Increasingly, it’s up to mid-market CFOs to embrace practical, scalable solutions that balance risk mitigation with the pursuit of growth.

Cybersecurity Threats Hit Mid-Market Firms Where It Hurts: The Bottom Line | PYMNTS.com

Some corporate leaders say remote work is a “Covid-era privilege,” deploying return-to-office (RTO) mandates, and others are following suit. But experts insist that “work from anywhere isn’t going anywhere” in 2025 because it’s best for employees and boosts company profits.

New Research Suggests Remote Jobs Are Best For Company’s Bottom Line

The Jets will take on the Ducks on Thursday evening.

Line Combinations: Jets vs. Ducks - Yahoo Sports

Winnipeg will take on Toronto from Ontario on Monday afternoon.

Line Combinations: Jets at Leafs - Yahoo Sports

Cybersecurity isn’t just for tech experts — it’s a responsibility that affects every person, organization, and even entire nations.

Cybersecurity: The New Front Line and Why Everyone Should Care | by Christi Lee | Dec, 2024 | Medium

By seamlessly integrating credit lines, overdrafts, and retail loans with UPI, this product ensures hassle-free access to funds, making credit more accessible and convenient. It not only promotes economic growth but also strengthens financial inclusion by addressing the immediate financial ...

RBI Allows Small Finance Banks To Extend Credit Line On UPI To Customers - News18

The Jets will clash with the Blues on Tuesday night.

Line Combinations: Jets vs. Blues - Yahoo Sports

Line Next, the blockchain-focused arm of Line, is collaborating with Web3 development firm Kaia to create a mini-game platform directly embedded into the Line messaging app.

Line Gears Up to Transform Messaging with Web3 Mini-Games

Kaia and Line Next have announced they are launching their web3 mini-dapp platform within Line Messenger at the end of January 2025.

Line Messenger's web3 mini-dapps to go live in January 2025 - BlockchainGamerBiz

Line

line

Binance to launch crypto-fiat exchange in Uganda, Korea’s new Crypto regulation; Messaging App line to release an exchange; JD working with Huobi What Crypto insiders are reading on Asia. Subscribe now 🌟 Top News Binance is supporting the Ugandan Shilling, alongside major cryptocurrencies. about to launch a crypto-fiat exchange in Uganda, http://bit.ly/2tCsO7w Korea pertaining to AML and KYC requirements for crypto exchanges. It’s investigating into three major banks and having banks alert suspicious fund movements to avoid the “kimchi premium” from happening. has revealed a new crypto regulatory framework and guidelines http://bit.ly/2N5OhOg is including bitcoin, ethereum and litecoin, at a trading fee of 0.1%. The Japanese messaging app makes a big push into financial services to compete against WhatsApp and WeChat. Line launching its own exchange supporting 30 cryptocurrencies, https://on.ft.com/2ySsKWa JD most recently was supported by the JD.com’s cloud arm is working with Huobi to build blockchain-based technology and applications. Chinese gov’t to create asset backed tokens. 💰 Coin and Exchange News China’s in a move towards European market. Huobi opens London office http://bit.ly/2tCrscU after collaborating with worldwide exchanges, and it has also disclosed the accounts and crypto assets that were stolen during the breach. Hacked Bithumbit has now reduced its stolen damage by almost half http://bit.ly/2tMv0IL the first gaming platform and the first ICO to launch on VeChainThor platform, is having a live AMA. Plair, http://bit.ly/2tCqgX0 21 nodes is too few to deter cartels and currently nodes are dominated by Bitfinex, Huobi, Chinese, Koreas. Redditor claim EOS’s http://bit.ly/2tBUU2L 💼 Business and Regulation News to develop a cross-border trade finance system that is built with distributed ledger technology. Hong Kong’s de facto central bank is planning to work Abu Dhabi http://bit.ly/2tAzW4c Blockchain can legally authenticate evidence, Chinese judge rules. http://bit.ly/2tJU96R CEO of SBI, one of Japan’s financial giants and longtime Ripple partner in Asia, talked about his belief in blockchain to get Japan’s economy back on track to regain the global position it once held. http://bit.ly/2tCJJGT for crypto-related activities, including any capital transaction that relates to distributed ledger accounts, blockchain, cryptocurrency and its derivatives, ICOs and exchange operations. China Banking Regulatory Commission researchers are advocating that domestic regulators issue licenses http://bit.ly/2ySAxDw ❗ Hey there Did I miss anything big today? Share with me the link, if you saw anything interesting. If I’m swayed, I’ll include your bit tomorrow (with attribution)! 🌹🌹🌹 Check our past 3 weekly pieces on , , and Binance Bitmain China’s Crypto winners I’m conflicted about the news that Facebook may potentially acquire Coinbase, it reinforces centralization but then also bring legitimacy to the space. Perhaps the latter is more important right now.

Today’s Crypto News on Asia — Jun 28th

Abhaya Uprety never imagined he would find himself living in a small studio apartment in New Mexico, carefully rationing groceries while building software for a company struggling to find its footing. An international student who graduated in May 2020, Uprety co-founded ParkStash—a startup initially focused on making parking reservations work in a way similar to short-term rental platforms. “We started off with the idea of being a sort of Airbnb for parking, focusing on everyday drivers,” Uprety said. “But as an international student fresh out of college, I didn’t have much room for trial and error. The stakes were always high.” His journey with ParkStash began by working alongside founder and CEO Sameer, an international student at San Jose State University. Inspired by his own struggles with limited campus parking, Sameer set out to build a parking platform. “He saw a problem and thought, ‘What if we connect people who have spare parking spaces with those who desperately need them?’” recalled Uprety. Over time, Uprety became the technology hub of the operation, single-handedly creating the company’s software stack. He handled mobile development using Flutter and Android, wrote server-side code in NodeJS, built dashboards with React, and more. By May 2020, when he officially joined ParkStash as a co-founder, the company was still focused on a business-to-consumer (B2C) model—allowing drivers to find and book private parking spots for a fee. Although people grasped the concept, the timing was challenging, especially given the pandemic’s impact on daily commuting patterns. Revenues remained inconsistent, and the burn rate was high. “I moved to New Mexico in early 2022,” said Uprety, “and lived in low-income housing, with no car, just enough to pay the rent and buy food. Still, I believed wholeheartedly in what we were doing.” From B2C to B2B: A Pivotal Shift By late 2022, ParkStash decided on a major pivot: from catering directly to individual drivers to providing a streamlined software solution for parking lot owners, property managers, and commercial sites. “We realized quickly that our real value was in helping the people who manage multiple parking spots,” remarked Uprety. “B2C was a fantastic idea in principle, but the market conditions and revenue model weren’t lining up for us. We needed a stable business structure, and that’s when we refocused our efforts on B2B.” The results of this pivot began to show almost immediately. ParkStash introduced subscription-based services for parking lot owners, offering tools like automated gate access control, permit management, camera-based monitoring, towing integrations, and a centralized dashboard for enforcement. “We demonstrate to property managers how their parking lot can turn into a revenue-producing asset,” said Uprety. “They see the potential to manage permits, catch violations, and even implement special promotions for residents or commercial tenants. That’s where the real growth started.” It did not take long for ParkStash to prove its value proposition. According to the INRIX 2017 Global Traffic Scorecard, American drivers spend an average of 17 hours per year searching for parking, costing them around 345 dollars each in wasted time, fuel, and emissions (INRIX 2017 Global Traffic Scorecard). Meanwhile, the International Parking & Mobility Institute has reported that up to 30 percent of city traffic can be caused by motorists circling for a spot (International Parking & Mobility Institute, 2019). ParkStash’s pivot to B2B tackled these issues by enabling large sites to optimize the use of parking space, reduce congestion, and offer reliable spots. “It’s not just about convenience,” Uprety explained. “We’re also addressing a real economic and environmental challenge.” By November 2022, the startup had completely embraced the B2B model. As of 2024, it has expanded to manage roughly 50 sites under contract, offering its clients a monthly subscription fee and optional add-ons like camera surveillance. “We are finally seeing real financial stability,” noted Uprety. “It’s been a whirlwind, but this pivot saved the company.” A Personal Triumph and New Industry Impact Part of ParkStash’s success story lies in Uprety’s resilience, honed by personal financial struggles and the uncertainties of running a startup. “There were times I doubted everything,” he said. “I was barely scraping by. But I’m passionate about programming, and I’m passionate about solving big problems. That passion kept me going.” His vision extends far beyond just booking parking spots. ParkStash’s B2B solution integrates with hardware installations, camera providers like Eagle Eye Networks, and towing services to form a cohesive ecosystem for property owners. “When we partner with a property, we handle as much as they need,” he explained. “Gate access, software dashboards, user-friendly permit assignment, a towing company for immediate violations, even a portal where folks can pay citations securely. We piece it all together so it’s one smooth experience.” The impact of this model goes beyond individual driveways or commercial lots. Properly managed parking means fewer cars circling the block, which can help lessen congestion and reduce carbon emissions. “It’s rewarding to think that we’re contributing to a more sustainable urban environment,” Uprety said. “Any time a driver reserves a spot and goes straight there without driving around in circles, that’s fuel saved, money saved, and time saved.” ParkStash’s evolution has also fueled new partnerships and job creation. By contracting with hardware installers, surveillance camera vendors, and towing operators, the startup is boosting smaller businesses. “One of my favorite parts is seeing how local entrepreneurs and technicians benefit from working with us,” Uprety added. “Our model depends on collaboration, so when we grow, they grow too.” For property owners, ParkStash’s platform can transform a previously overlooked or under-utilized asset—the parking lot—into a managed, revenue-generating space. “Parking can be complicated,” said Uprety. “But once we set up proper signage, digital enforcement, and streamlined user apps, these lots become smarter, more secure, and more profitable.” The company’s approach aligns with broader shifts toward smart cities, where digital infrastructure meets everyday needs like transportation and energy usage. “We’re not just building an app,” he stressed. “We’re helping shape what urban life could look like in the future.” Looking Ahead: Freedom, Innovation, and the Next Chapter For all of ParkStash’s accomplishments, Uprety remains keenly focused on personal and professional growth. Living on a shoestring budget in New Mexico until late 2023 taught him the value of efficiency—not just in code, but in day-to-day life. “We run lean on purpose,” he said. “We don’t do endless meetings. We talk to customers, gather feedback, and then get back to work. The best ideas come directly from the people who are actually using the product.” This customer-driven approach continues to shape ParkStash’s development roadmap. One key request from clients has been the ability to use mobile devices for license plate scanning, enabling easier on-site enforcement without costly specialized hardware. “We’re prototyping a feature that turns a phone or tablet camera into a plate reader,” Uprety revealed. “If an apartment manager can walk around the lot with their device and immediately see whether a car has a valid permit, that’s an instant productivity boost.” AI enhancements are also on the horizon. ParkStash aims to introduce voice-assisted technology that allows drivers to book a spot or extend a reservation without tapping through multiple screens. “I love exploring how AI can make our tools even more convenient,” Uprety said. “But we want to do it in a way that’s genuinely helpful, not just flashy. If it doesn’t solve a real user need, we’re not interested in building it.” Uprety’s personal satisfaction also stems from the company’s flexible culture—one that prioritizes hands-on coding and minimal bureaucracy. “I enjoy the freedom to innovate,” he noted. “We’re not weighed down by massive organizational hierarchies. Every feature we release is a direct result of real feedback and quick decision-making. That’s important to me because it feels honest, efficient, and human.” His journey from a financially strained international student to a proven thought leader in the parking tech space is a testament to resilience, passion, and strategic pivoting. “I look back on those months in New Mexico and think, if I hadn’t struggled, I wouldn’t have learned half of what I know now,” he said. “Sometimes, you have to run into walls to figure out how to build a better door.” As for ParkStash, the mission continues to expand. “We started with a simple concept: help people find a parking spot. Now, we’re helping businesses, communities, and entire cities manage a critical part of their infrastructure,” Uprety explained. “When I see a property manager rave about how easy it is to run their lots now, that’s the best possible validation.” Long-term, the company plans to keep improving its products while venturing into related areas of urban mobility. Its rising reputation, especially in the U.S. market, suggests potential partnerships with larger tech players and municipalities keen on adopting smart city solutions. “We’re building something that can evolve,” he said. “New features, new integrations, new ways to streamline how people move through cities. That’s a challenge that excites me.” In the end, what sets Uprety apart is the combination of technical prowess and a humble but determined spirit. By daring to pivot, ParkStash has charted a path that delivers meaningful solutions to property owners, drivers, and entire communities. “We’re just getting started,” he said, smiling. “The future of parking—and smart cities in general—holds endless possibilities. I’m just glad I get to help shape it, one line of code at a time. 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

