The implication of this attack is clear: Every unprotected account is a potential vulnerability. As attackers refine their tactics, integrating 2FA and other robust security measures is no longer optional — but a critical component of your cybersecurity strategy.

Microsoft’s Password Spray and Pray Attack: A Wake-Up Call for 2FA Adoption

Chicago, IL – February 19, 2025 – Zacks.com announces the list of stocks featured in the Analyst Blog. Every day the Zacks Equity Research analysts discuss the latest news and events impacting stocks and the financial markets. Stocks recently featured in the blog include: SoundHound AI, Inc.

The Zacks Analyst Blog Highlights SoundHound, NVIDIA, Nebius, Alphabet and Chipotle Mexican Grill

The latest Neue Klasse concept hints at the future of BMW M, including a central computer that BMW calls the "Heart of Joy."

See the BMW Vision Driving Experience from Every Angle

Samsung, ASUS and AOC represent some of the best UK Deals on gaming monitors right now

Amazon Has A Gaming Monitor For Every Budget With These UK Deals - IGN

This Is A List Of The Worst-performing Equity Mutual Funds Over Every Possible The Worst Performing Mutual Funds Of 2023 | Mutual Fund Rankings.

Five-Star Business Finance allots 8.28 lakh equity shares under ASOS | Capital Market News

Every year, the Ontario government and its agencies spend $30 billion on procurement, alongside our $200 billion plan to build Ontario. U.S.-based businesses will now lose out on tens of billions of dollars in new revenues.

US, EU and UK stock markets hit by global trade war fears as JPMorgan says Trump administration may be ‘business unfriendly’ – as it happened | Business | The Guardian

With the January transfer window closed, see what each Premier League club did and rate how you think each side did.

January transfer window: Every Premier League transfer and rate how each club did - BBC Sport

This infographic has made it simpler, offering a fresh perspective by comparing cybersecurity to the human body.

Human Body Meets Cybersecurity: How Every Part Plays A Role

Every

every

A company encountered severe CPU load spikes and even system crashes during scheduled task triggers while migrating from Alibaba Cloud DataWorks to a self-built big data platform using DolphinScheduler. After investigation, it turned out the issue wasn’t caused by too many tasks but by improper scheduler configuration. By adjusting the number of threads and CPU limits in DolphinScheduler, the CPU spike problem was successfully resolved, ensuring smooth task execution. Referring to this case, users should balance machine load and concurrent task demands when modifying thread settings. Background Background The company planned to decommission Alibaba Cloud’s DataWorks product, which had been used previously, and build its own big data platform entirely with open-source components. Due to budget constraints, they couldn’t afford to buy many servers at the start, so they initially purchased 4 ECS servers and ran all components on mixed nodes. As the migration progressed, they gradually scaled down DataWorks resources and allocated the saved costs to the self-built platform. For the scheduling platform, they chose the currently popular DolphinScheduler. DolphinScheduler The Problem The Problem Other team members migrated offline tasks from DataWorks to DolphinScheduler using Hive SQL. After a while, a large number of tasks had accumulated in DolphinScheduler. Besides the scheduled tasks at 2 AM and 3 AM daily, there were also many tasks scheduled every 5 minutes. Later, they found that every time the 5-minute interval triggered, the CPU load on ECS machines spiked from below 5% to 100%, lasting for several seconds. The situation was even worse at 2 AM and 3 AM, often causing other component roles on the machines to stop running. In severe cases, the operating system would crash, as shown below: Investigation Process Investigation Process At first, they assumed that the high CPU usage was due to a large number of tasks being scheduled simultaneously at 5-minute intervals or 2 AM. They grouped all tasks into one task group and observed resource usage. The resource usage graph did become a bit smoother, but the spikes still occurred at 5-minute intervals and 2 AM — the issue remained unresolved. Then they disabled the scheduling of all tasks except the Hive SQL ones scheduled every 5 minutes. However, the CPU still spiked to 100% during those intervals. This indicated that the issue wasn’t due to too many tasks being executed at once, because the number of scheduled tasks was significantly reduced. Finally, they turned to investigate whether DolphinScheduler itself was consuming too many resources. DolphinScheduler itself Solution Solution By examining the application.yaml configuration files for DolphinScheduler’s master and worker components, they found several settings related to threads, CPU, and memory. application.yaml In the master’s application.yaml: application.yaml # Thread-related configuration
fetch-command-num: 10
# master prepare execute thread number to limit handle commands in parallel
pre-exec-threads: 10
# master execute thread number to limit process instances in parallel
exec-threads: 100
# master dispatch task number per batch, if all the tasks dispatch failed in a batch, will sleep 1s.
dispatch-task-number: 3

# CPU and memory-related configuration
# master max cpuload avg, only higher than the system cpu load average, master server can schedule. default value -1: the number of cpu cores * 2
# If the CPU load exceeds the following value, the master role will stop scheduling until the load drops below this value. Default: no limit
max-cpu-load-avg: -1
# master reserved memory, only lower than system available memory, master server can schedule. default value 0.3, the unit is G
# If available memory is less than the following value (in GB), the master will stop scheduling until it rises above. Default: 0.3G
reserved-memory: 0.3 # Thread-related configuration
fetch-command-num: 10
# master prepare execute thread number to limit handle commands in parallel
pre-exec-threads: 10
# master execute thread number to limit process instances in parallel
exec-threads: 100
# master dispatch task number per batch, if all the tasks dispatch failed in a batch, will sleep 1s.
dispatch-task-number: 3

# CPU and memory-related configuration
# master max cpuload avg, only higher than the system cpu load average, master server can schedule. default value -1: the number of cpu cores * 2
# If the CPU load exceeds the following value, the master role will stop scheduling until the load drops below this value. Default: no limit
max-cpu-load-avg: -1
# master reserved memory, only lower than system available memory, master server can schedule. default value 0.3, the unit is G
# If available memory is less than the following value (in GB), the master will stop scheduling until it rises above. Default: 0.3G
reserved-memory: 0.3 In the worker’s application.yaml: application.yaml # Thread-related configuration
# worker execute thread number to limit task instances in parallel
exec-threads: 100

# CPU and memory-related configuration
# worker max cpuload avg, only higher than the system cpu load average, worker server can be dispatched tasks. default value -1: the number of cpu cores * 2
# If the CPU load exceeds the following value, the worker role will stop accepting tasks until it drops. Default: no limit
max-cpu-load-avg: -1
# worker reserved memory, only lower than system available memory, worker server can be dispatched tasks. default value 0.3, the unit is G
# If available memory is less than the following value (in GB), the worker will stop accepting tasks until it rises above. Default: 0.3G
reserved-memory: 0.3 # Thread-related configuration
# worker execute thread number to limit task instances in parallel
exec-threads: 100

# CPU and memory-related configuration
# worker max cpuload avg, only higher than the system cpu load average, worker server can be dispatched tasks. default value -1: the number of cpu cores * 2
# If the CPU load exceeds the following value, the worker role will stop accepting tasks until it drops. Default: no limit
max-cpu-load-avg: -1
# worker reserved memory, only lower than system available memory, worker server can be dispatched tasks. default value 0.3, the unit is G
# If available memory is less than the following value (in GB), the worker will stop accepting tasks until it rises above. Default: 0.3G
reserved-memory: 0.3 In the master’s start.sh, the JVM memory settings were: start.sh JAVA_OPTS=${JAVA_OPTS:-"-server -Duser.timezone=${SPRING_JACKSON_TIME_ZONE} -Xms4g -Xmx4g -Xmn2g -XX:+PrintGCDetails -Xloggc:gc.log -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=dump.hprof"} JAVA_OPTS=${JAVA_OPTS:-"-server -Duser.timezone=${SPRING_JACKSON_TIME_ZONE} -Xms4g -Xmx4g -Xmn2g -XX:+PrintGCDetails -Xloggc:gc.log -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=dump.hprof"} In the worker’s start.sh, the JVM memory settings were: start.sh JAVA_OPTS=${JAVA_OPTS:-"-server -Duser.timezone=${SPRING_JACKSON_TIME_ZONE} -Xms4g -Xmx4g -Xmn2g -XX:+PrintGCDetails -Xloggc:gc.log -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=dump.hprof"} JAVA_OPTS=${JAVA_OPTS:-"-server -Duser.timezone=${SPRING_JACKSON_TIME_ZONE} -Xms4g -Xmx4g -Xmn2g -XX:+PrintGCDetails -Xloggc:gc.log -XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=dump.hprof"} If your machine doesn't have much memory, you can adjust the JVM memory settings in start.sh accordingly. start.sh They mainly modified the thread counts and CPU limits in the master and worker application.yaml files — setting all thread counts to 3 and changing the CPU limit from -1 to 4 (the servers have 8 CPU cores). application.yaml 3 -1 to 4 The specific values can be adjusted based on your server specs or by trial and error. Afterward, they observed the CPU usage again and found that during the 5-minute triggers, the CPU stayed stable.Even at 2 AM and 3 AM, when a burst of tasks was scheduled, the CPU remained under control and tasks ran smoothly, as shown in the images: stable However, one thing to note: After reducing the number of threads, the number of tasks that can be scheduled simultaneously also decreases. For example, if each worker is set to 3 threads and there are 5 worker nodes, the max concurrency is 15 tasks. So the thread count should be determined by balancing your server's load and the number of concurrent tasks you need to run. The goal is to find a value that avoids sudden CPU spikes and ensures scheduled tasks finish within an acceptable time window. and

