Shriram Finance share price: Shriram Finance share split came into effect today as the company had fixed January 10, 2025, as the record date for the stock split

Shriram Finance share declines 6% as it trades ex-stock split today | News on Markets - Business Standard

Manappuram Finance Share Price: Morgan Stanley highlighted that the RBI has lifted restrictions on Asirvad Micro Finance with immediate effect. Morgan Stanley has factored in approximately 10% provisions for the MFI loan book for FY25-27 due to the previous embargo.

Manappuram Finance shares rally over 5% after RBI lifts restrictions on Asirvad Micro Finance - The Economic Times

In a different notification, HDFC Bank revealed that it has also obtained RBI approval to purchase a share of up to 9.5 per cent in Capital Small Finance Bank and Kotak Mahindra Bank. Until January 2, 2026, this approval is still in effect. The apex bank's letter, dated January 3, 2025, permits ...

HDFC Bank Gets Nod From RBI To Purchase 9.5% Stake In AU Small Finance Bank

Incidentally, this cyber ransom attack coincides with the Cybersecurity and Cyber Resilience Framework (CSCRF), which officially came into effect from January 1 for six Sebi-regulated entities (REs) that were already subject to previous cybersecurity guidelines, that include stock brokers, ...

Exclusive: Indian brokerages hit by ransomware amid rising cybersecurity threats | Fortune India

As the year 2025 approaches, various changes across financial, travel, and payment sectors will take effect, which could significantly impact your plans. From updated rules on Fixed Deposits (FDs) to revised credit card benefits and visa regulations, staying informed about these changes is ...

From Credit Card to UPI to FD: Key changes in financial rules from January 1 that will affect common man

Banks in the EU are gearing up for the implementation of new capital requirement rules when Basel 4 takes effect on January 1 next year, but doubts remain over the treatment of key trade finance products. Even though the deadline is just days away, there are still differing interpretations ...

EU trade finance banks puzzle over Basel rules as deadline looms | Global Trade Review (GTR)

Coote has been suspended by refereeing body Professional Game Match Officials Limited (PGMOL) with immediate effect pending a full investigation, while the Football Association has launched its own investigation.

'David Coote video on Liverpool and Jurgen Klopp fuels conspiracy theorists' - BBC Sport

effect

We adopt A/B testing as an indispensable method for measuring the real impact of the features we develop and want to release. Unfortunately, “before and after” analyses are often unusable due to temporal factors such as timing, campaigns, or other effects. When we want to proceed with an A/B test, we face scenarios where multiple tests must be run across different pages. In these situations, there are three basic approaches we can choose from: Running tests sequentially
Separating the test audiences
Running both tests simultaneously In this article, I will discuss the approach of running two tests simultaneously across the entire audience, what method we should follow, and what points to consider. How much do simultaneous tests influence each other?
How should we interpret the results?
How can we verify that randomization is achieved?
Can we analyze the variants together? Firstly, I would like to mention about central limit theorem and random distribution. Central Limit Theorem CLT forms the basis of many analyses in both theoretical and practical statistics. It states that the distribution of means from random samples of a population approaches a normal (gaussian) distribution as the sample size grows, regardless of the population’s initial distribution. In real life, this can be summarized as follows:Imagine you have a coin and flip it five times. If you get heads every time, can you conclude that the coin will always land on heads? Of course not. This happens because the sample size your coin flips is insufficient. With enough samples, the average of your random results will approach a normal distribution. Random Distribution Random distribution is a critical method for ensuring that statistical analyses and scientific studies produce unbiased, reliable, and generalizable results. It refers to allocating individuals in a population or data in a system to groups, points, or categories without any patterns, bias, or order. Without random distribution, CLT becomes difficult to apply, as biased sampling prevents results from approaching a normal distribution. Therefore, ensuring both random distribution and proper application of CLT is essential for successful analysis. How Can I Interpret the Results of Two Simultaneous A/B Tests? Let’s assume we are running two different tests on the homepage and the product detail page. In the first table, we see how the distribution between the two tests interacts. The homepage experiment’s control and test groups show an equal distribution of the PDP experiment’s control and test groups. The next step is to ensure that the sample size is sufficient. In the second table, however, the situation is different. The test group in the homepage experiment includes 90% of the PDP experiment’s test group. This indicates a problem with the distribution. If the test group in the PDP experiment performs poorly, this would unfairly favor the control group in the homepage experiment, leading to incorrect conclusions. To avoid such situations, it would be beneficial to run A/A tests beforehand and validate these distributions across pages. Conclusion Whether or not multiple tests are being run, don’t hesitate to interpret the results under the headings of sufficient sampling and random distribution. Running multiple tests simultaneously can bring efficiency to both your planning process and product insights. Tests conducted separately fail to analyze their combined performance when live simultaneously. In contrast, interaction effects between tests can be easily analyzed when they are run together. The most critical point to remember is that each test should have a clear purpose and support each other’s results. A well-executed A/B testing strategy will strengthen your approach and help you achieve long-term business objectives. Final Note:
If your website or application does not have sufficient traffic or sample size, the approaches discussed may not yield meaningful results. In such cases, adjust your strategy to match your available data volume. Thank you for your time; sharing is caring! 🌍 We adopt A/B testing as an indispensable method for measuring the real impact of the features we develop and want to release. Unfortunately, “before and after” analyses are often unusable due to temporal factors such as timing, campaigns, or other effects. When we want to proceed with an A/B test, we face scenarios where multiple tests must be run across different pages. In these situations, there are three basic approaches we can choose from: In these situations, there are three basic approaches we can choose from: Running tests sequentially Separating the test audiences Running both tests simultaneously Running tests sequentially Separating the test audiences Running both tests simultaneously In this article, I will discuss the approach of running two tests simultaneously across the entire audience, what method we should follow, and what points to consider. How much do simultaneous tests influence each other? How should we interpret the results? How can we verify that randomization is achieved? Can we analyze the variants together? How much do simultaneous tests influence each other? How should we interpret the results? How can we verify that randomization is achieved? Can we analyze the variants together? Firstly, I would like to mention about central limit theorem and random distribution. Central Limit Theorem Central Limit CLT forms the basis of many analyses in both theoretical and practical statistics. It states that the distribution of means from random samples of a population approaches a normal (gaussian) distribution as the sample size grows, regardless of the population’s initial distribution. In real life, this can be summarized as follows:Imagine you have a coin and flip it five times. If you get heads every time, can you conclude that the coin will always land on heads? Of course not. This happens because the sample size your coin flips is insufficient. With enough samples, the average of your random results will approach a normal distribution. Random Distribution Random Distribution Random distribution is a critical method for ensuring that statistical analyses and scientific studies produce unbiased, reliable, and generalizable results. It refers to allocating individuals in a population or data in a system to groups, points, or categories without any patterns, bias, or order. Without random distribution, CLT becomes difficult to apply, as biased sampling prevents results from approaching a normal distribution. Therefore, ensuring both random distribution and proper application of CLT is essential for successful analysis. How Can I Interpret the Results of Two Simultaneous A/B Tests? How Can I Interpret the Results of Two Simultaneous A/B Tests? Let’s assume we are running two different tests on the homepage and the product detail page. homepage product detail page. In the first table, we see how the distribution between the two tests interacts. The homepage experiment’s control and test groups show an equal distribution of the PDP experiment’s control and test groups. The next step is to ensure that the sample size is sufficient. In the second table, however, the situation is different. The test group in the homepage experiment includes 90% of the PDP experiment’s test group. This indicates a problem with the distribution. If the test group in the PDP experiment performs poorly, this would unfairly favor the control group in the homepage experiment, leading to incorrect conclusions. To avoid such situations, it would be beneficial to run A/A tests beforehand and validate these distributions across pages. Conclusion Conclusion Whether or not multiple tests are being run, don’t hesitate to interpret the results under the headings of sufficient sampling and random distribution. Running multiple tests simultaneously can bring efficiency to both your planning process and product insights. Tests conducted separately fail to analyze their combined performance when live simultaneously. In contrast, interaction effects between tests can be easily analyzed when they are run together. The most critical point to remember is that each test should have a clear purpose and support each other’s results. A well-executed A/B testing strategy will strengthen your approach and help you achieve long-term business objectives. Final Note: If your website or application does not have sufficient traffic or sample size, the approaches discussed may not yield meaningful results. In such cases, adjust your strategy to match your available data volume. Final Note: If your website or application does not have sufficient traffic or sample size, the approaches discussed may not yield meaningful results. In such cases, adjust your strategy to match your available data volume. Thank you for your time; sharing is caring! 🌍 Thank you for your time; sharing is caring! 🌍

