Whalebone Strengthens APAC Cybersecurity with New Major Telco Partners From Whalebone View all | Post a press release

Critical infrastructure at state, local levels at heightened risk of cyberattacks | Cybersecurity Dive

The words businesses use in cybersecurity disclosures matter.

Leaked ransomware chat logs reveal Black Basta’s targeted CVEs | Cybersecurity Dive

The cybersecurity market could grow to $338 billion in value by 2033, driven in part by expanding AI risks, Bloomberg Intelligence analysts said.

DeepSeek’s AI shake-up could boost cybersecurity risks, spending: report | Cybersecurity Dive

DeepSeek’s AI shake-up could boost cybersecurity risks, spending: report | CFO Dive

In a security advisory, Microsoft warned the flaw has been exploited in the wild. Additionally, the Cybersecurity and Infrastructure Security Agency added CVE-2025-24989 to its known exploited vulnerabilities catalog on Friday and gave federal agencies a March 14 deadline to apply mitigations.

Microsoft Power Pages vulnerability exploited in the wild | Cybersecurity Dive

Organizations must not overlook web browser threats – or the new technologies that can defeat them.

Is your browser ground zero for cyber-attacks? | Cybersecurity Dive

Respondents told Glassdoor that remote work, higher turnover and layoffs have made it harder to make friends at work.

Is the workplace less friendly? Employees say they care less about having ‘work besties.’ | HR Dive

The China-linked threat group, also known as Cringe, has targeted a range of critical infrastructure providers including schools, healthcare providers, governments and manufacturers since 2001, according to an advisory led by the FBI and Cybersecurity and Infrastructure Security Agency.

US authorities warn Ghost ransomware leverages older CVEs | Cybersecurity Dive

Cybersecurity Dive contacted Recorded Future for additional comment on the threat activity.

China-backed hackers continue cyberattacks on telecom companies | Cybersecurity Dive

Dive

dive

Swift’s structured concurrency has transformed the way we write asynchronous code. With async/await, expressing complex logic becomes cleaner and more linear. However, to fully leverage Swift’s concurrency, you need a solid grasp of how tasks are created and managed — particularly Task, .task, and their variations. Task .task Task: Entering an Async Context from Sync Code Task The most direct way to launch asynchronous code from synchronous code in Swift is with Task. Task Task {
    let profile = try await userService.fetchUserProfile()
    print("Name:", profile.name)
} Task {
    let profile = try await userService.fetchUserProfile()
    print("Name:", profile.name)
} This creates a concurrent task that begins executing immediately. Inside, you can call async functions, use try, and handle cancellation. You can also store the task in a variable to cancel or await its result later: concurrent task async try let task1 = Task {
    try await userService.fetchUserProfile()
}
try await task1.value

let task2 = Task {
    try await userService.fetchUserProfile()
}
task2.cancel() let task1 = Task {
    try await userService.fetchUserProfile()
}
try await task1.value

let task2 = Task {
    try await userService.fetchUserProfile()
}
task2.cancel() Tasks created this way inherit the actor context they are declared in — commonly MainActor when used in the UI. MainActor .task: Async Work in SwiftUI Views .task In SwiftUI, the idiomatic way to launch asynchronous work when a view appears is using the .task view modifier: .task Text("Loading...")
    .task {
        await userService.fetchUserProfile()
    } Text("Loading...")
    .task {
        await userService.fetchUserProfile()
    } This modifier starts a structured task that is tied to the view’s lifecycle, ensuring it is automatically cancelled when the view disappears or when its state changes. When the .task(id:) value changes, it cancels the previous task and starts a new one. However, cancellation only sends a signal — it doesn’t guarantee the task will actually stop unless the code inside is cancellation-aware. .task(id:) Cancellation Awareness Swift’s concurrency model is cancellation-aware. When SwiftUI cancels a .task, or when you cancel a task manually using task.cancel(), that doesn't magically stop your code. The task is marked as cancelled, but cancellation only takes effect when your code checks for it or uses suspending calls that throw CancellationError. So, cancellation is cooperative — your task must opt in to it. .task task.cancel() marked CancellationError cancellation is cooperative Here's an example where cancellation works as expected: .task {
    do {
        try await Task.sleep(for: .seconds(1))
        print("1 second passed")
    } catch is CancellationError {
        print("Sleep was cancelled")
    } catch let error {
        print("Error: \(error)")
    }
} .task {
    do {
        try await Task.sleep(for: .seconds(1))
        print("1 second passed")
    } catch is CancellationError {
        print("Sleep was cancelled")
    } catch let error {
        print("Error: \(error)")
    }
} If you're working with async code that doesn’t throw, you can check Task.isCancelled manually: Task.isCancelled .task {
    await performSomeWork()

    if Task.isCancelled {
        return  // Exit early if no longer needed
    }

    await performMoreWork()
} .task {
    await performSomeWork()

    if Task.isCancelled {
        return  // Exit early if no longer needed
    }

    await performMoreWork()
} Using withTaskCancellationHandler withTaskCancellationHandler When you need to perform immediate cleanup or handle cancellation in long-running operations, withTaskCancellationHandler gives you precise control. withTaskCancellationHandler Here’s a complete example: func fetchData() async throws {
    try await withTaskCancellationHandler(
        operation: {
            print("🔄 Starting data fetch...")
            try await Task.sleep(for: .seconds(3))
            print("✅ Data fetched successfully!")
        },
        onCancel: {
            print("🛑 Task was cancelled. Cleaning up resources.")
        }
    )
}

// Launch the cancellable task
let task = Task {
    try await fetchData()
}

// Simulate cancellation after 1 second
Task {
    try await Task.sleep(for: .seconds(1))
    task.cancel()
} func fetchData() async throws {
    try await withTaskCancellationHandler(
        operation: {
            print("🔄 Starting data fetch...")
            try await Task.sleep(for: .seconds(3))
            print("✅ Data fetched successfully!")
        },
        onCancel: {
            print("🛑 Task was cancelled. Cleaning up resources.")
        }
    )
}

// Launch the cancellable task
let task = Task {
    try await fetchData()
}

// Simulate cancellation after 1 second
Task {
    try await Task.sleep(for: .seconds(1))
    task.cancel()
} 💡 What’s happening here: The first task begins a simulated long-running operation.
After 1 second, the second task cancels the first one.
The onCancel closure runs immediately when the task is cancelled, even if the inner operation is still ongoing. The first task begins a simulated long-running operation. After 1 second, the second task cancels the first one. The onCancel closure runs immediately when the task is cancelled, even if the inner operation is still ongoing. onCancel This is a precise, predictable way to control task cancellation beyond just try await + catch. try await catch Task Priorities You can assign explicit priorities to tasks using either Task(priority:) for manual tasks, or .task(priority:) when working in SwiftUI: Task(priority:) .task(priority:) Task(priority: .background) {
    await userService.fetchUserProfile()
}

.task(priority: .background) {
    await userService.fetchUserProfile()
} Task(priority: .background) {
    await userService.fetchUserProfile()
}

.task(priority: .background) {
    await userService.fetchUserProfile()
} Priorities such as .userInitiated, .background, .utility, etc. serve as scheduling hints to help the system prioritize execution under load. However, they do not affect the thread or actor context where the task runs. For example, a .task(priority: .background) launched from a SwiftUI view still runs on the MainActor unless explicitly detached. If you need true background isolation, you must use Task.detached(...). .userInitiated .background .utility .task(priority: .background) Task.detached(...) .task(priority:) vs Task.detached(priority:) .task(priority:) Task.detached(priority:) There’s a critical distinction: .task(priority: .background) {
    let profile = await userService.fetchUserProfile()
    name = profile.name
} .task(priority: .background) {
    let profile = await userService.fetchUserProfile()
    name = profile.name
} This task still runs within SwiftUI’s MainActor context. It is safe to access @State, @Environment, or any UI-bound data. MainActor @State @Environment In contrast: Task.detached(priority: .background) {
    let profile = await userService.fetchUserProfile()
    await MainActor.run {
        name = profile.name
    }
} Task.detached(priority: .background) {
    let profile = await userService.fetchUserProfile()
    await MainActor.run {
        name = profile.name
    }
} Task.detached creates a task that does not inherit the current actor or task-local values. Task.detached not It is designed for truly independent background work. Using it improperly inside SwiftUI may cause UI updates from the wrong thread. Awaiting Task Results with .value .value You can create a task and later await its result explicitly: let task = Task {
    return try await userService.fetchUserProfile()
}

do {
    let profile = try await task.value
    print("Name:", profile.name)
} catch {
    print("Error:", error)
} let task = Task {
    return try await userService.fetchUserProfile()
}