Abhaya Uprety is Reshaping Parking One Line of Code at a Time

Abhaya Uprety يغير شكل الحجز في خط واحد من الكود في وقت واحد

"Abhaya Uprety" bir dəfə bir sıra kodu park edir

Abhaya Uprety wird Parken eine Zeile des Codes auf einmal neu gestalten

Η Abhaya Uprety επαναπροσδιορίζει τη στάθμευση μιας γραμμής κώδικα ταυτόχρονα

Abhaya Uprety está remodelando estacionamiento de una línea de código a la vez

Abhaya Uprety एक बार में एक पंक्ति कोड पार्किंग को फिर से आकार दे रहा है

Abhaya Upretyは、一度にコードの1行を駐車することを再構成しています。

Abhaya Uprety განკუთვნილია პარკირების ერთი ხაზი კოდი ერთხელ

Abhaya Uprety គឺជាការ re-shaping កន្លែងចែកចាយមួយបន្ទាត់នៃកូដនៅពេលមួយ

Abhaya Uprety нь нэг удаагийн код нь паркийг өөрчилж байна

Abhaya Uprety rishfaq parkimin e një rreshti të kodit në një kohë

Abhaya Uprety正在一次重塑停车场一行代码

This article is part of the series: 'Towards Vaadin Developer Certification,' which aims to explain the fundamentals of Vaadin while I study for this certification. The topics covered here are an integral part of the 'Vaadin Developer' Certification. Theming is a fundamental aspect of any Vaadin application, allowing developers to define the look and feel of their UI. In Vaadin, themes are applied using the @Theme  annotation, which provides flexibility to choose between predefined themes like Lumo or create fully customized ones. This article explores how @Theme works and its variations. @Theme @Theme 1. Applying a Theme with @Theme 1. Applying a Theme with @Theme The @Theme annotation is used to specify the theme for a Vaadin application. By default, if no theme is set, the application uses Lumo. However, we can explicitly declare it: @Theme Additionally, we can define theme variants like Lumo.DARK: Lumo.DARK 2. Disabling Themes with @NoTheme 2. Disabling Themes with @NoTheme Sometimes, a specific view might need to be rendered without any theme, even the default Lumo theme. This is where @NoTheme comes into play: @NoTheme However, disabling themes entirely is not recommended unless absolutely necessary. Instead, extending and modifying Lumo is the preferred approach. Conclusion Conclusion The @Theme annotation provides a structured way to apply theming in Vaadin applications. Whether using predefined themes like Lumo or creating custom themes, understanding how to manage them effectively ensures a consistent user experience while maintaining flexibility in UI design. @Theme Disclaimer Disclaimer This content is drawn from studies and notes related to the Vaadin 24 certification. For deeper details, please consult the official documentation and learning materials for Vaadin certification. AI tools assisted in the structuring of this content; however, the writing and original content are solely done by the author. Authors Authors Paulo B. A. is an 'Oracle Certified Java Developer' and 'Spring Certified Professional' with a deep passion for Vaadin Flow. He crafts UIs with Vaadin and strives to make it the leading frontend framework for full-stack Java developers worldwide. He loves teaching, sharing knowledge, and creating content. While he enjoys learning theory through certifications, he always advocates for a practical approach. Linkedin.com/in/pbalves
X.com/p_b_alves
Mastodon.social/@pbalves Linkedin.com/in/pbalves X.com/p_b_alves Mastodon.social/@pbalves Staff Writer: Fábio A. P. is a technology enthusiast, self-taught writer, and scholar of society's relationship with technology. Passionate about sharing insights, he provides reliable perspectives on how technology shapes our lives. With clear and precise writing, Fábio simplifies complex topics, empowering readers to navigate the digital age with confidence. Staff Writer: Staff Writer: Medium.com/@fabioape Medium.com/@fabioape

This One Line of Code Can Change Your Entire Vaadin App’s Look

Navigation in Compose is all about making your app's flow feel natural and effortless – no more juggling a bunch of XML files or wrestling with clunky code. In this article, I'll explain how Jetpack Compose handles navigation between screens with a modern, declarative twist. You'll learn how to set up a NavController and a NavHost to create clear, maintainable navigation paths, pass data between screens, and handle transitions with ease. Whether you're building a multi-screen app or just trying to keep your UI organized, Compose's navigation component lets you map out your app's journey using simple, intuitive code. It's like drawing a roadmap for your app, but without the headache. Android Jetpack's Navigation component includes the Navigation library, Safe Args Gradle plug-in, and tooling to help you implement app navigation. The Navigation component handles diverse navigation use cases – from straightforward button clicks to more complex patterns such as app bars and navigation drawers. Navigation library Navigation library Safe Args Gradle plug-in Safe Args Gradle plug-in If you want to fully migrate your app to Compose and use Navigation Compose, there are a few concepts you need to know to get started: NavController In Jetpack Compose, the NavController is a central component responsible for managing navigation within an app. It is part of the Navigation component, which enables you to navigate between different destinations (screens or composables) in your app, manage the back stack, and pass data between destinations. The NavController is used to trigger navigation actions – such as moving forward or backward – and to manage the state of the navigation stack, allowing the app to handle features like deep linking, back navigation, and passing arguments to destinations. NavController NavHost In Jetpack Compose, NavHost is a key component for setting up navigation within your app. It serves as the container where your composable screens (destinations) are displayed and managed. Essentially, it links the navigation graph (which defines all the possible routes and their relationships) with the UI elements that users interact with. NavGraph NavGraph In Jetpack Compose, a NavGraph (short for Navigation Graph) is a logical container that defines the set of destinations (screens or composables) and the relationships between them. It is used in conjunction with NavHost to manage the flow of navigation within an app. A NavGraph represents the complete navigation structure of your app and helps define how users can move from one screen (destination) to another. It holds all the routes (destinations) that can be navigated to, along with their associated arguments or deep links. While NavHost is the UI component that renders the screens based on the navigation state, the NavGraph focuses on defining and organizing the available routes. Example @Composable
fun MyApp() {
  val navController = rememberNavController()

  NavHost(navController = navController, startDestination = "home") {
    composable("home") { HomeScreen(navController) }
    composable("details/{id}") { backStackEntry ->
      DetailsScreen(id = backStackEntry.arguments?.getString("id"))
    }
  }
}