For Effective A/B/n Testing, Try Running Them Simultaneously

Hey folks! Congrats on scaling your team and growing your efforts! In our previous article, we covered the process of hiring salespeople—what steps to take and what questions to ask along the way. But now, we’re going to talk about how to scale that funnel! Specifically, how to scale the process of bringing awesome people onto your team. That’s the focus today, alright? We’re going to dive into the tools. Tools that will allow you to follow the same funnel but spend less time on manual tasks. Here’s a quick overview of the tools we’ll cover: Tools for research
Tools for sourcing candidates
Tools for finding their contact info (only the public kind, don’t worry—we’re staying on the right side of the law here)
Tools for sending email/LinkedIn sequences automatically bonus in the end: A tool for automatically generating custom emails And that’s pretty much it! These tools will help you build a hiring system, not just make a random hire once in a while. Let’s get started! Tools for research Step 0 is knowing what tools to use to properly research the talent pool, salaries, product market, and more. For researching the talent pool, I use LinkedIn Talent Insights. It provides a clear understanding of geographical markets, showing where the talent you need is located, which companies they’re working for, and what those companies are doing right now. It also helps you see what kind of education people have. Once you’ve identified locations, companies, and the demand levels, you can dig deeper into researching salaries for the required position, location, and company. Keep in mind, the higher the demand, the more prepared you should be to offer a salary above the range you see. For salary research, I use Indeed Career, Glassdoor or Salary.com. These platforms show salary ranges based on title, location, and company. Keep in mind that LinkedIn Talent Insights is a paid subscription. It comes as part of a bundle with the LinkedIn Recruiter subscription. So, if you're ready to go full pro mode, I recommend purchasing it as a bundle, since we’ll also be using LinkedIn Recruiter for sourcing candidates. Tools for Sourcing Candidates Once you know the location of your candidates, the companies they’re likely working for, and the approximate salary ranges, it's time to build a proper pipeline of matching talent. I use several tools for this, depending on how niche the candidates are. Here are the classic tools that will fit in 8 out of 10 situations: Sales Navigator
LinkedIn Recruiter For sourcing candidates, there are also tools that are primarily made for salespeople, but they’re incredibly useful for recruiting as well: Apollo
Snov.io
Clay All of these tools let you set filters for titles (or functions), location, years of experience, current or past companies, and hundreds of other settings. But the most important ones are location, function, and experience level. Tools for Finding Candidate’s Contact Info Now we get into more specific tools. We’ll be using "sales tools" rather than general recruitment tools. As mentioned, Apollo , Clay and Snov.io are originally sales tools. The bonus with both is that, in addition to building a pipeline, you can automatically scrape public contact details, like a candidate's work email. So, by using both of these tools, you can effectively build a database of candidates. If you’re using LinkedIn Sales Navigator or LinkedIn Recruiter, you’ll most likely only get the candidate’s LinkedIn profile link. To get their email, you can use the SalesQL extension. This tool scrapes data from the LinkedIn profile and provides all available public contact details. SalesQL is free for up to 100 contacts per month, which is usually enough for most recruiting needs Tools for Sending Email/LinkedIn Sequences Automatically Lemlist – Sends sequences via both email and LinkedIn


