaces can lead to poor decision-making, frustration, and operational inefficiencies. That’s why a human-centered approach is invaluable in such settings, enabling users to receive answers tailored to their needs. Often, users view data science solutions as “black boxes,” making it difficult to trust them or understand the results they produce. Developing tools and solutions that explain the relationships between inputs and outputs and the behavior of models can improve trust and usability, empowering users to confidently make informed decisions. This need for explainability and transparency is directly related to the need for human-centered design and more understandable solutions. When tools are designed with end-users in mind, they are more likely to be adopted and used effectively by users. When data scientists understand the nuances of user tasks, they can create models and interfaces that match users’ real workflows and contexts, rather than idealized or abstract ones. This increases trust in the tool’s output and minimizes the learning curve for users. Moreover, the accuracy and applicability of machine learning models can be highly dependent on the user context. These subtleties are better captured by a human-centered approach, which ultimately produces more accurate and meaningful predictions for users. By providing users with the tools they need to make informed decisions, human-centered design empowers users. Instead of relying on the user to validate a machine’s predictions, tools can allow users to make decisions alongside the machine, fostering collaboration rather than a directive relationship. Practical Steps to Move Toward Human-Centered Design While the benefits of human-centered design may be clear and obvious, shifting from a “human in the loop” requires both structural changes and cultural and team support. Here are some actionable steps teams can take to embrace human-centered design. 1. User Research First Researching users and their context is fundamental to human-centered design. Thus, interviews, ethnographic studies, observations of users’ workflows, and identification of pain points, motivations, and goals help to better understand and study users. Such research produces qualitative insights that can be complemented by quantitative data, providing a deeper understanding of users' context. Additionally, creating user journey maps that detail the workflows and touchpoints users have with various tools and other organizational functions allows for a more accurate assessment of what users are experiencing. This visual tool can help highlight opportunities where these tools can add more value or streamline user processes. 2. Prototype With a Purpose To make efficient use of a team's time by experimenting with different models, assumptions, and data visualizations while focusing not only on achieving technical goals but also on creating features that are clear, relevant, and useful to the user, rapid prototyping can help. It’s a fundamental practice of human-centered design that brings ideas to life and allows users to test the prototype early, for example in Figma, without spending time on development. This ensures that user feedback is obtained before a huge amount of time is spent on product development and helps to quickly get to the point where the user's problem is solved by matching the real needs and defining what tasks the user needs to perform to achieve it. 3. Design for Trust and Transparency A critical component of human-centered design is building trust and transparency so that when users receive forecasts or recommendations, they understand the validity of those results and can make business decisions and act on them with confidence. Trust in data tools can be achieved by helping users understand why the system suggests a certain outcome or path and by visualizing results that are understandable to the user and consistent with their mental model. 4. Development of a Collaborative and Interdisciplinary Team Culture Creating successful human-centered data tools requires collaboration across disciplines. Having a shared team vision based on the human-centered design will help bridge gaps in expertise and perspectives, ensuring that the final digital solution meets all team goals, prioritising user satisfaction, usability, and utility. Additionally, building empathy through shared UX activities helps develop a more empathetic understanding of the end user. 5. Measuring Success With User-Centric Metrics Implementing human-centred design means going beyond technical success metrics and focusing on including metrics that reflect user satisfaction and performance: a) regular feedback from users on their satisfaction with the tool and specific features; b) task success rates and time to complete them; c) feedback loops such as surveys or regular feedback sessions that help identify and resolve issues in real time, rather than waiting for problems to escalate. Conclusion Moving from “human-in-the-loop” to human-centred design is a mindset shift that emphasises designing for users rather than simply using users to validate machine output. In companies where decisions can have a high impact and significant risk, human-centred design helps better align tools with users' real-world needs and context. For data scientists, this mental shift paves the way for building digital tools that are not only functionally efficient but also truly empower users and enable them to engage a broader audience. When data tools are built with a human-centric mindset, they become tools that help users make better, faster, and more confident decisions, ultimately improving both the user experience and operational outcomes.