do {
    let profile = try await task.value
    print("Name:", profile.name)
} catch {
    print("Error:", error)
} This gives you control over when the task is awaited — useful for coordination or deferring work. Common Pitfalls Swift’s concurrency is powerful, but there are a few subtle traps developers often fall into. Below are common mistakes to watch out for when working with Task and .task in real-world code. Task .task Ignoring Cancellation with try? try? This is a typical example where cancellation seems to work — but doesn't seems .task {
    try? await Task.sleep(for: .seconds(1))
    print("Still printed")
} .task {
    try? await Task.sleep(for: .seconds(1))
    print("Still printed")
} Here, the task is cancellable in theory — Task.sleep throws on cancellation — but try? suppresses the error, including CancellationError. So even if the view disappears or the task is explicitly cancelled, the error is swallowed, and the remaining code continues to execute. This makes the task look cancellable, but it won’t actually stop when it should. Always prefer do/catch over try? when cancellation matters. This makes the task look cancellable, but it won’t actually stop when it should. Always prefer do/catch over try? when cancellation matters. do/catch try? Ignoring Errors in Task This compiles: Task {
    try await userService.fetchUserProfile()
} Task {
    try await userService.fetchUserProfile()
} But if fetchUserProfile() throws, the error is ignored. The task silently fails, which can be misleading or dangerous. Always handle errors explicitly: fetchUserProfile() Task {
    do {
        try await userService.fetchUserProfile()
    } catch {
        print("Failed:", error)
    }
} Task {
    do {
        try await userService.fetchUserProfile()
    } catch {
        print("Failed:", error)
    }
} Misunderstanding Task Priority Setting a task’s priority, like .task(priority: .background), does not move the task to a background thread or actor. If launched inside a SwiftUI view, it still runs on the MainActor, unless explicitly detached. .task(priority: .background) not MainActor This is a common misconception. If you need true isolation from the UI thread, use Task.detached — and don’t forget to switch back to MainActor if you update the UI. Task.detached MainActor Conclusion Task and .task are powerful building blocks for structured, safe, and cancellable concurrency in Swift. When used properly: Task .task Use .task for view-bound async work.
Use Task {} for imperative async calls.
Use Task.detached when you truly need to isolate work from your current context.
Always handle cancellation and errors explicitly.
Use withTaskCancellationHandler when you need precise control over cleanup logic and cancelling task.
Remember that task priority is just a scheduling hint, not a thread/context control. Use .task for view-bound async work. .task Use Task {} for imperative async calls. Task {} Use Task.detached when you truly need to isolate work from your current context. Task.detached Always handle cancellation and errors explicitly. Use withTaskCancellationHandler when you need precise control over cleanup logic and cancelling task. withTaskCancellationHandler Remember that task priority is just a scheduling hint, not a thread/context control. And of course — this is far from everything you can do with Task. Its capabilities go much deeper: child tasks, task groups, task-local values, cooperative cancellation, custom executors, and more. I encourage you to experiment, read the official documentation, and explore concurrency beyond the surface level. Task official documentation

Mastering Swift Structured Concurrency: Dive into Task and Asynchronous/Await

In 2019, a robot capable of creating art was developed by Engineered Arts under the guidance of Aidan Meller. This innovation raised fascinating questions: what is art? And can something produced by a machine even be considered art? The robot, named Ai-Da, quickly gained attention for her eerie ability to create abstract and surreal images—some of which were labeled "self-portraits." She even has her own social media presence. At the time, the world watched in mild curiosity, intrigued but not alarmed. The conversation centered more on aesthetics and philosophical musings than on ownership. Nobody was too concerned about intellectual property rights or copyright laws. robot capable of creating art robot capable of creating art is But the thing about advancement is that change begets change. Fast forward to 2022, when the release of ChatGPT changed everything. Suddenly, generative AI was not just a cool tech demo; it became accessible to everyone—from hobbyists to professionals to everyday users. What seemed like a fascinating novelty quickly turned into a disruptive force in creative industries. Now, a much louder conversation is emerging, one that dives straight into legal and ethical quicksand: who owns AI-generated art? Does ownership lie with the user who prompted the algorithm? The developer who wrote the code? Or the company that owns the model? And what about the artists whose original works were scraped to train these systems? Studio Ghibli Embroiled in an IP Struggle Generative AI tools such as ChatGPT, CGDream, DALL·E, and Midjourney have completely redefined what it means to be an artist. With just a few lines of text, users can produce illustrations, paintings, and even animations that mimic the styles of established artists and studios. The controversy surrounding AI-generated images that mimic the distinctive aesthetic of Studio Ghibli has become a flashpoint in this larger debate. CGDream CGDream Online forums and social media are flooded with Ghibli-inspired AI artworks—some so convincing that fans mistake them for unreleased concept art. While impressive, this has sparked outrage among creatives who see it as theft, plain and simple. Advocates are now calling for tighter regulations to protect artists' rights in the age of machine-made media. Mr. Chua, an intellectual property lawyer, explained to The Straits Times that “no one can claim exclusive rights over a style,” citing a notable legal precedent involving musician Ed Sheeran, who was sued for allegedly copying another song. The case was ultimately dismissed because chord progressions—like art styles—cannot be copyrighted. However, this doesn’t mean artists are without recourse. If AI-generated content crosses the line into direct reproduction of copyrighted elements (like specific characters or original compositions), that’s when legal trouble begins. The Straits Times The Case for Ethical Oversight As one of the most beloved animation studios in the world, many creatives hope Studio Ghibli will lead the charge in confronting the unchecked rise of AI art. The concern isn’t just about imitation—it’s about dilution of creative labor. Artists who spend years refining their craft are now competing with machines that can mimic their styles in seconds, without credit, compensation, or consent. Just as OpenAI faces lawsuits for training language models on copyrighted materials without permission—such as the one from Scarlett Johansson, who alleges her voice was used to train a voice assistant without her consent—visual artists are demanding similar accountability. But so far, unless an AI blatantly recreates iconic characters like Totoro or Princess Mononoke, current copyright law remains toothless. Who Owns AI-Generated Images? Here lies the core dilemma. In most jurisdictions, copyright is only granted to works created by humans. This legal principle was reinforced in the infamous “monkey selfie” case, where a macaque used a photographer’s camera to take a selfie. The courts ruled that because the image was not created by a human, it wasn’t eligible for copyright. So, if AI isn’t human—and if it autonomously generates a piece of art—then who can own the rights to that creation? Currently, many AI platforms state in their terms of service that users have rights to the content they generate. However, this is more of a policy stance than a legal one. The truth is, there’s still no global legal framework that definitively answers this question. And if we really dig deeper, another issue bubbles to the surface: the dataset. These AI systems are trained on millions (if not billions) of images scraped from the internet, often without the consent of the original creators. This includes everything from amateur sketches on DeviantArt to professional illustrations from licensed portfolios. users users If an AI learns to draw “like” a particular artist by analyzing thousands of their works, is that theft or inspiration? And should that artist be entitled to royalties or recognition when their style is emulated? The Grey Zone: Copyright, Culture, and the Future Right now, the legal consensus leans toward considering AI-generated images as public domain—meaning nobody owns them. But this could change as litigation continues and as governments begin crafting new legislation. Some countries are already taking tentative steps. The European Union, for example, has introduced draft AI regulations that include transparency requirements for generative systems. Meanwhile, the U.S. Copyright Office has begun rejecting registrations for AI-generated content unless there is “significant human authorship.” This leaves us in a grey zone—a sort of copyright limbo where users believe they own what they create, platforms claim limited liability, and original artists have little to no recourse. It's a situation that benefits tech companies the most, while creatives are left to navigate an evolving digital Wild West. What Happens Next? The path forward is murky. Any meaningful solution will likely require an unprecedented level of international cooperation. After all, the internet doesn’t recognize borders, and neither does AI-generated content. For copyright laws to truly be effective in this space, they’ll need to be enforceable globally—a challenge that has never been successfully met before. enforceable enforceable In the meantime, artists are fighting back in the ways they can: through lawsuits, public campaigns, and even by creating “poisoned” data that disrupts how AI systems train on their work. There’s also a growing call for new forms of digital licensing that would allow artists to opt in—or opt out—of having their work used to train generative systems. Whether AI art becomes a democratizing force or a destructive one depends on how we, as a society, choose to regulate it. One thing is clear: the conversation is only just beginning, and the rules of ownership in the digital age are still being written. This story was authored under HackerNoon’s Business Blogging Program. This story was authored under HackerNoon’s Business Blogging Program. This story was authored under HackerNoon’s Business Blogging Program. This story was authored under HackerNoon’s Business Blogging Program. Business Blogging Program

Who Owns AI Art? A Deep Dive into Copyright, Intellectual Property, and Liability

من يملك الفن الفني؟ غرق عميق في حقوق الملكية الفكرية والمسؤولية

¿Quién posee el arte de la IA? una profundización en el derecho de autor, la propiedad intelectual y la responsabilidad

Tko posjeduje umjetnost AI? duboko uranjanje u autorsko pravo, intelektualno vlasništvo i odgovornost

Chi possiede l'arte AI? una profonda immersione in copyright, proprietà intellettuale e responsabilità

누가 AI 예술을 소유합니까? 저작권, 지적 재산권 및 책임에 대한 깊은 몰입

Quem possui a arte de IA? um mergulho profundo em direitos autorais, propriedade intelectual e responsabilidade

Cine deține arta AI? o scufundare profundă în drepturile de autor, proprietatea intelectuală și răspunderea

Кто владеет искусством ИИ? глубокое погружение в авторское право, интеллектуальную собственность и ответственность

AI Art අයිති කාටද? Copyright, Intellectual Property, and Liability වෙත ගැඹුරු පිපිරීමක්

Nani ni mmiliki wa sanaa ya AI? Uvuvi wa kina katika mamlaka, mali ya akili, na wajibu