Apollo – Email sequences


Clay – Email sequences Lemlist is one of my favorite tools right now because it offers A/B testing for your emails. This allows you to track which pitches your candidates are responding to and adjust your emails accordingly. Plus, you can run multi-channel outreach, meaning you can reach out via both LinkedIn and email within the same tool. It's a great way to optimize your outreach strategy! Okay, Here’s the Cherry on Top! How to Automatically Personalize Your Email I know you’re aware that OpenAI exists, but hear me out—what if there was a platform that combined all the tools I mentioned earlier in one place? A platform that could automatically source people with the right experience, find their contact details, personalize emails via ChatGPT, and send sequences? That tool doesn’t exist yet, but you can create one for yourself, just like I did. I used Airtable and integrated all of it into one space. Here’s how it looks: Basically, you can connect all those tools (some of them via Zapier) to Airtable and automate everything with just one click. Conclusion I hope this helped broaden your hiring horizons and gave you some fresh ideas on tools that will make scaling your hiring efforts easier—and a lot more fun than dealing with manual work! PS: None of these tools were sponsored—this is just a genuine recommendation. Hey folks! Congrats on scaling your team and growing your efforts! In our previous article, we covered the process of hiring salespeople—what steps to take and what questions to ask along the way. But now, we’re going to talk about how to scale that funnel! Specifically, how to scale the process of bringing awesome people onto your team. That’s the focus today, alright? We’re going to dive into the tools. Tools that will allow you to follow the same funnel but spend less time on manual tasks. Here’s a quick overview of the tools we’ll cover: Here’s a quick overview of the tools we’ll cover: Tools for research Tools for sourcing candidates Tools for finding their contact info (only the public kind, don’t worry—we’re staying on the right side of the law here) Tools for sending email/LinkedIn sequences automatically Tools for research Tools for sourcing candidates Tools for finding their contact info (only the public kind, don’t worry—we’re staying on the right side of the law here) Tools for sending email/LinkedIn sequences automatically bonus in the end: A tool for automatically generating custom emails bonus in the end: A tool for automatically generating custom emails And that’s pretty much it! These tools will help you build a hiring system, not just make a random hire once in a while. Let’s get started! Tools for research Tools for research Step 0 is knowing what tools to use to properly research the talent pool, salaries, product market, and more. For researching the talent pool, I use LinkedIn Talent Insights. It provides a clear understanding of geographical markets, showing where the talent you need is located, which companies they’re working for, and what those companies are doing right now. It also helps you see what kind of education people have. LinkedIn Talent Insights. Once you’ve identified locations, companies, and the demand levels, you can dig deeper into researching salaries for the required position, location, and company. Keep in mind, the higher the demand, the more prepared you should be to offer a salary above the range you see. For salary research, I use Indeed Career, Glassdoor or Salary.com. These platforms show salary ranges based on title, location, and company. Indeed Career, Glassdoor or Salary.com. Keep in mind that LinkedIn Talent Insights is a paid subscription. It comes as part of a bundle with the LinkedIn Recruiter subscription. So, if you're ready to go full pro mode, I recommend purchasing it as a bundle, since we’ll also be using LinkedIn Recruiter for sourcing candidates. Keep in mind that LinkedIn Talent Insights is a paid subscription. It comes as part of a bundle with the LinkedIn Recruiter subscription. So, if you're ready to go full pro mode, I recommend purchasing it as a bundle, since we’ll also be using LinkedIn Recruiter for sourcing candidates. LinkedIn Talent Insights LinkedIn Recruiter Tools for Sourcing Candidates Tools for Sourcing Candidates Once you know the location of your candidates, the companies they’re likely working for, and the approximate salary ranges, it's time to build a proper pipeline of matching talent. I use several tools for this, depending on how niche the candidates are. Here are the classic tools that will fit in 8 out of 10 situations: Sales Navigator LinkedIn Recruiter Sales Navigator Sales Navigator LinkedIn Recruiter LinkedIn Recruiter For sourcing candidates, there are also tools that are primarily made for salespeople, but they’re incredibly useful for recruiting as well: Apollo Snov.io Clay Apollo Apollo Snov.io Snov.io Clay Clay All of these tools let you set filters for titles (or functions), location, years of experience, current or past companies, and hundreds of other settings. But the most important ones are location, function, and experience level. Tools for Finding Candidate’s Contact Info Tools for Finding Candidate’s Contact Info Now we get into more specific tools. We’ll be using "sales tools" rather than general recruitment tools. As mentioned, Apollo , Clay and Snov.io are originally sales tools. The bonus with both is that, in addition to building a pipeline, you can automatically scrape public contact details, like a candidate's work email. So, by using both of these tools, you can effectively build a database of candidates. If you’re using LinkedIn Sales Navigator or LinkedIn Recruiter , you’ll most likely only get the candidate’s LinkedIn profile link. To get their email, you can use the SalesQL extension . This tool scrapes data from the LinkedIn profile and provides all available public contact details. LinkedIn Sales Navigator LinkedIn Recruiter SalesQL extension SalesQL is free for up to 100 contacts per month, which is usually enough for most recruiting needs SalesQL is free for up to 100 contacts per month, which is usually enough for most recruiting needs Tools for Sending Email/LinkedIn Sequences Automatically Tools for Sending Email/LinkedIn Sequences Automatically Lemlist – Sends sequences via both email and LinkedIn Apollo – Email sequences Clay – Email sequences Lemlist – Sends sequences via both email and LinkedIn Lemlist – Sends sequences via both email and LinkedIn Lemlist and Apollo – Email sequences Apollo – Email sequences Apollo Clay – Email sequences Clay – Email sequences Clay Lemlist is one of my favorite tools right now because it offers A/B testing for your emails. This allows you to track which pitches your candidates are responding to and adjust your emails accordingly. Plus, you can run multi-channel outreach, meaning you can reach out via both LinkedIn and email within the same tool. It's a great way to optimize your outreach strategy! Lemlist is one of my favorite tools right now because it offers A/B testing for your emails. This allows you to track which pitches your candidates are responding to and adjust your emails accordingly. Plus, you can run multi-channel outreach, meaning you can reach out via both LinkedIn and email within the same tool. It's a great way to optimize your outreach strategy! Lemlist Okay, Here’s the Cherry on Top! Okay, Here’s the Cherry on Top! How to Automatically Personalize Your Email How to Automatically Personalize Your Email I know you’re aware that OpenAI exists, but hear me out—what if there was a platform that combined all the tools I mentioned earlier in one place? A platform that could automatically source people with the right experience, find their contact details, personalize emails via ChatGPT, and send sequences? That tool doesn’t exist yet, but you can create one for yourself, just like I did. I used Airtable and integrated all of it into one space. Airtable Here’s how it looks: Here’s how it looks: Basically, you can connect all those tools (some of them via Zapier ) to Airtable and automate everything with just one click. Zapier Airtable Conclusion Conclusion I hope this helped broaden your hiring horizons and gave you some fresh ideas on tools that will make scaling your hiring efforts easier—and a lot more fun than dealing with manual work! PS: None of these tools were sponsored—this is just a genuine recommendation. PS: PS: None of these tools were sponsored—this is just a genuine recommendation.