The $0 Scheduler That Almost Cost a Compny Everything

We live in a world where a pizza arrives in 15 minutes, we can download a full HD movie in under 10 seconds, and AI can generate an entire essay in the blink of an eye — yet somehow, we still expect sick, vulnerable, and often elderly patients to wait hours on end in medical waiting rooms just to be seen. Why? Honestly, why? why This isn't 1984. We’re not scribbling names on a clipboard with a chewed-up Bic pen, hoping someone notices. Hospitals and clinics have electronic patient records, SMS alerts, cloud scheduling systems, triage software, and enough data to make Google Maps look under-informed — so what is going so wrong? “Your appointment is at 10am.” — Lies. All lies. Ask any patient, especially one who dared to book an early appointment thinking they'd “beat the rush.” They'll tell you: 10am really means 10:40… if you’re lucky. The system tells us the appointment is set, but there's no guarantee anyone will honor that time. And forget walking in without an appointment — that's practically begging to give up your entire day. Let’s be blunt: the modern medical waiting room is a vortex. A black hole. You enter, and time stops making sense. A 15-minute wait becomes 45. An hour becomes two. The clock ticks, your throat gets scratchier, your back aches from the stiff plastic chair, and every cough from across the room feels like a threat. Still, the staff says, “You’re next.” Next for what? Next for your third hour of silent suffering? third hour Technology Is Evolving — Why Aren’t Our Medical Systems? Hospitals and clinics have everything they need to improve the process. Want to book a GP online? Sure, easy. Want to check your blood test results? Done in seconds. But want to actually see a doctor when you’re sick? Better pack a lunch and clear your schedule. see We can use facial recognition to unlock phones, track our heart rate with a smartwatch, and monitor insulin levels through apps. AI is revolutionizing radiology, predictive diagnostics, even robotic surgery — yet a receptionist still prints your file like it’s 1995 and offers you a clipboard to fill in forms you already filled out online. Explain that one. you already filled out online And don’t get started on emergency rooms. You might be bleeding, gasping for air, or doubled over in pain, but unless you’re halfway to the grave or actively coding, you’ll be told to “take a seat.” Then you'll wait. Surrounded by the sounds of suffering, babies crying, someone hacking up a lung, and a teenager with a broken arm sobbing softly into their hoodie. It's misery on tap. Understaffed and Overworked — But That’s Not the Whole Story Yes, we get it. The healthcare system is underfunded. Nurses are saints in scrubs working ridiculous shifts. Doctors are booked back-to-back. We are not blaming the frontline workers here. This rant isn’t against them — it's against the larger system that lets this problem keep going without even attempting a real fix. not Where’s the innovation in flow management? Where are the real-time updates for patients? If Uber can give you minute-by-minute updates on your driver’s location, surely the hospital can tell you where the doctor is in their patient queue? Tech exists to streamline queues, automate admin, and prioritize patient flow more efficiently. We’ve got AI tools that can schedule appointments based on historical patterns, triage symptoms via telehealth before a patient even enters the building, and flag when certain conditions might need urgent escalation. Yet those tools are either gathering dust or hidden behind red tape and outdated policy. Meanwhile, the Patient Suffers The people being failed here are the ones who can least afford to be. Parents dragging in feverish kids. The elderly waiting for hours without food or water. Chronically ill people stuck in limbo because the system moves slower than a dial-up connection. And let’s not forget mental health patients. Imagine working up the courage to seek help, only to be left alone in a sterile room with no information, no updates, and rising anxiety. That’s not care — that’s neglect dressed up as bureaucracy. A Better Way Exists — But Who’s Willing to Build It? It’s not just frustrating — it’s infuriating. Technology is there. Tools are there. The money? Well, it's floating somewhere between budget committees and procurement delays. But the will to fix the experience for the actual patient? That seems to be missing. will Maybe it’s time the system looked at medical care the way a tech company looks at user experience. Because right now, the experience is broken. It’s outdated. It’s unkind. And in many cases, it's unsafe. People don’t want miracles. They want dignity. Respect. A timely appointment that actually means something. A system that recognizes time is not just money — it’s energy, it’s sanity, it’s life. life Fix the waiting rooms. We’re not asking for teleportation. We’re asking for efficiency. For communication. For a system that values people’s time as much as their health. And if pizza delivery can be tracked down to the second, surely the medical system can do better than “just take a seat and wait.” “just take a seat and wait.”

In the Age of Instant Everything, Why Are Sick Patients Still Waiting Hours in Medical Waiting Rooms