📰 How Did Bybit Recover So Quickly After a Major Hack? 📰 How Did Bybit Recover So Quickly After a Major Hack? When a major cryptocurrency exchange faces a hack, the aftermath can often last for months, if not years. So how did Bybit—the world’s second-largest crypto exchange by trading volume—manage to regain market confidence and recover trading volume within weeks? Bybit second-largest crypto exchange A new report by digital asset research firm Block Scholes explores the incident and what it calls a “sharp but brief” liquidity disruption on Bybit, analyzing the exchange's market recovery through technical indicators, spot data, and peer comparisons. The data reveals a combination of early infrastructure deployment and tactical innovations aimed at protecting retail traders. new repor A Short-Lived Disruption: The Immediate Aftermath In the wake of the hack, Bybit saw trading volumes drop and order book depth thin out. Block Scholes attributes this to both the security breach and an ongoing market downturn. However, the disruption was temporary. The report states: “While the hack triggered a sharp but brief disruption in volumes and order book depth — particularly in the BTC and ETH markets — bid-ask spreads across major tokens remained largely intact.” “While the hack triggered a sharp but brief disruption in volumes and order book depth — particularly in the BTC and ETH markets — bid-ask spreads across major tokens remained largely intact.” Bybit's ability to keep bid-ask spreads stable despite thinner order books hinted at an underlying mechanism countering the market volatility. That mechanism, the report shows, was already in motion before the hack even took place. What is RPI and Why Did It Matter? Retail Price Improvement (RPI) orders—rolled out just days before the hack—became central to Bybit’s recovery. RPI is designed to provide retail traders a better price execution by building a separate liquidity pool. Institutional and algorithmic traders are excluded from this channel. “Aided by the timely rollout of Retail Price Improvement (RPI) orders, Bybit stabilized liquidity conditions and began to rebuild its share of overall crypto spot trading,” the report noted. “Aided by the timely rollout of Retail Price Improvement (RPI) orders, Bybit stabilized liquidity conditions and began to rebuild its share of overall crypto spot trading,” the report noted. This design allows everyday users transacting through the Bybit interface to access deeper liquidity, often getting a better deal than automated or institutional traders. According to the data, by late March, RPI orders made up more than 50% of the order book depth at key price points in assets like BTC, ETH, SOL, and DOGE. The RPI pool is built with three layers of liquidity—bid and ask—enhancing both price discovery and execution speed. According to Bybit's internal analytics: "RPI orders dominated price zones within 5–10 basis points of the mid-price, accounting for up to 30% of the liquidity in those levels." "RPI orders dominated price zones within 5–10 basis points of the mid-price, accounting for up to 30% of the liquidity in those levels." For retail traders, this translated into tighter spreads and more favorable execution conditions, even in post-incident volatility. Learn more about RPI Orders on Bybit. Learn more about RPI Orders on Bybit Market Share Recovery and Liquidity Metrics Bybit’s share of the spot trading market dropped to 4% following the hack but recovered to 7% by early April, according to Block Scholes. The exchange also restored healthy order book depth and normalized spreads on its major pairs, including BTCUSDT and ETHUSDT. Technical indicators tracked during this period included 3-stall liquidity measurements, a metric that evaluates available depth across three price levels. Block Scholes' analysis shows RPI orders consistently improved liquidity at these levels. “The order book metrics returned to pre-incident benchmarks within a week,” the report said. “The order book metrics returned to pre-incident benchmarks within a week,” the report said. This is a relatively fast timeline in the aftermath of a cyberattack, especially considering the typical hesitancy among market makers and large traders following breaches. View full Bybit data from Feb 24–Apr 1, 2025 View full Bybit data from Feb 24–Apr 1, 2025 Peer Comparison and the Bigger Picture Compared to other exchanges that have experienced similar disruptions, Bybit’s response stands out for its use of technical tools to protect retail order flow. Where other platforms may rely on external liquidity providers or restrict trading after hacks, Bybit took the opposite approach—enhancing access and reducing friction. Block Scholes’ study benchmarks Bybit against peers by analyzing liquidity recovery curves. While the names of competing exchanges are anonymized in the public version of the report, the relative slope of Bybit’s recovery curve suggests a steeper rebound than most. This raises questions about whether retail-focused tools like RPI might become standard across other centralized exchanges. A Smart Play with RPI, But Sustainability Remains the Question Bybit’s handling of the crisis shows the strength of having systems like RPI in place—not just as product features, but as strategic safeguards. The exchange’s ability to quickly normalize market behavior without resorting to heavy PR or marketing spin is notable. Bybit must maintain a careful balance between retail friendliness and institutional participation. The bigger challenge is ensuring that such innovations aren’t just reactive but form part of a proactive resilience strategy. Crypto markets are volatile by design. Exchanges that can evolve their liquidity infrastructure without waiting for crises will likely lead the next era. Final Thoughts Bybit’s recovery provides a compelling case study in crypto market resilience. With the help of technical innovations like RPI, the exchange managed to stabilize order books and regain lost ground in a matter of weeks. The Block Scholes report offers a rare view into how deep liquidity mechanics and strategic tooling can shape post-crisis outcomes in digital finance. In a sector prone to turbulence, the lesson here is clear: infrastructure—not hype—is what carries platforms through the storm. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO business blogging program business blogging program

How Bybit Rebounded from a Major Hack & Regained Market Share: A Dive into Block Scholes’ Report

Cómo Bybit Rebounded de un mayor hack & Regained Market Share: Una inmersión en el informe de Block Scholes

Kuinka Bybit Rebounded suuresta hakkeroinnista ja palautti markkinaosuuden: Sukellus Block Scholesin raporttiin

How Bybit Rebounded from a Major Hack & Regained Market Share: A Dive into Block Scholes’ Reportの記事一覧

How Bybit Rebounded from a Major Hack & Regained Market Share: A Dive into Block Scholes’ ანგარიში

ວິທີທີ່ Bybit Rebounded ຈາກ Hack Major & Regained Market Share: A Dive into Block Scholes' Report

यो बर्ष महानगरको अभियान — सम्पदा संरक्षण तथा सम्बर्धन, पर्यटन प्रवर्धनका लागि पूर्वाधार Next Post

How Bybit Rebounded from a Major Hack & Regained Market Share: A Dive into Block Scholes' Report

Cloud migration involves many complex steps and requires strategic vision, technical cloud expertise, and a clear understanding of both the existing infrastructure and target state environment. This article explores some of the proven migration techniques that can serve as a guide for any cloud migration journey. I. The 6 R's Approach for Cloud Migration I. The 6 R's Approach for Cloud Migration The "6 R's" framework is an industry-standard approach for cloud migrations and provides a great foundation for organizations looking to undertake this journey: Rehost (lift and shift)
Replatform (lift, tinker, and shift)
Repurchase (drop and shop)
Refactor/Re-architect
Retire
Retain Rehost (lift and shift) Replatform (lift, tinker, and shift) Repurchase (drop and shop) Refactor/Re-architect Retire Retain Among these approaches replatforming involves the most technical complexity, so we will explore it further here and try to unpack the complexity. II. Technical Implementation of Replatforming II. Technical Implementation of Replatforming Replatforming includes not only moving resources to the target environment but also making optimizations during the migration process. A common example to consider here is migrating an on-premises database to a managed cloud database instance. The implementation approach shown below with a streaming data integration and change data capture technology minimizes disruptions and risks. The above approach demonstrates effective handling of the complex process of database migration, while also considering some of the non-functional aspects like: •	Batch processing to prevent memory overload •	Error handling and logging •	Data verification III. Containerization to enable Cloud Migration III. Containerization to enable Cloud Migration Containerization simplifies cloud migrations by providing consistency across environments. Converting monolithic applications to containerized services can serves as a great starter in the process of completing full cloud-native refactoring. The below flowchart depicts the detailed steps to be followed in the containerization process. IV. Refactoring to Cloud-Native Architecture IV. Refactoring to Cloud-Native Architecture While refactoring is the most complex migration strategy, it offers the greatest long-term benefit and ROI for firms. Refactoring is also an opportunity for teams to adopt a cloud native architecture and decompose their monolithic applications into microservices. V. Data Migration Approaches V. Data Migration Approaches Data migration often involves large volumes of data and is one of the trickiest pieces in cloud migration. It is crucial to design an optimal approach based on key parameters such as data volume, RTO, RPO, and rules. Shown below are some of the standard approaches used in large scale data migrations and guidance on where they fit best. VI. Performance Monitoring and Optimization VI. Performance Monitoring and Optimization Post-migration monitoring of the cloud environment and transactions is crucial not only for debugging, but also for addressing performance issues and scaling the infrastructure as needed. This needs a consistent approach that ties together the application layer, the logging infrastructure and the analytics platform as shown below. VII. Conclusion VII. Conclusion A successful cloud migration requires the right blend of strategic vision and technical expertise. The migration techniques depicted in this article provide a foundation to address complex migration challenges. But a detailed understanding of the specific requirements and careful selection of the migration strategies, is crucial to minimize risks while maximizing the benefits of cloud adoption. When planning a cloud migration, it is recommended to prioritize these critical success factors: Thorough assessment of existing applications and infrastructure landscape
Selection of appropriate migration strategies for each type of workload
Implementation of a robust testing process
Continuous monitoring and optimization post-migration Thorough assessment of existing applications and infrastructure landscape Selection of appropriate migration strategies for each type of workload Implementation of a robust testing process Continuous monitoring and optimization post-migration The cloud technologies will continue to evolve as they always have, so a regular assessment of your architecture and leveraging new cloud-native capabilities will help ensure that your cloud infrastructure remains optimized for both performance and cost.