Automate Hiring, Build Effective Funnels, And Go for Top Talent With This Guide

Table of Links Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.6 Distributed Execution 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.5 Chatbot 7 Ablation Studies 8 Discussion 9 Related Work 10 Conclusion, Acknowledgement and References 6.4 Shared prefix We explore the effectiveness of vLLM for the case a prefix is shared among different input prompts, as illustrated in Fig. 10. For the model, we use LLaMA-13B [52], which is multilingual. For the workload, we use the WMT16 [4] Englishto-German translation dataset and synthesize two prefixes that include an instruction and a few translation examples. The first prefix includes a single example (i.e., one-shot) while the other prefix includes 5 examples (i.e., few-shot). As shown in Fig. 16 (a), vLLM achieves 1.67× higher throughput than Orca (Oracle) when the one-shot prefix is shared. Furthermore, when more examples are shared (Fig. 16 (b)), vLLM achieves 3.58× higher throughput than Orca (Oracle). This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Woosuk Kwon, UC Berkeley with Equal contribution;
(2) Zhuohan Li, UC Berkeley with Equal contribution;
(3) Siyuan Zhuang, UC Berkeley;
(4) Ying Sheng, UC Berkeley and Stanford University;
(5) Lianmin Zheng, UC Berkeley;
(6) Cody Hao Yu, Independent Researcher;
(7) Cody Hao Yu, Independent Researcher;
(8) Joseph E. Gonzalez, UC Berkeley;
(9) Hao Zhang, UC San Diego;
(10) Ion Stoica, UC Berkeley. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Background and 2.1 Transformer-Based Large Language Models 2 Background and 2.1 Transformer-Based Large Language Models 2.2 LLM Service & Autoregressive Generation 2.2 LLM Service & Autoregressive Generation 2.3 Batching Techniques for LLMs 2.3 Batching Techniques for LLMs 3 Memory Challenges in LLM Serving 3 Memory Challenges in LLM Serving 3.1 Memory Management in Existing Systems 3.1 Memory Management in Existing Systems 4 Method and 4.1 PagedAttention 4 Method and 4.1 PagedAttention 4.2 KV Cache Manager 4.2 KV Cache Manager 4.3 Decoding with PagedAttention and vLLM 4.3 Decoding with PagedAttention and vLLM 4.4 Application to Other Decoding Scenarios 4.4 Application to Other Decoding Scenarios 4.5 Scheduling and Preemption 4.5 Scheduling and Preemption 4.6 Distributed Execution 4.6 Distributed Execution 5 Implementation 5 Implementation 6 Evaluation and 6.1 Experimental Setup 6 Evaluation and 6.1 Experimental Setup 6.2 Basic Sampling 6.2 Basic Sampling 6.3 Parallel Sampling and Beam Search 6.3 Parallel Sampling and Beam Search 6.4 Shared prefix 6.4 Shared prefix 6.5 Chatbot 6.5 Chatbot 7 Ablation Studies 7 Ablation Studies 8 Discussion 8 Discussion 9 Related Work 9 Related Work 10 Conclusion, Acknowledgement and References 10 Conclusion, Acknowledgement and References 6.4 Shared prefix We explore the effectiveness of vLLM for the case a prefix is shared among different input prompts, as illustrated in Fig. 10. For the model, we use LLaMA-13B [52], which is multilingual. For the workload, we use the WMT16 [4] Englishto-German translation dataset and synthesize two prefixes that include an instruction and a few translation examples. The first prefix includes a single example (i.e., one-shot) while the other prefix includes 5 examples (i.e., few-shot). As shown in Fig. 16 (a), vLLM achieves 1.67× higher throughput than Orca (Oracle) when the one-shot prefix is shared. Furthermore, when more examples are shared (Fig. 16 (b)), vLLM achieves 3.58× higher throughput than Orca (Oracle). 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 Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley. Authors: Authors: (1) Woosuk Kwon, UC Berkeley with Equal contribution; (2) Zhuohan Li, UC Berkeley with Equal contribution; (3) Siyuan Zhuang, UC Berkeley; (4) Ying Sheng, UC Berkeley and Stanford University; (5) Lianmin Zheng, UC Berkeley; (6) Cody Hao Yu, Independent Researcher; (7) Cody Hao Yu, Independent Researcher; (8) Joseph E. Gonzalez, UC Berkeley; (9) Hao Zhang, UC San Diego; (10) Ion Stoica, UC Berkeley.

How Effective is vLLM When a Prefix Is Thrown Into the Mix?

Master React by Designing Effective APIs With the useImperativeHandle Hook

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使用 useImperativeHandle Hook 设计有效的 API 来掌握 React