Can you introduce yourself and tell us about LATOKEN? I’m Valentin Preobrazhenskiy, CEO and founder of LATOKEN, one of the leading cryptocurrency exchanges focused on altcoins. Previously, I managed a hedge fund where I developed a passion for discovering promising investments and now help traders find hidden crypto gems while empowering founders to successfully launch tokens that gain real community traction. How did you get the idea to launch LATOKEN? Before LATOKEN, I ran a long-short equity hedge fund where I successfully predicted geopolitical risks in 2014, moving my portfolio away from exposure to Russian markets towards more global opportunities. At that turning point, I had two choices: join the highly competitive Wall Street environment or pursue an entirely new avenue—blockchain technology—that very few understood at the time. Organizing hundreds of tech meetups opened my eyes to blockchain and ICO trends. Ethereum, launched by Vitalik Buterin, particularly intrigued me because startups suddenly had the tools to essentially “print their own money” through tokenization. It became clear to me that blockchain would soon revolutionize asset ownership. Millions of assets would be tokenized, demanding a completely new infrastructure. That’s when I transitioned part of my team from my home equity loan platform to build LATOKEN. We launched our ICO in 2017 and quickly grew from there. How has LATOKEN evolved since launching? Today, LATOKEN supports more than 4 million user accounts from 127 countries and lists over 2,000 digital assets. We're consistently ranked among the top 50 crypto exchanges globally according to CoinMarketCap and CoinGecko, and Forbes named us one of the Top 30 Remote Employers worldwide. Our strength lies in identifying innovative tokens early, providing investors a first-mover advantage. What fuels your passion for this mission? My passion is deeply personal. Growing up in post-Soviet Russia during the economic collapse of the 1990s profoundly shaped my worldview. I vividly remember being with my family, picking berries in a peaceful forest, while at home, hyperinflation had wiped out our savings overnight. My parents, both highly skilled engineers, lost their jobs during that turbulent time. Studying economics at the New Economic School further reinforced my belief in secure property rights as the foundation for stability and growth. Blockchain captivated me precisely because it provides a transparent, secure method to protect wealth, independent of unreliable institutions or politics. That potential continues to inspire everything I do at LATOKEN. How will blockchain and altcoins reshape the financial industry? Blockchain addresses one critical global issue: secure property rights without costly institutional oversight. Traditional financial systems require extensive infrastructure—armies, police, courts, central banks—to guarantee property ownership. Many countries simply can't sustain such complex institutions, leaving billions excluded from financial stability. Blockchain technology eliminates these barriers, allowing anyone to open accounts and trade digital assets easily, like signing up for an email service. Beyond inclusivity, altcoins offer a hedge against fiat currency devaluation and create a web3 economy where users become stakeholders and founders scale rapidly. What role does AI play in the crypto world? AI significantly streamlines crypto operations, helping traders discover new tokens through advanced analytics, recommendations, and market summarization. At LATOKEN, we've embraced AI to automate processes like customer interactions during token listings, qualifying leads, and even transforming our HR department. AI-powered tools screen resumes, conduct candidate assessments, and organize streamlined interviews. This allowed us to efficiently build and manage a fully remote global team of over 150 experts, reflecting the blockchain ethos of transparency and efficiency internally. What's your outlook on the current crypto market? Currently, altcoins face challenging liquidity conditions due to high interest rates drawing capital away from the crypto sector. However, I view this as a temporary downturn. As soon as macroeconomic pressures ease, liquidity will rapidly return—first into Bitcoin and Ethereum, and later into strong altcoins backed by vibrant communities and practical use-cases. Hedge funds are already positioning themselves to capitalize on these undervalued assets, signaling an upcoming market revival. What are your thoughts on recent crypto policy developments in the US? Recent regulatory moves in the US have been cautiously optimistic. Authorities recognize the value of stablecoins and tokenized treasuries to expand the dollar's global footprint without cumbersome intermediaries. By halting the push for a central bank digital currency, the US has subtly embraced decentralized innovation over centralized alternatives, potentially unlocking institutional capital and fostering wider adoption. Tell us about your involvement with the Founder Institute. At the Founder Institute, a global startup accelerator with alumni companies exceeding $20 billion in valuation, I served as both a Director and Mentor. I guided early-stage entrepreneurs through the critical phases of refining their ideas, crafting viable products, and raising funds. Drawing on my LATOKEN journey, I shared firsthand experiences on scaling operations, overcoming fundraising challenges, and staying competitive in fast-evolving markets. What about your role in the Blockchain Economic Forum? Launching the Blockchain Economic Forum (BEF) in 2017 is among my proudest achievements. BEF became a unique global platform connecting blockchain innovators with policymakers and institutional investors. We held impactful forums in cities like Davos, New York, San Francisco, and London, gathering influential leaders including presidents, EU policymakers, SEC leadership, and top economists. The aim was to facilitate transition of DeFi into the mainstream financial system. I love connecting founders to build the future and made over 500 documented tech meetups. You've also judged startup pitch competitions—can you elaborate on this experience? I’ve had the privilege of judging international startup competitions focused on Web3 at Gain Web3 Pitch Competitions in partnership with Duke University's Web3 Ecosystem, DevCon and Blockchain life forums in 2024. Judging allowed me to help founders sharpen their market strategies, refine their products, and prepare for scaling. Do you have any unique or controversial views you'd like to share? I believe that the universal goal of life can be measured as global economy capitalization with a Nasdaq as a well-known proxy. Currency is a control right to use energy for the self-replication of symbiosis of programs, constituting Sapience. Future cash flow essentially reflects strategic viability of life. I find this concept useful to prioritize conflicting goals and run a website to share this framework. Any final thoughts? My mission remains steadfast: ensure every person has meaningful access to economic opportunity. Whether through LATOKEN, mentoring founders, or facilitating global forums, my goal is removing barriers to financial inclusion. Access to the global financial system shouldn't be a privilege—it should be universally accessible, creating a more stable and united world. -- Can you introduce yourself and tell us about LATOKEN? Can you introduce yourself and tell us about LATOKEN? I’m Valentin Preobrazhenskiy, CEO and founder of LATOKEN, one of the leading cryptocurrency exchanges focused on altcoins. Previously, I managed a hedge fund where I developed a passion for discovering promising investments and now help traders find hidden crypto gems while empowering founders to successfully launch tokens that gain real community traction. How did you get the idea to launch LATOKEN? How did you get the idea to launch LATOKEN? Before LATOKEN, I ran a long-short equity hedge fund where I successfully predicted geopolitical risks in 2014, moving my portfolio away from exposure to Russian markets towards more global opportunities. At that turning point, I had two choices: join the highly competitive Wall Street environment or pursue an entirely new avenue—blockchain technology—that very few understood at the time. Organizing hundreds of tech meetups opened my eyes to blockchain and ICO trends. Ethereum, launched by Vitalik Buterin, particularly intrigued me because startups suddenly had the tools to essentially “print their own money” through tokenization. It became clear to me that blockchain would soon revolutionize asset ownership. Millions of assets would be tokenized, demanding a completely new infrastructure. That’s when I transitioned part of my team from my home equity loan platform to build LATOKEN. We launched our ICO in 2017 and quickly grew from there. How has LATOKEN evolved since launching? How has LATOKEN evolved since launching? Today, LATOKEN supports more than 4 million user accounts from 127 countries and lists over 2,000 digital assets. We're consistently ranked among the top 50 crypto exchanges globally according to CoinMarketCap and CoinGecko, and Forbes named us one of the Top 30 Remote Employers worldwide. Our strength lies in identifying innovative tokens early, providing investors a first-mover advantage. What fuels your passion for this mission? What fuels your passion for this mission? My passion is deeply personal. Growing up in post-Soviet Russia during the economic collapse of the 1990s profoundly shaped my worldview. I vividly remember being with my family, picking berries in a peaceful forest, while at home, hyperinflation had wiped out our savings overnight. My parents, both highly skilled engineers, lost their jobs during that turbulent time. Studying economics at the New Economic School further reinforced my belief in secure property rights as the foundation for stability and growth. Blockchain captivated me precisely because it provides a transparent, secure method to protect wealth, independent of unreliable institutions or politics. That potential continues to inspire everything I do at LATOKEN. How will blockchain and altcoins reshape the financial industry? How will blockchain and altcoins reshape the financial industry? Blockchain addresses one critical global issue: secure property rights without costly institutional oversight. Traditional financial systems require extensive infrastructure—armies, police, courts, central banks—to guarantee property ownership. Many countries simply can't sustain such complex institutions, leaving billions excluded from financial stability. Blockchain technology eliminates these barriers, allowing anyone to open accounts and trade digital assets easily, like signing up for an email service. Beyond inclusivity, altcoins offer a hedge against fiat currency devaluation and create a web3 economy where users become stakeholders and founders scale rapidly. What role does AI play in the crypto world? What role does AI play in the crypto world? AI significantly streamlines crypto operations, helping traders discover new tokens through advanced analytics, recommendations, and market summarization. At LATOKEN, we've embraced AI to automate processes like customer interactions during token listings, qualifying leads, and even transforming our HR department. AI-powered tools screen resumes, conduct candidate assessments, and organize streamlined interviews. This allowed us to efficiently build and manage a fully remote global team of over 150 experts, reflecting the blockchain ethos of transparency and efficiency internally. What's your outlook on the current crypto market? What's your outlook on the current crypto market? Currently, altcoins face challenging liquidity conditions due to high interest rates drawing capital away from the crypto sector. However, I view this as a temporary downturn. As soon as macroeconomic pressures ease, liquidity will rapidly return—first into Bitcoin and Ethereum, and later into strong altcoins backed by vibrant communities and practical use-cases. Hedge funds are already positioning themselves to capitalize on these undervalued assets, signaling an upcoming market revival. What are your thoughts on recent crypto policy developments in the US? What are your thoughts on recent crypto policy developments in the US? Recent regulatory moves in the US have been cautiously optimistic. Authorities recognize the value of stablecoins and tokenized treasuries to expand the dollar's global footprint without cumbersome intermediaries. By halting the push for a central bank digital currency, the US has subtly embraced decentralized innovation over centralized alternatives, potentially unlocking institutional capital and fostering wider adoption. Tell us about your involvement with the Founder Institute. Tell us about your involvement with the Founder Institute. At the Founder Institute, a global startup accelerator with alumni companies exceeding $20 billion in valuation, I served as both a Director and Mentor. I guided early-stage entrepreneurs through the critical phases of refining their ideas, crafting viable products, and raising funds. Drawing on my LATOKEN journey, I shared firsthand experiences on scaling operations, overcoming fundraising challenges, and staying competitive in fast-evolving markets. What about your role in the Blockchain Economic Forum? Launching the Blockchain Economic Forum (BEF) in 2017 is among my proudest achievements. BEF became a unique global platform connecting blockchain innovators with policymakers and institutional investors. We held impactful forums in cities like Davos, New York, San Francisco, and London, gathering influential leaders including presidents, EU policymakers, SEC leadership, and top economists. The aim was to facilitate transition of DeFi into the mainstream financial system. I love connecting founders to build the future and made over 500 documented tech meetups. You've also judged startup pitch competitions—can you elaborate on this experience? You've also judged startup pitch competitions—can you elaborate on this experience? I’ve had the privilege of judging international startup competitions focused on Web3 at Gain Web3 Pitch Competitions in partnership with Duke University's Web3 Ecosystem, DevCon and Blockchain life forums in 2024. Judging allowed me to help founders sharpen their market strategies, refine their products, and prepare for scaling. Do you have any unique or controversial views you'd like to share? Do you have any unique or controversial views you'd like to share? I believe that the universal goal of life can be measured as global economy capitalization with a Nasdaq as a well-known proxy. Currency is a control right to use energy for the self-replication of symbiosis of programs, constituting Sapience. Future cash flow essentially reflects strategic viability of life. I find this concept useful to prioritize conflicting goals and run a website to share this framework. website Any final thoughts? Any final thoughts? My mission remains steadfast: ensure every person has meaningful access to economic opportunity. Whether through LATOKEN, mentoring founders, or facilitating global forums, my goal is removing barriers to financial inclusion. Access to the global financial system shouldn't be a privilege—it should be universally accessible, creating a more stable and united world. --