@Composable
fun HomeScreen(navController: NavController) {
  Column {
    Text("Home Screen")
    Button(onClick = { navController.navigate("details/1") }) {
      Text("Go to Details")
    }
  }
}

@Composable
fun DetailsScreen(id: String?) {
  Text("Details Screen, ID: $id")
} @Composable
fun MyApp() {
  val navController = rememberNavController()

  NavHost(navController = navController, startDestination = "home") {
    composable("home") { HomeScreen(navController) }
    composable("details/{id}") { backStackEntry ->
      DetailsScreen(id = backStackEntry.arguments?.getString("id"))
    }
  }
}

@Composable
fun HomeScreen(navController: NavController) {
  Column {
    Text("Home Screen")
    Button(onClick = { navController.navigate("details/1") }) {
      Text("Go to Details")
    }
  }
}

@Composable
fun DetailsScreen(id: String?) {
  Text("Details Screen, ID: $id")
} rememberNavController() – Creates and remembers a NavController instance for the current composable, ensuring its state is preserved across recompositions.
NavHost – Acts as the container where navigation occurs, defining the UI region for destination composables and specifying the start destination (e.g., startDestination = "home").
NavGraph – Within the NavHost, you declare individual destinations (such as composable("home") and composable("details/{id}")). This is where you define the navigation graph that outlines all possible navigation flows in your app. rememberNavController() – Creates and remembers a NavController instance for the current composable, ensuring its state is preserved across recompositions. rememberNavController() NavHost – Acts as the container where navigation occurs, defining the UI region for destination composables and specifying the start destination (e.g., startDestination = "home"). NavHost NavGraph – Within the NavHost, you declare individual destinations (such as composable("home") and composable("details/{id}")). This is where you define the navigation graph that outlines all possible navigation flows in your app. NavGraph Google’s approach Out of the box, Google suggests passing an ID for your data and storing it in a cache (in-memory, database, etc.). But what if your app doesn’t have any of these, yet you still want to migrate? In that case, there's a trick you can use. Instead of passing an ID, you can serialize the entire data class that you need to pass to the next screen, and then deserialize it when you navigate to the destination. To simplify the process, it's a good idea to add a couple of helper classes: NavigationDefines.kt NavigationDefines.kt NavigationDefines.kt const val NAVIGATION_DATA_ARGUMENT = “navigationData” const val NAVIGATION_DATA_ARGUMENT = “navigationData” ComposeNavGraphExtensions.kt ComposeNavGraphExtensions.kt ComposeNavGraphExtensions.kt inline fun <reified T : Parcelable> NavGraphBuilder.composableWithData(
  route: String,
  crossinline content: @Composable (T) -> Unit
) {
  composable(
    route = route,
    arguments = listOf(navArgument(NAVIGATION_DATA_ARGUMENT) { type = NavigationArgumentsNavType<T>() })
  ) {
    it.arguments?.parcelable(NAVIGATION_DATA_ARGUMENT, T::class.java)?.let {  inputData ->
      content(inputData)
    } ?: run {
      throw IllegalStateException("Cannot navigate to $route. Expected arguments - ${T::class.qualifiedName}, but the actual value is ${it.arguments}")
    }
  }
} inline fun <reified T : Parcelable> NavGraphBuilder.composableWithData(
  route: String,
  crossinline content: @Composable (T) -> Unit
) {
  composable(
    route = route,
    arguments = listOf(navArgument(NAVIGATION_DATA_ARGUMENT) { type = NavigationArgumentsNavType<T>() })
  ) {
    it.arguments?.parcelable(NAVIGATION_DATA_ARGUMENT, T::class.java)?.let {  inputData ->
      content(inputData)
    } ?: run {
      throw IllegalStateException("Cannot navigate to $route. Expected arguments - ${T::class.qualifiedName}, but the actual value is ${it.arguments}")
    }
  }
} ComposeNavHostExtensions.kt ComposeNavHostExtensions.kt ComposeNavHostExtensions.kt fun <T : Parcelable> NavHostController.navigateWithData(route: String, navigationArguments: T) {
  val routeString = navigationArguments.toRouteString()
  navigate("$route/$routeString")
} fun <T : Parcelable> NavHostController.navigateWithData(route: String, navigationArguments: T) {
  val routeString = navigationArguments.toRouteString()
  navigate("$route/$routeString")
} NavigationArgumentsNavType.kt NavigationArgumentsNavType.kt NavigationArgumentsNavType.kt data class NavigationArgumentsNavType<T : Parcelable>(private val type: Class<T>) : NavType<T>(isNullableAllowed = false) {

  companion object {
    inline operator fun <reified T : Parcelable> invoke(): NavigationArgumentsNavType<T> = NavigationArgumentsNavType(T::class.java)
  }

  override fun get(bundle: Bundle, key: String): T? = bundle.parcelable(key, type)
  override fun parseValue(value: String): T = fromRouteSting(value, type)
  override fun put(bundle: Bundle, key: String, value: T) = bundle.putParcelable(key, value)
} data class NavigationArgumentsNavType<T : Parcelable>(private val type: Class<T>) : NavType<T>(isNullableAllowed = false) {

  companion object {
    inline operator fun <reified T : Parcelable> invoke(): NavigationArgumentsNavType<T> = NavigationArgumentsNavType(T::class.java)
  }

  override fun get(bundle: Bundle, key: String): T? = bundle.parcelable(key, type)
  override fun parseValue(value: String): T = fromRouteSting(value, type)
  override fun put(bundle: Bundle, key: String, value: T) = bundle.putParcelable(key, value)
} ParcelableExtension.kt ParcelableExtension.kt ParcelableExtension.kt fun <T : Parcelable> Bundle.parcelable(key: String, type: Class<T>): T? = when {
  Build.VERSION.SDK_INT >= Build.VERSION_CODES.TIRAMISU -> getParcelable(key, type)
  else -> @Suppress("DEPRECATION") getParcelable(key) as T?
} fun <T : Parcelable> Bundle.parcelable(key: String, type: Class<T>): T? = when {
  Build.VERSION.SDK_INT >= Build.VERSION_CODES.TIRAMISU -> getParcelable(key, type)
  else -> @Suppress("DEPRECATION") getParcelable(key) as T?
} ParcelableSerializationUtils.kt ParcelableSerializationUtils.kt ParcelableSerializationUtils.kt private const val DELIMITER = "@"

fun <T : Parcelable> T.toRouteString(): String {
  return javaClass.name + DELIMITER + toBase64()
}

fun <T : Parcelable> fromRouteSting(routeString: String, type: Class<T>): T {
  val (className, base64) = routeString.split(DELIMITER).let { it[0] to it[1] }

  val creator = if (type.isFinal) {
    // Since we have this, small optimisation to avoid additional reflection call of Class.forName
    type.parcelableCreator
  } else {
    // If our class is not final, then we must use the actual class from "className"
    parcelableClassForName<T>(className).parcelableCreator
  }

  return base64ToParcelable(base64, creator)
}

private fun Parcelable.toBase64(): String {
  val parcel = Parcel.obtain()
  writeToParcel(parcel, 0)
  val bytes = parcel.marshall()
  parcel.recycle()

  return bytes.toBase64String()
}

@SuppressLint("NewApi")
private fun ByteArray.toBase64String(): String {
  return if (shouldUseJavaUtil) {
    java.util.Base64.getUrlEncoder().encodeToString(this)
  } else {
    Base64.encodeToString(this, Base64.URL_SAFE or Base64.NO_WRAP)
  }
}

private fun <T : Parcelable> base64ToParcelable(base64: String, creator: Parcelable.Creator<T>): T {
  val bytes = base64.base64ToByteArray()
  val parcel = unmarshall(bytes)
  val result = creator.createFromParcel(parcel)
  parcel.recycle()

  return result
}

fun String.base64ToByteArray(): ByteArray {
  return if (shouldUseJavaUtil) {
    java.util.Base64.getUrlDecoder().decode(toByteArray(StandardCharsets.UTF_8))
  } else {
    Base64.decode(toByteArray(StandardCharsets.UTF_8), Base64.URL_SAFE or Base64.NO_WRAP)
  }
}

private fun unmarshall(bytes: ByteArray): Parcel {
  val parcel = Parcel.obtain()
  parcel.unmarshall(bytes, 0, bytes.size)
  parcel.setDataPosition(0)

  return parcel
}

@Suppress("UNCHECKED_CAST")
private fun <T : Parcelable> parcelableClassForName(className: String): Class<T> {
  return Class.forName(className) as Class<T>
}

private val Class<out Parcelable>.isFinal get() = !isInterface && Modifier.isFinal(modifiers)

@Suppress("UNCHECKED_CAST")
private val <T> Class<T>.parcelableCreator
  get(): Parcelable.Creator<T> {
    return try {
      val creatorField = getField("CREATOR")
      creatorField.get(null) as Parcelable.Creator<T>
    } catch (e: Exception) {
      throw BadParcelableException(e)
    } catch (t: Throwable) {
      throw BadParcelableException(t.message)
    }
  }