Authors:
(1) Hoon Kim, Beeble AI, and contributed equally to this work;
(2) Minje Jang, Beeble AI, and contributed equally to this work;
(3) Wonjun Yoon, Beeble AI, and contributed equally to this work;
(4) Jisoo Lee, Beeble AI, and contributed equally to this work;
(5) Donghyun Na, Beeble AI, and contributed equally to this work;
(6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction
2. Related Work
3. SwitchLight and 3.1. Preliminaries
3.2. Problem Formulation
3.3. Architecture
3.4. Objectives
4. Multi-Masked Autoencoder Pre-training
5. Data
6. Experiments
7. Conclusion Appendix A. Implementation Details
B. User Study Interface
C. Video Demonstration
D. Additional Qualitative Results & References 2. Related Work Human Portrait Relighting is an ill-posed problem due to its under-constrained nature. To tackle this, earlier methods incorporated 3D facial priors [44], exploited image intrinsics [3, 40], or framed the task as a style transfer [43]. Light stage techniques [49] offer a more powerful solution by recording subject’s reflectance fields under varying lighting conditions [10, 14], though they are labor-intensive and require specialized equipment. A promising alternative has emerged with deep learning, utilizing neural networks trained on light stage data. Sun et al. [45] pioneered this approach, but their method had limitations in representing non-Lambertian effects. This was improved upon by Nestmeyer et al. [32], who integrated rendering physics into network design, albeit limited to directional light. Building upon this, Pandey et al. [34] incorporated the Phong reflection model and a high dynamic range (HDR) lighting map [9] into their network, enabling a more accurate representation of global illumination. Simultaneously, efforts have been made to explore portrait relighting without light stage data [20, 21, 42, 47, 55]. Moreover, introduction of NeRF [7] and diffusion-based [38] models has opened new avenues in the field. However, networks trained with lightstage data maintain superior accuracy and realism, thanks to physics-based composited relight image training pairs and precise ground truth image intrinsics [56]. Our work furthers this domain by integrating the Cook-Torrance model into our network design, shifting from the empirical Phong model to a more physics-based approach, thereby enhancing the realism and detail in relit images. Self-supervised Pre-training has become a standard training scheme in the development of large language models like BERT [11] and GPT [39], and is increasingly influential in vision models, aiming to replicate the ‘BERT moment’. This approach typically involves pre-training on extensive unlabeled data, followed by fine-tuning on specific tasks. While early efforts in vision models focused on simple pretext tasks [13, 17, 33, 36, 53], the field has evolved through stages like contrastive learning [5, 18] and masked image modeling [2, 19, 50]. However, the primary focus has remained on visual recognition, with less attention to other domains. Exceptions include low-level image processing tasks [4, 6, 27, 30] using the vision transformer [15]. Our research takes a different route, focusing on human portrait relighting—a complex challenge of manipulating illumination in the image. This direction is crucial because acquiring accurate ground truth data, especially from light stage, is both expensive and difficult. We modify the MAE framework [19], previously successful in robust image representation learning and developing locality biases [35], to suit the unique requirements of effective relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Authors: Authors: (1) Hoon Kim, Beeble AI, and contributed equally to this work; (2) Minje Jang, Beeble AI, and contributed equally to this work; (3) Wonjun Yoon, Beeble AI, and contributed equally to this work; (4) Jisoo Lee, Beeble AI, and contributed equally to this work; (5) Donghyun Na, Beeble AI, and contributed equally to this work; (6) Sanghyun Woo, New York University, and contributed equally to this work. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Editor's Note: This is Part 2 of 14 of a study introducing a method for improving how light and shadows can be applied to human portraits in digital images. Read the rest below. Table of Links Abstract and 1. Introduction 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.3. Architecture 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 5. Data 6. Experiments 7. Conclusion Abstract and 1. Introduction Abstract and 1. Introduction 2. Related Work 2. Related Work 3. SwitchLight and 3.1. Preliminaries 3. SwitchLight and 3.1. Preliminaries 3.2. Problem Formulation 3.2. Problem Formulation 3.3. Architecture 3.3. Architecture 3.4. Objectives 3.4. Objectives 4. Multi-Masked Autoencoder Pre-training 4. Multi-Masked Autoencoder Pre-training 5. Data 5. Data 6. Experiments 6. Experiments 7. Conclusion 7. Conclusion Appendix A. Implementation Details B. User Study Interface C. Video Demonstration D. Additional Qualitative Results & References A. Implementation Details A. Implementation Details B. User Study Interface B. User Study Interface C. Video Demonstration C. Video Demonstration D. Additional Qualitative Results & References D. Additional Qualitative Results & References 2. Related Work Human Portrait Relighting is an ill-posed problem due to its under-constrained nature. To tackle this, earlier methods incorporated 3D facial priors [44], exploited image intrinsics [3, 40], or framed the task as a style transfer [43]. Light stage techniques [49] offer a more powerful solution by recording subject’s reflectance fields under varying lighting conditions [10, 14], though they are labor-intensive and require specialized equipment. A promising alternative has emerged with deep learning, utilizing neural networks trained on light stage data. Sun et al. [45] pioneered this approach, but their method had limitations in representing non-Lambertian effects. This was improved upon by Nestmeyer et al. [32], who integrated rendering physics into network design, albeit limited to directional light. Building upon this, Pandey et al. [34] incorporated the Phong reflection model and a high dynamic range (HDR) lighting map [9] into their network, enabling a more accurate representation of global illumination. Simultaneously, efforts have been made to explore portrait relighting without light stage data [20, 21, 42, 47, 55]. Moreover, introduction of NeRF [7] and diffusion-based [38] models has opened new avenues in the field. However, networks trained with lightstage data maintain superior accuracy and realism, thanks to physics-based composited relight image training pairs and precise ground truth image intrinsics [56]. Human Portrait Relighting Our work furthers this domain by integrating the Cook-Torrance model into our network design, shifting from the empirical Phong model to a more physics-based approach, thereby enhancing the realism and detail in relit images. Self-supervised Pre-training has become a standard training scheme in the development of large language models like BERT [11] and GPT [39], and is increasingly influential in vision models, aiming to replicate the ‘BERT moment’. This approach typically involves pre-training on extensive unlabeled data, followed by fine-tuning on specific tasks. While early efforts in vision models focused on simple pretext tasks [13, 17, 33, 36, 53], the field has evolved through stages like contrastive learning [5, 18] and masked image modeling [2, 19, 50]. However, the primary focus has remained on visual recognition, with less attention to other domains. Exceptions include low-level image processing tasks [4, 6, 27, 30] using the vision transformer [15]. Self-supervised Pre-training Our research takes a different route, focusing on human portrait relighting—a complex challenge of manipulating illumination in the image. This direction is crucial because acquiring accurate ground truth data, especially from light stage, is both expensive and difficult. We modify the MAE framework [19], previously successful in robust image representation learning and developing locality biases [35], to suit the unique requirements of effective relighting. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Beeble Researchers Use Physics-Based Models to Achieve Realistic Lighting Effects in Images