Design and Data Science: From a Human-in-the-Loop Approach to Human-Centered Design

Next.js v15 — What’s new under the hood

Building Better React & Next.js Apps: Mistakes, Tips, and Best Practices

The Case for Software Supply Chain Security. A couple of days ago, on Oct 22, 2024, the Securities and Exchange Commission fined millions of dollars for mislabeling the risks of the SolarWinds attack as “hypothetical.” Four companies Unisys, Avaya, Check Point, and Mimecast were fined $7 million. In 2020, the SolarWinds attack affected major organizations when malware gave backdoor access to its Orion software. Supply chain attacks have only grown in both quantity and magnitude since then. Though companies continue to downplay the impact of Supply Chain security, on average, less than 50% of the code base in your organization is written by your employees. Supply chain security controls mitigate risks coming from the “external” software that ships with your product. The Cure to Make Robust Software Supply Chains. Google has invested heavily in insider risk since 2010 and has been building multi-layered security controls to defend against advanced attacks. Around 2019/20, Google Chrome started developing a framework that applies to a broader community, starting with Chromium. Collaboration across the company led to the formation of the supply chain levels for software artifacts aka SLSA. Since then, Google has donated this project to OpenSSF, one of the largest open-source software foundations. SLSA version 1.0 was launched earlier this year and it provides an incremental solution to gradually improve security posture from development to deployment for the software supply chain. SLSA Threat Model Let’s take a look into how malicious actors exploit weaknesses throughout the process right from build systems to the artifact distribution level. The SLSA threat model states that issues like inadequate code review, misconfigured build systems, and unverified third-party code-introduced risks, etc. can cause significant security breaches. The successful attacks on SolarWinds where the hackers injected malicious code into the build process require strong controls. Knowing your dependencies and preventing unilateral access remains crucial for ensuring malware can’t be inserted. Applicability of SLSA in Real World Scenarios There are three major use cases for SLSA, First Party, Open Source Projects, and Vendors. In First Party, since there is no need to transfer trust across organizational boundaries, this makes it the easiest use case. SLSA can be used entirely within an organization. In the case of Open Source Projects, SLSA uses a canonical source repository approach where users of open-source software need to only trust a handful of build platforms instead of trusting thousands of developers who have upload permissions to many packages. SLSA would simply reject an upload if it was not from a canonical source repository. And finally, for users of vendor-provided software, SLSA would require a vendor to be certified by a third-party auditor. This would make it easier for consumers to trust vendor software products. Also, provenance is the main aspect of using SLSA, it would provide traceability and verifiability of where the software originated. The goal is not just to prevent tampering but also to trace who was involved and where the exact origin of the software was. Since it facilitates transparency, companies can easily adapt to regulatory restrictions while ensuring that their business operations are sound. Hardening the Chain of Trust The core of SLSA is provenance, which documents the origin and security of artifacts. SLSA can pinpoint tampering or any other malicious behavior using cryptographic validation and automated attestation. Since it verifies that each step from creation to distribution of the software is indeed secure, the chain of trust is therefore solidified. The majority of well-secured development environments apply the principles of provenance including Google. One of the innovators of the SLSA framework at Google is Akash Mukherjee. He discusses how Chrome ensures verifiability in its builds in this article Chromium Chronicle #23. The article focuses on the fundamentals of the framework and the four pillars that we see today in SLSA. Meeting Government Mandates With SLSA Another advantage of SLSA compared to other security libraries is its ability to meet U.S. government regulations. The National Institute Of Standards and Technology aka NIST plays a crucial role in cybersecurity by creating frameworks, guidelines, etc. like the Secure Software Development Framework (SSDF). SLSA is able to help companies meet these requirements. Following this framework will also align companies with the Executive Order 14028 directive which has a higher standard for cybersecurity along the entire software supply chain. Also in highly regulated defense, healthcare, and finance industries, SLSA is reliable enough to be the security blueprint for organizations that can help generate evidence of compliance even when it touches critical cybersecurity concerns. The Future of SLSA as a Blueprint for Secure Software Development But this journey does not stop here; it continues evolving the industry. With the flexibility in its structure, SLSA allows organizations to enhance their security step by step while preparing for future threats. As SBOMs become central to software development, experts focus on securing development practices in general, “SBOMs can help lift security health from the status quo, but frameworks like Supply-chain Levels for Software Artifacts (SLSA) and Secure Software Development Framework (SSDF) must be used to improve security as you’re building new packages.”, Akash Mukherjee, an early developer of the SLSA framework in his recent post on RSA conference. By integrating the best practices from SLSA with development insights gleaned from resources such as Chromium Chronicle, an organization is well-equipped to design secure software. At its core, SLSA revolves around provenance, providing cryptographic assurances and compliance with regulatory requirements, making it an extremely important tool for securing the global software ecosystem. The Case for Software Supply Chain Security. A couple of days ago, on Oct 22, 2024, the Securities and Exchange Commission fined millions of dollars for mislabeling the risks of the SolarWinds attack as “hypothetical.” Four companies