Restti

restti

In modern applications, data security is a top priority, especially when sensitive information may be copied or transferred to external applications. By preventing certain actions like "cut," "copy," and "paste" on text fields within iOS apps, we can safeguard text data, ensuring it remains within a trusted group of applications. This article demonstrates how to achieve this protection using Swift, while maintaining full control over text-related operations. Goals of This Approach Restrict External Text Sharing: By managing text actions, we can prevent unauthorized access to sensitive information.
Safe Text Storage and Management: Creating an internal buffer ensures that sensitive text remains protected within the app.
Controlled Clipboard Access: By limiting access to the system clipboard, we can ensure that only authorized applications within a defined group can access this information. Implementation Overview Our approach centers around the TextActionInterceptor class, which "swizzles" standard text-related methods, replacing them with secure alternatives. This class intercepts and overrides the "copy," "paste," and "cut" methods, with an option to enable or disable swizzling dynamically. Core Components Enum for Text Actions: The TextAction enumeration defines all the actions related to text that we want to control.
Saved Method Structure: The SavedTextAction struct stores original implementations of methods, allowing us to restore them as needed.
Swizzling Methods: The toggleSwizzling method manages the swizzling state, enabling or disabling the replacement of standard implementations. Code Example Let’s go through key code segments that bring this functionality to life. class TextActionInterceptor {

    enum TextAction: String {
        case cut
        case copy
        case paste
        case define
        case translate
        case modify
        case update
        case previewItem
    }

    struct SavedTextAction {
        var methodRef: Method
        var originalImplementation: IMP
        var selector: Selector
    }

    private var isSwizzled = false
    var savedActions: [TextAction: SavedTextAction] = [:]
    
    // Method to intercept the "copy" action
    func interceptCopy(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = text
    }

    // Similar methods for "cut" and "paste"
    func interceptPaste(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = SecureClipboard.shared.contents
        else { return }

        inputField.replace(range, withText: text)
    }

    func interceptCut(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let cutText = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = cutText
        inputField.replace(range, withText: "")
    }
    
    // Method to enable or disable swizzling
    func toggleSwizzling(_ enable: Bool) {
        guard enable != isSwizzled else { return }
        isSwizzled = enable
        savedActions.forEach { (_, action) in
            let swizzledMethod = class_getInstanceMethod(
                object_getClass(self),
                action.selector
            )
            guard enable else {
                method_setImplementation(action.methodRef, action.originalImplementation)
                return
            }
            method_exchangeImplementations(action.methodRef, swizzledMethod!)
        }
    }
} Implementing a Secure Clipboard The SecureClipboard class is responsible for managing text stored in the clipboard. It blocks any attempt to share text data externally, ensuring it stays within the app's defined ecosystem. final class SecureClipboard {

    static let restrictedTextPlaceholder = "Access Restricted"
    static var shared = SecureClipboard()
    private let placeholderItem: [String: String]
    private var clipboard: UIPasteboard
    private(set) var internalBuffer: String?

    init() {
        placeholderItem = ["public.utf8-plain-text": SecureClipboard.restrictedTextPlaceholder]
        clipboard = UIPasteboard.general
    }

    var contents: String? {
        get {
            guard
                let clipboardText = clipboard.string,
                clipboardText != SecureClipboard.restrictedTextPlaceholder
            else { return internalBuffer }
            return clipboardText
        }
        set {
            guard let newText = newValue else {
                internalBuffer = nil
                return
            }
            clipboard.items = [placeholderItem]
            internalBuffer = newText.isEmpty ? nil : newText
        }
    }
} How This Works Swizzling of Methods: We replace the standard text action methods with customized, secure methods.
Secure Copying: The interceptCopy method intercepts "copy" actions and transfers the text to the SecureClipboard instead of the system clipboard.
Flexible Management: Swizzling can be dynamically enabled or disabled, allowing for adaptable data security. Summary and Results Applying this approach achieves multiple levels of data security: Access Control: All text operations are managed within the application, preventing potential data leaks.


Safe Data Storage: Text data is accessible only within a controlled application group.


Flexible configuration: The ability to enable and disable swizzling allows flexible control of data security based on conditions. By using swizzling and an internal clipboard, we ensure a high level of data protection in iOS applications. This approach offers a robust solution for maintaining text security across applications in a defined environment. In modern applications, data security is a top priority, especially when sensitive information may be copied or transferred to external applications. By preventing certain actions like "cut," "copy," and "paste" on text fields within iOS apps, we can safeguard text data, ensuring it remains within a trusted group of applications. This article demonstrates how to achieve this protection using Swift, while maintaining full control over text-related operations. Goals of This Approach Restrict External Text Sharing: By managing text actions, we can prevent unauthorized access to sensitive information. Safe Text Storage and Management: Creating an internal buffer ensures that sensitive text remains protected within the app. Controlled Clipboard Access: By limiting access to the system clipboard, we can ensure that only authorized applications within a defined group can access this information. Restrict External Text Sharing : By managing text actions, we can prevent unauthorized access to sensitive information. Restrict External Text Sharing Safe Text Storage and Management : Creating an internal buffer ensures that sensitive text remains protected within the app. Safe Text Storage and Management Controlled Clipboard Access : By limiting access to the system clipboard, we can ensure that only authorized applications within a defined group can access this information. Controlled Clipboard Access Implementation Overview Our approach centers around the TextActionInterceptor class, which "swizzles" standard text-related methods, replacing them with secure alternatives. This class intercepts and overrides the "copy," "paste," and "cut" methods, with an option to enable or disable swizzling dynamically. Core Components Enum for Text Actions: The TextAction enumeration defines all the actions related to text that we want to control. Saved Method Structure: The SavedTextAction struct stores original implementations of methods, allowing us to restore them as needed. Swizzling Methods: The toggleSwizzling method manages the swizzling state, enabling or disabling the replacement of standard implementations. Enum for Text Actions: The TextAction enumeration defines all the actions related to text that we want to control. Enum for Text Actions: The TextAction enumeration defines all the actions related to text that we want to control. Saved Method Structure: The SavedTextAction struct stores original implementations of methods, allowing us to restore them as needed. Saved Method Structure: The SavedTextAction struct stores original implementations of methods, allowing us to restore them as needed. Swizzling Methods: The toggleSwizzling method manages the swizzling state, enabling or disabling the replacement of standard implementations. Swizzling Methods: The toggleSwizzling method manages the swizzling state, enabling or disabling the replacement of standard implementations. Code Example Let’s go through key code segments that bring this functionality to life. class TextActionInterceptor {

    enum TextAction: String {
        case cut
        case copy
        case paste
        case define
        case translate
        case modify
        case update
        case previewItem
    }

    struct SavedTextAction {
        var methodRef: Method
        var originalImplementation: IMP
        var selector: Selector
    }

    private var isSwizzled = false
    var savedActions: [TextAction: SavedTextAction] = [:]
    
    // Method to intercept the "copy" action
    func interceptCopy(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = text
    }

    // Similar methods for "cut" and "paste"
    func interceptPaste(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = SecureClipboard.shared.contents
        else { return }

        inputField.replace(range, withText: text)
    }

    func interceptCut(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let cutText = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = cutText
        inputField.replace(range, withText: "")
    }
    
    // Method to enable or disable swizzling
    func toggleSwizzling(_ enable: Bool) {
        guard enable != isSwizzled else { return }
        isSwizzled = enable
        savedActions.forEach { (_, action) in
            let swizzledMethod = class_getInstanceMethod(
                object_getClass(self),
                action.selector
            )
            guard enable else {
                method_setImplementation(action.methodRef, action.originalImplementation)
                return
            }
            method_exchangeImplementations(action.methodRef, swizzledMethod!)
        }
    }
} class TextActionInterceptor {

    enum TextAction: String {
        case cut
        case copy
        case paste
        case define
        case translate
        case modify
        case update
        case previewItem
    }

    struct SavedTextAction {
        var methodRef: Method
        var originalImplementation: IMP
        var selector: Selector
    }

    private var isSwizzled = false
    var savedActions: [TextAction: SavedTextAction] = [:]
    