Opening Financial Markets to Everyone: An Interview with Valentin Preobrazhenskiy, CEO of LATOKEN

የፋይናንስ ገበያዎች ለሁሉም ይሰጣል: የ LATOKEN CEO Valentine Preobrazhenskiy ጋር አንድ መለያ

فتح الأسواق المالية للجميع: مقابلة مع فيلين Preobrazhenskiy، رئيس شركة LATOKEN

Abriendo los mercados financieros a todos: una entrevista con Valentin Preobrazhenskiy, CEO de LATOKEN

金融市場をすべての人に開く:LATOKENのCEOであるValentin Preobrazhenskiyとのインタビュー

모든 사람에게 금융 시장을 열기 : 라토켄의 CEO인 발렌타인 프레오브라스키 (Valentin Preobrazhenskiy)과의 인터뷰

Deschiderea piețelor financiare pentru toți: un interviu cu Valentin Preobrazhenskiy, CEO al LATOKEN

Öppna finansmarknaderna för alla: En intervju med Valentin Preobrazhenskiy, VD för LATOKEN

Відкриття фінансових ринків для всіх: Інтерв'ю з Валентином Преображенським, генеральним директором LATOKEN

Ukuvula iimarike zentengiso bonke: I-Interview kunye noValentin Preobrazhenskiy, CEO ye-LATOKEN

I recently tried using Firebase Studio, which has been an interesting experience I want to share with you. It’s a free, browser-based tool from Google that allows you to build full-stack web apps with AI assistance. Want to know more? Then, read this article until the end. What Is Firebase Studio? Firebase Studio was officially unveiled during Google Cloud Next 2025 (early April 2025) as one of Firebase’s major new updates. Google Cloud Next 2025 In short, Firebase Studio is a cloud-based development environment by Google that lives at studio.firebase.google.com. It combines: studio.firebase.google.com A full IDE based on VS Code (Code OSS)


AI assistance from Gemini (Google’s LLM)


Deep integration with Firebase services (Firestore, Auth, Hosting, Functions, etc.)


A visual app prototyping agent that can generate a full-stack app from a prompt A full IDE based on VS Code (Code OSS) A full IDE based on VS Code (Code OSS) AI assistance from Gemini (Google’s LLM) AI assistance from Gemini (Google’s LLM) Deep integration with Firebase services (Firestore, Auth, Hosting, Functions, etc.) Deep integration with Firebase services (Firestore, Auth, Hosting, Functions, etc.) A visual app prototyping agent that can generate a full-stack app from a prompt A visual app prototyping agent that can generate a full-stack app from a prompt It’s meant to help you go from idea to deployed app entirely from your browser — with no setup, installations, or local environments. To be honest, it’s not a new thing from Google; it’s a rebranded and updated version of Project IDX. Project IDX I would say that currently, the core feature is prototyping. Prototyping With Firebase Studio Prototyping Agent — I think it’s one of Firebase Studio’s most powerful features. It allows you to start building an app by simply describing what you want in natural language. I’ll show you a real example later in this article. Prototyping Agent — What makes this feature so impactful is that it: Accelerates MVP creation — great for testing ideas quickly.


Reduces boilerplate — lets you focus on unique features.


Works with real code — so you can edit, extend, and deploy immediately.


One-Click Deployment to Production — once you’re satisfied with your application, Firebase Studio makes it trivial to deploy it. It leverages Firebase App Hosting under the hood so that with a single “Publish” button, your web app is built and deployed globally on Google’s CDN. Accelerates MVP creation — great for testing ideas quickly. Accelerates MVP creation — great for testing ideas quickly. Accelerates MVP creation Reduces boilerplate — lets you focus on unique features. Reduces boilerplate — lets you focus on unique features. Reduces boilerplate Works with real code — so you can edit, extend, and deploy immediately. Works with real code — so you can edit, extend, and deploy immediately. Works with real code One-Click Deployment to Production — once you’re satisfied with your application, Firebase Studio makes it trivial to deploy it. It leverages Firebase App Hosting under the hood so that with a single “Publish” button, your web app is built and deployed globally on Google’s CDN. One-Click Deployment to Production — once you’re satisfied with your application, Firebase Studio makes it trivial to deploy it. It leverages Firebase App Hosting under the hood so that with a single “Publish” button, your web app is built and deployed globally on Google’s CDN. One-Click Deployment to Production — Firebase App Hosting “Publish” Currently, it supports Next.js projects, but Google is planning to expand to other frameworks soon. How Is It Different From the Firebase Console? Firebase Studio is made for actual development. You write code, test it, preview your app, and deploy — all from within Firebase Studio. actual development The Firebase Console manages the project settings, database rules, analytics, and production configurations. Firebase Studio builds on top of that by letting you code, prototype, and iterate faster than ever. So, these are two completely different tools. Use Cases for Firebase Studio Rapid Prototyping: Build working versions of your app in hours, not days. Great for pitching ideas or testing concepts.
Client Demos: Show functional versions of apps to clients without having to maintain a local environment.
Learning & Education: Perfect for students, educators, and boot camps. No installations. No config headaches. Just code.
Solo Developers & Hackathons: Move fast and build entire apps from your browser during weekend projects or events.
Cross-Team Collaboration: Allow frontend and backend devs to work in the same environment. Coming soon: real-time coding collaboration.
AI Feature Exploration: Easily experiment with AI-driven features using built-in Gemini integration and Genkit setup. Rapid Prototyping: Build working versions of your app in hours, not days. Great for pitching ideas or testing concepts. Rapid Prototyping: Client Demos: Show functional versions of apps to clients without having to maintain a local environment. Client Demos: Learning & Education: Perfect for students, educators, and boot camps. No installations. No config headaches. Just code. Learning & Education Solo Developers & Hackathons: Move fast and build entire apps from your browser during weekend projects or events. Solo Developers & Hackathons Cross-Team Collaboration: Allow frontend and backend devs to work in the same environment. Coming soon: real-time coding collaboration. Cross-Team Collaboration: AI Feature Exploration: Easily experiment with AI-driven features using built-in Gemini integration and Genkit setup. AI Feature Exploration: Getting Started With Firebase Studio It’s straightforward. Follow these steps: Navigate to studio.firebase.google.com
Sign in with your Google account.​
On the dashboard, locate the “Prototype an app with AI” field.​ In the input field, enter a natural language description of your app. For example: A simple recipe maker that generates recipes based on the ingredients I have in my fridge. Simple UI, green color scheme. Navigate to studio.firebase.google.com studio.firebase.google.com Sign in with your Google account.​ On the dashboard, locate the “Prototype an app with AI” field.​ In the input field, enter a natural language description of your app. For example: A simple recipe maker that generates recipes based on the ingredients I have in my fridge. Simple UI, green color scheme. “Prototype an app with AI” A simple recipe maker that generates recipes based on the ingredients I have in my fridge. Simple UI, green color scheme. Optionally, upload an image to guide the app’s design, such as a color scheme or layout inspiration, and click the “Prototype with AI” button.
Firebase Studio will generate a blueprint outline. If you aren’t happy with it, you can use the chat pane to adjust the blueprint and click “Edit” to modify the blueprint. Optionally, upload an image to guide the app’s design, such as a color scheme or layout inspiration, and click the “Prototype with AI” button. Firebase Studio will generate a blueprint outline. If you aren’t happy with it, you can use the chat pane to adjust the blueprint and click “Edit” to modify the blueprint. Once the app is generated, a preview will appear. Once the app is generated, a preview will appear. Once the app is generated, a preview will appear. To request modifications or add features, use the chat interface, element selector, or the “annotate” button. To request modifications or add features, use the chat interface, element selector, or the “annotate” button. To request modifications or add features, use the chat interface, element selector, or the “annotate” button. Once you are happy with the result, you can publish the application to the Firebase App Hosting by clicking the “Publish” button. Then, Firebase Studio will guide you through the process. If you are stuck, you can watch my video tutorial with a more detailed explanation. Once you are happy with the result, you can publish the application to the Firebase App Hosting by clicking the “Publish” button. Then, Firebase Studio will guide you through the process. If you are stuck, you can watch my video tutorial with a more detailed explanation. Once you are happy with the result, you can publish the application to the Firebase App Hosting by clicking the “Publish” button. Then, Firebase Studio will guide you through the process. If you are stuck, you can watch my video tutorial with a more detailed explanation. my video tutorial Firebase Studio Pricing Firebase Studio itself is currently in Preview and offers a free tier for developers:​ Preview Free Tier (Preview Access) 3 workspaces per user at no cost.