Unisys, Avaya, Check Point, and Mimecast were fined $7 million. In 2020, the SolarWinds attack affected major organizations when malware gave backdoor access to its Orion software. Supply chain attacks have only grown in both quantity and magnitude since then. Though companies continue to downplay the impact of Supply Chain security, on average, less than 50% of the code base in your organization is written by your employees. Supply chain security controls mitigate risks coming from the “external” software that ships with your product. The Cure to Make Robust Software Supply Chains. The Cure to Make Robust Software Supply Chains. Google has invested heavily in insider risk since 2010 and has been building multi-layered security controls to defend against advanced attacks. Around 2019/20, Google Chrome started developing a framework that applies to a broader community, starting with Chromium. Collaboration across the company led to the formation of the supply chain levels for software artifacts aka SLSA. Since then, Google has donated this project to OpenSSF, one of the largest open-source software foundations. SLSA version 1.0 was launched earlier this year and it provides an incremental solution to gradually improve security posture from development to deployment for the software supply chain. SLSA version 1.0 SLSA version 1.0 SLSA Threat Model SLSA Threat Model Let’s take a look into how malicious actors exploit weaknesses throughout the process right from build systems to the artifact distribution level. The SLSA threat model states that issues like inadequate code review, misconfigured build systems, and unverified third-party code-introduced risks, etc. can cause significant security breaches. SLSA threat model SLSA threat model The successful attacks on SolarWinds where the hackers injected malicious code into the build process require strong controls. Knowing your dependencies and preventing unilateral access remains crucial for ensuring malware can’t be inserted. Applicability of SLSA in Real World Scenarios Applicability of SLSA in Real World Scenarios There are three major use cases for SLSA, First Party, Open Source Projects, and Vendors. In First Party, since there is no need to transfer trust across organizational boundaries, this makes it the easiest use case. SLSA can be used entirely within an organization. use cases use cases In the case of Open Source Projects, SLSA uses a canonical source repository approach where users of open-source software need to only trust a handful of build platforms instead of trusting thousands of developers who have upload permissions to many packages. SLSA would simply reject an upload if it was not from a canonical source repository. And finally, for users of vendor-provided software, SLSA would require a vendor to be certified by a third-party auditor. This would make it easier for consumers to trust vendor software products. Also, provenance is the main aspect of using SLSA, it would provide traceability and verifiability of where the software originated. The goal is not just to prevent tampering but also to trace who was involved and where the exact origin of the software was. Since it facilitates transparency, companies can easily adapt to regulatory restrictions while ensuring that their business operations are sound. Hardening the Chain of Trust Hardening the Chain of Trust The core of SLSA is provenance , which documents the origin and security of artifacts. SLSA can pinpoint tampering or any other malicious behavior using cryptographic validation and automated attestation. Since it verifies that each step from creation to distribution of the software is indeed secure, the chain of trust is therefore solidified. provenance provenance The majority of well-secured development environments apply the principles of provenance including Google. One of the innovators of the SLSA framework at Google is Akash Mukherjee. He discusses how Chrome ensures verifiability in its builds in this article Chromium Chronicle #23 . The article focuses on the fundamentals of the framework and the four pillars that we see today in SLSA. Chromium Chronicle #23 Chromium Chronicle #23 Meeting Government Mandates With SLSA Meeting Government Mandates With SLSA Another advantage of SLSA compared to other security libraries is its ability to meet U.S. government regulations. The National Institute Of Standards and Technology aka NIST plays a crucial role in cybersecurity by creating frameworks, guidelines, etc. like the Secure Software Development Framework ( SSDF ). SLSA is able to help companies meet these requirements. SSDF SSDF Following this framework will also align companies with the Executive Order 14028 directive which has a higher standard for cybersecurity along the entire software supply chain. Also in highly regulated defense, healthcare, and finance industries, SLSA is reliable enough to be the security blueprint for organizations that can help generate evidence of compliance even when it touches critical cybersecurity concerns. The Future of SLSA as a Blueprint for Secure Software Development The Future of SLSA as a Blueprint for Secure Software Development But this journey does not stop here; it continues evolving the industry. With the flexibility in its structure, SLSA allows organizations to enhance their security step by step while preparing for future threats. As SBOMs become central to software development, experts focus on securing development practices in general, “SBOMs can help lift security health from the status quo, but frameworks like Supply-chain Levels for Software Artifacts (SLSA) and Secure Software Development Framework (SSDF) must be used to improve security as you’re building new packages.” , Akash Mukherjee, an early developer of the SLSA framework in his recent post on RSA conference. “SBOMs can help lift security health from the status quo, but frameworks like Supply-chain Levels for Software Artifacts (SLSA) and Secure Software Development Framework (SSDF) must be used to improve security as you’re building new packages.” recent post recent post By integrating the best practices from SLSA with development insights gleaned from resources such as Chromium Chronicle, an organization is well-equipped to design secure software. At its core, SLSA revolves around provenance, providing cryptographic assurances and compliance with regulatory requirements, making it an extremely important tool for securing the global software ecosystem.