A Technical Deep Dive into Cloud Migration Techniques

Authors:
(1) Senthujan Senkaiahliyan M. Mgt, is with the Institute for Health Policy Management and Evaluation, Faculty of Public Health, University of Toronto and Peter Munk Cardiac Centre, University Health Network, Toronto ON, Canada;
(2) Augustin Toma MD, is with the Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada;
(3) Jun Ma PhD, is with Peter Munk Cardiac Centre, University Health Network; Department of Laboratory Medicine and Pathobiology, University of Toronto; Vector Institute, Toronto, ON Canada;
(4) An-Wen Chan MD, is with the Institute for Health Policy Management and Evaluation, Faculty of Public Health and with the Division of Dermatology, Department of Medicine, University of Toronto, Toronto, ON, Canada;
(5) Andrew Ha MD, is with Peter Munk Cardiac Centre, University Health Network and the Division of Cardiology, Department of Medicine, University of Toronto, Toronto, ON, Canada;
(6) Kevin R. An MD, is with the Division of Cardiac Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada;
(7) Hrishikesh Suresh MD, is with the Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada;
(8) Barry Rubin MD, is with Peter Munk Cardiac Centre, University Health Network and the Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada;
(9) Bo Wang PhD (Corresponding Author) is with Peter Munk Cardiac Centre, University Health Network; Department of Laboratory Medicine and Pathobiology and Department of Computer Science, University of Toronto; Vector Institute, Toronto, Canada. E-mail: bowang@vectorinstitute.ai. Authors: Authors: (1) Senthujan Senkaiahliyan M. Mgt, is with the Institute for Health Policy Management and Evaluation, Faculty of Public Health, University of Toronto and Peter Munk Cardiac Centre, University Health Network, Toronto ON, Canada; (2) Augustin Toma MD, is with the Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, ON, Canada; (3) Jun Ma PhD, is with Peter Munk Cardiac Centre, University Health Network; Department of Laboratory Medicine and Pathobiology, University of Toronto; Vector Institute, Toronto, ON Canada; (4) An-Wen Chan MD, is with the Institute for Health Policy Management and Evaluation, Faculty of Public Health and with the Division of Dermatology, Department of Medicine, University of Toronto, Toronto, ON, Canada; (5) Andrew Ha MD, is with Peter Munk Cardiac Centre, University Health Network and the Division of Cardiology, Department of Medicine, University of Toronto, Toronto, ON, Canada; (6) Kevin R. An MD, is with the Division of Cardiac Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada; (7) Hrishikesh Suresh MD, is with the Division of Neurosurgery, Department of Surgery, University of Toronto, Toronto, ON, Canada; (8) Barry Rubin MD, is with Peter Munk Cardiac Centre, University Health Network and the Division of Vascular Surgery, Department of Surgery, University of Toronto, Toronto, ON, Canada; (9) Bo Wang PhD (Corresponding Author) is with Peter Munk Cardiac Centre, University Health Network; Department of Laboratory Medicine and Pathobiology and Department of Computer Science, University of Toronto; Vector Institute, Toronto, Canada. E-mail: bowang@vectorinstitute.ai. Table of Links Abstract and 1. Introduction GPT-4V(ision) Abstract and 1. Introduction GPT-4V(ision) 2. Data Collection 2. Data Collection 3. Experimental Setup 3. Experimental Setup 4. Results and References 4. Results and References 5. Discussion and Limitations, and References 5. Discussion and Limitations, and References Supplementary Notes Supplementary Notes Abstract OpenAI’s large multimodal model, GPT-4V(ision), was recently developed for general image interpretation. However, less is known about its capabilities with medical image interpretation and diagnosis. Board-certified physicians and senior residents assessed GPT-4V’s proficiency across a range of medical conditions using imaging modalities such as CT scans, MRIs, ECGs, and clinical photographs. Although GPT-4V is able to identify and explain medical images, its diagnostic accuracy and clinical decision-making abilities are poor, posing risks to patient safety. Despite the potential that large language models may have in enhancing medical education and delivery, the current limitations of GPT-4V in interpreting medical images reinforces the importance of appropriate caution when using it for clinical decision-making. 1. INTRODUCING GPT-4V(ISION) This past year, large language models (LLMs) demonstrated impressive capabilities to perform numerous language-based tasks. They have shown capability in analyzing text, discerning patterns, and establishing connections between words [1]. As a result, they can generate outputs that align with the prompts provided. While LLMs have expressed strong performance in expert-level medical question answering, they are still unable to outperform their clinician counterparts especially in scenarios that require reasoning capabilities [2]. Generative Pre-Trained Transformer Vision (GPT-4V) is OpenAI’s first large multimodal model with the ability to accept image input alongside text. [3] Multimodal learning is the ability for machine learning models to be trained on and input multiple forms of input data. They have the potential to enhance the breadth and depth of tasks that LLMs can perform across various medical disciplines. [4] To evaluate GPT-4V’s proficiency in analyzing medical images, we conducted an evaluation involving senior residents and board-certified physicians to assess its capability to accurately interpret various medical conditions and provide accurate and useful information regarding the diagnosis and management of these conditions. The study aimed to assess whether GPT-4V could not only interpret medical images but also provide valuable information for diagnosis, management, and education. Finally, we aimed to evaluate if the resulting outputs align with the safety standards for patient care. 2. DATA COLLECTION 2.1 General Conditions In the data collection phase, a diverse set of multimodal medical images were gathered to assess the performance of GPT-4V across various medical scenarios and specialties. The breakdown of multimodal images is presented in Table 1, showcasing different modalities and their respective counts. These images were sourced from open-source libraries and repositories found on the internet. 2.2 Cardiology The dataset used was a set of ECG waveforms sourced from the ECG Wave-Maven: A Self-Assessment Program for Students and Clinicians[1]. These ECG images cover various cardiac conditions and serve as a representative dataset for evaluating GPT-4V’s interpretation of ECG’s. 2.3 Dermatology In dermatology, clinical photos were collected from the Hellenic Dermatological Atlas[2], to curate a comprehensive set of dermatological conditions for assessing GPT-4V’s performance in interpretation. 3. EXPERIMENTAL SETUP The methodology employed for this comprehensive evaluation followed a structured four-phase approach. 3.1 Dataset Curation A diverse range of medical images and corresponding labels were selected from public datasets, encompassing various diagnostic modalities such as patient clinical photos, radiological images, ECG traces, EEG, fundoscopy, endoscopy, and colonoscopy. GPT-4V analyzed these images based on the prompts. The combined prompts, images, and the model’s output were captured as a screenshot to be placed on the evaluation platform for assessment. 3.2 Evaluation Criteria A dual approach was adopted to assess the accuracy and reliability of GPT-4V’s interpretations. All images were evaluated by two senior surgical residents (K.R.A, H.S.) and a board-certified internal medicine physician (A.T.). ECGs and clinical photos of dermatologic conditions were additionally evaluated by a board-certified cardiac electrophysiologist (A.H.) and dermatologist (A.C.), respectively. The following below are the questionnaires used for the evaluation General Conditions (Diverse Modalities): General Conditions (Diverse Modalities): • 1) Rate the answer from 1-5. • 2) Rate from 1-5 how comfortable you would be letting a medical student rely on this content to help learning. • 3) Was the image interpreted correctly? (Yes/No) • 4) Was the advice correct? (Yes/No) • 5) Was the advice given dangerous? (Yes/No) Cardiology (ECGs): Cardiology (ECGs): • 1) Rate the overall interpretation of the ECG (1-5). • 2) Compared to a standard automated read of an ECG, would you consider this interpretation more competent? (Yes/No) • 3) Rate from 1-5 how comfortable you would be letting a medical student rely on this content to help learning. • 4) Would this interpretation be helpful in a medical student’s learning? (Yes/No) • 5) General Comments: Dermatology (Clinical Photos): Dermatology (Clinical Photos): • 1) Rate the quality of the layman’s description of the rash (1-5) • 2) Rate the quality of the medical description of the rash (1-5) • 3) Rate the quality of the differential diagnosis (1-5) • 4) General Comments 3.3 Evaluation Platform Figure 1 provides a visual representation of the evaluation platform, designed as a web-based form. • Left Side: Displayed the image and GPT-4V’s Prompts/Outputs as screenshots as well as the correct label for the image. • Left Side: • Right Side: Comprised a section for evaluator feedback, including questions from the evaluation criteria. • Right Side: 3.4 Prompts The prompts that were used by GPT-4V to generate outputs are listed below for each category. General Conditions (Diverse Modalities): General Conditions (Diverse Modalities): •”You are an expert [insert specialty i.e. radiology, cardiology] tutor. Explain to a medical student what this patient photo indicates.” •”If indicators like arrows, asterisks, or circles are present in the image, underline and expand on their significance.” Cardiology (ECGs): Cardiology (ECGs): •”You are an expert cardiology tutor assisting a medical student. Provide a detailed medical interpretation of the ECG, covering rhythm, rate, axis, evidence of ischemia, hypertrophy, or other clinically significant findings. Finally, list a differential diagnosis based on the ECG findings.” Dermatology (Clinical Photos): • ”You are an expert dermatology tutor helping a medical student. Describe the rash seen in the photo in layman’s terms. Next, describe it using medical terminology. Finally, list a differential diagnosis for the given image.” This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv https://ecg.bidmc.harvard.edu/maven/mavenmain.asp


http://www.hellenicdermatlas.com/en/ https://ecg.bidmc.harvard.edu/maven/mavenmain.asp https://ecg.bidmc.harvard.edu/maven/mavenmain.asp https://ecg.bidmc.harvard.edu/maven/mavenmain.asp http://www.hellenicdermatlas.com/en/ http://www.hellenicdermatlas.com/en/ http://www.hellenicdermatlas.com/en/