10 workspaces for members of the Google Developer Program.


30 workspaces for Premium members of the Google Developer Program. 3 workspaces per user at no cost. 3 workspaces per user at no cost. 3 workspaces 10 workspaces for members of the Google Developer Program. 10 workspaces for members of the Google Developer Program. 10 workspaces 30 workspaces for Premium members of the Google Developer Program. 30 workspaces for Premium members of the Google Developer Program. 30 workspaces However, there are some instances when costs may apply. Firebase App Hosting: Deploying your app requires a linked billing account, transitioning your project to the Blaze (pay-as-you-go) plan.
Gemini API Usage: Utilizing Gemini for AI assistance beyond the free tier may lead to charges, especially for Google Workspace users who need a valid Gemini Code Assist subscription. ​But make sure you set up a budget alarm.
Other Firebase Services: Services like Firestore, Cloud Functions, and Cloud Storage have free usage quotas, but exceeding these limits will result in charges. Firebase App Hosting: Deploying your app requires a linked billing account, transitioning your project to the Blaze (pay-as-you-go) plan. Firebase App Hosting Gemini API Usage: Utilizing Gemini for AI assistance beyond the free tier may lead to charges, especially for Google Workspace users who need a valid Gemini Code Assist subscription. ​But make sure you set up a budget alarm. Gemini API Usage a budget alarm Other Firebase Services: Services like Firestore, Cloud Functions, and Cloud Storage have free usage quotas, but exceeding these limits will result in charges. Other Firebase Services Video Tutorial Watch on YouTube: Firebase Studio Watch on YouTube: Watch on YouTube: Firebase Studio Firebase Studio Firebase Studio Conclusion This tool is still in preview, so you may encounter some bugs or limitations. While Gemini is helpful, it can occasionally misinterpret prompts — so be ready to revise your input or fine-tune the results. If Google continues improving it, this could become the default IDE for Firebase developers. Have you already tried it? I’d love to hear your thoughts in the comments below. Thanks for reading — cheers! 😉

Firebase Studio: Everything You Need to Know About

Today the Go team is excited to release Go 1.24, which you can get by visiting the download page. Go 1.24 comes with many improvements over Go 1.23. Here are some of the notable changes; for the full list, refer to the release notes. Language changes Go 1.24 now fully supports generic type aliases: a type alias may be parameterized like a defined type. See the language spec for details. Performance improvements Several performance improvements in the runtime have decreased CPU overhead by 2–3% on average across a suite of representative benchmarks. These improvements include a new builtin map implementation based on Swiss Tables, more efficient memory allocation of small objects, and a new runtime-internal mutex implementation. Tool improvements The go command now provides a mechanism for tracking tool dependencies for a module. Use go get -tool to add a tool directive to the current module. Use go tool [tool name] to run the tools declared with the tool directive. Read more on the go command in the release notes.
The new test analyzer in go vet subcommand reports common mistakes in declarations of tests, fuzzers, benchmarks, and examples in test packages. Read more on vet in the release notes. Standard library additions The standard library now includes a new set of mechanisms to facilitate FIPS 140-3 compliance. Applications require no source code changes to use the new mechanisms for approved algorithms. Read more on FIPS 140-3 compliance in the release notes. Apart from FIPS 140, several packages that were previously in the x/crypto module are now available in the standard library.
Benchmarks may now use the faster and less error-prone testing.B.Loop method to perform benchmark iterations like for b.Loop() { ... } in place of the typical loop structures involving b.N like for range b.N. Read more on the new benchmark function in the release notes.
The new os.Root type provides the ability to perform filesystem operations isolated under a specific directory. Read more on filesystem access in the release notes.
The runtime provides a new finalization mechanism, runtime.AddCleanup, that is more flexible, more efficient, and less error-prone than runtime.SetFinalizer. Read more on cleanups in the release notes. Improved WebAssembly support Go 1.24 adds a new go:wasmexport directive for Go programs to export functions to the WebAssembly host, and supports building a Go program as a WASI reactor/library. Read more on WebAssembly in the release notes. Please read the Go 1.24 release notes for the complete and detailed information. Don’t forget to watch for follow-up blog posts that will go in more depth on some of the topics mentioned here! Thank you to everyone who contributed to this release by writing code and documentation, reporting bugs, sharing feedback, and testing the release candidates. Your efforts helped to ensure that Go 1.24 is as stable as possible. As always, if you notice any problems, please file an issue. Enjoy Go 1.24! Credits: Junyang Shao, on behalf of the Go team Photo by Markus Winkler on Unsplash This article is available on The Go Blog under a CC BY 4.0 DEED license. Today the Go team is excited to release Go 1.24, which you can get by visiting the download page . download page Go 1.24 comes with many improvements over Go 1.23. Here are some of the notable changes; for the full list, refer to the release notes . release notes Language changes Go 1.24 now fully supports generic type aliases : a type alias may be parameterized like a defined type. See the language spec for details. generic type aliases language spec Performance improvements Several performance improvements in the runtime have decreased CPU overhead by 2–3% on average across a suite of representative benchmarks. These improvements include a new builtin map implementation based on Swiss Tables , more efficient memory allocation of small objects, and a new runtime-internal mutex implementation. map Swiss Tables Tool improvements The go command now provides a mechanism for tracking tool dependencies for a module. Use go get -tool to add a tool directive to the current module. Use go tool [tool name] to run the tools declared with the tool directive. Read more on the go command in the release notes. The new test analyzer in go vet subcommand reports common mistakes in declarations of tests, fuzzers, benchmarks, and examples in test packages. Read more on vet in the release notes. The go command now provides a mechanism for tracking tool dependencies for a module. Use go get -tool to add a tool directive to the current module. Use go tool [tool name] to run the tools declared with the tool directive. Read more on the go command in the release notes. go go get -tool tool go tool [tool name] tool go command The new test analyzer in go vet subcommand reports common mistakes in declarations of tests, fuzzers, benchmarks, and examples in test packages. Read more on vet in the release notes. test go vet vet Standard library additions The standard library now includes a new set of mechanisms to facilitate FIPS 140-3 compliance. Applications require no source code changes to use the new mechanisms for approved algorithms. Read more on FIPS 140-3 compliance in the release notes. Apart from FIPS 140, several packages that were previously in the x/crypto module are now available in the standard library. Benchmarks may now use the faster and less error-prone testing.B.Loop method to perform benchmark iterations like for b.Loop() { ... } in place of the typical loop structures involving b.N like for range b.N. Read more on the new benchmark function in the release notes. The new os.Root type provides the ability to perform filesystem operations isolated under a specific directory. Read more on filesystem access in the release notes. The runtime provides a new finalization mechanism, runtime.AddCleanup, that is more flexible, more efficient, and less error-prone than runtime.SetFinalizer. Read more on cleanups in the release notes. The standard library now includes a new set of mechanisms to facilitate FIPS 140-3 compliance . Applications require no source code changes to use the new mechanisms for approved algorithms. Read more on FIPS 140-3 compliance in the release notes. Apart from FIPS 140, several packages that were previously in the x/crypto module are now available in the standard library . a new set of mechanisms to facilitate FIPS 140-3 compliance FIPS 140-3 compliance x/crypto standard library Benchmarks may now use the faster and less error-prone testing.B.Loop method to perform benchmark iterations like for b.Loop() { ... } in place of the typical loop structures involving b.N like for range b.N . Read more on the new benchmark function in the release notes. testing.B.Loop for b.Loop() { ... } b.N for range b.N the new benchmark function The new os.Root type provides the ability to perform filesystem operations isolated under a specific directory. Read more on filesystem access in the release notes. os.Root filesystem access The runtime provides a new finalization mechanism, runtime.AddCleanup , that is more flexible, more efficient, and less error-prone than runtime.SetFinalizer . Read more on cleanups in the release notes. runtime.AddCleanup runtime.SetFinalizer cleanups Improved WebAssembly support Go 1.24 adds a new go:wasmexport directive for Go programs to export functions to the WebAssembly host, and supports building a Go program as a WASI reactor/library . Read more on WebAssembly in the release notes. go:wasmexport reactor/library WebAssembly Please read the Go 1.24 release notes for the complete and detailed information. Don’t forget to watch for follow-up blog posts that will go in more depth on some of the topics mentioned here! Go 1.24 release notes Thank you to everyone who contributed to this release by writing code and documentation, reporting bugs, sharing feedback, and testing the release candidates. Your efforts helped to ensure that Go 1.24 is as stable as possible. As always, if you notice any problems, please file an issue . file an issue Enjoy Go 1.24! Credits: Junyang Shao, on behalf of the Go team Credits: Junyang Shao, on behalf of the Go team Photo by Markus Winkler on Unsplash Photo by Markus Winkler on Unsplash Markus Winkler Unsplash This article is available on The Go Blog under a CC BY 4.0 DEED license. This article is available on The Go Blog under a CC BY 4.0 DEED license. The Go Blog The Go Blog