    // Method to intercept the "copy" action
    func interceptCopy(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = text
    }

    // Similar methods for "cut" and "paste"
    func interceptPaste(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let text = SecureClipboard.shared.contents
        else { return }

        inputField.replace(range, withText: text)
    }

    func interceptCut(_ sender: Any?) {
        guard
            let inputField = retrieveTextInput(sender),
            let range = inputField.selectedTextRange,
            let cutText = inputField.text(in: range)
        else { return }

        SecureClipboard.shared.contents = cutText
        inputField.replace(range, withText: "")
    }
    
    // Method to enable or disable swizzling
    func toggleSwizzling(_ enable: Bool) {
        guard enable != isSwizzled else { return }
        isSwizzled = enable
        savedActions.forEach { (_, action) in
            let swizzledMethod = class_getInstanceMethod(
                object_getClass(self),
                action.selector
            )
            guard enable else {
                method_setImplementation(action.methodRef, action.originalImplementation)
                return
            }
            method_exchangeImplementations(action.methodRef, swizzledMethod!)
        }
    }
} Implementing a Secure Clipboard The SecureClipboard class is responsible for managing text stored in the clipboard. It blocks any attempt to share text data externally, ensuring it stays within the app's defined ecosystem. final class SecureClipboard {

    static let restrictedTextPlaceholder = "Access Restricted"
    static var shared = SecureClipboard()
    private let placeholderItem: [String: String]
    private var clipboard: UIPasteboard
    private(set) var internalBuffer: String?

    init() {
        placeholderItem = ["public.utf8-plain-text": SecureClipboard.restrictedTextPlaceholder]
        clipboard = UIPasteboard.general
    }

    var contents: String? {
        get {
            guard
                let clipboardText = clipboard.string,
                clipboardText != SecureClipboard.restrictedTextPlaceholder
            else { return internalBuffer }
            return clipboardText
        }
        set {
            guard let newText = newValue else {
                internalBuffer = nil
                return
            }
            clipboard.items = [placeholderItem]
            internalBuffer = newText.isEmpty ? nil : newText
        }
    }
} final class SecureClipboard {

    static let restrictedTextPlaceholder = "Access Restricted"
    static var shared = SecureClipboard()
    private let placeholderItem: [String: String]
    private var clipboard: UIPasteboard
    private(set) var internalBuffer: String?

    init() {
        placeholderItem = ["public.utf8-plain-text": SecureClipboard.restrictedTextPlaceholder]
        clipboard = UIPasteboard.general
    }

    var contents: String? {
        get {
            guard
                let clipboardText = clipboard.string,
                clipboardText != SecureClipboard.restrictedTextPlaceholder
            else { return internalBuffer }
            return clipboardText
        }
        set {
            guard let newText = newValue else {
                internalBuffer = nil
                return
            }
            clipboard.items = [placeholderItem]
            internalBuffer = newText.isEmpty ? nil : newText
        }
    }
} How This Works Swizzling of Methods: We replace the standard text action methods with customized, secure methods. Secure Copying: The interceptCopy method intercepts "copy" actions and transfers the text to the SecureClipboard instead of the system clipboard. Flexible Management: Swizzling can be dynamically enabled or disabled, allowing for adaptable data security. Swizzling of Methods : We replace the standard text action methods with customized, secure methods. Swizzling of Methods Secure Copying: The interceptCopy method intercepts "copy" actions and transfers the text to the SecureClipboard instead of the system clipboard. Secure Copying: The interceptCopy method intercepts "copy" actions and transfers the text to the SecureClipboard instead of the system clipboard. Flexible Management : Swizzling can be dynamically enabled or disabled, allowing for adaptable data security. Flexible Management Summary and Results Applying this approach achieves multiple levels of data security: Access Control: All text operations are managed within the application, preventing potential data leaks. Safe Data Storage: Text data is accessible only within a controlled application group. Flexible configuration: The ability to enable and disable swizzling allows flexible control of data security based on conditions. Access Control: All text operations are managed within the application, preventing potential data leaks. Access Control : All text operations are managed within the application, preventing potential data leaks. Access Control Safe Data Storage: Text data is accessible only within a controlled application group. Safe Data Storage : Text data is accessible only within a controlled application group. Safe Data Storage Flexible configuration: The ability to enable and disable swizzling allows flexible control of data security based on conditions. Flexible configuration : The ability to enable and disable swizzling allows flexible control of data security based on conditions. Flexible configuration By using swizzling and an internal clipboard, we ensure a high level of data protection in iOS applications. This approach offers a robust solution for maintaining text security across applications in a defined environment.

Securing Text Fields in iOS Apps: Restricting 'Cut,' 'Copy,' and 'Paste' Operations Using Swift