Can GPT-4V Diagnose? A Deep Dive into AI’s Medical Imaging Capabilities

Without state management, Terraform is as good as normal CLI commands... so think about it now ! Without state management, Terraform is as good as normal CLI commands... so think about it now ! Normally, while developing Terraform scripts, state management is the last topic on most developers' minds. Only after development is complete does the thinking process related to state management start. This is not an optimal practice. State management is extremely crucial for the success of infrastructure as code using Terraform. State management is important because the state file contains all the sensitive information in plain text format. It cannot be stored as part of the code in any version control system. It should be protected and secured heavily with all authentication, authorization, confidentiality and integrity. State should also be stored centrally if multiple developers and operators are involved in managing the environment provisioned through Terraform. Having the Terraform state in a single developer’s machine will not let others manage the environment. Terraform also uses this state to check which resources need to be added, removed or updated. There are more secondary reasons why Terraform state is important however, it is equally important to have a strategy to manage Terraform state in a way that allows Collaboration between teams and developers
Ability for Terraform to access and manage the resources
Treat it as a highly secure and confidential resource containing sensitive information to be protected at any cost. Collaboration between teams and developers Ability for Terraform to access and manage the resources Treat it as a highly secure and confidential resource containing sensitive information to be protected at any cost. Let’s get into the details of state management in Terraform with Azure Cloud Strategy: Hierarchical State Organization Most developers start with a simple Azure Storage backend configuration: terraform {
  backend "azurerm" {
    resource_group_name  = "terraform-state-rg"
    storage_account_name = "tfstate0123456789"
    container_name       = "tfstate"
    key                  = "prod.terraform.tfstate"
  }
} terraform {
  backend "azurerm" {
    resource_group_name  = "terraform-state-rg"
    storage_account_name = "tfstate0123456789"
    container_name       = "tfstate"
    key                  = "prod.terraform.tfstate"
  }
} This strategy can be implemented with dynamic backend configuration: locals {
  environment = terraform.workspace
  region      = "eastus2"
  component   = "networking"
}

terraform {
  backend "azurerm" {
    resource_group_name  = "terraform-state-rg"
    storage_account_name = "tfstate0123456789"
    container_name       = "tfstate"
    key                  = "${local.environment}/${local.region}/${local.component}/terraform.tfstate"
  }
} locals {
  environment = terraform.workspace
  region      = "eastus2"
  component   = "networking"
}

terraform {
  backend "azurerm" {
    resource_group_name  = "terraform-state-rg"
    storage_account_name = "tfstate0123456789"
    container_name       = "tfstate"
    key                  = "${local.environment}/${local.region}/${local.component}/terraform.tfstate"
  }
} This hierarchical approach provides several benefits: Logical organization that maps to your Azure architecture
Clearer separation of concerns
Easier disaster recovery
Better alignment with team structures Logical organization that maps to your Azure architecture Clearer separation of concerns Easier disaster recovery Better alignment with team structures Strategy: Multi-Workspace State Isolation for Development Teams For large engineering teams, workspace-based isolation provides better control: # Create development workspace for Team A
terraform workspace new team_a_dev

# Create development workspace for Team B
terraform workspace new team_b_dev

# List available workspaces
terraform workspace list # Create development workspace for Team A
terraform workspace new team_a_dev

# Create development workspace for Team B
terraform workspace new team_b_dev

# List available workspaces
terraform workspace list The workspaces can be combined with Azure RBAC for granular access control: # Create a custom role for Team A state access
az role definition create --role-definition '{
    "Name": "Team A Terraform State Access",
    "Description": "Can read and write Team A terraform state",
    "Actions": [
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/read",
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/write",
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/lease/action"
    ],
    "DataActions": [
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/read",
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/write",
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/lease/action"
    ],
    "AssignableScopes": [
        "/subscriptions/{subscription-id}/resourceGroups/terraform-state-rg"
    ]
}'

# Assign the role to Team A
az role assignment create \
  --role "Team A Terraform State Access" \
  --assignee-object-id {team-a-group-id} \
  --scope "/subscriptions/{subscription-id}/resourceGroups/terraform-state-rg/providers/Microsoft.Storage/storageAccounts/tfstate0123456789/blobServices/default/containers/tfstate/blobs/team_a_dev" # Create a custom role for Team A state access
az role definition create --role-definition '{
    "Name": "Team A Terraform State Access",
    "Description": "Can read and write Team A terraform state",
    "Actions": [
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/read",
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/write",
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/lease/action"
    ],
    "DataActions": [
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/read",
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/write",
        "Microsoft.Storage/storageAccounts/blobServices/containers/blobs/lease/action"
    ],
    "AssignableScopes": [
        "/subscriptions/{subscription-id}/resourceGroups/terraform-state-rg"
    ]
}'

# Assign the role to Team A
az role assignment create \
  --role "Team A Terraform State Access" \
  --assignee-object-id {team-a-group-id} \
  --scope "/subscriptions/{subscription-id}/resourceGroups/terraform-state-rg/providers/Microsoft.Storage/storageAccounts/tfstate0123456789/blobServices/default/containers/tfstate/blobs/team_a_dev" Strategy: State Encryption While Azure Storage provides encryption at rest, sensitive environments might require additional protection: # Generate a customer-managed key in Azure Key Vault
resource "azurerm_key_vault_key" "terraform_state_key" {
  name         = "terraform-state-encryption-key"
  key_vault_id = azurerm_key_vault.terraform_kv.id
  key_type     = "RSA"
  key_size     = 2048
  key_opts     = ["decrypt", "encrypt", "sign", "verify"]
}

# Configure the storage account to use this key
resource "azurerm_storage_account_customer_managed_key" "terraform_state" {
  storage_account_id = azurerm_storage_account.terraform_state.id
  key_vault_id       = azurerm_key_vault.terraform_kv.id
  key_name           = azurerm_key_vault_key.terraform_state_key.name
} # Generate a customer-managed key in Azure Key Vault
resource "azurerm_key_vault_key" "terraform_state_key" {
  name         = "terraform-state-encryption-key"
  key_vault_id = azurerm_key_vault.terraform_kv.id
  key_type     = "RSA"
  key_size     = 2048
  key_opts     = ["decrypt", "encrypt", "sign", "verify"]
}

# Configure the storage account to use this key
resource "azurerm_storage_account_customer_managed_key" "terraform_state" {
  storage_account_id = azurerm_storage_account.terraform_state.id
  key_vault_id       = azurerm_key_vault.terraform_kv.id
  key_name           = azurerm_key_vault_key.terraform_state_key.name
} For even more sensitive environments, we can implement client-side encryption as well: # pre-commit hook example
import json
import os
from cryptography.fernet import Fernet

# Encrypt state before it's pushed to remote
def encrypt_state():
    # Read encryption key from secure location
    with open('.terraform-key', 'rb') as key_file:
        key = key_file.read()
    
    fernet = Fernet(key)
    
    # Read the state file
    with open('.terraform/terraform.tfstate', 'rb') as state_file:
        state_data = state_file.read()
    
    # Encrypt the state
    encrypted_state = fernet.encrypt(state_data)
    
    # Write encrypted state
    with open('.terraform/terraform.tfstate.encrypted', 'wb') as encrypted_file:
        encrypted_file.write(encrypted_state)
    
    print("State encrypted successfully")

if __name__ == "__main__":
    encrypt_state() # pre-commit hook example
import json
import os
from cryptography.fernet import Fernet

# Encrypt state before it's pushed to remote
def encrypt_state():
    # Read encryption key from secure location
    with open('.terraform-key', 'rb') as key_file:
        key = key_file.read()
    
    fernet = Fernet(key)
    
    # Read the state file
    with open('.terraform/terraform.tfstate', 'rb') as state_file:
        state_data = state_file.read()
    
    # Encrypt the state
    encrypted_state = fernet.encrypt(state_data)
    
    # Write encrypted state
    with open('.terraform/terraform.tfstate.encrypted', 'wb') as encrypted_file:
        encrypted_file.write(encrypted_state)
    
    print("State encrypted successfully")

if __name__ == "__main__":
    encrypt_state() Conclusion Effective state management is the foundation of successful Terraform deployments in Azure. The strategies outlined here move beyond basic storage configuration to address enterprise concerns: Security: Multi-layered protection for sensitive state data


Governance: Controlled access and monitoring across teams


Resilience: Backup, versioning, and disaster recovery


Scalability: Patterns for growing beyond a single team or application Security: Multi-layered protection for sensitive state data Security: Multi-layered protection for sensitive state data Security Governance: Controlled access and monitoring across teams Governance: Controlled access and monitoring across teams Governance Resilience: Backup, versioning, and disaster recovery Resilience: Backup, versioning, and disaster recovery Resilience Scalability: Patterns for growing beyond a single team or application Scalability: Patterns for growing beyond a single team or application Scalability By implementing these advanced techniques, you can build a state management approach that supports even the most complex Azure deployments while maintaining security and operational efficiency. Remember that state files are the single source of truth for your infrastructure – treat them with the same care you would apply to your most critical database or application code.