Table of Links Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 5.5 Ablation Study Before we compare the model with other baselines in the TTS and VC tasks, we conducted ablation studies by comparing Reconstruction[10], Resynthesis [11], and VC performance to verify the effectiveness of each component in HierSpeech++. First, although previous E2E models have shown highquality waveform audio generation, the zero-shot speech synthesis performance was considerably low, and some studies must fine-tune or use speaker id for speaker adaptation. Recently, HierVST has significantly improved a voice style transfer performance of the E2E model; therefore so we conduct ablation studies by building up on HierVST AMP Block We first replaced the MRF block of HiFi-GAN with the AMP of BigVGAN for OOD generation. The AMP improved the performance of all tasks in terms of all metrics without F0 consistency. The results show that the BigVGANbased HAG performs better but the loss balance may lean toward optimizing the waveform reconstruction rather than F0 prediction; however, the naturalness and similarity of the converted speech improved in terms of all metrics. Specifically, objective naturalness exhibited better UTMOS. SF Encoder To address F0 consistency, we utilize an SF encoder (SFE) for a dual-path semantic encoder, which enhances the semantic prior in terms of all metrics. This significantly improved the F0 consistency of inference scenario. It is worth noting that F0 can be manually controlled. Dual-audio Encoder We also utilized a dual-audio posterior encoder (DAE) to increase the acoustic capacity of the acoustic representation, which significantly increases the reconstruction performance. Although the linear spectrogram contains useful information for reconstructing a waveform audio, this representation still lacks the ability to reproduce all information; therefore, additional information from waveform audio could complement a wave-level acoustic representation. It is worth noting that the DAE was only utilized during training but significantly improved reconstruction and pronunciation. However, we found that the enhanced acoustic posterior contains a large information resulting in reducing a VC performance. T-Flow To bridge the gap between each representation, we replace a wavenet-based normalizing flow with Transformerbased normalizing flow (T-Flow) using AdaLN-Zero for style adaptation. This also improved the entire performance of all metrics. Moreover, speaker similarity significantly improved. Bi-Flow Moreover, we adopt a bidirectional normalizing flow (Bi-Flow) to reduce the train-inference mismatch problem. The results show that Bi-Flow slightly decreases the reconstruction quality. However, this could regularize a posterior by conditioning the information used in the inference scenario, thereby improving the VC performance. We also found that high weight of Bi-Flow significantly decreased a reconstruction performance, and thus, we use λ of 0.5 for weak regularization. Large-scale Data In addition, we demonstrated that our model is robust to data scale-up. We did not use any labels to train the model, and only used a low-resolution speech dataset of 16 kHz, which we could obtain simply due to SpeechSR. For scaling-up, we did not conduct any preprocessing to train the model without down-sampling (any to 16 kHz), so there are noisy samples in our dataset however we did not experience any problems. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. [10] Reconstruction: Posterior Encoder → Generator → Audio [11] Resynthesis: Prior Encoder → Generator → Audio Authors:
(1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea;
(4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.1 Neural Codec Language Models and 2.2 Non-autoregressive Models 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 2.3 Diffusion Models and 2.4 Zero-shot Voice Cloning 3 Hierspeech++ and 3.1 Speech Representations 3 Hierspeech++ and 3.1 Speech Representations 3.2 Hierarchical Speech Synthesizer 3.2 Hierarchical Speech Synthesizer 3.3 Text-to-Vec 3.3 Text-to-Vec 3.4 Speech Super-resolution 3.4 Speech Super-resolution 3.5 Model Architecture 3.5 Model Architecture 4 Speech Synthesis Tasks 4.1 Voice Conversion and 4.2 Text-to-Speech 4.1 Voice Conversion and 4.2 Text-to-Speech 4.3 Style Prompt Replication 4.3 Style Prompt Replication 5 Experiment and Result, and Dataset 5 Experiment and Result, and Dataset 5.2 Preprocessing and 5.3 Training 5.2 Preprocessing and 5.3 Training 5.4 Evaluation Metrics 5.4 Evaluation Metrics 5.5 Ablation Study 5.5 Ablation Study 5.6 Zero-shot Voice Conversion 5.6 Zero-shot Voice Conversion 5.7 High-diversity but High-fidelity Speech Synthesis 5.7 High-diversity but High-fidelity Speech Synthesis 5.8 Zero-shot Text-to-Speech 5.8 Zero-shot Text-to-Speech 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.9 Zero-shot Text-to-Speech with 1s Prompt 5.10 Speech Super-resolution 5.10 Speech Super-resolution 5.11 Additional Experiments with Other Baselines 5.11 Additional Experiments with Other Baselines 6 Limitation and Quick Fix 6 Limitation and Quick Fix 7 Conclusion, Acknowledgement and References 7 Conclusion, Acknowledgement and References 5.5 Ablation Study Before we compare the model with other baselines in the TTS and VC tasks, we conducted ablation studies by comparing Reconstruction[10], Resynthesis [11], and VC performance to verify the effectiveness of each component in HierSpeech++. First, although previous E2E models have shown highquality waveform audio generation, the zero-shot speech synthesis performance was considerably low, and some studies must fine-tune or use speaker id for speaker adaptation. Recently, HierVST has significantly improved a voice style transfer performance of the E2E model; therefore so we conduct ablation studies by building up on HierVST AMP Block We first replaced the MRF block of HiFi-GAN with the AMP of BigVGAN for OOD generation. The AMP improved the performance of all tasks in terms of all metrics without F0 consistency. The results show that the BigVGANbased HAG performs better but the loss balance may lean toward optimizing the waveform reconstruction rather than F0 prediction; however, the naturalness and similarity of the converted speech improved in terms of all metrics. Specifically, objective naturalness exhibited better UTMOS. AMP Block SF Encoder To address F0 consistency, we utilize an SF encoder (SFE) for a dual-path semantic encoder, which enhances the semantic prior in terms of all metrics. This significantly improved the F0 consistency of inference scenario. It is worth noting that F0 can be manually controlled. SF Encoder Dual-audio Encoder We also utilized a dual-audio posterior encoder (DAE) to increase the acoustic capacity of the acoustic representation, which significantly increases the reconstruction performance. Although the linear spectrogram contains useful information for reconstructing a waveform audio, this representation still lacks the ability to reproduce all information; therefore, additional information from waveform audio could complement a wave-level acoustic representation. It is worth noting that the DAE was only utilized during training but significantly improved reconstruction and pronunciation. However, we found that the enhanced acoustic posterior contains a large information resulting in reducing a VC performance. Dual-audio Encoder T-Flow To bridge the gap between each representation, we replace a wavenet-based normalizing flow with Transformerbased normalizing flow (T-Flow) using AdaLN-Zero for style adaptation. This also improved the entire performance of all metrics. Moreover, speaker similarity significantly improved. T-Flow Bi-Flow Moreover, we adopt a bidirectional normalizing flow (Bi-Flow) to reduce the train-inference mismatch problem. The results show that Bi-Flow slightly decreases the reconstruction quality. However, this could regularize a posterior by conditioning the information used in the inference scenario, thereby improving the VC performance. We also found that high weight of Bi-Flow significantly decreased a reconstruction performance, and thus, we use λ of 0.5 for weak regularization. Bi-Flow Large-scale Data In addition, we demonstrated that our model is robust to data scale-up. We did not use any labels to train the model, and only used a low-resolution speech dataset of 16 kHz, which we could obtain simply due to SpeechSR. For scaling-up, we did not conduct any preprocessing to train the model without down-sampling (any to 16 kHz), so there are noisy samples in our dataset however we did not experience any problems. Large-scale Data This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv [10] Reconstruction: Posterior Encoder → Generator → Audio [11] Resynthesis: Prior Encoder → Generator → Audio Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author. Authors: Authors: (1) Sang-Hoon Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (2) Ha-Yeong Choi, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (3) Seung-Bin Kim, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea; (4) Seong-Whan Lee, Fellow, IEEE with the Department of Artificial Intelligence, Korea University, Seoul 02841, South Korea and a Corresponding author.