How SLSA Can Secure Your Software Supply Chain

There is an annual developer survey called State of CSS which explores which tools developers use to produce CSS code, what are the current industry preferences and sentiments. Looking at the results of section "CSS frameworks" a Tailwind has been an indisputable leader in satisfaction rankings for consecutive 4 years from 2019 to 2023, meaning it has been so since its initial release in May 2019. No wonder it accumulated usage rankings throughout these 4 years from 6% in 2019 to 50.5% in 2023 making it the 2nd most popular CSS framework after Bootstrap. I would not call myself a huge fan of Tailwind and to be honest I was a bit skeptical about it for quite some time. But I think I understand the appeal to it. In this article I will try to explore why Tailwind and other utility CSS solutions are so popular and what I think the future holds for it. But first a little bit of history Let's get back to 2013 when the first commits to Basscss were made public. While traditional CSS frameworks at the time focused on certain components, like alerts, inputs, modal windows, etc., Basscss took a different approach focusing on abstract non-semantic utilities. For instance, take a look at Bootstrap. It has alert which is a certain semantic component with a well-defined purpose. There are even more complex components like dropdown and components that combine multiple ones, but there is always semantics in mind. It's almost impossible to reuse the parts of alert component in a dropdown - it doesn't make any sense because they serve different business purposes and therefore their semantics are completely different. Basscss does not have any of this. Zero components. What it has is a set of simple and pre-defined CSS rules that could be used in any context where this rule is suitable to be applied. Check this out. .m0 {
  margin: 0;
}

.p0 {
  padding: 0;
} Using narrow utility classes aimed to be highly reusable. Consider the following CSS code. .success {
  background-color: green;
  color: white;
} You can use .success class only when both `green` background and white text color are required. You would need to create a new class with a different name when only one rule of these two is required. Basscss confronts the semantic CSS approach. Instead of coming up with a class name and assigning properties for a specific semantic meaning <style>
  .panel {
    margin: 0;
    padding: 0;
  }
</style>
<div class="panel">This is a panel</div> you use multiple classes like m0 and p0 mentioned above to achieve the same effect. <style>
  .m0 {
    margin: 0;
  }

  .p0 {
    padding: 0;
  }
</style>
<div class="m0 p0">This is a panel</div> Basscss contains shorthands not only for padding and margin, but for typography, borders, positions, colors, etc. It was a strange approach but it got its fans and its own name called "Atomic CSS" inspiring a lot of other frameworks. One of them was Tachyons which introduced pseudo-classes for :hover, :active, :focused. In this example zero margin is applied only when an element is hovered. <div class="ma0:hover">This is a panel</div> Another framework was ACSS, now known as Atomizer which used even more complex syntax and required a build step. <div class="M(0)">This is a panel</div> Inline styles or something more? At first sight it looks like an inline styling, but it is actually more than that. It is a composition tool giving us an ability to construct different small pieces of software to produce a complete solution. This is exactly what we are getting by using Basscss and other atomic CSS solutions. To achieve high quality composition one needs to have pieces of software that are reusable in different semantic contexts
easily combined with each other in an arbitrary way Going back to the example of .success class let's break it down into 2 classes .bg-green {
  background-color: green;
}