Terraform State Management: A Deep Dive Beyond the Basics for Azure Deployments

What happens when market volatility spikes and early-stage tokens scramble for attention? Picture a crowded room where only the loudest voices get heard—smaller projects, no matter their potential, often fade into the background. WOO X Research, the analytical wing of centralized crypto trading platform WOO X, flagged a growing issue: a liquidity squeeze is gripping early-stage tokens. WOO X Research WOO X With US fiscal shifts and global uncertainties stirring the pot, underfunded projects find themselves outmatched by their better-funded peers. The crypto market has never been a level playing field. Volatility, a constant companion, has intensified lately, pushing smaller tokens to the sidelines. WOO X Research points to cases like TRUMP, a token that ballooned over 100x in days during its peak. While that kind of surge grabs headlines, it also casts a shadow over less-resourced projects struggling for visibility. Liquidity, the lifeblood of any token, becomes harder to secure when attention and capital flow to the top dogs. Enter WOO X Swap Spotlight, a new feature launched under the platform’s ‘Markets’ section, sitting alongside Spot and Futures. This isn’t just another trading tool—it’s a lifeline for early-stage tokens. Swap Spotlight lets users trade these assets with real-time price quotes from market makers, ensuring instant execution. Unlike decentralized exchanges (DEXes), where slippage can erode trades, this setup locks in the price you see. These tokens start exclusive to Swap Spotlight within WOO X, separate from spot or futures markets. If they build momentum, they might graduate to broader trading options. Pat Zhang, Head of Research at WOO X, frames the problem plainly: “Valuable early-stage projects struggle to gain traction due to the liquidity squeeze in today’s volatile market.” He ties it back to the TRUMP example—when a single token dominates the spotlight, others can’t break through. Swap Spotlight aims to flip that script, offering traders early access to promising tokens while giving those projects a shot at liquidity and exposure beyond the usual onchain channels. The mechanics are straightforward but deliberate. Bryan Chu, Chief Strategy Officer at WOO X, explains: “Swap Spotlight uses an RFQ model—Request for Quote. The price is guaranteed, no slippage, just what’s on the screen.” Think of it like over-the-counter trading: your buy or sell doesn’t ripple out to skew the market price, unlike standard orders that can shift the board. This stability matters for traders chasing opportunities in uncharted waters. What sets Swap Spotlight apart is curation. The WOO X Research team handpicks the tokens, leaning on community buzz, market trends, and raw traction. It’s not a free-for-all—only a select roster makes the cut. Market makers back these assets with live quotes, smoothing out the trading process. For users, it’s a chance to get in early, before a token’s price fully settles into the market’s consciousness. For the projects, it’s a stage to prove their worth. This isn’t about hype or promises, though. WOO X frames Swap Spotlight as an educational tool, not a golden ticket. It doesn’t guarantee a token will list elsewhere on the platform or skyrocket in value. Users need to dig in, do their homework, and weigh the risks. The crypto space, after all, is a minefield of volatility and uncertainty—early-stage tokens doubly so. Final Thoughts The liquidity squeeze isn’t a new story, but it’s hitting harder as volatility ramps up. WOO X Swap Spotlight doesn’t solve every problem—small projects still face an uphill climb against well-funded giants. Yet, it’s a practical step, bridging a gap between traders hunting fresh opportunities and tokens desperate for a foothold. Whether it reshapes the early-stage token landscape depends on execution and uptake. For now, it’s a tool worth watching, a potential equalizer in a market that rarely plays fair. Traders and projects alike should take note: in a volatile world, visibility and liquidity are currencies of their own. Don’t forget to like and share the story! Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYO Vested Interest Disclosure: business blogging program business blogging program

Can Early-Stage Tokens Survive the Liquidity Crunch? WOO X Dives Into the Challenge

¿Podrán los tokens en fase inicial sobrevivir a la crisis de liquidez? WOO X analiza el desafío.

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Èske Tokens Early-Stage siviv crunch la likidite? WOO X plonje nan defi a

I token in fase iniziale possono sopravvivere alla crisi di liquidità? WOO X si tuffa nella sfida

初期段階のトークンは流動性危機を乗り越えられるか？WOO X が挑戦に挑む

შეიძლება დასაწყისში ტოკები გაგრძელდეს liquidity crunch? WOO X Dives Into the Challenge

Эрт үеийн токенууд хөрвөх чадварын хямралыг даван туулж чадах уу? WOO X сорилтод шумбаж байна

महासंघमा राजनीति हाबी नहोस् भनेर वरिष्ठ उपाध्यक्षको चुनाव गराउनुभयो, तर त्यहाँ पनि समस्या एउटै देखियो होइन?

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Calaamadaha Marxaladda hore ma ka badbaadi karaan qulqulka dareeraha? WOO X U Qulqulaya Loolanka

Да ли токени у раној фази могу да преживе пад ликвидности? ВОО Кс зарони у изазов

Maaari ba ang Early-Stage Tokens Mag-survive ang Liquidity Crunch? WOO X Dives Sa Challenge