**SINGAPORE, Singapore, October 3rd, 2024/CyberNewsWire/--**At DEF CON 32, the SquareX research team delivered a hard-hitting presentation titled Sneaky Extensions: The MV3 Escape Artists where they shared their findings on how malicious browser extensions are bypassing Google’s latest standard for building chrome extensions: Manifest V3 (MV3)’s security features, putting millions of users and businesses at risk. SquareX’s research team publicly demonstrated rogue extensions built on MV3. The key findings include: Extensions can steal live video streams, such as those from Google Meet and Zoom Web, without requiring special permissions.
The rogue extensions can act on a user’s behalf to add collaborators to private GitHub repositories.
The extensions are capable of hooking into login events to redirect users to a page disguised as a password manager login.
Extensions built on MV3 can steal site cookies, browsing history, bookmarks, and download history with ease, like their MV2 counterparts.
The rogue extensions can add pop-ups to the active webpage, such as fake software update prompts, tricking users into downloading malware. Browser extensions have long been a target for malicious actors — a Stanford University report estimates that 280 million malicious Chrome extensions were installed in recent years. Google has struggled to address this issue, often relying on independent researchers to identify malicious extensions. In some cases, Google has had to manually remove them, such as the 32 extensions taken down in June last year. By the time they were removed, these extensions had already been installed 75 million times. Most of these issues arose because the Chrome extension standard, Manifest Version 2 (MV2), was riddled with loopholes that granted extensions excessive permissions, and allowed scripts to be injected on the fly, often without users' knowledge. This allowed malicious actors to easily exploit these vulnerabilities to steal data, inject malware, and access sensitive information. MV3 was introduced to address these problems by tightening security, limiting permissions, and requiring extensions to declare their scripts beforehand. However, SquareX’s research shows that MV3 falls short in many critical areas, demonstrating how attackers are still able to exploit minimal permissions to carry out malicious activity. Both individual users and enterprises are exposed, even under the newer MV3 framework. Today's security solutions, such as endpoint security, SASE/SSE, and Secure Web Gateways (SWG), lack visibility into installed browser extensions. There is currently no mature tool or platform capable of dynamically instrumenting these extensions, leaving enterprises without the ability to accurately assess whether an extension is safe or malicious. SquareX is committed to the highest level of cybersecurity protection for enterprises and has built key innovative features to solve this problem, which include; Fine grained policies to decide which extensions to allow / block and parameters include extension permissions, creation date, last update, reviews, ratings, user count, author attributes etc
SquareX blocks network requests sent by extensions at run time – based on policies, heuristics and machine learning insights
SquareX is also experimenting with dynamic analysis of Chrome Extensions using a modified Chromium browser in its cloud server These are part of SquareX’s Browser Detection and Response solution which is being deployed at medium-large enterprises and is effectively blocking these attacks. Vivek Ramachandran, Founder & CEO of SquareX, warned about the mounting risks: “Browser extensions are a blind spot for EDR/XDR and SWGs have no way to infer their presence. This has made browser extensions a very effective and potent technique to silently be installed and monitor enterprise users, and attackers are leveraging them to monitor communication over web calls, act on the victim’s behalf to give permissions to external parties, steal cookies and other site data and so on.” “Our research proves that without dynamic analysis and the ability for enterprises to apply stringent policies, it will not be possible to identify and block these attacks. Google MV3, though well intended, is still far away from enforcing security at both a design and implementation phase,” said Vivek Ramachandran. About SquareX SquareX helps organizations detect, mitigate and threat-hunt client-side web attacks happening against their users in real time. SquareX's industry-first Browser Detection and Response (BDR) solution, takes an attack-focused approach to browser security, ensuring enterprise users are protected against advanced threats like malicious QR Codes, Browser-in-the-Browser phishing, macro-based malware, malicious extensions and other web attacks encompassing malicious files, websites, scripts, and compromised networks. With SquareX, enterprises can also provide contractors and remote workers with secure access to internal applications, enterprise SaaS, and convert the browsers on BYOD / unmanaged devices into trusted browsing sessions. Contact Head of PR Junice Liew SquareX junice@sqrx.com This story was distributed as a release by CyberNewsWire under HackerNoon’s Business Blogging Program. Learn more about the program here **SINGAPORE, Singapore, October 3rd, 2024/CyberNewsWire/--**At DEF CON 32, the SquareX research team delivered a hard-hitting presentation titled Sneaky Extensions: The MV3 Escape Artists where they shared their findings on how malicious browser extensions are bypassing Google’s latest standard for building chrome extensions: Manifest V3 (MV3)’s security features, putting millions of users and businesses at risk. SquareX SquareX SquareX’s research team publicly demonstrated rogue extensions built on MV3. The key findings include: Extensions can steal live video streams, such as those from Google Meet and Zoom Web, without requiring special permissions. The rogue extensions can act on a user’s behalf to add collaborators to private GitHub repositories. The extensions are capable of hooking into login events to redirect users to a page disguised as a password manager login. Extensions built on MV3 can steal site cookies, browsing history, bookmarks, and download history with ease, like their MV2 counterparts. The rogue extensions can add pop-ups to the active webpage, such as fake software update prompts, tricking users into downloading malware. Extensions can steal live video streams, such as those from Google Meet and Zoom Web, without requiring special permissions. The rogue extensions can act on a user’s behalf to add collaborators to private GitHub repositories. The extensions are capable of hooking into login events to redirect users to a page disguised as a password manager login. Extensions built on MV3 can steal site cookies, browsing history, bookmarks, and download history with ease, like their MV2 counterparts. The rogue extensions can add pop-ups to the active webpage, such as fake software update prompts, tricking users into downloading malware. Browser extensions have long been a target for malicious actors — a Stanford University report estimates that 280 million malicious Chrome extensions were installed in recent years. Google has struggled to address this issue, often relying on independent researchers to identify malicious extensions. report report In some cases, Google has had to manually remove them, such as the 32 extensions taken down in June last year. By the time they were removed, these extensions had already been installed 75 million times. Most of these issues arose because the Chrome extension standard, Manifest Version 2 (MV2), was riddled with loopholes that granted extensions excessive permissions, and allowed scripts to be injected on the fly, often without users' knowledge. 32 extensions 32 extensions This allowed malicious actors to easily exploit these vulnerabilities to steal data, inject malware, and access sensitive information. MV3 was introduced to address these problems by tightening security, limiting permissions, and requiring extensions to declare their scripts beforehand. However, SquareX’s research shows that MV3 falls short in many critical areas, demonstrating how attackers are still able to exploit minimal permissions to carry out malicious activity. Both individual users and enterprises are exposed, even under the newer MV3 framework. Today's security solutions, such as endpoint security, SASE/SSE, and Secure Web Gateways (SWG), lack visibility into installed browser extensions. There is currently no mature tool or platform capable of dynamically instrumenting these extensions, leaving enterprises without the ability to accurately assess whether an extension is safe or malicious. SquareX is committed to the highest level of cybersecurity protection for enterprises and has built key innovative features to solve this problem, which include; Fine grained policies to decide which extensions to allow / block and parameters include extension permissions, creation date, last update, reviews, ratings, user count, author attributes etc SquareX blocks network requests sent by extensions at run time – based on policies, heuristics and machine learning insights SquareX is also experimenting with dynamic analysis of Chrome Extensions using a modified Chromium browser in its cloud server Fine grained policies to decide which extensions to allow / block and parameters include extension permissions, creation date, last update, reviews, ratings, user count, author attributes etc SquareX blocks network requests sent by extensions at run time – based on policies, heuristics and machine learning insights SquareX is also experimenting with dynamic analysis of Chrome Extensions using a modified Chromium browser in its cloud server These are part of SquareX’s Browser Detection and Response solution which is being deployed at medium-large enterprises and is effectively blocking these attacks. Browser Detection and Response Browser Detection and Response Vivek Ramachandran, Founder & CEO of SquareX, warned about the mounting risks: “Browser extensions are a blind spot for EDR/XDR and SWGs have no way to infer their presence. This has made browser extensions a very effective and potent technique to silently be installed and monitor enterprise users, and attackers are leveraging them to monitor communication over web calls, act on the victim’s behalf to give permissions to external parties, steal cookies and other site data and so on.” Vivek Ramachandran , Founder & CEO of SquareX , warned about the mounting risks: “Browser extensions are a blind spot for EDR/XDR and SWGs have no way to infer their presence. This has made browser extensions a very effective and potent technique to silently be installed and monitor enterprise users, and attackers are leveraging them to monitor communication over web calls, act on the victim’s behalf to give permissions to external parties, steal cookies and other site data and so on.” Vivek Ramachandran Vivek Ramachandran SquareX SquareX “Our research proves that without dynamic analysis and the ability for enterprises to apply stringent policies, it will not be possible to identify and block these attacks. Google MV3, though well intended, is still far away from enforcing security at both a design and implementation phase,” said Vivek Ramachandran. “Our research proves that without dynamic analysis and the ability for enterprises to apply stringent policies, it will not be possible to identify and block these attacks. Google MV3, though well intended, is still far away from enforcing security at both a design and implementation phase,” said Vivek Ramachandran. About SquareX SquareX helps organizations detect, mitigate and threat-hunt client-side web attacks happening against their users in real time. SquareX SquareX SquareX's industry-first Browser Detection and Response (BDR) solution, takes an attack-focused approach to browser security, ensuring enterprise users are protected against advanced threats like malicious QR Codes, Browser-in-the-Browser phishing, macro-based malware, malicious extensions and other web attacks encompassing malicious files, websites, scripts, and compromised networks. With SquareX, enterprises can also provide contractors and remote workers with secure access to internal applications, enterprise SaaS, and convert the browsers on BYOD / unmanaged devices into trusted browsing sessions. Contact Head of PR Junice Liew SquareX junice@sqrx.com This story was distributed as a release by CyberNewsWire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by CyberNewsWire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

SquareX Shows How Malicious Extensions Bypass Google’s MV3 Restrictions

SquareX يوضح كيف تتجاوز الإضافات الضارة قيود MV3 الخاصة بشركة Google

SquareX показва как злонамерените разширения заобикалят MV3 ограниченията на Google

SquareX muestra cómo las extensiones maliciosas eluden las restricciones MV3 de Google

SquareX montre comment les extensions malveillantes contournent les restrictions MV3 de Google

A SquareX megmutatja, hogy a rosszindulatú bővítmények hogyan kerülik meg a Google MV3 korlátozásait

SquareX mostra come le estensioni dannose aggirano le restrizioni MV3 di Google

SquareX、悪質な拡張機能がGoogleのMV3制限を回避する方法を紹介

SquareX აჩვენებს, თუ როგორ აცილებენ მავნე გაფართოებები Google-ის MV3 შეზღუდვებს

SquareX покажува како злонамерните екстензии ги заобиколуваат ограничувањата за MV3 на Google

SquareX laat zien hoe kwaadaardige extensies de MV3-beperkingen van Google omzeilen

SquareX mostra como extensões maliciosas contornam as restrições MV3 do Google

SquareX waxay tusinaysaa sida Fidinta xaasidnimada leh ay uga gudubto Google's MV3

When working with APIs, there may be a need to send a request with JSON data and receive a response in multipart/form-data format containing a file. In this article, we will discuss how to configure RestTemplate to handle such data. Create a class for serializing JSON to send import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;