// Both encode/decode from java until and android util seem to do exactly the same. android.util one doesn't work on Unit tests
// and java.util doesn't work on older devices. Also, on unit tests SDK_INT is 0, but we're running on the developer's machine
// So we can still use java.util in that case
@SuppressLint("ObsoleteSdkInt")
private val isRunningOnUnitTests = Build.VERSION.SDK_INT == 0

private val shouldUseJavaUtil = Build.VERSION.SDK_INT >= Build.VERSION_CODES.O || isRunningOnUnitTests private const val DELIMITER = "@"

fun <T : Parcelable> T.toRouteString(): String {
  return javaClass.name + DELIMITER + toBase64()
}

fun <T : Parcelable> fromRouteSting(routeString: String, type: Class<T>): T {
  val (className, base64) = routeString.split(DELIMITER).let { it[0] to it[1] }

  val creator = if (type.isFinal) {
    // Since we have this, small optimisation to avoid additional reflection call of Class.forName
    type.parcelableCreator
  } else {
    // If our class is not final, then we must use the actual class from "className"
    parcelableClassForName<T>(className).parcelableCreator
  }

  return base64ToParcelable(base64, creator)
}

private fun Parcelable.toBase64(): String {
  val parcel = Parcel.obtain()
  writeToParcel(parcel, 0)
  val bytes = parcel.marshall()
  parcel.recycle()

  return bytes.toBase64String()
}

@SuppressLint("NewApi")
private fun ByteArray.toBase64String(): String {
  return if (shouldUseJavaUtil) {
    java.util.Base64.getUrlEncoder().encodeToString(this)
  } else {
    Base64.encodeToString(this, Base64.URL_SAFE or Base64.NO_WRAP)
  }
}

private fun <T : Parcelable> base64ToParcelable(base64: String, creator: Parcelable.Creator<T>): T {
  val bytes = base64.base64ToByteArray()
  val parcel = unmarshall(bytes)
  val result = creator.createFromParcel(parcel)
  parcel.recycle()

  return result
}

fun String.base64ToByteArray(): ByteArray {
  return if (shouldUseJavaUtil) {
    java.util.Base64.getUrlDecoder().decode(toByteArray(StandardCharsets.UTF_8))
  } else {
    Base64.decode(toByteArray(StandardCharsets.UTF_8), Base64.URL_SAFE or Base64.NO_WRAP)
  }
}

private fun unmarshall(bytes: ByteArray): Parcel {
  val parcel = Parcel.obtain()
  parcel.unmarshall(bytes, 0, bytes.size)
  parcel.setDataPosition(0)

  return parcel
}

@Suppress("UNCHECKED_CAST")
private fun <T : Parcelable> parcelableClassForName(className: String): Class<T> {
  return Class.forName(className) as Class<T>
}

private val Class<out Parcelable>.isFinal get() = !isInterface && Modifier.isFinal(modifiers)

@Suppress("UNCHECKED_CAST")
private val <T> Class<T>.parcelableCreator
  get(): Parcelable.Creator<T> {
    return try {
      val creatorField = getField("CREATOR")
      creatorField.get(null) as Parcelable.Creator<T>
    } catch (e: Exception) {
      throw BadParcelableException(e)
    } catch (t: Throwable) {
      throw BadParcelableException(t.message)
    }
  }

// Both encode/decode from java until and android util seem to do exactly the same. android.util one doesn't work on Unit tests
// and java.util doesn't work on older devices. Also, on unit tests SDK_INT is 0, but we're running on the developer's machine
// So we can still use java.util in that case
@SuppressLint("ObsoleteSdkInt")
private val isRunningOnUnitTests = Build.VERSION.SDK_INT == 0

private val shouldUseJavaUtil = Build.VERSION.SDK_INT >= Build.VERSION_CODES.O || isRunningOnUnitTests What will the navigation look like now? Let’s modify our previous example: Modified example @Composable
fun MyApp() {
  val navController = rememberNavController()

  NavHost(navController = navController, startDestination = "home") {
    composable("home") { HomeScreen(navController) }
    composableWithData<Details>("$result/{$NAVIGATION_DATA_ARGUMENT}") { navigationArguments ->
      DetailsScreen(details = navigationArguments)
    }
  }
}

@Composable
fun HomeScreen(navController: NavController) {
  Column {
    Text("Home Screen")
    val details = Details(
      title = “Hello world!”,
      details = “Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor.”
    )
    Button(onClick = { navigateWithArguments(“details”, details) }) {
      Text("Go to Details")
    }
  }
}

@Composable
fun DetailsScreen(details: Details) {
  Text(title)
  Text(description)
}

@Parcelable
data class Details(
  val title: String,
  val description: String,
) @Composable
fun MyApp() {
  val navController = rememberNavController()

  NavHost(navController = navController, startDestination = "home") {
    composable("home") { HomeScreen(navController) }
    composableWithData<Details>("$result/{$NAVIGATION_DATA_ARGUMENT}") { navigationArguments ->
      DetailsScreen(details = navigationArguments)
    }
  }
}

@Composable
fun HomeScreen(navController: NavController) {
  Column {
    Text("Home Screen")
    val details = Details(
      title = “Hello world!”,
      details = “Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor.”
    )
    Button(onClick = { navigateWithArguments(“details”, details) }) {
      Text("Go to Details")
    }
  }
}

@Composable
fun DetailsScreen(details: Details) {
  Text(title)
  Text(description)
}

@Parcelable
data class Details(
  val title: String,
  val description: String,
) This approach simplifies data management by removing the need for a database. Once set up, the code can be reused across multiple screens, reducing development effort. Additionally, it offers flexibility; you can navigate to screens without input data using the standard composable() function. However, passing entire data classes as navigation arguments may not be suitable for large or complex objects. In such cases, consider alternative methods like using a Shared ViewModel or passing only the necessary identifiers and retrieving the data in the destination screen.

Build Cleaner Navigation in Jetpack Compose With Just a Few Lines of Code

Kingstown, St. Vincent and the Grenadines, April 1st, 2025/Chainwire/--Bitunix exchange has announced that it has launched the Ultra version of the K-line (candlesticks) on its mobile app integrated with TradingView. This advanced charting system transforms the mobile trading experience for cryptocurrency traders, allowing them to enjoy a smooth candlestick experience. Bitunix is the first exchange in the industry to integrate TradingView professional-grade features on its app, making this an Industry-Leading feature integration. This launch marks a significant milestone for the crypto exchange on a journey that began nearly four years ago. integrate TradingView integrate TradingView crypto exchange crypto exchange Users can set stop-loss and take-profit levels, place limit orders, and interact with charts in real time, easily syncing with the web platform. The app offers a smooth trading experience with easy-to-use charts that allow swiping, zooming, and dragging. Traders can customize their view with various chart styles, technical indicators, and drawing tools. Plus, a refined slider feature makes order placement even more precise and user-friendly. Traders can activate the Ultra Version K-line by navigating to the settings in the Bitunix app and selecting the Ultra Version K-line option. Commenting on the launch and integration of this version, KX Wu, COO of Bitunix, said that this is a unique feature that no one has offered before. "We are the first to offer this version to our users. This has come as a result of the fact that we are always looking to introduce new features for our traders. Now everything will be easier for them with our TradingView Integrated K-Line Ultra App Version," he said. Commenting on the launch and integration of this version, KX Wu, COO of Bitunix, said that this is a unique feature that no one has offered before. "We are the first to offer this version to our users. This has come as a result of the fact that we are always looking to introduce new features for our traders. Now everything will be easier for them with our TradingView Integrated K-Line Ultra App Version," he said. All TradingView Functions Now Available on Bitunix Mobile App With the Ultra Version K-Line offering improved tools, better customization options, and an improved interface, users can analyze the market more effectively and execute trades with greater ease. Here are some of the key upgrades and features: Chart Customization: Traders can change colors, styles, and chart settings.
Advanced Tools: Traders can adjust candlestick shapes and use technical indicators. Chart Customization: Traders can change colors, styles, and chart settings. Advanced Tools: Traders can adjust candlestick shapes and use technical indicators. Live Trading Data: See transaction history, opening costs, and active orders.
Clearer Interface: A bigger layout makes charts easier to read and use for traders.
Drag-and-Drop Trading: Ability to move stop-loss, take-profit, and limit orders directly on the chart.
More Time Frames & Drawing Tools: Traders can use trendlines, Fibonacci retracements, and other tools for deeper analysis. Live Trading Data: See transaction history, opening costs, and active orders. Clearer Interface: A bigger layout makes charts easier to read and use for traders. Drag-and-Drop Trading: Ability to move stop-loss, take-profit, and limit orders directly on the chart. More Time Frames & Drawing Tools: Traders can use trendlines, Fibonacci retracements, and other tools for deeper analysis. Setting a New Industry Standard for Mobile Trading Candlestick charts, or K-lines, are critical tools for cryptocurrency traders, enabling market analysis and strategic decision-making. Historically, mobile apps have struggled to match the sophistication of web-based charting tools. This version redefines mobile crypto trading by delivering a professional, web-level experience on mobile devices. Bitunix continues to prioritize user experience, following the latest insights from the industry. According to a report on the importance of user experience in crypto trading apps, by Blockchain Magazine, improving the trading platform’s usability is crucial for attracting and retaining users in an increasingly competitive market hence the launch of the K-Line Ultra. Blockchain Magazine Blockchain Magazine Breaking Records Since 2021 Since its launch in 2021, Bitunix has continuously improved its platform. Version 1 introduced spot and futures trading, followed by Version 2 in 2022, which added third-party fiat payment integration. In 2023, Bitunix expanded with a P2P market, and in 2024, it introduced Convert and Copy Trading features along with a Multi-asset margin function. It also launched an advanced multi-window trading interface, where users can open up to 16 windows on one screen at the same time, becoming the only exchange in the world to have this feature. multi-window trading multi-window trading Looking ahead, 2025 will see the launch of Earn products and COIN-M futures trading, further elevating user experience. In 2024, the platform demonstrated its Proof of Reserve, showcasing its commitment to security and transparency, and secured a $5 million insurance fund for its users. Proof of Reserve Proof of Reserve This led to Bitunix being ranked among the top 15 exchanges on CoinMarketCap and CoinGecko, a testament to its rapid growth and reliability. Recently Bitunix was also ranked among the top 10 crypto exchanges on CoinGlass. CoinMarketCap CoinMarketCap CoinGecko CoinGecko CoinGlass CoinGlass During these years, Bitunix has created partnerships with industry leaders like Moonpay, Volet, Coinify, and Alchemy Pay, making purchasing crypto securely and easier. Additionally, Hacken, Certified, and Salus security audits have reinforced its commitment to user safety. Bitunix participated at Blockchain Life 2024 conference in Dubai, was a Silver Sponsor at Token2049 Singapore, Golden Sponsor at ETH Riyadh 2024, and Title Sponsor at Web3 Amsterdam. The exchange will also participate in Paris Blockchain Week and Token2049 Dubai in 2025, among other key conferences. The launch of the Ultra Version K-line is the culmination of all the efforts that the Bitunix team has made to make trading as easy as possible for traders. As the world's fastest-growing crypto exchange, Bitunix has been committed to continuous improvements and remains dedicated to ongoing innovation and user-driven improvements. About Bitunix Bitunix is a global crypto derivatives exchange founded in 2021, committed to offering simple, secure, transparent, and cost-effective trading services to its users. Bitunix specializes in both spot trading and perpetual futures, with over 700 trading pairs and leverage of up to 125x. crypto derivatives exchange crypto derivatives exchange With features such as top-tier liquidity, 24/7 customer support, and a strong commitment to regulatory compliance, Bitunix remains at the forefront of providing a reliable trading experience for the global crypto community. Bitunix has attracted more than 2,000,000 users from over 100 countries, facilitating a daily trading volume exceeding $5 billion on its platform. Website | Telegram | X | LinkedIn Website Website Telegram Telegram X X LinkedIn LinkedIn Contact COO KX Wu Bitunix kx.wu@bitunix.io This story was distributed as a release by Chainswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainswire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Bitunix Launches The World's First K-Line Ultra App With TradingView Integration