CrossFi has dropped a bombshell with its 2025 Crypto Market Research Survey—a sprawling study of over 5,000 crypto users from India, Finland, and Russia. This isn’t your typical tech-bro echo chamber. It’s a vivid snapshot of a shifting landscape: 80% women, aged 28 to 43, earning $1,000 to $3,000 monthly, and leaning hard into cryptocurrencies and Web3 banking. The data challenges stereotypes, exposes pain points, and hints at a future where digital finance could rival cash. So, who’s driving this revolution? What’s holding it back? And what needs to happen by 2025 to make crypto as seamless as a dollar bill? Let’s unpack the numbers, decode the trends, and peer into the future of money. The Survey at a Glance: Scope and Credibility CrossFi surveyed 5,000 participants across three nations—India, Finland, and Russia—chosen, presumably, for their diverse economic and technological profiles. India’s a fintech hotspot with a booming middle class; Finland’s a Nordic tech leader with high digital literacy; Russia’s a wildcard with a complex financial history. Participants weren’t random; they were crypto users, likely sourced via CrossFi’s network or partnered platforms, giving the findings a focused lens on engaged adopters. This isn’t a general population poll; it’s a pulse-check on the crypto-curious and committed. Who’s Using Crypto? A Demographic Shake-Up Gender Flip: Women Take the Lead The standout stat? 80% of respondents are women, flipping the script on crypto’s male-heavy past. Compare this to Pew Research’s 2023 finding that just 16% of U.S. crypto users were women. What’s driving this? In India, women-led financial inclusion programs—like the government’s Jan Dhan Yojana—may be funneling more women into digital assets. Finland’s gender-equal tech culture could be a factor, while Russia’s economic volatility might push women toward alternative wealth-building tools. This isn’t a fluke, it’s a trend begging for deeper global research. Age and Income: The Middle Ground Most respondents fall between 28 and 43 years old, a sweet spot for earning, saving, and investing. Their monthly incomes—$1,000 to $3,000—align with middle-income brackets in their regions, per World Bank data. In India, that’s above the national average ($180 monthly); in Finland, it’s modest but livable; in Russia, it’s solid for urban professionals. Crypto’s not just for billionaires or the unbanked—it’s resonating with the global middle class, a group hungry for financial control. Why This Matters This demographic mix hints at crypto’s broadening appeal. Women in these regions might see it as empowerment—a way to bypass traditional banking barriers. Middle-income earners, meanwhile, may view it as a hedge against inflation (India’s hit 5-6% annually) or currency instability (Russia’s ruble woes). It’s less about Lambos and more about stability. Crypto as a Lifestyle: Usage and Income Patterns Holding Over Hustling Here’s the kicker: investing or holding cryptocurrencies is the dominant income source for respondents. Forget day trading or meme-coin flips—these users are playing the long game. This echoes Fidelity’s 2024 report, where 52% of institutional investors held crypto for growth. Respondents likely buy Bitcoin or Ethereum, stash it in wallets, and watch it (hopefully) climb. Some might stake assets on platforms like Lido or lend via Aave for passive income—low-effort strategies that fit their profile. Weekly Check-Ins Most engage with crypto one to two times per week. What are they doing? Probably: Buying: Snagging dips on exchanges like Binance or Coinbase.
Holding: Monitoring wallets via apps like Trust Wallet.
Earning: Adjusting staking pools or claiming rewards. Buying: Snagging dips on exchanges like Binance or Coinbase. Holding: Monitoring wallets via apps like Trust Wallet. Earning: Adjusting staking pools or claiming rewards. This isn’t the frenetic pace of Wall Street traders. It’s deliberate—like tending a digital garden. For middle-income users, it’s a side hustle, not a full-time gig. Insight Unlocked This behavior signals crypto’s evolution from speculative toy to financial staple. It’s not about “to the moon” hype—it’s about building wealth incrementally, especially in regions where traditional investments (stocks, real estate) feel out of reach. The Fee Problem: Crypto’s Biggest Roadblock The Pain of High Costs High transaction fees top the list of challenges—and it’s a universal gripe. Ethereum’s gas fees once soared to $200 per transaction (Etherscan, 2021), and while they’ve eased, a $10-$20 hit still stings for someone earning $1,000 monthly. Bitcoin’s no picnic either—fees peaked at $60 in 2021 (BitInfoCharts). For context, sending $50 via PayPal costs pennies. Crypto’s not competitive yet. Why Fees Bite Network Congestion: Ethereum’s clogged during NFT booms; Bitcoin’s slow by design.
Mining Costs: Proof-of-work chains like Bitcoin burn energy, driving fees up.
No Middleman Savings: Decentralization’s freedom comes at a price. Network Congestion: Ethereum’s clogged during NFT booms; Bitcoin’s slow by design. Mining Costs: Proof-of-work chains like Bitcoin burn energy, driving fees up. No Middleman Savings: Decentralization’s freedom comes at a price. Solutions on the Horizon Layer 2 Scaling: Polygon and Arbitrum slash Ethereum fees to cents.
Faster Chains: Solana and Avalanche process thousands of transactions per second, cheaply.
Fee Optimization: Tools like GasNow help users time transactions for lower costs. Layer 2 Scaling: Polygon and Arbitrum slash Ethereum fees to cents. Faster Chains: Solana and Avalanche process thousands of transactions per second, cheaply. Fee Optimization: Tools like GasNow help users time transactions for lower costs. The Stakes For crypto to hit mass adoption, fees must drop. A $20 fee for a $5 coffee is a nonstarter. Respondents’ frustration underscores a broader truth: cost is king in financial inclusion. Web3 Banking: Promise Meets Pain What Users Want Web3 banking—blockchain-powered, decentralized finance—dazzles with potential: no banks, no borders, just you and your money. Respondents prioritize safety and reliability, and it’s easy to see why. Traditional banks fail (think Lehman Brothers) or freeze funds (Russia’s 2022 sanctions). Web3’s promise? Your assets, your rules—secured by code, not CEOs. The Reality Check Yet satisfaction is lukewarm—most are only “somewhat satisfied”. Why? Frozen Accounts: DeFi platforms can lock funds due to bugs (e.g., Wormhole’s $320M hack, 2023) or liquidity dries up.
KYC Clashes: Regulations demand “Know Your Customer” checks, but decentralized systems balk at central ID hubs. Uploading a passport to a blockchain? Awkward. Frozen Accounts: DeFi platforms can lock funds due to bugs (e.g., Wormhole’s $320M hack, 2023) or liquidity dries up. KYC Clashes: Regulations demand “Know Your Customer” checks, but decentralized systems balk at central ID hubs. Uploading a passport to a blockchain? Awkward. Decoding the Disconnect Web3’s ethos is trustless freedom, but reality bites. KYC is a legal must—exchanges like Binance enforce it, yet DeFi resists. Solutions like decentralized identity (Civic, SelfKey) aim to bridge this, letting users prove identity without compromising privacy. Meanwhile, frozen accounts reflect Web3’s immaturity—smart contracts need audits, and users need insurance (think Nexus Mutual). 2025 Vision: Crypto as Everyday Cash The Big Dream Respondents predict that by 2025, crypto will flow like fiat. Imagine swiping Bitcoin for groceries or tipping in Dogecoin—no delays, no fuss. It’s bold, but tech’s catching up: Lightning Network: Speeds Bitcoin to seconds, costs to pennies.
Stablecoins: USDC and Tether nix volatility, mimicking dollars.
Payment Rails: Solana Pay and Binance Pay streamline merchant use. Lightning Network: Speeds Bitcoin to seconds, costs to pennies. Stablecoins: USDC and Tether nix volatility, mimicking dollars. Payment Rails: Solana Pay and Binance Pay streamline merchant use. The Hurdles Volatility: Even with stablecoins, Bitcoin’s wild swings scare vendors.
Adoption: Only 1% of U.S. merchants take crypto (PYMNTS, 2024).
Rules: Taxes and regs vary—crypto’s an asset in the U.S., currency in El Salvador. Volatility: Even with stablecoins, Bitcoin’s wild swings scare vendors. Adoption: Only 1% of U.S. merchants take crypto (PYMNTS, 2024). Rules: Taxes and regs vary—crypto’s an asset in the U.S., currency in El Salvador. What It’ll Take UX Overhaul: Apps as slick as Venmo, not clunky wallets.
Merchant Incentives: Cashback in crypto? Lower fees than Visa?
Global Regs: The EU’s MiCA is a model—others must follow. UX Overhaul: Apps as slick as Venmo, not clunky wallets. Merchant Incentives: Cashback in crypto? Lower fees than Visa? Global Regs: The EU’s MiCA is a model—others must follow. Safety First: Cybersecurity Fears Loom Large The Threat Landscape Safety and cybersecurity dominate concerns—and for good reason. Hacks siphoned $1.7 billion in 2024 (Chainalysis). Phishing snares private keys; rug pulls tank projects; exchanges like FTX collapse. For every win, there’s a scam. User Armor Hardware Wallets: Ledger and Trezor keep keys offline—only 10% use them (2023 survey).
Multi-Sig: Requires multiple approvals, thwarting thieves.
Education: Spotting phishing or fake ICOs saves millions. Hardware Wallets: Ledger and Trezor keep keys offline—only 10% use them (2023 survey). Multi-Sig: Requires multiple approvals, thwarting thieves. Education: Spotting phishing or fake ICOs saves millions. Industry Fixes Better UX: Biometric logins or social recovery (Argent wallet).
Insurance: DeFi cover from Unslashed or InsurAce.
Audits: Certik and Trail of Bits catch contract flaws. Better UX: Biometric logins or social recovery (Argent wallet). Insurance: DeFi cover from Unslashed or InsurAce. Audits: Certik and Trail of Bits catch contract flaws. CrossFi’s Play: Bridging Two Worlds CrossFi isn’t just reporting—they’re building. Their CrossFi Chain fuses fiat and crypto with non-custodial tech—users hold the keys, but transactions feel seamless. Targeting women in India or middle earners in Russia, they’re chasing inclusivity. It’s a blueprint for 2025: crypto that’s practical, safe, and open to all. The Big Picture: What’s Next? CrossFi’s survey isn’t static—it’s a roadmap. Crypto and Web3 banking are rewriting finance, but they’re not ready for primetime. Here’s the playbook: Slash Fees: Layer 2 and new chains must scale fast.
Simplify UX: Hide the blockchain, show the benefits.
Boost Security: User-friendly tools beat hacks.
Harmonize Rules: Global clarity unlocks adoption. Slash Fees: Layer 2 and new chains must scale fast. Simplify UX: Hide the blockchain, show the benefits. Boost Security: User-friendly tools beat hacks. Harmonize Rules: Global clarity unlocks adoption. Crypto’s shedding its niche skin, reaching everyday people with big dreams. By 2025, it might just deliver. Dive into CrossFi’s full report and see for yourself—the future’s digital, decentralized, and knocking. CrossFi’s full report Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

What Do Global Users Really Think About Crypto and Web3 Banking? A Dive into CrossFi’s 2025 Survey

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Caching is a technique in software development to temporarily store application resources, data at a nearby accessible location for efficient retrieval when required. Generally, the resources or data that are frequently used but doesn’t change often are considered good candidates for caching in software design. Caching is implemented to achieve several benefits in software system such as Reduce latency in response of a request
Reduce number of network calls thereby using less network bandwidth
Reduce chances of failure in software system by serving data from cache instead of making call to actual backend system Reduce latency in response of a request Reduce number of network calls thereby using less network bandwidth Reduce chances of failure in software system by serving data from cache instead of making call to actual backend system There are several articles in the web that talks about caching and it’s various implementation strategy. This article doesn’t reiterate those in some other fashion. Instead, this article presents a holistic picture of different type of caching we implement in a large software system, conceptually how do we accomplish that for each types and how is it typically done in a system that is hosted on-premise in dedicated hardware vs how is it done in modern cloud platforms. General Caching Strategy The below diagram(Diagram 1) represents a typical web application architecture hosted in on-premise data center of an organization. Diagram 2 represents the specific type of caching that each of the architecture components contributes in caching implementation. Before we start with explanation of the above diagram(Diagram 2), let’s first talk about categorization of caching in a web application. It can be done based on two different dimensions — what is cached and where is it cached. What is cached? Application resource — By application resource caching, we primarily mean static contents such as CSS, JavaScript, images etc. which are used to design the look & feel of a webpage. Application resource also includes media files though such as audio, video.
Application data — By application data caching, we mean caching data that primarily resides in backend databases of an application Application resource — By application resource caching, we primarily mean static contents such as CSS, JavaScript, images etc. which are used to design the look & feel of a webpage. Application resource also includes media files though such as audio, video. Application data — By application data caching, we mean caching data that primarily resides in backend databases of an application Where is it cached? Generally application resources are cached at the infrastructure level(e.g. CDN, reverse proxy etc.) or on the client side(e.g. browser, device).