@Data
@Builder
@NoArgsConstructor
@AllArgsConstructor
public class JsonRequest {
    String field1;
    int field2;
} Configure RestTemplate, prepare, and send a POST request public Optional<Resource> postSomeReq(JsonRequest rq) {
    
    String url = "http://somehost/services/retrieve-multipart";

    try {
        HttpHeaders headers = new HttpHeaders();
        headers.setContentType(MediaType.APPLICATION_JSON);
        headers.setAccept(List.of(MediaType.MULTIPART_FORM_DATA));

        RestTemplate restClient = new RestTemplate();
        ResponseEntity<byte[]> response = restClient.postForEntity(url, getRequestData(rq, headers), byte[].class);

        return getResource(response.getBody());

    } catch (RestClientException | MessagingException | IOException e) {
        return Optional.empty();
    }
}

private HttpEntity<String> getRequestData(Object request, HttpHeaders headers) {
    String requestBody;
    try {
        requestBody = new ObjectMapper().writeValueAsString(request);
    } catch (JsonProcessingException e) {
        throw new RuntimeException(e);
    }
    return new HttpEntity<>(removeShielding(requestBody), headers);
}

private Optional<Resource> getResource(byte[] responseBody) throws MessagingException, IOException {
    ByteArrayDataSource dataSource = new ByteArrayDataSource(responseBody, "multipart/form-data");
    MimeMultipart multipart = new MimeMultipart(dataSource);

    int count = multipart.getCount();
    for (int i = 0; i < count; i++) {
        BodyPart bodyPart = multipart.getBodyPart(i);
        String[] contentType = bodyPart.getHeader("Content-Type");

        if ("application/octet-stream".equals(contentType[0])) {
            SharedByteArrayInputStream inputStream = (SharedByteArrayInputStream) bodyPart.getContent();
            final String filename = bodyPart.getFileName();
            Resource resource = new ByteArrayResource(inputStream.readAllBytes()) {
                @Override
                public String getFilename() {
                    return filename;
                }
            };
            return Optional.of(resource);
        }
    }
    return Optional.empty();
} Alternative Approach You can also use a third-party converter MultipartMessageConverter (GitHub Repository). It provides a simple implementation of HttpMessageConverter that can read multipart/form-data and prepare a DTO model, even if a part contains either application/json or application/octet-stream. public Optional<Resource> postSomeReq(JsonRequest rq) {
    
    String url = "http://somehost/services/retrieve-multipart";

    try {
        HttpHeaders headers = new HttpHeaders();
        headers.setContentType(MediaType.APPLICATION_JSON);
        headers.setAccept(List.of(MediaType.MULTIPART_FORM_DATA));

        RestTemplate restClient = new RestTemplate();
        restClient.getMessageConverters().add(new MultiPartMessageConverter(objectMapper));

        ResponseEntity<ResultModel> responseEntity =
                restClient.postForEntity(url, getRequestData(rq, headers), ResultModel.class);

        final ResultModel resultModel = responseEntity.getBody();
        return Optional.of(resultModel.fileModel.resource);

    } catch (RestClientException | MessagingException | IOException e) {
        return Optional.empty();
    }
} Conclusion We have reviewed how to use RestTemplate to send a JSON request and receive a response in multipart/form-data format. The approach presented allows for effective handling of multipart responses, which is useful in various scenarios, such as downloading files from a server. An alternative approach using a third-party converter was also presented. You can now use these methods in your applications to work with multipart data, enhancing the functionality and flexibility of your code. When working with APIs , there may be a need to send a request with JSON data and receive a response in multipart/form-data format containing a file. In this article, we will discuss how to configure RestTemplate to handle such data. APIs JSON Create a class for serializing JSON to send import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;

@Data
@Builder
@NoArgsConstructor
@AllArgsConstructor
public class JsonRequest {
    String field1;
    int field2;
} import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;

@Data
@Builder
@NoArgsConstructor
@AllArgsConstructor
public class JsonRequest {
    String field1;
    int field2;
} Configure RestTemplate, prepare, and send a POST request public Optional<Resource> postSomeReq(JsonRequest rq) {
    
    String url = "http://somehost/services/retrieve-multipart";

    try {
        HttpHeaders headers = new HttpHeaders();
        headers.setContentType(MediaType.APPLICATION_JSON);
        headers.setAccept(List.of(MediaType.MULTIPART_FORM_DATA));

        RestTemplate restClient = new RestTemplate();
        ResponseEntity<byte[]> response = restClient.postForEntity(url, getRequestData(rq, headers), byte[].class);

        return getResource(response.getBody());

    } catch (RestClientException | MessagingException | IOException e) {
        return Optional.empty();
    }
}

private HttpEntity<String> getRequestData(Object request, HttpHeaders headers) {
    String requestBody;
    try {
        requestBody = new ObjectMapper().writeValueAsString(request);
    } catch (JsonProcessingException e) {
        throw new RuntimeException(e);
    }
    return new HttpEntity<>(removeShielding(requestBody), headers);
}

private Optional<Resource> getResource(byte[] responseBody) throws MessagingException, IOException {
    ByteArrayDataSource dataSource = new ByteArrayDataSource(responseBody, "multipart/form-data");
    MimeMultipart multipart = new MimeMultipart(dataSource);

    int count = multipart.getCount();
    for (int i = 0; i < count; i++) {
        BodyPart bodyPart = multipart.getBodyPart(i);
        String[] contentType = bodyPart.getHeader("Content-Type");

        if ("application/octet-stream".equals(contentType[0])) {
            SharedByteArrayInputStream inputStream = (SharedByteArrayInputStream) bodyPart.getContent();
            final String filename = bodyPart.getFileName();
            Resource resource = new ByteArrayResource(inputStream.readAllBytes()) {
                @Override
                public String getFilename() {
                    return filename;
                }
            };
            return Optional.of(resource);
        }
    }
    return Optional.empty();
} public Optional<Resource> postSomeReq(JsonRequest rq) {
    
    String url = "http://somehost/services/retrieve-multipart";

    try {
        HttpHeaders headers = new HttpHeaders();
        headers.setContentType(MediaType.APPLICATION_JSON);
        headers.setAccept(List.of(MediaType.MULTIPART_FORM_DATA));

        RestTemplate restClient = new RestTemplate();
        ResponseEntity<byte[]> response = restClient.postForEntity(url, getRequestData(rq, headers), byte[].class);

        return getResource(response.getBody());

    } catch (RestClientException | MessagingException | IOException e) {
        return Optional.empty();
    }
}

private HttpEntity<String> getRequestData(Object request, HttpHeaders headers) {
    String requestBody;
    try {
        requestBody = new ObjectMapper().writeValueAsString(request);
    } catch (JsonProcessingException e) {
        throw new RuntimeException(e);
    }
    return new HttpEntity<>(removeShielding(requestBody), headers);
}

private Optional<Resource> getResource(byte[] responseBody) throws MessagingException, IOException {
    ByteArrayDataSource dataSource = new ByteArrayDataSource(responseBody, "multipart/form-data");
    MimeMultipart multipart = new MimeMultipart(dataSource);

    int count = multipart.getCount();
    for (int i = 0; i < count; i++) {
        BodyPart bodyPart = multipart.getBodyPart(i);
        String[] contentType = bodyPart.getHeader("Content-Type");

        if ("application/octet-stream".equals(contentType[0])) {
            SharedByteArrayInputStream inputStream = (SharedByteArrayInputStream) bodyPart.getContent();
            final String filename = bodyPart.getFileName();
            Resource resource = new ByteArrayResource(inputStream.readAllBytes()) {
                @Override
                public String getFilename() {
                    return filename;
                }
            };
            return Optional.of(resource);
        }
    }
    return Optional.empty();
} Alternative Approach You can also use a third-party converter MultipartMessageConverter ( GitHub Repository ). It provides a simple implementation of HttpMessageConverter that can read multipart/form-data and prepare a DTO model, even if a part contains either application/json or application/octet-stream . MultipartMessageConverter GitHub Repository HttpMessageConverter multipart/form-data application/json application/octet-stream public Optional<Resource> postSomeReq(JsonRequest rq) {
    