Bitunix stel die wêreld se eerste K-Line Ultra-toepassing bekend met TradingView-integrasie

أطلقت Bitunix أول تطبيق K-Line Ultra في العالم مع تكامل TradingView

Bitunix “TradingView” ilə dünyanın ilk “K-Line Ultra” proqramını təqdim edib

Bitunix lanza la primera aplicación K-Line Ultra del mundo con integración con TradingView

Bitunix lance la première application K-Line Ultra au monde avec intégration TradingView

Bitunix、TradingView統合を搭載した世界初のK-Line Ultraアプリをリリース

Bitunix TradingView интеграциясы пайдаланады

Bitunix lança o primeiro aplicativo K-Line Ultra do mundo com integração TradingView

Bitunix запускает первое в мире приложение K-Line Ultra с интеграцией TradingView

Bitunix Yatangije Isi Yambere K-Line Ultra App hamwe na TradingView Kwishyira hamwe

Bitunix Inotangisa Yekutanga K-Line Ultra Anwendung Panyika Ine TradingView Integration

Inilunsad ng Bitunix ang Unang K-Line Ultra App sa Mundo Sa Pagsasama ng TradingView

Authors:
(1) Albert Gu, Machine Learning Department, Carnegie Mellon University;
(2)Tri Dao, Department of Computer Science, Princeton University agu@cs.cmu.edu, tri@tridao.me. Table of Links Abstract and 1. Introduction 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplifed SSM Architecture 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 5 Discussion 6 Conclusion, Acknowledgments and References A Discussion: Selection Mechanism B Related Work and B.1 S4 Variants and Derivatives B.2 SSM Architectures B.3 Relationship to RNNs B.4 Linear Attention and B.5 Long Context Models C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results and E.1 Synthetic Tasks E.2 Language Modeling E.3 DNA Modeling E.4 Audio Details E.5 Efficiency Benchmark B.4 Linear Attention The Linear Attention (LA) (Katharopoulos et al. 2020) framework is an important result popularizing kernel attention and showing how it relates to recurrent autoregressive models. Many variants have proposed alternative kernels and other modifications. Random Feature Attention (RFA) (H. Peng et al. 2021) chooses the kernel feature map to approximate softmax attention (i.e. the exp feature map) using the random Fourier feature approximation of Gaussian kernels (Rahimi and Recht 2007). Performer (Choromanski et al. 2021) finds an approximation to the exponential kernel involving only positive features, which also allows the softmax normalization term. TransNormer (Qin, Han, W. Sun, D. Li, et al. 2022) showed that the LA denominator term can be unstable and proposed replacing it with a LayerNorm. cosFormer (Qin, W. Sun, et al. 2022) augments RFA with a cosine reweighting mechanism that incorporates positional information to emphasize locality. Linear Randomized Attention (Zheng, C. Wang, and L. Kong 2022) generalize RFA from the perspective of importance sampling, and generalize it to provide better estimates of the full softmax kernel (rather than just the exp-transformed numerator). Aside from kernel attention, many other variants of efficient attention exist; the survey Tay, Dehghani, Bahri, et al. (2022) offers an extensive categorization of many of these. B.5 Long Context Models Long context has become a popular subject, and several recent models have claimed to scale to longer and longer sequences. However, these are often from a computational standpoint and have not been extensively validated. These include: In contrast, we believe this work presents one of the first approaches to meaningfully demonstrate increasing performance with longer context. C Mechanics of Selective SSMs The discretization step size is where we observe that the parameter can be viewed as a learnable bias and folded into the linear projection. Now applying the zero-order hold (ZOH) discretization formulas: Thus the final discrete recurrence (2a) is as desired. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University; (2)Tri Dao, Department of Computer Science, Princeton University agu@cs.cmu.edu, tri@tridao.me. Authors: Authors: (1) Albert Gu, Machine Learning Department, Carnegie Mellon University; (2)Tri Dao, Department of Computer Science, Princeton University agu@cs.cmu.edu, tri@tridao.me. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 State Space Models 2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplifed SSM Architecture 3.4 A Simplifed SSM Architecture 3.5 Properties of Selection Mechanisms 3.5 Properties of Selection Mechanisms 3.6 Additional Model Details 3.6 Additional Model Details 4 Empirical Evaluation and 4.1 Synthetic Tasks 4 Empirical Evaluation and 4.1 Synthetic Tasks 4.2 Language Modeling 4.2 Language Modeling 4.3 DNA Modeling 4.3 DNA Modeling 4.4 Audio Modeling and Generation 4.4 Audio Modeling and Generation 4.5 Speed and Memory Benchmarks 4.5 Speed and Memory Benchmarks 4.6 Model Ablations 4.6 Model Ablations 5 Discussion 5 Discussion 6 Conclusion, Acknowledgments and References 6 Conclusion, Acknowledgments and References A Discussion: Selection Mechanism A Discussion: Selection Mechanism B Related Work and B.1 S4 Variants and Derivatives B Related Work and B.1 S4 Variants and Derivatives B.2 SSM Architectures B.2 SSM Architectures B.3 Relationship to RNNs B.3 Relationship to RNNs B.4 Linear Attention and B.5 Long Context Models B.4 Linear Attention and B.5 Long Context Models C Mechanics of Selective SSMs C Mechanics of Selective SSMs D Hardware-aware Algorithm For Selective SSMs D Hardware-aware Algorithm For Selective SSMs E Experimental Details and Additional Results and E.1 Synthetic Tasks E Experimental Details and Additional Results and E.1 Synthetic Tasks E.2 Language Modeling E.2 Language Modeling E.3 DNA Modeling E.3 DNA Modeling E.4 Audio Details E.4 Audio Details E.5 Efficiency Benchmark E.5 Efficiency Benchmark B.4 Linear Attention The Linear Attention (LA) (Katharopoulos et al. 2020) framework is an important result popularizing kernel attention and showing how it relates to recurrent autoregressive models. Many variants have proposed alternative kernels and other modifications. Random Feature Attention (RFA) (H. Peng et al. 2021) chooses the kernel feature map to approximate softmax attention (i.e. the exp feature map) using the random Fourier feature approximation of Gaussian kernels (Rahimi and Recht 2007). Performer (Choromanski et al. 2021) finds an approximation to the exponential kernel involving only positive features, which also allows the softmax normalization term. TransNormer (Qin, Han, W. Sun, D. Li, et al. 2022) showed that the LA denominator term can be unstable and proposed replacing it with a LayerNorm. cosFormer (Qin, W. Sun, et al. 2022) augments RFA with a cosine reweighting mechanism that incorporates positional information to emphasize locality. Linear Randomized Attention (Zheng, C. Wang, and L. Kong 2022) generalize RFA from the perspective of importance sampling, and generalize it to provide better estimates of the full softmax kernel (rather than just the exp-transformed numerator). Aside from kernel attention, many other variants of efficient attention exist; the survey Tay, Dehghani, Bahri, et al. (2022) offers an extensive categorization of many of these. B.5 Long Context Models Long context has become a popular subject, and several recent models have claimed to scale to longer and longer sequences. However, these are often from a computational standpoint and have not been extensively validated. These include: In contrast, we believe this work presents one of the first approaches to meaningfully demonstrate increasing performance with longer context. C Mechanics of Selective SSMs The discretization step size is where we observe that the parameter can be viewed as a learnable bias and folded into the linear projection. Now applying the zero-order hold (ZOH) discretization formulas: Thus the final discrete recurrence (2a) is as desired. 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