.color-white {
  color: white;
} By splitting the .success class we are no longer tied to a certain semantic and we can reuse individual classes in completely different and unrelated to each other contexts combining them with other such classes. And finally… Tailwind Of course, Tailwind took inspiration from all of the libraries mentioned above. Composition is the core concept in Tailwind and that’s how developers are expected to reason about it - by composing utility classes forming complex components. It focuses on being easy to use and very friendly to newcomers and developers who are not keen on frontend development. What Tailwind got right is a good balance between API and functionality and a tooling which has nice defaults being highly-configurable at the same time.  But is it actually a sustainable story? Critics of functional CSS. There are a lot of critics of utility (functional) CSS. But I would mention only one, which is the strongest one, and that is mixing markup and styling results in having a messy class string in HTML. <div class="bg-indigo-500 text-white px-4 py-2 rounded-md shadow-md hover:bg-indigo-600 transition duration-200 ease-in-out cursor-pointer flex items-center justify-center"> You might argue that it is a developer’s responsibility to keep it clean, but it is actually quite common to see this. Tailwind does not offer a way to clean the long class strings other than apply directive but even that is discouraged by Adam himself. Here is a quote. I can say with absolute certainty that if I started Tailwind CSS over from scratch, there would be no `@​apply`
The behavior is outrageously complicated, everyone struggles to build the right mental model of what it's supposed to do, and it encourages bad CSS architecture. And it makes sense, because it defeats the whole purpose of writing utility CSS in the first place. Why even bother with it if you end up writing a separate semantic class name with a vendor specific keyword? You don’t need a library for that, just write a plain CSS. Another way to fight it is to use a component based approach with a JavaScript library of your choice (React, Vue, etc.) and use something like tailwind-variants. This is also not a new concept, as it was popularized by styled-system, a Basscss author by the way. It works, and it works great despite introducing an imminent complexity. But it still leaves critics with the question: Why go through all the hassle when I could just write a CSS class? For now I will leave this question open. But I’m sure in the future we will see new approaches as CSS standards are constantly evolving. There is an annual developer survey called State of CSS which explores which tools developers use to produce CSS code, what are the current industry preferences and sentiments. State of CSS Looking at the results of section "CSS frameworks" a Tailwind has been an indisputable leader in satisfaction rankings for consecutive 4 years from 2019 to 2023, meaning it has been so since its initial release in May 2019. No wonder it accumulated usage rankings throughout these 4 years from 6% in 2019 to 50.5% in 2023 making it the 2nd most popular CSS framework after Bootstrap. "CSS frameworks" 6% in 2019 50.5% in 2023 I would not call myself a huge fan of Tailwind and to be honest I was a bit skeptical about it for quite some time. But I think I understand the appeal to it. In this article I will try to explore why Tailwind and other utility CSS solutions are so popular and what I think the future holds for it. But first a little bit of history Let's get back to 2013 when the first commits to Basscss were made public. While traditional CSS frameworks at the time focused on certain components, like alerts, inputs, modal windows, etc., Basscss took a different approach focusing on abstract non-semantic utilities. Basscss For instance, take a look at Bootstrap. It has alert which is a certain semantic component with a well-defined purpose. There are even more complex components like dropdown and components that combine multiple ones, but there is always semantics in mind. It's almost impossible to reuse the parts of alert component in a dropdown - it doesn't make any sense because they serve different business purposes and therefore their semantics are completely different. alert dropdown semantics Basscss does not have any of this. Zero components. What it has is a set of simple and pre-defined CSS rules that could be used in any context where this rule is suitable to be applied. simple pre-defined Check this out. .m0 {
  margin: 0;
}