    String url = "http://somehost/services/retrieve-multipart";

    try {
        HttpHeaders headers = new HttpHeaders();
        headers.setContentType(MediaType.APPLICATION_JSON);
        headers.setAccept(List.of(MediaType.MULTIPART_FORM_DATA));

        RestTemplate restClient = new RestTemplate();
        restClient.getMessageConverters().add(new MultiPartMessageConverter(objectMapper));

        ResponseEntity<ResultModel> responseEntity =
                restClient.postForEntity(url, getRequestData(rq, headers), ResultModel.class);

        final ResultModel resultModel = responseEntity.getBody();
        return Optional.of(resultModel.fileModel.resource);

    } catch (RestClientException | MessagingException | IOException e) {
        return Optional.empty();
    }
} public Optional<Resource> postSomeReq(JsonRequest rq) {
    
    String url = "http://somehost/services/retrieve-multipart";

    try {
        HttpHeaders headers = new HttpHeaders();
        headers.setContentType(MediaType.APPLICATION_JSON);
        headers.setAccept(List.of(MediaType.MULTIPART_FORM_DATA));

        RestTemplate restClient = new RestTemplate();
        restClient.getMessageConverters().add(new MultiPartMessageConverter(objectMapper));

        ResponseEntity<ResultModel> responseEntity =
                restClient.postForEntity(url, getRequestData(rq, headers), ResultModel.class);

        final ResultModel resultModel = responseEntity.getBody();
        return Optional.of(resultModel.fileModel.resource);

    } catch (RestClientException | MessagingException | IOException e) {
        return Optional.empty();
    }
} Conclusion We have reviewed how to use RestTemplate to send a JSON request and receive a response in multipart/form-data format. The approach presented allows for effective handling of multipart responses, which is useful in various scenarios, such as downloading files from a server. An alternative approach using a third-party converter was also presented. multipart/form-data You can now use these methods in your applications to work with multipart data, enhancing the functionality and flexibility of your code.

How to Receive Files from multipart/form-data Responses Using RestTemplate in Java

4 Strategies for Targeting Children on YouTube Despite Advertising Restrictions

Authors:
(1) Bobby He, Department of Computer Science, ETH Zurich (Correspondence to: bobby.he@inf.ethz.ch.);
(2) Thomas Hofmann, Department of Computer Science, ETH Zurich. Table of Links Abstract and Introduction Related Work Preliminaries Simplifying Transformer Blocks Further Experimental Analysis Discussion, Reproducibility Statement, Acknowledgements and References A Duality Between Downweighted Residual and Restricting Updates In Linear Layers B Block Layouts C Additional Experiments D Implementation Details A DUALITY BETWEEN DOWNWEIGHTED RESIDUALS AND RESTRICTING UPDATES IN LINEAR LAYERS In Sec. 4.1, we motivated our reparameterisation of the value and projection parameters, Eq. (6), through a duality between downweighted residuals branches and restricting parameter updates (materialised through smaller learning rates) in linear layers. This is a relatively simple argument, found elsewhere in the literature e.g. Ding et al. (2023), which we outline here for completeness. We suppose we have a (differentiable) loss function L(W), which is a function of some parameter matrix W. We consider taking a gradient step to minimise L, with learning rate ηW from initialisation W0. This would give new parameters W1: This paper is available on arxiv under CC 4.0 license. Authors: (1) Bobby He, Department of Computer Science, ETH Zurich (Correspondence to: bobby.he@inf.ethz.ch.); (2) Thomas Hofmann, Department of Computer Science, ETH Zurich. Authors: Authors: (1) Bobby He, Department of Computer Science, ETH Zurich (Correspondence to: bobby.he@inf.ethz.ch.); (2) Thomas Hofmann, Department of Computer Science, ETH Zurich. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Preliminaries Preliminaries Simplifying Transformer Blocks Simplifying Transformer Blocks Further Experimental Analysis Further Experimental Analysis Discussion, Reproducibility Statement, Acknowledgements and References Discussion, Reproducibility Statement, Acknowledgements and References A Duality Between Downweighted Residual and Restricting Updates In Linear Layers A Duality Between Downweighted Residual and Restricting Updates In Linear Layers B Block Layouts B Block Layouts C Additional Experiments C Additional Experiments D Implementation Details D Implementation Details A DUALITY BETWEEN DOWNWEIGHTED RESIDUALS AND RESTRICTING UPDATES IN LINEAR LAYERS In Sec. 4.1, we motivated our reparameterisation of the value and projection parameters, Eq. (6), through a duality between downweighted residuals branches and restricting parameter updates (materialised through smaller learning rates) in linear layers. This is a relatively simple argument, found elsewhere in the literature e.g. Ding et al. (2023), which we outline here for completeness. We suppose we have a (differentiable) loss function L(W), which is a function of some parameter matrix W. We consider taking a gradient step to minimise L, with learning rate ηW from initialisation W0. This would give new parameters W1: This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