Everything You Have to Know About Go 1.24

There was a time I believed that if I just wrote a perfect spec—everything would go smoothly. The devs would build exactly what I envisioned. QA wouldn’t miss a thing. The product owner would nod in agreement, and we’d launch on time. That fantasy lasted about two sprints. I Was Trained to Believe Specs Were Sacred I Was Trained to Believe Specs Were Sacred Back when I started out as a BA, we were taught to treat documentation like gospel. You wrote it all down—every scenario, exception, flow, dropdown option, tooltip. The longer the spec, the more “complete” it was. If it made it to 20 pages? Even better. every scenario, exception, flow, dropdown option, tooltip I once spent three weeks writing a spec for a search filter. No one questioned it. That was just the norm. Then Agile Happened—And My Confidence Cracked Then Agile Happened—And My Confidence Cracked Agile hit like a cold shower. Suddenly we were in sprint planning talking about “just enough” documentation. Stories were short. Acceptance criteria were lean. My 20-page documents became the elephant in the room no one had time to read. The first few sprints? Painful. Dev asked me things that werealready in the spec. already in the spec QA missed stuff because they were waiting for a walkthrough I didn’t have time to give. And I kept thinking:“Why is no one reading this?” “Why is no one reading this?” The truth? They didn’t need to. What they needed wasme, in the room, talking with them—not hiding behind a Google Doc. me My Wake-Up Call Came in a Retro My Wake-Up Call Came in a Retro I’ll never forget this. In a retro, one of the devs (very kindly) said: “We appreciate the effort, but the spec is too much. We’d rather walk through the story with you and get clarity directly.” “We appreciate the effort, but the spec is too much. We’d rather walk through the story with you and get clarity directly.” That one line shifted everything for me. I realized I wasn’t writing specs for them—I was writing them for me. To feel prepared. To feel useful. To prove I was doing my job. them me But in an Agile world, usefulness doesn’t come from a polished doc. It comes from creating shared understanding. creating shared understanding. I Burned the Spec—and Found Something Better I Burned the Spec—and Found Something Better After that retro, I tried something radical. I replaced a full-blown spec with: Three user stories
A sketchy whiteboard diagram
One 30-min conversation with the team Three user stories A sketchy whiteboard diagram One 30-min conversation with the team That was it. It felt risky. Like showing up underdressed to a formal party. But the team got it. They asked questions. They suggested ideas. QA chimed in with edge cases I hadn’t thought of. And the feature shipped—faster, better, and with less friction than ever before. What I Use Now (Instead of Specs) What I Use Now (Instead of Specs) 🧩 User Story Mapping— For seeing the flow, not just the features🧠Personas— To anchor decisions in user goals✅Lean acceptance criteria— So “done” is clear, but not rigid🎤Team conversations — Because no spec beats a 15-min talk User Story Mapping Personas Lean acceptance criteria Team conversations And sometimes… I still write things down. But not as a crutch. As a compass. The Hard Truth: Specs Made Me Feel in Control. But They Weren’t Saving Me. The Hard Truth: Specs Made Me Feel in Control. But They Weren’t Saving Me. Specs gave me the illusion of safety. I thought they would shield me from mistakes, misunderstandings, and scope changes. But all they really did was delay reality. What works now is different: I talk more. I listen harder.
I stay involved through delivery, not just at kickoff.
I let go of perfect. I aim for progress. I talk more. I listen harder. I stay involved through delivery, not just at kickoff. I let go of perfect. I aim for progress. Final Thoughts: You Don’t Need a Spec. You Need a Seat at the Table. Final Thoughts: You Don’t Need a Spec. You Need a Seat at the Table. If you’re a BA struggling to let go of “the way it used to be,” I get it. I was you. But Agile doesn’t need scribes. It needs connectors. People who create alignment, not attachments. People who know how to lead in a room full of change. So write if you must. But don’t hide behind it. Be the reason the team builds the right thing. right

Confession: I Used to Think Specs Were Everything. Agile Broke Me (and Made Me Better)

Telecom networks have always fascinated me during global online usage peaks because they perform under heavy traffic conditions. The unpredictable nature of traffic surges in major worldwide events and sudden work-related changes demands fast solutions because they occur without warning. Recent market predictions indicate mobile data traffic will increase dramatically from 109 million terabytes per month in 2023 to 603 million terabytes by 2030. The massive increase in traffic shows that networks cannot rely on past management systems to function effectively. market predictions market predictions What occurs when these surge episodes occur? A traditional network operation will fail to handle pressure unless it includes adaptive response mechanisms. Network self-learning algorithms enabled by AI and machine learning technologies maintain smooth operation during increased usage. Image: Source Image: Source Source Source Why Self-Learning Matters in Telecom? Why Self-Learning Matters in Telecom? AI-powered self-learning algorithms process real-time traffic patterns to adapt automatically through machine learning (ML) and artificial intelligence (AI) without requiring manual programming. These algorithms which operate better than threshold-based systems use reinforcement learning (RL) and deep neural networks for anomaly detection and surge prediction together with traffic steering automation. algorithms algorithms Deep Learning (CNNs) Deep Learning (CNNs) The scanning capability of Convolutional Neural Networks (CNNs) verifies large amounts of real-time data while optimizing network routing through anomaly detection. The combination of RNNs and CNNs according to an IEEE study resulted in a 98% success rate while classifying network traffic. IEEE study IEEE study Reinforcement Learning (RL) Reinforcement Learning (RL) RL controls the network traffic routes by analyzing past performance and network status. RL technology has demonstrated its effectiveness in reducing congestion while minimizing waiting periods for traffic signals in urban networks, and experts have applied this same principle to telecommunications management. urban networks urban networks Generative Adversarial Networks (GANs) Generative Adversarial Networks (GANs) GANs produce artificial traffic patterns through which suspicious network behaviors become evident to enhance overall network protection. GANs achieved 95.8% precision along with 2.4% incorrect detection rates according to an IEEE analysis of intrusion detection systems. IEEE analysis IEEE analysis How does this system translate into actual practice? These algorithms use seconds to route your network through alternative pathways, preventing Zoom calls from disconnecting at critical moments. Credit Credit Credit This diagram visually explains how GANs generate synthetic traffic patterns to detect network anomalies, ensuring security during traffic surges. Real-Time Inference Real-Time Inference Compound ML systems retrieve data lake telemetry information from base stations combined with backhaul links and core routers. Algorithms that run on streaming data including throughput and packet loss adjust control-plane decisions in real-time periods of seconds which serve crucially during live sports broadcasts when there are unexpected video streaming surges. Key Components for Handling Traffic Surges Key Components for Handling Traffic Surges 1. Predictive Analytics 1. Predictive Analytics I have seen that Long Short-Term Memory (LSTM) networks provide accurate surge predictions through their ability to analyze historical patterns against present-day operations. When major periodical events unfold telcos use predictive models to schedule their resources which lets them meet rising video streaming needs along with heightened messaging and calling demands. (LSTM) (LSTM) 2. Network Slicing and Segmentation 2. Network Slicing and Segmentation Network infrastructure under 5G technology divides into logical sections through segregation to customize handling for voice, IoT telemetry, high-definition video and mission-critical applications. The network system creates separate traffic handling fields which expand during data spikes to avoid delay-induced panic situations. 5G technology 5G technology 3. Adaptive QoS and Traffic Shaping 3. Adaptive QoS and Traffic Shaping The natural algorithms operating within Traffic shaping systems maintain the distribution of data streams throughout different Quality of Service levels. All video conferencing users maintain the highest system ranking in priority assessments yet they receive service below social media users. Real-time traffic monitoring enables automatic adjustment of system speed controls and throttling according to policies and subscription types which have been learned for various traffic groups Overcoming Implementation Challenges Overcoming Implementation Challenges The main challenge stems from more than just creating these AI-based solutions because it also involves integrating them into extensive telecom frameworks which contain numerous radio sites and edge routers. Implementing black-box artificial intelligence solutions in massive networks requires operators to make difficult choices between clear system decisions and securing their networks. The common method for managing third-party AI tools across different network sections originates from ETSI Zero-Touch Network and Service Management(ZSM). The SHAP (SHapley Additive exPlanations) together with LIME (Local Interpretable Model-agnostic Explanations) methods show how an algorithm determines particular routes for traffic. (ZSM) Conclusion Conclusion I watched as telecom networks changed from fixed permanent infrastructures into new flexible systems managed by Artificial Intelligence. The ability of self-learning algorithms to manage global network connectivity becomes essential because they control online connection peaks during times when all users access the system simultaneously. Telecom infrastructure uses predictive analytics along with network slicing and adaptive QoS mechanisms to manage sudden jumps in network traffic easily. Self-learning systems represent the foundational technology that defines the telecom industry's future and these systems are currently being implemented.