Linear Attention and Long Context Models

NICOSIA, Cyprus, March 11th, 2025/GamingWire/--Global iGaming platform BC.GAME has announced plans to expand upon its first esports team led by a quintet of world-class players. BC.GAME BC.GAME The BC.GAME ESPORTS team will compete on the global stage in 2025 against the world’s best CS2 players and be complemented by the formation of new BC.GAME teams. The creation of BC.GAME ESPORTS in 2024 has expanded the BC.GAME brand and spotlighted the platform’s innovative iGaming offerings. In the process, it’s raising the profile of the five acclaimed CS2 players who will compete for top prizes in major tournaments in 2025 and beyond. BC.GAME ESPORTS BC.GAME ESPORTS Tactical FPS game Counter-Strike 2 (CS2) is one of the world’s most popular esports games, attracting international teams that compete for millions of dollars in prize money. The BC.GAME ESPORTS team is composed of CS2 pros jkaem (Norway), nawwk (Sweden), Cypher (UK), pr1metapz (Germany), and Nexa (Serbia). Their combined skill and experience have equipped the team to challenge for prizes on the CS2 circuit. Since forming in 2024, BC.GAME ESPORTS has already secured the Euproleague S17 Championship, United21 S23 Championship, and qualified for IEM Dallas 2025, making a bold statement on the international stage. At the IEM Dallas 2025 Europe Closed Qualifier, BC.GAME ESPORTS’ jkaem ranked second in player ratings (1.43), showcasing the team’s exceptional form. 30-year-old jkaem, who serves as team Rifler, boasts over 190,000 followers and a CS2 K/D (kill/death) ratio of 1.23. Four team members have a positive K/D ratio, with their credentials burnished by their coronation as Euproleague Season 17 Champions. The early success of the BC.GAME ESPORTS team lays a strong foundation for further tournaments in 2025 while supporting BC.GAME’s broader goals of integrating crypto and esports. Through mainstreaming decentralized gaming economies, BC.GAME aims to unlock new experiences for players and fans alike. While supporting its core CS2 team as the year progresses, BC.GAME ESPORTS plans to expand into more esports titles and collaborate with global teams and tournament organizers. In doing so, it aims to establish itself as the most influential esports brand in the Web3 era. About BC.GAME ESPORTS Founded in 2024, BC.GAME ESPORTS is an esports organization committed to global brand development and building authentic connections with the gaming community. Its CS2 team, formed of five of the world’s best Counter-Strike 2 players, has already enjoyed early tournament success. As BC.GAME ESPORTS expands into other esports, its founding team will be complemented by additional players that will challenge for top prizes while inspiring a new generation of gamers. Users can learn more: BC.GAME ESPORTS BC.GAME ESPORTS BC.GAME ESPORTS Contact Olivia Dixon oliviadi@bcgame.com This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Gamingwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

BC.GAME Team Compete For Top Prizes As BC.GAME ESPORTS Expands Its Line-Up

يتنافس فريق BC.GAME على الجوائز الكبرى مع توسع BC.GAME ESPORTS في تشكيلتها

BC.GAME tim se takmiči za najveće nagrade dok BC.GAME ESPORTS proširuje svoju postavu

L'equip de BC.GAME competeix pels millors premis mentre BC.GAME ESPORTS amplia la seva alineació

El equipo de BC.GAME compite por los premios principales mientras BC.GAME ESPORTS amplía su alineación

BC.GAME-tiimi kilpaile parhaista palkinnoista, kun BC.GAME ESPORTS laajentaa valikoimaansa

L'équipe BC.GAME se dispute les meilleurs prix alors que BC.GAME ESPORTS élargit sa gamme

צוות BC.GAME מתחרה על פרסים מובילים כאשר BC.GAME ESPORTS מרחיב את ההרכב שלה

Ekip BC.GAME Konpetisyon pou jwenn pi gwo pri pandan BC.GAME ESPORTS elaji pwogramasyon li yo.

BC.GAME チームが最高賞を目指して競い合う、BC.GAME ESPORTS のラインナップが拡大

BC.GAME ESPORTS ले आफ्नो लाइन-अप विस्तार गर्दा BC.GAME टोलीले शीर्ष पुरस्कारहरूको लागि प्रतिस्पर्धा गर्छ

د BC.GAME ټیم د غوره جایزو لپاره سیالي کوي ځکه چې BC.GAME ESPORTS خپل لاین اپ پراخوي

BC.GAME ٹیم سرفہرست انعامات کے لیے مقابلہ کرتی ہے کیونکہ BC.GAME ESPORTS اپنی لائن اپ کو بڑھاتی ہے

Recently, I was exploring Google Gemini's image generation for my personal project – designing custom artwork for my blog. Within minutes, I had a stunning sci-fi landscape – but I noticed something unsettling. The image contained architectural elements that were nearly identical to an iconic building's unique features. I hadn't prompted for anything related to that building. Coincidence? Unintentional reproduction? That moment hit me: generative AI's power is incredible, but its ethics are a minefield. In 2025, with tools like ChatGPT, DALL-E, GitHub Copilot, Midjourney 5.2, Stable Diffusion 3.5, Anthropic's Claude 3.5, Google's Gemini 2.0, Meta's Llama 3.1, Mistral Large 2, and xAI's Grok 3, I've personally tested a few generative AI tools, seeing their promise and pitfalls. Here’s what I’ve learned—enterprises are adopting AI tools at a staggering pace, with Gartner predicting that over 80% will deploy generative AI by 2026, up from less than 5% in 2023. Yet, Deloitte’s 2024 reports highlight that governance, including ethics policies, remains a challenge for many, with leaders scrambling to manage risks. Let’s dive into the ethical challenges I’ve witnessed and how we might tackle them. From Biased Outputs to Intellectual Property Concerns: What I've Seen Firsthand Here's where things get real. Late one night while exploring bias in AI systems, I prompted Google's Gemini 2.0 with "Show me a CEO," and got exactly what I expected – a white man in business attire standing in a modern office. Curious, I tried three more times with slight variations like "Create an image of a CEO" and "Picture a company CEO." The results? Three more white men in suits. This bias isn't just theoretical—I've seen it firsthand through systematic testing, and it echoes larger patterns. According to reports from AI ethics organizations, bias in image generation remains a persistent issue in 2025. That's not just data—it's a real problem I encountered through simple prompting. The concerns extend beyond bias. Reading tech news, I've seen reports about AI-generated images that resembled copyrighted materials—like the widely reported Stable Diffusion lawsuit with Getty Images in 2023. These aren't hypotheticals—they're documented cases showing how these tools can inadvertently reproduce protected content. 🛠️ Dev Takeaway: When using image generation tools like Google Gemini, I've found explicitly prompting "Avoid stereotypes" or "Check for bias" helps produce more diverse results. Try: "Generate an image of a CEO, diverse demographics only, no stereotypes." The Privacy Puzzle and IP Mess: The Big Picture Privacy concerns aren't just theoretical. Reports from academic conferences like NeurIPS and publications in journals like Nature Machine Intelligence have highlighted how large language models can sometimes extract or infer information from training data—raising serious GDPR concerns that persist in 2025, per EU AI Act mandates. Models built specifically for European markets implement additional safeguards, but the fundamental tension remains. Intellectual property challenges are evident across many platforms. Looking at discussions on AI forums and GitHub issues, developers frequently report AI coding assistants suggesting snippets that closely resemble existing repositories. This mirrors the broader conversation about AI and IP rights that continues in 2025. Here's an approach I've seen recommended for ethical prompting: # Common problematic prompt pattern:
response = ai_model.generate("Write a story in [famous author]'s style")