Conducting Ablation Studies to Verify the Effectiveness of Each Component in HierSpeech++

How Effective Are AI Models at Biomedical Problem Solving?

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JavaScript challenges are like stealthy ninjas lurking in the shadows 🌃, ready to block your web scraping efforts without you even realizing it. They may not be visible, but their presence can thwart your data collection attempts! Dig into how these challenges operate and explore effective strategies for bypassing them. Time to enhance your web scraping capabilities! 🦾 What Are JavaScript Challenges? Nope, we’re not talking about those fun JavaScript coding challenges we all love. That’s a whole different game... Here, we’re exploring a different type of challenge. 🤔 In the world of bot protection, JavaScript challenges—also known as JS challenges—are the digital bouncers that stand between your scraper and a page's juicy content. They’re there to keep automated scraping bots from accessing a site's data. 🚫 🤖 🚫 Web servers embed these challenges directly into the web pages they deliver to the client. To bypass them and access the site’s content, you need a browser that can execute the JavaScript code within these challenge scripts. Otherwise, you’re not getting in! 🛑 Sites use the JavaScript challenge mechanism to automatically detect and block bots. Think of it as a “prove you’re human” test. To gain entry to the site, your scraper must be able to run some specific obfuscated script in a browser and pass the underlying test! What Does a JavaScript Challenge Look Like? Usually, a JavaScript challenge is like a ghost 👻—you can sense it, but you rarely see it. More specifically, it’s just a script hiding in the web page that your browser must execute to gain access to the site’s content. To get a clearer picture of these challenges, let’s look at a real-world example. Cloudflare is known for using JS challenges. When you enable the Managed Challenge feature of its WAF (Web Application Firewall) solution, the popular CDN starts embedding JavaScript challenges in your pages. According to official docs, a JS challenge doesn’t require user interaction. Instead, it’s processed quietly by the browser in the background. ⚙️ During this process, the JavaScript code runs tests to confirm if the visitor is human👤—like checking for the presence of specific fonts installed on the user’s device. In detail, Cloudflare uses Google’s Picasso fingerprinting protocol. This analyzes the client’s software and hardware stack with data collected via JavaScript. The entire verification process might happen behind the scenes without the user noticing, or it might stall them briefly with a screen like this: Want to avoid this screen altogether? Read the guide on Cloudflare bypass! Now, three scenarios can play out: You pass the test: You access the page, and the JavaScript challenge won’t reappear during the same browsing session.
You fail the test: Expect to face additional anti-bot measures, like CAPTCHAs.
You can’t run the test: If you’re using an HTTP client that can’t execute JavaScript, you’re out of luck—blocked, and possibly banned! (Pro tip: Learn how to avoid IP bans with proxies!). How to Challenge JavaScript Protections for Seamless Web Scraping Want to bypass mandatory JavaScript challenges? First, you need an automation tool that runs web pages in a browser 🌐. In other words, you have to use a browser automation library like Selenium, Puppeteer, or Playwright. Those tools empower you to write scraping scripts that make a real browser interact with web pages just like a human would. This strategy helps you bypass the dreaded scenario 3 (you can’t run the test) from earlier, limiting your outcomes to either scenario 1 (you pass the test) or scenario 2 (you fail the test). For simple JavaScript challenges that just check if you can run JS, a browser automation tool is usually enough to do the trick 😌. But when it comes to more advanced challenges from services like Cloudflare or Akamai, things get tricky… To control browsers, these tools set configurations that can raise suspicion with WAFs. You can try to hide them using technologies like Puppeteer Extra, but that doesn’t always guarantee success either. 🥷 Suspicious settings are especially evident when checking browsers in headless mode, which is popular in scraping due to its resource efficiency. However, don’t forget that headless browsers are still resource-intensive compared to HTTP clients. So, they require a solid server setup to run at scale. ⚖️ So, what’s the ultimate answer for overcoming JavaScript challenges and doing scraping without getting blocked and at scale? Best Solution to Overcome a JavaScript Challenge The issue isn't with the browser automation tools themselves. Quite the opposite, it’s all about the browsers those solutions control! 💡 Now, picture a browser that: Runs in headed mode like a regular browser, reducing the chances of bot detection.


Scales effortlessly in the cloud, saving you both time and money on infrastructure management.


Automatically tackles CAPTCHA solving, browser fingerprinting, cookie and header customization, and retries for optimal efficiency.


Provides rotating IPs backed by one of the largest and most reliable proxy networks out there.