.p0 {
  padding: 0;
} .m0 {
  margin: 0;
}

.p0 {
  padding: 0;
} Using narrow utility classes aimed to be highly reusable . Consider the following CSS code. highly reusable .success {
  background-color: green;
  color: white;
} .success {
  background-color: green;
  color: white;
} You can use .success class only when both `green` background and white text color are required. You would need to create a new class with a different name when only one rule of these two is required. Basscss confronts the semantic CSS approach. .success white Instead of coming up with a class name and assigning properties for a specific semantic meaning semantic <style>
  .panel {
    margin: 0;
    padding: 0;
  }
</style>
<div class="panel">This is a panel</div> <style>
  .panel {
    margin: 0;
    padding: 0;
  }
</style>
<div class="panel">This is a panel</div> you use multiple classes like m0 and p0 mentioned above to achieve the same effect. m0 p0 <style>
  .m0 {
    margin: 0;
  }

  .p0 {
    padding: 0;
  }
</style>
<div class="m0 p0">This is a panel</div> <style>
  .m0 {
    margin: 0;
  }

  .p0 {
    padding: 0;
  }
</style>
<div class="m0 p0">This is a panel</div> Basscss contains shorthands not only for padding and margin, but for typography, borders, positions, colors, etc. It was a strange approach but it got its fans and its own name called "Atomic CSS" inspiring a lot of other frameworks. One of them was Tachyons which introduced pseudo-classes for :hover, :active, :focused. Tachyons In this example zero margin is applied only when an element is hovered. <div class="ma0:hover">This is a panel</div> <div class="ma0:hover">This is a panel</div> Another framework was ACSS, now known as Atomizer which used even more complex syntax and required a build step. <div class="M(0)">This is a panel</div> <div class="M(0)">This is a panel</div> Inline styles or something more? At first sight it looks like an inline styling, but it is actually more than that. It is a composition tool giving us an ability to construct different small pieces of software to produce a complete solution . This is exactly what we are getting by using Basscss and other atomic CSS solutions. composition an ability to construct different small pieces of software to produce a complete solution To achieve high quality composition one needs to have pieces of software that are reusable in different semantic contexts easily combined with each other in an arbitrary way reusable in different semantic contexts easily combined with each other in an arbitrary way Going back to the example of .success class let's break it down into 2 classes .success .bg-green {
  background-color: green;
}

.color-white {
  color: white;
} .bg-green {
  background-color: green;
}

.color-white {
  color: white;
} By splitting the .success class we are no longer tied to a certain semantic and we can reuse individual classes in completely different and unrelated to each other contexts combining them with other such classes. .success And finally… Tailwind Of course, Tailwind took inspiration from all of the libraries mentioned above. Composition is the core concept in Tailwind and that’s how developers are expected to reason about it - by composing utility classes forming complex components. It focuses on being easy to use and very friendly to newcomers and developers who are not keen on frontend development. What Tailwind got right is a good balance between API and functionality and a tooling which has nice defaults being highly-configurable at the same time.  But is it actually a sustainable story? Critics of functional CSS. There are a lot of critics of utility (functional) CSS. But I would mention only one, which is the strongest one, and that is mixing markup and styling results in having a messy class string in HTML. a messy class string in HTML. <div class="bg-indigo-500 text-white px-4 py-2 rounded-md shadow-md hover:bg-indigo-600 transition duration-200 ease-in-out cursor-pointer flex items-center justify-center"> <div class="bg-indigo-500 text-white px-4 py-2 rounded-md shadow-md hover:bg-indigo-600 transition duration-200 ease-in-out cursor-pointer flex items-center justify-center"> You might argue that it is a developer’s responsibility to keep it clean, but it is actually quite common to see this. Tailwind does not offer a way to clean the long class strings other than apply directive but even that is discouraged by Adam himself . Here is a quote. discouraged by Adam himself I can say with absolute certainty that if I started Tailwind CSS over from scratch, there would be no `@​apply` The behavior is outrageously complicated, everyone struggles to build the right mental model of what it's supposed to do, and it encourages bad CSS architecture. I can say with absolute certainty that if I started Tailwind CSS over from scratch, there would be no `@​apply` The behavior is outrageously complicated, everyone struggles to build the right mental model of what it's supposed to do, and it encourages bad CSS architecture. And it makes sense, because it defeats the whole purpose of writing utility CSS in the first place. Why even bother with it if you end up writing a separate semantic class name with a vendor specific keyword? You don’t need a library for that, just write a plain CSS. Why even bother with it if you end up writing a separate semantic class name with a vendor specific keyword? Another way to fight it is to use a component based approach with a JavaScript library of your choice (React, Vue, etc.) and use something like tailwind-variants . This is also not a new concept, as it was popularized by styled-system, a Basscss author by the way. It works, and it works great despite introducing an imminent complexity. tailwind-variants But it still leaves critics with the question: Why go through all the hassle when I could just write a CSS class? Why go through all the hassle when I could just write a CSS class? Why go through all the hassle when I could just write a CSS class? Why go through all the hassle when I could just write a CSS class? For now I will leave this question open. But I’m sure in the future we will see new approaches as CSS standards are constantly evolving.