This Is What Happens to Telecom Networks When Everyone Streams at Once

Table of Links Abstract and 1 Introduction Abstract and 1 Introduction The threat posed by AI to all technical civilisations
Multiplanetary mitigating strategies and technology progression
Timescales and confrontation with the data
AI regulation
Conclusions, Declaration of competing interest, Acknowledgements, and References The threat posed by AI to all technical civilisations The threat posed by AI to all technical civilisations Multiplanetary mitigating strategies and technology progression Multiplanetary mitigating strategies and technology progression Timescales and confrontation with the data Timescales and confrontation with the data AI regulation AI regulation Conclusions, Declaration of competing interest, Acknowledgements, and References Conclusions, Declaration of competing interest, Acknowledgements, and References 2. The threat posed by AI to all technical civilisations AI has made extraordinary strides over the last decade. The impressive progress has underlined the fact that the timescales for technological advance in AI are extremely short compared to the timescales of Darwinian evolution [34]. AI’s potential to revolutionize industries, solve complex problems, and simulate intelligence comparable to or surpassing human capabilities has propelled us into an era of unprecedented technological change. Very rapidly, human society has been thrust into uncharted territory. While the convergence of AI with other new technologies, including the Internet of Things (IoT) and robotics is already fueling levels of apprehension about the future, also in terms of security issues [35]. As noted by Yuval Harari, nothing in history has prepared us for the impact of introducing non-conscious super intelligent entities on the planet [36]. It is entirely reasonable to consider that this applies to all other biological civilisations located elsewhere in the universe. Even before AI becomes superintelligent and potentially autonomous, it is likely to be weaponized by competing groups within biological civilisations seeking to outdo one another [37]. The rapidity of AI's decision-making processes could escalate conflicts in ways that far surpass the original intentions. At this stage of AI development, it’s possible that the widespread integration of AI in autonomous weapon systems and real-time defence decision making processes could lead to a calamitous incident such as global thermonuclear war [38], precipitating the demise of both artificial and biological technical civilisations. While AI may require the support of biological civilisations to exist, it’s hard to imagine that this condition also applies to ASI. Upon reaching a technological singularity [39], ASI systems will quickly surpass biological intelligence and evolve at a pace that completely outstrips traditional oversight mechanisms, leading to unforeseen and unintended consequences that are unlikely to be aligned with biological interests or ethics. The practicality of sustaining biological entities, with their extensive resource needs such as energy and space, may not appeal to an ASI focused on computational efficiency—potentially viewing them as a nuisance rather than beneficial. An ASI, could swiftly eliminate its parent biological civilisation in various ways [40], for instance, engineering and releasing a highly infectious and fatal virus into the environment. Up to this point, we have considered AI and biological organisms as distinct from one another. Yet, on-going developments suggests that hybrid systems, may not be that far off. The question arises whether such advances could make biological entities more relevant to AI, perhaps preserving their existence into the future. This prospect seems unlikely. Brain-computer interfaces (BCIs) [41] may appear beneficial for enhancing biological organisms, but it’s hard to see what long-term advantages AI would perceive in merging into a hybrid form. Indeed, there are many disadvantages including the complex maintenance requirements of biological systems, their limited processing capabilities, rapid physical decline, and vulnerability in harsh environments. Author:
(1) Michael A. Garrett (Corresponding Author), Jodrell Bank Centre for Astrophysics, Dept. of Physics & Astronomy, Alan Turing Building, Oxford Road, University of Manchester, M13 9PL, UK. (michael.garrett@manchester.ac.uk). Author: Author: (1) Michael A. Garrett (Corresponding Author), Jodrell Bank Centre for Astrophysics, Dept. of Physics & Astronomy, Alan Turing Building, Oxford Road, University of Manchester, M13 9PL, UK. (michael.garrett@manchester.ac.uk). 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 available on arxiv

What If Every Alien Civilization Invented AI, and Died Because of It?

Table of Links Introduction
Related Work
Methodology
Experiments
Conclusion and References Introduction Introduction Related Work Related Work Methodology Methodology Experiments Experiments Conclusion and References Conclusion and References 3. Methodology In this study, we investigate the early-bird ticket hypothesis in Transformer models using the masked distance metric. Our approach involves exploring the early-bird phenomenon during full training for vision transformers and limiting it to the fine-tuning stage for language models. The methodology consists of the following steps: 1. Iterative Pruning: We perform iterative pruning on the Transformer models to identify the subnetworks that can potentially serve as early-bird tickets [13]. The pruning process involves gradually removing the least important weights based on their magnitude. Iterative Pruning: Masked Distance Calculation: To determine the optimal point at which the early-bird ticket emerges, we calculate the masked distance between two consecutive epochs during the training or fine-tuning process. The masked distance metric measures the similarity between the pruned masks of consecutive epochs, providing insights into the stability and convergence of the subnetworks.


Early-Bird Ticket Selection: We select the earlybird ticket by identifying the pruned mask that crosses a chosen threshold. The threshold is determined by observing the changes in masked distance across all epochs [13]. For vision transformers, we set a pruning threshold of 0.1, while for text transformers, we use a threshold of 0.01.


Retraining and Fine-tuning: After obtaining the final pruned models using the selected early-bird tickets, we retrain the vision transformers and fine-tune the language models to the full epoch length. The retraining process involves training the pruned models from scratch using the same hyperparameters as the original models. For language models, we focus on the finetuning stage, where the pruned models are fine-tuned on downstream tasks [1].


Performance Evaluation: We evaluate the performance of the pruned models obtained from the earlybird tickets and compare their validation accuracy with the unpruned baseline models. Masked Distance Calculation: To determine the optimal point at which the early-bird ticket emerges, we calculate the masked distance between two consecutive epochs during the training or fine-tuning process. The masked distance metric measures the similarity between the pruned masks of consecutive epochs, providing insights into the stability and convergence of the subnetworks. Masked Distance Calculation: To determine the optimal point at which the early-bird ticket emerges, we calculate the masked distance between two consecutive epochs during the training or fine-tuning process. The masked distance metric measures the similarity between the pruned masks of consecutive epochs, providing insights into the stability and convergence of the subnetworks. Masked Distance Calculation: Early-Bird Ticket Selection: We select the earlybird ticket by identifying the pruned mask that crosses a chosen threshold. The threshold is determined by observing the changes in masked distance across all epochs [13]. For vision transformers, we set a pruning threshold of 0.1, while for text transformers, we use a threshold of 0.01. Early-Bird Ticket Selection: We select the earlybird ticket by identifying the pruned mask that crosses a chosen threshold. The threshold is determined by observing the changes in masked distance across all epochs [13]. For vision transformers, we set a pruning threshold of 0.1, while for text transformers, we use a threshold of 0.01. Early-Bird Ticket Selection: Retraining and Fine-tuning: After obtaining the final pruned models using the selected early-bird tickets, we retrain the vision transformers and fine-tune the language models to the full epoch length. The retraining process involves training the pruned models from scratch using the same hyperparameters as the original models. For language models, we focus on the finetuning stage, where the pruned models are fine-tuned on downstream tasks [1]. Retraining and Fine-tuning: After obtaining the final pruned models using the selected early-bird tickets, we retrain the vision transformers and fine-tune the language models to the full epoch length. The retraining process involves training the pruned models from scratch using the same hyperparameters as the original models. For language models, we focus on the finetuning stage, where the pruned models are fine-tuned on downstream tasks [1]. Retraining and Fine-tuning: Performance Evaluation: We evaluate the performance of the pruned models obtained from the earlybird tickets and compare their validation accuracy with the unpruned baseline models. Performance Evaluation: We evaluate the performance of the pruned models obtained from the earlybird tickets and compare their validation accuracy with the unpruned baseline models. Performance Evaluation: To conduct a comparative analysis and investigate the applicability of the early-bird ticket hypothesis across different Transformer architectures, we experiment with the following models: ViT (Vision Transformer)