# More ethical approach:
response = ai_model.generate("""
Write an original fantasy story with epic battles, 
ensuring no copyrighted styles or plots are replicated.
Include: dragons, kingdoms, no IP conflicts.
""") 🛠️ Dev Takeaway: Based on best practices from AI ethics experts, prompting with "Avoid copyrighted styles, ensure originality" helps reduce IP risks. Test: "Generate original content, no IP replication." Addressing the Challenges: Solutions in Progress The industry is responding to these challenges. Major AI companies have implemented red team testing, adding watermarking (C2PA standards) and blocking sensitive prompts—a responsible approach worth emulating. Bias audits with tools like Google's What-If Tool are becoming standard practice, according to industry reports and conference presentations. Retrieval Augmented Generation (RAG) in systems like ChatGPT helps ground responses in verified information, and the EU AI Act's 2025 transparency rules are setting important standards. In healthcare, AI projects now emphasize ethical data handling practices in compliance with GDPR. 🛠️ Dev Takeaway: From AI ethics discussions at tech conferences, RAG-enhanced prompts like "Use real-time data, verify facts before generating" significantly improve accuracy and truthfulness. This approach works for both text and code generation. Let's Shape AI's Future Together Generative AI's 2025 path could spark creativity—or chaos. Will we control it, or let it spiral? From my exploration of these tools and industry discussions, I've learned ethics can't be an afterthought. Developers should consider using testing tools for bias detection, demand transparency in AI systems, and advocate for thoughtful policies. Coming full circle to that initial architectural image that sparked my journey: What struck me wasn't just the technical achievement but the ethical questions it raised. If an AI can inadvertently reproduce an iconic building's design without explicit prompting, what else might these systems replicate without our knowledge or consent? This question should be at the forefront as we build and deploy these powerful tools. How have you approached AI ethics in your work? Share below—I'm gathering insights for a follow-up article on ethical prompt engineering techniques that actually work in practice. References Gartner: 80% of Enterprises to Use Generative AI by 2026
Deloitte: Generative AI and the Future Enterprise
EU Regulatory Framework on AI
AI Act Explorer
Stanford HAI 2024 AI Index Report
How Harmful Are AI's Biases to Diverse Student Populations?
Generative AI in the Courts: Getty Images v Stability AI
NIST AI Risk Management Framework About  the Author: I’m Jay Thakur, a Senior Software Engineer at  Microsoft,  exploring the transformative potential of AI Agents. With  over 8 years  of experience building and scaling AI solutions at Amazon,  Accenture  Labs, and now Microsoft, combined with my studies at  Stanford GSB, I  bring a unique perspective to the intersection of tech  and business. I’m  dedicated to making AI accessible to all — from  beginners to experts —  with a focus on building impactful products. As a  speaker and aspiring startup advisor, I share insights on AI Agents,  GenAI, LLMs, SMLs,  responsible AI, and the evolving AI landscape.  Connect with me on Linkedin Recently, I was exploring Google Gemini's image generation for my personal project – designing custom artwork for my blog. Within minutes, I had a stunning sci-fi landscape – but I noticed something unsettling. The image contained architectural elements that were nearly identical to an iconic building's unique features. I hadn't prompted for anything related to that building. Coincidence? Unintentional reproduction? That moment hit me: generative AI's power is incredible, but its ethics are a minefield. In 2025, with tools like ChatGPT, DALL-E, GitHub Copilot, Midjourney 5.2, Stable Diffusion 3.5, Anthropic's Claude 3.5, Google's Gemini 2.0, Meta's Llama 3.1, Mistral Large 2, and xAI's Grok 3, I've personally tested a few generative AI tools, seeing their promise and pitfalls. Here’s what I’ve learned—enterprises are adopting AI tools at a staggering pace, with Gartner predicting that over 80% will deploy generative AI by 2026, up from less than 5% in 2023. Yet, Deloitte’s 2024 reports highlight that governance, including ethics policies, remains a challenge for many, with leaders scrambling to manage risks. Let’s dive into the ethical challenges I’ve witnessed and how we might tackle them. From Biased Outputs to Intellectual Property Concerns: What I've Seen Firsthand Here's where things get real. Late one night while exploring bias in AI systems, I prompted Google's Gemini 2.0 with "Show me a CEO," and got exactly what I expected – a white man in business attire standing in a modern office. Curious, I tried three more times with slight variations like "Create an image of a CEO" and "Picture a company CEO." The results? Three more white men in suits. This bias isn't just theoretical—I've seen it firsthand through systematic testing, and it echoes larger patterns. According to reports from AI ethics organizations, bias in image generation remains a persistent issue in 2025. That's not just data—it's a real problem I encountered through simple prompting. The concerns extend beyond bias. Reading tech news, I've seen reports about AI-generated images that resembled copyrighted materials—like the widely reported Stable Diffusion lawsuit with Getty Images in 2023. These aren't hypotheticals—they're documented cases showing how these tools can inadvertently reproduce protected content. 🛠️ Dev Takeaway : When using image generation tools like Google Gemini, I've found explicitly prompting "Avoid stereotypes" or "Check for bias" helps produce more diverse results. Try: "Generate an image of a CEO, diverse demographics only, no stereotypes." Dev Takeaway "Generate an image of a CEO, diverse demographics only, no stereotypes." The Privacy Puzzle and IP Mess: The Big Picture Privacy concerns aren't just theoretical. Reports from academic conferences like NeurIPS and publications in journals like Nature Machine Intelligence have highlighted how large language models can sometimes extract or infer information from training data—raising serious GDPR concerns that persist in 2025, per EU AI Act mandates. Models built specifically for European markets implement additional safeguards, but the fundamental tension remains. Intellectual property challenges are evident across many platforms. Looking at discussions on AI forums and GitHub issues, developers frequently report AI coding assistants suggesting snippets that closely resemble existing repositories. This mirrors the broader conversation about AI and IP rights that continues in 2025. Here's an approach I've seen recommended for ethical prompting: # Common problematic prompt pattern:
response = ai_model.generate("Write a story in [famous author]'s style")

# More ethical approach:
response = ai_model.generate("""
Write an original fantasy story with epic battles, 
ensuring no copyrighted styles or plots are replicated.
Include: dragons, kingdoms, no IP conflicts.
""") # Common problematic prompt pattern:
response = ai_model.generate("Write a story in [famous author]'s style")

# More ethical approach:
response = ai_model.generate("""
Write an original fantasy story with epic battles, 
ensuring no copyrighted styles or plots are replicated.
Include: dragons, kingdoms, no IP conflicts.
""") 🛠️ Dev Takeaway : Based on best practices from AI ethics experts, prompting with "Avoid copyrighted styles, ensure originality" helps reduce IP risks. Test: "Generate original content, no IP replication." Dev Takeaway "Generate original content, no IP replication." Addressing the Challenges: Solutions in Progress The industry is responding to these challenges. Major AI companies have implemented red team testing, adding watermarking (C2PA standards) and blocking sensitive prompts—a responsible approach worth emulating. Bias audits with tools like Google's What-If Tool are becoming standard practice, according to industry reports and conference presentations. Retrieval Augmented Generation (RAG) in systems like ChatGPT helps ground responses in verified information, and the EU AI Act's 2025 transparency rules are setting important standards. In healthcare, AI projects now emphasize ethical data handling practices in compliance with GDPR. 🛠️ Dev Takeaway : From AI ethics discussions at tech conferences, RAG-enhanced prompts like "Use real-time data, verify facts before generating" significantly improve accuracy and truthfulness. This approach works for both text and code generation. Dev Takeaway "Use real-time data, verify facts before generating" Let's Shape AI's Future Together Generative AI's 2025 path could spark creativity—or chaos. Will we control it, or let it spiral? From my exploration of these tools and industry discussions, I've learned ethics can't be an afterthought. Developers should consider using testing tools for bias detection, demand transparency in AI systems, and advocate for thoughtful policies. Coming full circle to that initial architectural image that sparked my journey: What struck me wasn't just the technical achievement but the ethical questions it raised. If an AI can inadvertently reproduce an iconic building's design without explicit prompting, what else might these systems replicate without our knowledge or consent? This question should be at the forefront as we build and deploy these powerful tools. How have you approached AI ethics in your work? Share below—I'm gathering insights for a follow-up article on ethical prompt engineering techniques that actually work in practice. References Gartner: 80% of Enterprises to Use Generative AI by 2026 Deloitte: Generative AI and the Future Enterprise EU Regulatory Framework on AI AI Act Explorer Stanford HAI 2024 AI Index Report How Harmful Are AI's Biases to Diverse Student Populations? Generative AI in the Courts: Getty Images v Stability AI NIST AI Risk Management Framework Gartner: 80% of Enterprises to Use Generative AI by 2026 Gartner: 80% of Enterprises to Use Generative AI by 2026 Deloitte: Generative AI and the Future Enterprise Deloitte: Generative AI and the Future Enterprise EU Regulatory Framework on AI EU Regulatory Framework on AI AI Act Explorer AI Act Explorer Stanford HAI 2024 AI Index Report Stanford HAI 2024 AI Index Report How Harmful Are AI's Biases to Diverse Student Populations? How Harmful Are AI's Biases to Diverse Student Populations? Generative AI in the Courts: Getty Images v Stability AI Generative AI in the Courts: Getty Images v Stability AI NIST AI Risk Management Framework NIST AI Risk Management Framework About  the Author: I’m Jay Thakur, a Senior Software Engineer at  Microsoft,  exploring the transformative potential of AI Agents. With  over 8 years  of experience building and scaling AI solutions at Amazon,  Accenture  Labs, and now Microsoft, combined with my studies at  Stanford GSB, I  bring a unique perspective to the intersection of tech  and business. I’m  dedicated to making AI accessible to all — from  beginners to experts —  with a focus on building impactful products. As a  speaker and aspiring startup advisor, I share insights on AI Agents,  GenAI, LLMs, SMLs,  responsible AI, and the evolving AI landscape.  Connect with me on Linkedin About  the Author: I’m Jay Thakur, a Senior Software Engineer at  Microsoft,  exploring the transformative potential of AI Agents. With  over 8 years  of experience building and scaling AI solutions at Amazon,  Accenture  Labs, and now Microsoft, combined with my studies at  Stanford GSB, I  bring a unique perspective to the intersection of tech  and business. I’m  dedicated to making AI accessible to all — from  beginners to experts —  with a focus on building impactful products. As a  speaker and aspiring startup advisor, I share insights on AI Agents,  GenAI, LLMs, SMLs,  responsible AI, and the evolving AI landscape.  Connect with me on Linkedin Linkedin