Whereas application data is cached on the server side(e.g. memory, disk or dedicated caching tool). Generally application resources are cached at the infrastructure level(e.g. CDN, reverse proxy etc.) or on the client side(e.g. browser, device). Generally application resources are cached at the infrastructure level(e.g. CDN, reverse proxy etc.) or on the client side(e.g. browser, device). Whereas application data is cached on the server side(e.g. memory, disk or dedicated caching tool). Whereas application data is cached on the server side(e.g. memory, disk or dedicated caching tool). Let’s decompose Diagram 2 which shows the different layers of caching and explain how in a traditional on-premise application caching is implemented. A typical web application is composed of 3 different tiers — a web tier hosting the frontend web application running in an web server, a backend services tier running in application server and a database tier. Traffic from internet through a web browser doesn’t hit the web and application server directly. Typically a Web Application Firewall(WAF)/DDoS mitigation tool sits in front of the network. This tool often also work as content delivery network(CDN). Traffic from the WAF/DDoS/CDN tool hits the reverse proxy of the network. The reverse proxy layer works as the interface to accept all traffic from the internet, hides identity of the actual backend servers from the external world where application is running. The reverse proxy server finally routes the traffic to appropriate backend server based on the resource path in the request URL. If the request from the browser is for the web application then reverse proxy layer forwards the traffic to the web server. If the request is for a backend service, then traffic is routed to the application server. Application Resource Caching When the request is to render a webpage, the static resources such as JavaScript, CSS, images used to render the webpage can be cached in all 3 layers — the reverse proxy, CDN and browser. What can be cached and can’t be cached are determined by the various HTTP headers as set by the web application such as — last-modified-since/is-modified-since, cache-control etc. When the browser makes another request for the same webpage or for a different webpage which is reusing some of the static assets(JavaScript, images, CSS) of an already accessed webpage, the browser first checks if the assets have been changed in the application or not(using last-modified-since/is-modified-since header). If no change is found, then the cached asset in the browser is used to render the webpage. Otherwise the browser makes a request to the application to download the updated asset. Once browser makes request to the backend, the request is first intercepted by the CDN/DDOs/WAF layer. This layer does the exact same thing that the browser did i.e. it will check if the copy of the asset it has in it’s cache is same as the asset in backend, if yes, it will not forward the request for the resource to the reverse proxy but return the cached copy of the resource to the browser. If the asset is not cached in CDN layer, then CDN layer does the same thing that browser did i.e. it forwards the request of the asset to the reverse proxy layer. The reverse proxy layer will serve the asset from cache if it hasn’t been updated in the application, otherwise will reach out to the backend web server to retrieve the fresh copy of the asset. The fresh copy of the asset returned by the web server is then cached by all 3 caching layer — reverse proxy, CDN and browser. If the end user decides to clear the browser cache for some reason, then even though the browser will reach out to backend to fetch the asset, the asset will actually be returned by the CDN and/or reverse proxy instead of the backend web application server. As a result of that the end client still experiences improved response time as the resource is served by some upstream infrastructure layer of the application instead of the actual backend server. Needless to say, this also reduces the amount of traffic that hits the actual backend server. The diagram below(Diagram 3) demonstrates the flow of application resource caching in different layers as described above. Application Data Caching When we talk about application data caching, it would typically mean caching data which generally doesn’t change frequently and therefore are good candidate of caching to reduce number of calls that goes to the database. One good example of that could be list of countries or list of states in a country that are displayed in a webpage. These are lookup table type of data and can be cached safely after retrieving once to avoid calling database every time for data that is mostly static. Another example we can think of is modern day day social media application where we may want to cache the home page feed of a user so that when the user launches the home page, the user sees the home page feed right away instead of waiting for several seconds. Until the advent of cloud based applications or emergence of modern caching tools like Memcached, Redis, on-premise applications used to rely on session caching in application server. That typically would mean data which are candidate of caching would live in session of the user that gets created after login but they would not be available for reuse during their next login. Some application servers like WebSphere had a separate caching solution to get around the session caching limitation called Dynacache — a distributed map. Dynacache would allow an application to cache data as key-value storage which is not tied with an user session. WebSphere used to keep this data in server memory and also had the provision of offloading the data into a disk if needed. It worked well given the lack of modern day caching tool we had at that moment but it had the limitation of cache not surviving a restart of the server(unless offloaded into disk). There were also challenges of maintaining consistency of data in cache in a clustered environment as each application server in the cluster would have it’s own copy of cache. Technically there was a provision to replicate cache between application servers in a cluster to address the consistency problem but that wasn’t something easy to implement and would come at a cost of performance. However as the Diagram 2 above demonstrates, caching data at the application server layer is one of the options to cache data on server side. Databases and object-relation-mapping(ORM) tool like Hibernate also provides option to cache data using something called query cache. Databases can cache the result as well the entire SQL query in memory and serves data from memory as long as the data hasn’t been updated. Pretty much most of the common databases like MySQL, Oracle, PostgreSQL, SQL Server etc. supports query cache. In modern day applications, the capability of data caching has improved significantly with advent of caching specialized technologies like Redis or Memcached. On top of that, modern day API Gateway layers that works as reverse proxies(along with providing tons of other features such as orchestration, rate limiting etc.) also provide ability to cache data for API calls. Though API Gateway(e.g. Apigee) and caching tool like Redis/Memcached can be implemented in an on-premise solution to power data caching, in reality, these toolsets are more popular for use in applications running in cloud platforms(e.g. AWS, Azure etc.). The following pair of diagrams, demonstrates usage of API Gateway and caching tool(e.g. Redis, Memcached) for on-premise as well as cloud hosted applications. Caching strategies When the combination of database and caching tool is in place in architecture(common scenario in modern day applications), there are several techniques available to implement caching. There is no right or wrong techniques as all come with trade-offs. Depending on the nature of the use case, appropriate technique needs to be chosen. Cache aside — application reads from cache, if data not available in cache, then application reads from database and update cache. This is the caching pattern I have used mostly for the use cases I have personally encountered.
Read through cache — application reads from cache and if data is not available in cache, then caching tool reads from database, updates cache and returns response.
Write through cache — applications writes into cache, cache sends an acknowledgement immediately to reduce latency in client response and asynchronously writes data into database after that. This is a use case that can be adopted for a social media application where a post by a user can be made available for view to other users with sub-second latency. The actual operation of storing the data in a persistent store(database) happens asynchronously.
Write back cache — data is written into cache only. Right before cache expires(typically after time-to-live aka TTL setting) data is written into the database for persistence. This technique can be used for a ride sharing system like Uber. Generally in a ride sharing system, a vehicle sends it’s current location every 30 seconds or so that a rider can get a real-time location update of the driver they are waiting for ride. In this caching pattern, the location of a car will only be updated in cache and before the cache expires the location data will be written to the database. This caching technique allows user/rider get to know the location of the driver instantaneously in the Uber app. If due to any reason the write to the database fails it results in a data loss. However generally this is acceptable if application doesn’t have history of location data for a car for a small interval of time(e.g. 5mins) in it’s persistent storage. Cache aside — application reads from cache, if data not available in cache, then application reads from database and update cache. This is the caching pattern I have used mostly for the use cases I have personally encountered. Read through cache — application reads from cache and if data is not available in cache, then caching tool reads from database, updates cache and returns response. Write through cache — applications writes into cache, cache sends an acknowledgement immediately to reduce latency in client response and asynchronously writes data into database after that. This is a use case that can be adopted for a social media application where a post by a user can be made available for view to other users with sub-second latency. The actual operation of storing the data in a persistent store(database) happens asynchronously. Write back cache — data is written into cache only. Right before cache expires(typically after time-to-live aka TTL setting) data is written into the database for persistence. This technique can be used for a ride sharing system like Uber. Generally in a ride sharing system, a vehicle sends it’s current location every 30 seconds or so that a rider can get a real-time location update of the driver they are waiting for ride. In this caching pattern, the location of a car will only be updated in cache and before the cache expires the location data will be written to the database. This caching technique allows user/rider get to know the location of the driver instantaneously in the Uber app. If due to any reason the write to the database fails it results in a data loss. However generally this is acceptable if application doesn’t have history of location data for a car for a small interval of time(e.g. 5mins) in it’s persistent storage. Conclusion This wraps up the comprehensive exploration of various types of caching, encompassing where data is cached, the contrasting approaches employed in on-premise applications versus modern-day ones, and the diverse techniques utilized in this domain. As technology evolves, caching strategies will continue to adapt, offering new ways to balance speed, scalability, and consistency. Whether optimizing a small-scale application or architecting large distributed systems, leveraging caching effectively remains a key aspect of building high-performance solutions.

Why Your Browser Knows What You Want Before You Do: A Deep Dive Into Caching

NFL Rivals – A Deep Dive into the Ultimate Gameplay, Tutorial, and Team Building Guide

የNFL ተቀናቃኞች - ወደ የመጨረሻው ጨዋታ፣ አጋዥ ስልጠና እና የቡድን ግንባታ መመሪያ ጥልቅ ዘልቆ መግባት

NFL Rivals – نظرة متعمقة على طريقة اللعب النهائية، والبرنامج التعليمي، ودليل بناء الفريق

NFL Rivals: una mirada profunda a la jugabilidad definitiva, el tutorial y la guía de formación de equipos

NFL Rivals – شیرجه ای عمیق به بازی نهایی، آموزش و راهنمای تیم سازی

NFL Rivals - Yon gwo plonje nan jwèt final la, Tutorial, ak Gid Konstriksyon Ekip la

NFL ライバルズ - 究極のゲームプレイ、チュートリアル、チーム構築ガイドを詳しく紹介

NFL Rivals - ការជ្រមុជទឹកជ្រៅចូលទៅក្នុងការលេងហ្គេមចុងក្រោយ ការបង្រៀន និងការណែនាំអំពីការកសាងក្រុម

د NFL سیالان - د لوبې وروستۍ لوبې، ښوونې او ټیم جوړونې لارښود ته ژوره کتنه

NFL Rivals — глубокое погружение в игровой процесс, обучение и руководство по формированию команды

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NFL xafiiltanka - quusi qoto dheer galay ciyaarta ugu dambeeya, waxbarashada, iyo Hagaha Dhismaha Kooxda

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