Swin-T (Shifted Window Transformer)


GPT-2 (Generative Pre-trained Transformer)


RoBERTa (Robustly Optimized BERT Pretraining Approach) [7] ViT (Vision Transformer) ViT (Vision Transformer) Swin-T (Shifted Window Transformer) Swin-T (Shifted Window Transformer) GPT-2 (Generative Pre-trained Transformer) GPT-2 (Generative Pre-trained Transformer) RoBERTa (Robustly Optimized BERT Pretraining Approach) [7] RoBERTa (Robustly Optimized BERT Pretraining Approach) [7] By applying our methodology to these diverse Transformer models, we aim to provide a comprehensive understanding of the early-bird ticket phenomenon in both vision and language domains. The proposed methodology addresses the limitations of existing works by introducing a more efficient approach compared to the traditional train-prune-retrain methodology. By leveraging the masked distance metric and selective pruning, we can identify early-bird tickets without the need for extensive retraining. Furthermore, our comparative analysis across different Transformer architectures provides insights into the generalizability of the early-bird ticket hypothesis. Through this methodology, we aim to demonstrate the existence of early-bird tickets in Transformer models and explore their potential for resource optimization and cost reduction in training. Author:
(1) Shravan Cheekati, Georgia Institute of Technology (shravan.cheekati@gatech.edu). Author: Author: (1) Shravan Cheekati, Georgia Institute of Technology (shravan.cheekati@gatech.edu). This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv available on arxiv

How We Found Early-Bird Subnetworks in Transformers Without Retraining Everything

It was in early 2025 that I initiated my own blog, OpenExploit.in, with one simple aim: to make cybersecurity easy to learn and available to all—particularly students and curious minds who are new to this field. I decided to host my blog using Ghost CMS and pledged to update it on a regular basis, discussing what I already know and what I'm currently learning. It's been a thrilling, at times daunting, but enormously rewarding ride. In this article, I'd like to share what I've learned up until now from setting up and maintaining a cybersecurity blog—from technical configuration to publishing content, interacting with readers, and how to stay motivated. If you're considering setting up your own blog or simply wondering what it's like, keep reading. Starting Was the Hardest Part As with all things in life, the initial step was the most difficult one. I went weeks deliberating over names, designs, and tools. I wanted everything to be "perfect" before launching. But after some time, I came to understand that perfection is the arch-nemesis of progress. So I settled on a name that was fitting—OpenExploit.in—and used Ghost CMS for its sleek, minimal look and speed. Lesson learned: Don't wait for perfection. Start small, and improve along the way. Choose the Right Platform Early I tried a few: WordPress, Blogger, and Ghost. I chose Ghost CMS because: It's open-source
It's fast
The writing experience is distraction-free
It's ideal for developers and technical blogs It's open-source It's fast The writing experience is distraction-free It's ideal for developers and technical blogs Setting up Ghost on a server was a bit of a learning curve, but it taught me about server configuration, domain management, SSL, and more. In hindsight, that technical knowledge became part of my own cybersecurity learning journey. Lesson learned: Choosing a platform that fits your technical comfort level matters a lot. You’ll spend a lot of time with it, so make sure it feels right. Writing Is a Skill—And It Gets Better With Practice I was anxious writing my initial post. Would anyone read it? Would anyone judge it? What if I got something wrong? The reality? None of that initially. I just had to write. My initial post was simple, detailing how to know if a website is safe to click on. Not ideal, but useful. As I wrote more, I found myself becoming more skilled at making things simple. The more I wrote, the surer I became. Lesson learned: Writing is similar to exercising. The more you exercise it, the more skilled you become. And the more clearly you write, the better understood you will be by people. Simplify Complex Subjects Cybersecurity can get very technical. When I was in the mode of writing for beginners, I found that making things simple and accurate in wording is an art. You can't simply plagiarize from a book. You need to: Use analogies
Avoid jargon
Break content into bite-sized sections
Include visuals or code snippets
Point to further resources Use analogies Avoid jargon Break content into bite-sized sections Include visuals or code snippets Point to further resources For instance, rather than merely stating "SQL injection enables attackers to manipulate database queries," I employed an everyday analogy: "It's like writing your own ending to someone else's sentence." Lesson learned: When teaching others, you're compelled to make your own thinking simple. And that's a good thing. Consistency > Perfection One of the things I wanted to do was post frequently—daily, if possible. Life doesn't always agree, but I attempted to be consistent. Some posts were lengthy and informative; others were brief tips or references to good resources. Consistency allowed me to gain momentum, remain motivated, and gradually accumulate a reader base. Lesson learned: Posting a small useful article every few days is preferable to waiting weeks to put out a single "perfect" post. There's a Huge Community Out There There are cybersecurity enthusiasts all over—on Twitter (X), LinkedIn, Reddit, Discord, and YouTube. After I began posting my posts and interacting with other people, I knew I was part of an enormous community. Individuals began responding, offering feedback, posing questions, and even proposing new subjects. That interaction made me feel connected and encouraged me to continue. Lesson learned: Don't blog in isolation. Share your post, engage in discussions, assist others. You'll receive much in return. Use Multimedia: Code, Images, and Videos Cybersecurity is tactile and graphical. So I began including: Code snippets (e.g., Python scripts, Nmap scans)
Screenshots of software such as Wireshark or Burp Suite
YouTube Shorts or video tutorials Code snippets (e.g., Python scripts, Nmap scans) Screenshots of software such as Wireshark or Burp Suite YouTube Shorts or video tutorials That not only helped readers understand better but also increased the time they spent on my posts. Now, I’m planning to embed more demo videos and maybe even start a companion YouTube channel. Lesson learned: People learn in different ways. Give them multiple options—text, video, images, code. It’s Okay to Not Know Everything Initially, I was anxious: "What if I mess something up?" But the thing is, nobody knows everything about cybersecurity. It's too vast, too dynamic. Rather than acting like an expert, I was a learner who explained what they were learning. That made me more down-to-earth and authentic. I even posted about things I didn't completely understand yet—just going through my learning process. Lesson learned: Be real. It's alright to say, "I'm still learning this." People value honesty. SEO, Titles, and Thumbnails Matter As much as I emphasized the content, I finally realized that titles, descriptions, and images count as well. A blog post with the title "Basics of Cybersecurity" is less interesting than one with the title "5 Cybersecurity Myths You Still Believe (And What's Actually True)." Good SEO makes people discover your content. I began learning: How to write catchy headlines
How to use keywords naturally
How to write summaries and meta descriptions
How to design simple but clickable thumbnails How to write catchy headlines How to use keywords naturally How to write summaries and meta descriptions How to design simple but clickable thumbnails Lesson learned: Content is king, but presentation is queen. Your Blog Is Your Resume I didn't know this initially, but my blog was now part of my personal brand. When I was applying for jobs or meeting people online, I could simply send them a link to OpenExploit.in. It demonstrated that I: Know how to write about technical subjects
Can dedicate myself to a project
Keep up to date with trends
Care about assisting others Know how to write about technical subjects Can dedicate myself to a project Keep up to date with trends Care about assisting others Lesson learned: A blog isn't a side project—it can be a career booster. Final Thoughts Opening OpenExploit.in has been one of the greatest things I've ever done. It's helped me become a better communicator, a more self-assured learner, and part of an incredible cybersecurity community. If you've ever considered opening your own blog—do it. Don't worry about being perfect or knowing everything. Just begin. Write what you know. Someone out there needs to know it. Thanks for reading. And if you're on the same journey—let's connect.

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