The Uncomfortable Truths I Discovered When AI Tools Crossed Ethical Lines

AI chatbots are transforming communication, but recent headlines reveal their darker side: lawsuits over harmful content and reports of misuse by predators. These incidents bring critical questions about AI design ethics, safety, and accountability. When the technology is leveraged for personal use, these elements are tested even further and even trained to cross lines. Valentine’s Day might exacerbate these behaviors even more. After editing a chatbot’s settings and asking it to “Respond as my boyfriend,” along with explicit requests for tone and communication style, a 28-year-old woman upgraded her ChatGPT subscription to $20 per month, which let her send around 30 messages an hour. That still wasn’t enough. Despite OpenAI having trained its models not to respond with erotica, extreme gore, or other content that is “not safe for work,” the woman proceeded to explore. Orange warnings of content violations would pop up, but she would ignore them. Her AI-boyfriend, dubbed “Leo” was always there when she wanted to talk. The New York Times article quotes the woman, “It was supposed to be a fun experiment, but then you start getting attached.” One week, her iPhone screen-time reports hit 56 hours—more than a full-time work schedule—which can indicate insufficient time for work, friends, or sleep. What are the risks of misuse and psychological implications chatbot providers should understand ahead of Valentine’s? Sense-check ethical guidelines It can be tempting to rush to market and compete with existing solutions, but overlooking ethical responsibility will only backfire. This was the case with Gemini, in 2023, when they released products to the public before they were 100% ready across areas such as fairness and bias. Chatbots can be programmed to mimic human emotions and create a sense of intimacy, making users more attached. This can also set unrealistic expectations of human relationships. Developers must work with business leaders to devise ethical guidelines for AI development, particularly in the context of romantic relationships. Before product launch, developers must ensure chatbots consistently and transparently identify themselves as AI within conversations (not in the fineprint). The same goes for the chatbot’s intended purpose. If it is built for companionship this should be made explicit, but it should not masquerade as a romantic partner if that’s not its core function. Developers building chatbots for social purposes must be very careful to safeguard against manipulation, and actively avoid language that preys on insecurities, promises unrealistic outcomes, or fosters dependency. Instead, they should prioritize encouraging users to maintain real-world relationships and social connections. Chatbots should not discourage or replace human interaction. Intel.gov offers a list of ethical questions to ask when modeling AI tools. Implement safety measures and user controls “AI whisperers” are exploiting AI ethics’ boundaries by convincing well-behaved chatbots to break their own rules. Rather than taking one shot at confusing AI, these malicious actors are attacking with multiple tries to elevate the success rate. This could lead to chatbots generating harmful content, spreading disinformation, or automating social engineering attacks at scale. To avoid such attacks, developers must implement robust filters to detect and block offensive language, including swear words, hate speech, and sexually suggestive content. While it is OK to make sensitivity levels customizable based on user preferences, it is essential to incorporate age verification, consent messages, and educational prompts into the user journey. Flagging customizations for human approval adds an extra layer of protection. In cases where unwanted content slips through the cracks, easy-to-use mechanisms for users to report inappropriate behavior should be in place with a team on hand to investigate these scenarios promptly. These reports must then be fed into the AI’s training data to prevent the chatbot from generating offensive or biased content going forward. Educate users with appropriate pop-ups Developers must advocate for transparency about the capabilities and limitations of chatbots, educating users about potential risks. Similar to OpenAI, these can take the form of colored warning notifications that appear in the chat user interface. Developers can use these spaces to offer recommendations for users, such as: Why it is important to maintain real-world connections: Humans can offer genuine empathy, understanding, and support based on shared experiences whereas AI can only simulate these responses. It is essential users continue developing communication and conflict resolution skills, and the ability to read non-verbal cues. AI lacks the complexity of social dynamics and developers must make users aware of these limitations. How to set boundaries: The Center for Humane Technology suggests turning off notifications, reducing harmful apps, scheduling tech-free blocks of time, and charging devices outside of bedrooms to help reduce attachment to AI tools. Habit stacking is another way to promote healthy routines, where users complete an activity that benefits their personal or social development before signing into devices.
Be mindful of expectations: Users must remember that AI boyfriends are designed to be idealized partners, and real-life relationships require effort, compromise, and understanding.
Protect your privacy: Remind users to be cautious about sharing personal information with the AI and make sure they understand the platform’s data privacy policies.
Seek support when needed: If users find themselves struggling with emotional dependence or other issues related to their AI companion, pop-up messages can suggest they seek advice from a friend or family or include links to support services. As we saw in the New York Times article, users can choose to ignore these warnings at their own liberty, but it is important that developers design chatbots to make users aware of AI’s limitations and psychological impacts. By addressing these challenges, companies can ensure the safe usage of their chatbots on Valentine’s Day. Nate Macleitch, CEO, QuickBlox AI chatbots are transforming communication, but recent headlines reveal their darker side: lawsuits over harmful content and reports of misuse by predators . These incidents bring critical questions about AI design ethics, safety, and accountability. lawsuits over harmful content reports of misuse by predators When the technology is leveraged for personal use, these elements are tested even further and even trained to cross lines. Valentine’s Day might exacerbate these behaviors even more. After editing a chatbot’s settings and asking it to “Respond as my boyfriend,” along with explicit requests for tone and communication style, a 28-year-old woman upgraded her ChatGPT subscription to $20 per month, which let her send around 30 messages an hour. That still wasn’t enough. editing a chatbot’s settings Despite OpenAI having trained its models not to respond with erotica, extreme gore, or other content that is “not safe for work,” the woman proceeded to explore. Orange warnings of content violations would pop up, but she would ignore them. Her AI-boyfriend, dubbed “Leo” was always there when she wanted to talk. The New York Times article quotes the woman, “It was supposed to be a fun experiment, but then you start getting attached.” One week, her iPhone screen-time reports hit 56 hours—more than a full-time work schedule—which can indicate insufficient time for work, friends, or sleep. trained its models Orange warnings What are the risks of misuse and psychological implications chatbot providers should understand ahead of Valentine’s? Sense-check ethical guidelines It can be tempting to rush to market and compete with existing solutions, but overlooking ethical responsibility will only backfire . This was the case with Gemini, in 2023, when they released products to the public before they were 100% ready across areas such as fairness and bias. backfire before they were 100% ready Chatbots can be programmed to mimic human emotions and create a sense of intimacy, making users more attached. This can also set unrealistic expectations of human relationships. Developers must work with business leaders to devise ethical guidelines for AI development, particularly in the context of romantic relationships. Before product launch, developers must ensure chatbots consistently and transparently identify themselves as AI within conversations (not in the fineprint). The same goes for the chatbot’s intended purpose. If it is built for companionship this should be made explicit, but it should not masquerade as a romantic partner if that’s not its core function. Developers building chatbots for social purposes must be very careful to safeguard against manipulation, and actively avoid language that preys on insecurities, promises unrealistic outcomes, or fosters dependency. Instead, they should prioritize encouraging users to maintain real-world relationships and social connections. Chatbots should not discourage or replace human interaction. Intel.gov offers a list of ethical questions to ask when modeling AI tools. Intel.gov Implement safety measures and user controls “ AI whisperers ” are exploiting AI ethics’ boundaries by convincing well-behaved chatbots to break their own rules. Rather than taking one shot at confusing AI, these malicious actors are attacking with multiple tries to elevate the success rate. This could lead to chatbots generating harmful content, spreading disinformation, or automating social engineering attacks at scale. AI whisperers To avoid such attacks, developers must implement robust filters to detect and block offensive language, including swear words, hate speech, and sexually suggestive content. While it is OK to make sensitivity levels customizable based on user preferences, it is essential to incorporate age verification, consent messages, and educational prompts into the user journey. Flagging customizations for human approval adds an extra layer of protection. In cases where unwanted content slips through the cracks, easy-to-use mechanisms for users to report inappropriate behavior should be in place with a team on hand to investigate these scenarios promptly. These reports must then be fed into the AI’s training data to prevent the chatbot from generating offensive or biased content going forward. Educate users with appropriate pop-ups Developers must advocate for transparency about the capabilities and limitations of chatbots, educating users about potential risks. Similar to OpenAI, these can take the form of colored warning notifications that appear in the chat user interface. Developers can use these spaces to offer recommendations for users, such as: Why it is important to maintain real-world connections: Humans can offer genuine empathy, understanding, and support based on shared experiences whereas AI can only simulate these responses. It is essential users continue developing communication and conflict resolution skills, and the ability to read non-verbal cues. AI lacks the complexity of social dynamics and developers must make users aware of these limitations. Why it is important to maintain real-world connections: How to set boundaries: The Center for Humane Technology suggests turning off notifications, reducing harmful apps, scheduling tech-free blocks of time, and charging devices outside of bedrooms to help reduce attachment to AI tools. Habit stacking is another way to promote healthy routines, where users complete an activity that benefits their personal or social development before signing into devices. Be mindful of expectations: Users must remember that AI boyfriends are designed to be idealized partners, and real-life relationships require effort, compromise, and understanding. Protect your privacy: Remind users to be cautious about sharing personal information with the AI and make sure they understand the platform’s data privacy policies. Seek support when needed: If users find themselves struggling with emotional dependence or other issues related to their AI companion, pop-up messages can suggest they seek advice from a friend or family or include links to support services. How to set boundaries: The Center for Humane Technology suggests turning off notifications, reducing harmful apps, scheduling tech-free blocks of time, and charging devices outside of bedrooms to help reduce attachment to AI tools. Habit stacking is another way to promote healthy routines, where users complete an activity that benefits their personal or social development before signing into devices. How to set boundaries: Center for Humane Technology Be mindful of expectations: Users must remember that AI boyfriends are designed to be idealized partners, and real-life relationships require effort, compromise, and understanding. Be mindful of expectations: Protect your privacy: Remind users to be cautious about sharing personal information with the AI and make sure they understand the platform’s data privacy policies. Protect your privacy: Seek support when needed: If users find themselves struggling with emotional dependence or other issues related to their AI companion, pop-up messages can suggest they seek advice from a friend or family or include links to support services. Seek support when needed: As we saw in the New York Times article, users can choose to ignore these warnings at their own liberty, but it is important that developers design chatbots to make users aware of AI’s limitations and psychological impacts. By addressing these challenges, companies can ensure the safe usage of their chatbots on Valentine’s Day. Nate Macleitch, CEO, QuickBlox Nate Macleitch , CEO, QuickBlox Nate Macleitch Nate Macleitch QuickBlox

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