Seamlessly integrates with popular browser automation libraries like Playwright, Selenium, and Puppeteer. If such a solution existed, it would allow you to wave goodbye to JavaScript challenges and most other anti-scraping measures. Well, this isn’t just a distant fantasy—it's a reality! Enter Bright Data’s Scraping Browser: https://www.youtube.com/watch?v=kuDuJWvho7Q&embedable=true Final Thoughts Now you’re in the loop about JavaScript challenges and why they’re not just tests to level up your coding skills. In the realm of web scraping, these challenges are pesky barriers that can stop your data retrieval efforts. Want to scrape without hitting those frustrating blocks? Take a look at Bright Data's suite of tools! Join our mission to make the Internet accessible to everyone—even via automated browsers. 🌐 Until next time, keep surfing the Internet with freedom! JavaScript challenges are like stealthy ninjas lurking in the shadows 🌃, ready to block your web scraping efforts without you even realizing it. They may not be visible, but their presence can thwart your data collection attempts! Dig into how these challenges operate and explore effective strategies for bypassing them. Time to enhance your web scraping capabilities! 🦾 What Are JavaScript Challenges? Nope, we’re not talking about those fun JavaScript coding challenges we all love. That’s a whole different game... Here, we’re exploring a different type of challenge. 🤔 In the world of bot protection, JavaScript challenges—also known as JS challenges—are the digital bouncers that stand between your scraper and a page's juicy content. They’re there to keep automated scraping bots from accessing a site's data. 🚫 🤖 🚫 scraping bots Web servers embed these challenges directly into the web pages they deliver to the client. To bypass them and access the site’s content, you need a browser that can execute the JavaScript code within these challenge scripts. Otherwise, you’re not getting in! 🛑 Sites use the JavaScript challenge mechanism to automatically detect and block bots. Think of it as a “prove you’re human” test. To gain entry to the site, your scraper must be able to run some specific obfuscated script in a browser and pass the underlying test! What Does a JavaScript Challenge Look Like? Usually, a JavaScript challenge is like a ghost 👻—you can sense it, but you rarely see it. More specifically, it’s just a script hiding in the web page that your browser must execute to gain access to the site’s content. To get a clearer picture of these challenges, let’s look at a real-world example. Cloudflare is known for using JS challenges. When you enable the Managed Challenge feature of its WAF ( Web Application Firewall ) solution, the popular CDN starts embedding JavaScript challenges in your pages. Managed Challenge Web Application Firewall According to official docs, a JS challenge doesn’t require user interaction. Instead, it’s processed quietly by the browser in the background. ⚙️ JS challenge During this process, the JavaScript code runs tests to confirm if the visitor is human👤—like checking for the presence of specific fonts installed on the user’s device. In detail, Cloudflare uses Google’s Picasso fingerprinting protocol . This analyzes the client’s software and hardware stack with data collected via JavaScript. Google’s Picasso fingerprinting protocol The entire verification process might happen behind the scenes without the user noticing, or it might stall them briefly with a screen like this: Want to avoid this screen altogether? Read the guide on Cloudflare bypass ! Cloudflare bypass Now, three scenarios can play out: You pass the test: You access the page, and the JavaScript challenge won’t reappear during the same browsing session. You fail the test: Expect to face additional anti-bot measures, like CAPTCHAs. You can’t run the test: If you’re using an HTTP client that can’t execute JavaScript, you’re out of luck—blocked, and possibly banned! (Pro tip: Learn how to avoid IP bans with proxies!). You pass the test : You access the page, and the JavaScript challenge won’t reappear during the same browsing session. You pass the test You fail the test : Expect to face additional anti-bot measures, like CAPTCHAs. You fail the test You can’t run the test : If you’re using an HTTP client that can’t execute JavaScript, you’re out of luck—blocked, and possibly banned! (Pro tip: Learn how to avoid IP bans with proxies! ). You can’t run the test Learn how to avoid IP bans with proxies! How to Challenge JavaScript Protections for Seamless Web Scraping Want to bypass mandatory JavaScript challenges? First, you need an automation tool that runs web pages in a browser 🌐. In other words, you have to use a browser automation library like Selenium, Puppeteer, or Playwright . browser automation library like Selenium, Puppeteer, or Playwright Those tools empower you to write scraping scripts that make a real browser interact with web pages just like a human would. This strategy helps you bypass the dreaded scenario 3 (you can’t run the test) from earlier, limiting your outcomes to either scenario 1 (you pass the test) or scenario 2 (you fail the test). For simple JavaScript challenges that just check if you can run JS, a browser automation tool is usually enough to do the trick 😌. But when it comes to more advanced challenges from services like Cloudflare or Akamai, things get tricky… To control browsers, these tools set configurations that can raise suspicion with WAFs. You can try to hide them using technologies like Puppeteer Extra , but that doesn’t always guarantee success either. 🥷 Puppeteer Extra Suspicious settings are especially evident when checking browsers in headless mode, which is popular in scraping due to its resource efficiency. However, don’t forget that headless browsers are still resource-intensive compared to HTTP clients. So, they require a solid server setup to run at scale. ⚖️ So, what’s the ultimate answer for overcoming JavaScript challenges and doing scraping without getting blocked and at scale ? scraping without getting blocked and at scale Best Solution to Overcome a JavaScript Challenge The issue isn't with the browser automation tools themselves. Quite the opposite, it’s all about the browsers those solutions control! 💡 Now, picture a browser that: Runs in headed mode like a regular browser, reducing the chances of bot detection. Scales effortlessly in the cloud, saving you both time and money on infrastructure management. Automatically tackles CAPTCHA solving, browser fingerprinting, cookie and header customization, and retries for optimal efficiency. Provides rotating IPs backed by one of the largest and most reliable proxy networks out there. Seamlessly integrates with popular browser automation libraries like Playwright, Selenium, and Puppeteer. Runs in headed mode like a regular browser, reducing the chances of bot detection. Runs in headed mode like a regular browser, reducing the chances of bot detection. Scales effortlessly in the cloud, saving you both time and money on infrastructure management. Scales effortlessly in the cloud, saving you both time and money on infrastructure management. Automatically tackles CAPTCHA solving, browser fingerprinting, cookie and header customization, and retries for optimal efficiency. Automatically tackles CAPTCHA solving, browser fingerprinting, cookie and header customization, and retries for optimal efficiency. Provides rotating IPs backed by one of the largest and most reliable proxy networks out there. Provides rotating IPs backed by one of the largest and most reliable proxy networks out there. Seamlessly integrates with popular browser automation libraries like Playwright, Selenium, and Puppeteer. Seamlessly integrates with popular browser automation libraries like Playwright, Selenium, and Puppeteer. If such a solution existed, it would allow you to wave goodbye to JavaScript challenges and most other anti-scraping measures . Well, this isn’t just a distant fantasy—it's a reality! anti-scraping measures Enter Bright Data’s Scraping Browser : Scraping Browser https://www.youtube.com/watch?v=kuDuJWvho7Q&embedable=true https://www.youtube.com/watch?v=kuDuJWvho7Q&embedable=true Final Thoughts Now you’re in the loop about JavaScript challenges and why they’re not just tests to level up your coding skills. In the realm of web scraping, these challenges are pesky barriers that can stop your data retrieval efforts. Want to scrape without hitting those frustrating blocks? Take a look at Bright Data 's suite of tools! Join our mission to make the Internet accessible to everyone—even via automated browsers. 🌐 Bright Data Until next time, keep surfing the Internet with freedom!

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