A Duality Between Downweighted Residual and Restricting Updates In Linear Layers

United States v. Apple INC Court Filing, retrieved on March 21, 2024 is part of . You can jump to any part in this filing . This part is 7 of 25. HackerNoon’s Legal PDF Series here A. Apple harms competition by imposing contractual restrictions, fees, and taxes on app creation and distribution 52. Apple’s internal documents show that, soon after the iPhone’s introduction and notwithstanding its success, the company began to fear that disintermediation of its platform and the commoditization of the iPhone would threaten Apple’s substantial profits from iPhone sales and related revenue streams. 53. Accordingly, Apple exercised its control of app creation and app distribution in key cases to cement the iPhone and App Store as the primary gateway to apps, products, and services. Apple often claims these rules and restrictions are necessary to protect user privacy or security, but Apple’s documents tell a different story. In reality, Apple imposes certain restrictions to benefit its bottom line by thwarting direct and disruptive competition for its iPhone platform fees and/or for the importance of the iPhone platform itself. 54. Three aspects of Apple’s efforts to protect and exploit its smartphone monopoly are worth noting. First, Apple exercises its control over app distribution and app creation to dictate how developers innovate for the iPhone, enforcing rules and contractual restrictions that stop or delay developers from innovating in ways that threaten Apple’s power. In so doing, Apple influences the direction of innovation both on and off the iPhone. 55. Second, Apple drives iPhone users away from products and services that compete with or threaten Apple. In doing so, Apple increases the cost and friction of switching from the iPhone to another smartphone and generates extraordinary profits through subscription services (like Apple’s proprietary music, gaming, cloud storage, and news services), advertisements within the App Store, and accessories like headphones and smartwatches. 56. Third, Apple uses these restrictions to extract monopoly rents from third parties in a variety of ways, including app fees and revenue-share requirements. For most of the last 15 years, Apple collected a tax in the form of a 30 percent commission on the price of any app downloaded from the App Store, a 30 percent tax on in-app purchases, and fees to access the tools needed to develop iPhone native apps in the first place. While Apple has reduced the tax it collects from a subset of developers, Apple still extracts 30 percent from many app makers. Apple also generates substantial and increasing revenue by charging developers to help users find their apps in the App Store—something that, for years, Apple told developers was part of the reason they paid a 30 percent tax in the first place. For example, Apple will sell keyword searches for an app to someone other than the owner of the app. Apple is able to command these rents from companies of all sizes, including some of the largest and most sophisticated companies in the world. 57. As Apple exercised its control of app distribution and app creation, Apple slowed its own iPhone innovation and extracted more revenue and profit from its existing customers through subscriptions, advertising, and cloud services. These services increase the cost of switching from the iPhone to another smartphone because many of these services—including its proprietary gaming, cloud storage, and news service—are exclusive to the Apple ecosystem, causing significant frictions for iPhone users who try to use alternative services on another smartphone. Moreover, Apple’s conduct demonstrates that Apple recognized the importance of digital products and services for the success of the iPhone while at the same time it restricted the development and growth of non-iPhone products and services—especially those that might make it easier for users to switch from the iPhone to another smartphone. 58. Each step in Apple’s course of conduct built and reinforced the moat around its smartphone monopoly. The cumulative effect of this course of conduct has been to maintain and entrench Apple’s smartphone monopoly at the expense of the users, developers, and other third parties who helped make the iPhone what it is today. Despite major technological changes over the years, Apple’s power to control app creation and distribution and extract fees from developers has remained largely the same, unconstrained by competitive pressures or market forces. That this conduct is impervious to competition reflects the success of Apple’s efforts to create and maintain its smartphone monopoly, the strength of that monopoly, and the durability of Apple’s power. 59. Apple’s monopoly maintenance has taken many forms and continues to evolve today; however, Apple’s anticompetitive and exclusionary course of conduct is exemplified by its contractual rules and restrictions targeting several products and services: super apps, cloud streaming apps, messaging apps, smartwatches, and digital wallets. By stifling these technologies, and many others, Apple reinforces the moat around its smartphone monopoly not by making its products more attractive to users, but by discouraging innovation that threatens Apple’s smartphone monopoly or the disintermediation of the iPhone. Apple continues to expand and shift the scope and categories of anticompetitive conduct such that the cumulative anticompetitive effect of Apple’s conduct is even more powerful than that of each exclusionary act standing alone. i. Super Apps: Apple prevented apps from threatening its smartphone monopoly by undermining mini programs that reduce user dependence on the iPhone 60. For years, Apple denied its users access to super apps because it viewed them as “fundamentally disruptive” to “existing app distribution and development paradigms” and ultimately Apple’s monopoly power. Apple feared super apps because it recognized that as they become popular, “demand for iPhone is reduced.” So, Apple used its control over app distribution and app creation to effectively prohibit developers from offering super apps instead of competing on the merits. 61. A super app is an app that can serve as a platform for smaller “mini” programs developed using programming languages such as HTML5 and JavaScript. By using programming languages standard in most web pages, mini programs are cross platform, meaning they work the same on any web browser and on any device. Developers can therefore write a single mini program that works whether users have an iPhone or another smartphone. 62. Super apps can provide significant benefits to users. For example, a super app that incorporates a multitude of mini programs might allow users to easily discover and access a wide variety of content and services without setting up and logging into multiple apps, not unlike how Netflix and Hulu allow users to find and watch thousands of movies and television shows in a single app. As one Apple executive put it, “who doesn’t want faster, easier to discover apps that do everything a full app does?” Restricting super apps makes users worse off and sacrifices the short-term profitability of iPhones for Apple. 63. Super apps also reduce user dependence on the iPhone, including the iOS operating system and Apple’s App Store. This is because a super app is a kind of middleware that can host apps, services, and experiences without requiring developers to use the iPhone’s APIs or code. 64. As users interact with a super app, they rely less on the smartphone’s proprietary software and more on the app itself. Eventually, users become more willing to choose a different smartphone because they can access the same interface, apps, and content they desire on any smartphone where the super app is also present. Moreover, developers can write mini programs that run on the super app without having to write separate apps for iPhones and other smartphones. This lowers barriers to entry for smartphone rivals, decreases Apple’s control over third-party developers, and reduces switching costs. 65. Apple recognizes that super apps with mini programs would threaten its monopoly. As one Apple manager put it, allowing super apps to become “the main gateway where people play games, book a car, make payments, etc.” would “let the barbarians in at the gate.” Why? Because when a super app offers popular mini programs, “iOS stickiness goes down.” 66. Apple’s fear of super apps is based on first-hand experience with enormously popular super apps in Asia. Apple does not want U.S. companies and U.S. users to benefit from similar innovations. For example, in a Board of Directors presentation, Apple highlighted the “[u]ndifferentiated user experience on [a] super platform” as a “major headwind” to growing iPhone sales in countries with popular super apps due to the “[l]ow stickiness” and “[l]ow switching cost.” For the same reasons, a super app created by a U.S. company would pose a similar threat to Apple’s smartphone dominance in the United States. Apple noted as a risk in 2017 that a potential super app created by a specific U.S. company would “replace[ ] usage of native OS and apps resulting in commoditization of smartphone hardware.” 67. Apple did not respond to the risk that super apps might disrupt its monopoly by innovating. Instead, Apple exerted its control over app distribution to stifle others’ innovation. Apple created, strategically broadened, and aggressively enforced its App Store Guidelines to effectively block apps from hosting mini programs. Apple’s conduct disincentivized investments in mini program development and caused U.S. companies to abandon or limit support for the technology in the United States. 68. In particular, part of what makes super apps valuable to consumers is that finding and using mini programs is easier than using an app store and navigating many separate apps, passwords, and set-up processes. Instead of making mini program discovery easy for users, however, Apple made it nearly impossible. 69. Since at least 2017, Apple has arbitrarily imposed exclusionary requirements that unnecessarily and unjustifiably restrict mini programs and super apps. For example, Apple required apps in the United States to display mini programs using a flat, text-only list of mini programs. Apple also banned displaying mini programs with icons or tiles, such as descriptive pictures of the content or service offered by the mini program. Apple also banned apps from categorizing mini programs, such as by displaying recently played games or more games by the same developer. These restrictions throttle the popularity of mini programs and ultimately make the iPhone worse because it discourages developers from creating apps and other content that would be attractive to iPhone users. 70. Apple also selectively enforced its contractual rules with developers to prevent developers from monetizing mini programs, hurting both users and developers. For example, Apple blocked mini programs from accessing the APIs needed to implement Apple’s in-app payment (IAP) system—even if developers were willing to pay Apple’s monopoly tax. Similarly, Apple blocked developers’ ability to use in-app payment methods other than directly using IAP. For instance, super apps could create a virtual currency for consumers to use in mini programs, but Apple blocked this too. Apple, however, allows other, less-threatening apps to do so. ii. Cloud Streaming Apps: Apple prevented developers from offering cloud gaming apps that reduce dependence on the iPhone’s expensive hardware 71. For years, Apple blocked cloud gaming apps that would have given users access to desirable apps and content without needing to pay for expensive Apple hardware because this would threaten its monopoly power. In Apple’s own words, it feared a world where “all that matters is who has the cheapest hardware” and consumers could “buy[] a [expletive] Android for 25 bux at a garage sale and . . . have a solid cloud computing device” that “works fine.” Apple’s conduct made its own product worse because consumers missed out on apps and content. This conduct also cost Apple substantial revenues from third-party developers. At the same time, Apple also made other smartphones worse by stifling the growth of these cross-platform apps on other smartphones. Importantly, Apple prevented the emergence of technologies that could lower the price that consumers pay for iPhones. 72. Cloud streaming apps let users run a computationally intensive program without having to process or store the program on the smartphone itself. Instead, a user’s smartphone leverages the computing power of a remote server, which runs the program and streams the result back to the phone. Cloud streaming allows developers to bring cutting-edge technologies and services to smartphone consumers—including gaming and interactive artificial intelligence services—even if their smartphone includes hardware that is less powerful than an iPhone. 73. Cloud streaming has significant benefits for users. For example, Apple has promoted the iPhone 15 by promising that its hardware is powerful enough to enable “next-level performance and mobile gaming.” But powerful hardware is unnecessary if games are played via cloud streaming apps. For a cloud game, the user experiences and plays the game on the smartphone, but the game is run by hardware and software in remote computing centers (“the cloud”). Thus, cloud gaming apps deliver rich gaming experiences on smartphones without the need for users to purchase powerful, expensive hardware. As a result, users with access to cloud streamed games may be more willing to switch from an iPhone to a smartphone with less expensive hardware because both smartphones can run desirable games equally well. 74. Cloud streaming also has significant advantages for developers. For example, instead of re-writing the same game for multiple operating systems, cloud platforms can act as middleware that allow developers to create a single app that works across iOS, Android, and other operating systems. Cloud streaming provides more and simpler options for offering subscriptions, collecting payments, and distributing software updates as well. All of this helps game developers reach economies of scale and profitability they might not achieve without offering cloud gaming apps and reduces their dependence on iOS and Apple’s App Store. 75. Apple wielded its power over app distribution to effectively prevent third-party developers from offering cloud gaming subscription services as a native app on the iPhone. Even today, none are currently available on the iPhone. 76. For years, Apple imposed the onerous requirement that any cloud streaming game—or any update to a cloud streaming game—be submitted as a stand-alone app for approval by Apple. Having to submit individual cloud streaming games for review by Apple increased the cost of releasing games on the iPhone and limited the number of games a developer could make available to iPhone users. For example, the highest quality games, referred to as AAA games, typically require daily or even hourly updates across different platforms. If these updates need to be individually approved by Apple, developers must either delay their software updates across all platforms or only update their games on non-iOS platforms, potentially making the iOS version of the game incompatible with other versions on other platforms until Apple approves the update. Neither option is tenable for players or developers. 77. Until recently, Apple would have required users to download cloud streaming software separately for each individual game, install identical app updates for each game individually, and make repeated trips to Apple’s App Store to find and download games. Apple’s conduct made cloud streaming apps so unattractive to users that no developer designed one for the iPhone. 78. Apple undermines cloud gaming apps in other ways too, such as by requiring cloud games to use Apple’s proprietary payment system and necessitating game overhauls and payment redesigns specifically for the iPhone. Apple’s rules and restrictions effectively force developers to create a separate iOS-specific version of their app instead of creating a single cloud-based version that is compatible with several operating systems, including iOS. As a result, developers expend considerable time and resources re-engineering apps to bring crossplatform apps like multiplayer games to the iPhone. 79. Cloud streaming apps broadly speaking—not just gaming—could force Apple to compete more vigorously against rivals. As one Apple manager recognized, cloud streaming eliminates “a big reason for high-performance local compute” and thus eliminates one of the iPhone’s advantages over other smartphones because then “all that matters is who has the cheapest hardware.” Accordingly, it reduces the need for users to buy expensive phones with advanced hardware. This problem does not “stop at high-end gaming,” but applies to “a number of high-compute requirement applications.” Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 21, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. justice.gov