Why Functional CSS Got so Popular?

In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold. In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold.

Can The Government, The Technology Platforms, or Anyone Else Control the Spread of Deepfakes?

Können die Regierung, die Technologieplattformen oder sonst jemand die Verbreitung von Deepfakes kontrollieren?

¿Pueden el gobierno, las plataformas tecnológicas o cualquier otra persona controlar la propagación de deepfakes?

آیا دولت، پلتفرم‌های فناوری یا هر کس دیگری می‌توانند گسترش دیپ‌فیک را کنترل کنند؟

Poden o goberno, as plataformas tecnolóxicas ou calquera outra persoa controlar a propagación dos Deepfakes?

क्या सरकार, प्रौद्योगिकी प्लेटफॉर्म या कोई अन्य डीपफेक के प्रसार को नियंत्रित कर सकता है?

政府、テクノロジープラットフォーム、あるいは他の誰かがディープフェイクの拡散を制御できるのでしょうか?

Afaka mifehy ny fiparitahan'ny Deepfakes ve ny governemanta, ny sehatra teknolojia, na olon-kafa?

Дали владата, технолошките платформи или некој друг може да го контролира ширењето на Deepfakes?

के सरकार, टेक्नोलोजी प्लेटफर्म, वा अरू कसैले Deepfakes को फैलावट नियन्त्रण गर्न सक्छ?

Czy rząd, platformy technologiczne lub ktokolwiek inny może kontrolować rozprzestrzenianie się deepfake'ów?

Dawladdu, Platform-yada Tignoolajiyada, ama cid kale ma xakamayn kartaa fiditaanka been-abuurka?

Чи може уряд, технологічні платформи чи будь-хто інший контролювати поширення Deepfakes?

Mastering LLM Knowledge Graphs: Build and Implement GraphRAG in Just 5 Minutes

إتقان الرسوم البيانية المعرفية في مجال القانون: إنشاء وتنفيذ GraphRAG في 5 دقائق فقط

Domine los gráficos de conocimiento de LLM: cree e implemente GraphRAG en solo 5 minutos

Dominar os gráficos de coñecemento de LLM: crea e implementa GraphRAG en só 5 minutos

Savladavanje LLM grafikona znanja: izgradite i implementirajte GraphRAG u samo 5 minuta

שליטה בגרפי ידע LLM: בנה והטמיע GraphRAG תוך 5 דקות בלבד

LLM ナレッジ グラフをマスターする: わずか 5 分で GraphRAG を構築して実装する

LLM билим графиктерин өздөштүрүү: GraphRAGди 5 мүнөттүн ичинде куруп, ишке ашырыңыз

Koyeba malamu ba graphiques ya boyebi ya LLM: Kotonga pe kosalela GraphRAG na Minutes 5 Kaka

Mastering LLM Knowledge Graphs: ສ້າງ ແລະປະຕິບັດ GraphRAG ໃນເວລາພຽງ 5 ນາທີ

Stăpânirea graficelor de cunoștințe LLM: construiți și implementați GraphRAG în doar 5 minute

Zvládnutie LLM Knowledge Graphs: Zostavte a implementujte GraphRAG len za 5 minút

เรียนรู้กราฟความรู้ LLM: สร้างและใช้งาน GraphRAG ในเวลาเพียง 5 นาที

Stories, News & Mentions about DIREKTIV

Direktiv’s Stories on HackerNoon