Apple Harms Competition by Imposing Restrictions, Fees, and Taxes on App Creation and Distribution

USA v. Samuel Bankman-Fried Court Filing, retrieved on March 15, 2024 is part of . You can jump to any part in this filing . This part is 32 of 33. HackerNoon’s Legal PDF Series here III. The Court Should Order Victim Compensation Through Remission as an Alternative to Restitution The Government seeks to return all stolen assets that are finally forfeited to victims of the defendant’s crimes in the most efficient manner possible. Because of the complexity of the case and the number of victims involved, awarding restitution to victims in accordance with 18 U.S.C. § 3663A would be extremely costly and administratively impractical. The cost and time associated with calculating each victim’s loss, determining whether the victim has already been compensated through the pending bankruptcy, and then paying out a percentage of the victim’s losses, will delay payment and diminish the amount of money actually paid to victims. Therefore, consistent with the Government’s frequent approach in complex cases with numerous victims, the Government moves for entry of an order authorizing the United States to compensate victims with finally forfeited assets through a remission process, as restitution would be impracticable in this case. 18 U.S.C. § 3663A(c)(3). As noted in the defendant’s submission, he consents to this request. Restitution to persons “directly and proximately harmed” by fraud and money laundering is ordinarily mandatory. See 18 U.S.C. §§ 3663A(a)(1), (c)(1)(A)(ii). In such a case, the Court is authorized to determine a restitution amount and a schedule of victims and payments. However, an order of restitution is not required when “the number of identifiable victims is so large as to make restitution impracticable,” or when imposing restitution would require “determining complex issues of fact related to the cause or amount of the victim’s losses” which “would complicate or prolong the sentencing process.” Id. § 3663A(c)(3). In those situations, instead of ordering restitution, courts have authorized the Government to compensate victims through the process of remission under the forfeiture statutes and related regulations. See, e.g., United States v. Madoff, 09 Cr. 213 (DC), Dkt. 106 & United States v. Bonventre, 10 Cr. 228, Dkt. 318 (Madoff Ponzi scheme); United States v. Sharma, 18 Cr. 340, Dkt. 407 (multi-million dollar cryptocurrency scheme); United States v. Dos Santos, 20 Cr. 398, Dkt. 283 (multi-million dollar cryptocurrency scheme). The forfeiture statutes authorize the Attorney General to “grant petitions for mitigation or remission of forfeiture, restore forfeited property to victims of a violation of this subchapter, or take any other action to protect the rights of innocent persons which is in the interest of justice and which is not inconsistent with the provisions of this section.” 21 U.S.C. § 853(i)(1). The applicable regulations are set forth in 28 C.F.R. Part 9. In order to qualify for remission, a crime victim must provide information to support specific elements, including that they suffered a specific pecuniary loss directly caused by the criminal offense, that the victim did not contribute to the offense, that the victim has not been in compensated for the loss from another means, and that the victim has no other recourse available. See generally 28 C.F.R. § 9.8(b). It is the policy of the Department of Justice, consistent with the Crime Victims’ Rights Act, to ensure that crime victims receive “full and timely restitution as provided in law.” See 18 U.S.C. § 3771(c)(1). Accordingly, when the Government seizes property in connection with a criminal case, the Government’s goal is to forfeit the property and then, in remission proceedings administered by the Attorney General through his delegee, the Chief of the Money Laundering and Asset Recovery Section of the Department of Justice’s Criminal Division (“MLARS”), to distribute funds to victims. Frequently MLARS will distribute those assets through a remission program by which victims may petition for portions of forfeited property. E.g. United States v. Sharma, 18 Cr. 340 (LGS), 2022 WL 1910026, at *2 (S.D.N.Y. June 3, 2022). In a multi-victim or complex case, the process of notifying potential victims, processing petitions, verifying losses, and recommending a distribution of available funds may be managed on behalf of the Department of Justice by a Special Master or trustee, as authorized by 28 C.F.R. § 9.9(c). While use of such means is common, there are other methods of distribution that the Department has used. In cases that also involve bankruptcy proceedings, the Department has at times worked with the bankruptcy estate to distribute forfeited assets. E.g. United States v. $7,206,157,717 on Deposit at JP Morgan Chase Bank, N.A., 10 Civ. 9398, Dkt. 2 (S.D.N.Y. December 17, 2010) (stipulation and court ordered settlement whereby the estate of Jeffry Picower, who had received billions of dollars from Bernie Madoff, forfeited more than $7 billion; the Government agreed to credit $5 billion in payment to the bankruptcy estate to be allocated under bankruptcy rules, and then named the trustee as administrator for the remaining funds). Victims’ interests will be best served in this case by ordering remission of finally forfeited funds and by using the remission process. The Government has been in frequent communication with MLARS about the most efficient way to do this, including by retaining a claims administrator or by transferring funds into the FTX bankruptcy estate upon specified conditions.[16] An alternative arrangement whereby restitution is calculated and distributed to victims by the Government and the Court would be nearly impossible administratively, and would come at great expense to victims’ recovery. In order to enter an order of restitution, the Court would need to determine and corroborate each victim’s losses. That would require determining and corroborating for each customer victim both the amount of funds he or she transferred to FTX, and any funds withdrawn. That task, challenging as it would ever be for the global fraud which the defendant led, is compounded here further for victims who transferred funds via cryptocurrencies, given their ever-fluctuating value. There are millions of potential victims and substantial complexity in computing each victim’s investments and withdrawals. Restitution in this case is even more complicated by the different classes of victims: FTX’s dozens of equity investors, and the Alameda lenders who often lent or received funds via cryptocurrency. Instead, a process whereby the Government distributes finally forfeited funds, either through a claims administrator or through a distribution to the bankruptcy estate, will more be efficient, and not “would complicate or prolong the sentencing process.” 18 U.S.C. § 3663A(c)(3). Continue Reading . Here [16] The creditor class in the bankruptcy overlaps substantially but is not a perfect match with the victims of the defendant’s crimes. For example, customers of FTX’s US branch are not victims of the defendant’s crimes as charged in the Indictment, but are expected to recover in the FTX bankruptcy; conversely, equity investors are victims of the charged offenses but may not recover their losses in the bankruptcy. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on March 15, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. storage.courtlistener

Proposed Strategy for Compensating Bankman-Fried's Victims: Remission Over Restitution

What's Next for AI: Interpreting Anthropic CEO's Vision

Què hi ha a continuació per a la IA: interpretar la visió del CEO antròpic

Co bude dál pro AI: Interpretace vize generálního ředitele společnosti Anthropic

Τι ακολουθεί για την AI: Ερμηνεύοντας το όραμα του CEO της Anthropic

¿Qué viene después en el mundo de la IA?: interpretación de la visión del director ejecutivo de Anthropic

AIの今後: Anthropic CEOのビジョンを解釈する

Nini ekoya sima pona AI: Ko interpréter Vision ya CEO Anthropic

AI-ийн дараагийн зүйл: Anthropic CEO-ийн алсын харааг тайлбарлах нь

Seo se Latelago bakeng sa AI: Go Hlatholla Pono ya CEO ya Anthropic

Leswi landzelaka eka AI: Ku hlamusela Xivono xa CEO wa Anthropic

Що далі для ШІ: інтерпретація бачення генерального директора Anthropic

AI uchun navbatdagi narsa: Antropik bosh direktorning qarashlarini talqin qilish

人工智能的下一步：解读 Anthropic 首席执行官的愿景

The NikolAI project has announced the release of its first NFT collection on the TON blockchain, coinciding with mathematician Nikolai Durov's 44th birthday. The collection, comprising 1,000 unique profile pictures (PFPs), represents the project's expansion into digital collectibles. The initiative targets existing token holders, with eligibility restricted to the top 44% of $NIKO holders on the TON blockchain. The qualification period extends until November 28, 2024, at 23:59 UTC, after which eligible holders can participate in the free mint. Market data indicates significant growth for the project, with the $NIKO token reaching a fully diluted valuation of $180 million within two weeks of launch. The project has gained attention for its AI integration, including an automated social media presence across X.com and Telegram platforms. The NFT collection introduces several features for holders: Participation rights in governance decisions


Priority access to future project developments


Potential yield opportunities


Integration with upcoming AI tools The collection's launch occurs amid increasing interest in AI-blockchain integrations within the cryptocurrency sector. Industry analysts note that such projects often aim to combine technological innovation with community engagement mechanisms. Technical implementation details include: AI-generated artwork for each NFT


Smart contract integration on TON blockchain


Automated eligibility verification system


Decentralized distribution mechanism Participating users must hold their tokens on-chain, as centralized exchange positions do not qualify for the NFT distribution. This requirement aligns with broader industry trends encouraging on-chain asset custody. The development represents a significant milestone in TON blockchain's NFT ecosystem, potentially influencing future projects in the space. Market observers suggest that successful implementation could establish new standards for AI-integrated blockchain applications. For cryptocurrency markets, this launch demonstrates continued evolution in NFT utility beyond simple collectibles. The integration of governance rights and future benefits indicates a shift toward more complex token economics in digital asset projects. The collection's timing coincides with increased attention on AI applications in blockchain technology, as projects explore combinations of artificial intelligence and distributed ledger systems. Industry participants suggest such integrations could influence future developments in both sectors. The project's expansion into NFTs reflects broader market trends toward multi-faceted cryptocurrency platforms combining various technological elements. Success metrics will likely depend on community engagement and technological implementation quality. For TON blockchain ecosystem participants, this development potentially signals increasing platform capabilities in NFT and AI integrations. The project's performance could influence future development directions on the network. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR The NikolAI project has announced the release of its first NFT collection on the TON blockchain, coinciding with mathematician Nikolai Durov's 44th birthday. The collection, comprising 1,000 unique profile pictures (PFPs), represents the project's expansion into digital collectibles. The initiative targets existing token holders, with eligibility restricted to the top 44% of $NIKO holders on the TON blockchain. The qualification period extends until November 28, 2024, at 23:59 UTC, after which eligible holders can participate in the free mint. Market data indicates significant growth for the project, with the $NIKO token reaching a fully diluted valuation of $180 million within two weeks of launch. The project has gained attention for its AI integration, including an automated social media presence across X.com and Telegram platforms. X.com The NFT collection introduces several features for holders: Participation rights in governance decisions Priority access to future project developments Potential yield opportunities Integration with upcoming AI tools Participation rights in governance decisions Participation rights in governance decisions Priority access to future project developments Priority access to future project developments Potential yield opportunities Potential yield opportunities Integration with upcoming AI tools Integration with upcoming AI tools The collection's launch occurs amid increasing interest in AI-blockchain integrations within the cryptocurrency sector. Industry analysts note that such projects often aim to combine technological innovation with community engagement mechanisms. Technical implementation details include: AI-generated artwork for each NFT Smart contract integration on TON blockchain Automated eligibility verification system Decentralized distribution mechanism AI-generated artwork for each NFT AI-generated artwork for each NFT Smart contract integration on TON blockchain Smart contract integration on TON blockchain Automated eligibility verification system Automated eligibility verification system Decentralized distribution mechanism Decentralized distribution mechanism Participating users must hold their tokens on-chain, as centralized exchange positions do not qualify for the NFT distribution. This requirement aligns with broader industry trends encouraging on-chain asset custody. The development represents a significant milestone in TON blockchain's NFT ecosystem, potentially influencing future projects in the space. Market observers suggest that successful implementation could establish new standards for AI-integrated blockchain applications. For cryptocurrency markets, this launch demonstrates continued evolution in NFT utility beyond simple collectibles. The integration of governance rights and future benefits indicates a shift toward more complex token economics in digital asset projects. The collection's timing coincides with increased attention on AI applications in blockchain technology, as projects explore combinations of artificial intelligence and distributed ledger systems. Industry participants suggest such integrations could influence future developments in both sectors. The project's expansion into NFTs reflects broader market trends toward multi-faceted cryptocurrency platforms combining various technological elements. Success metrics will likely depend on community engagement and technological implementation quality. For TON blockchain ecosystem participants, this development potentially signals increasing platform capabilities in NFT and AI integrations. The project's performance could influence future development directions on the network. Don’t forget to like and share the story! Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: This author is an independent contributor publishing via our business blogging program . HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure: business blogging program business blogging program

NikolAI Launches AI-Generated NFT Collection on TON Blockchain

**TALLINN, Estonia, November 20th, 2024/Chainwire/--**Following its global unveiling on the main stage of Binance’s Blockchain Week  conference in Dubai, AI platform bitGPT is experiencing sizable growth across every vertical of the project, from active partnership conversations to the number of users currently testing its technology. bitGPT’s X and Telegram communities grew in size by more than 1,000% in the days following its presentation at Binance in which hundreds of onlookers watched as its AI platform was launched and tested for the first time in front of a public audience. The demonstration replay surpassed a quarter-million views on Binance’s website within hours of being posted – the most popular replay event of the conference. bitGPT’s live demo and subsequent growth have been covered by several major new outlets, including reporting from Decrypt that predicted bitGPT’s AI platform “will help onboard the next billion Web3 users and kickstart the AI Agent economy.” https://youtu.be/Lm8hI_ZuiI8 https://www.youtube.com/embed/Lm8hI_ZuiI8 Backed by former SingularityNET co-founders and a growing team of AI and Web3 experts, bitGPT bridges the mass acceleration and adoption of generative AI technologies with the entire crypto ecosystem, providing users – both humans and AI agents – with a unified UX to connect and transact across any decentralized network or service using natural language commands. Available in more than 25 languages, its platform overcomes language, literacy, and interface barriers in what appears to be the Web3 industry’s most legitimate effort yet to onboard its first billion Web3 users, all while positioning the platform as a central node for kickstarting the AI Agent economy. Developed in stealth for more than two years, bitGPT’s AI platform has already been tested by a wide audience, with a growing community of dedicated users generating valuable data and insights that have been continually used to refine and expand the capabilities of its model. After advancing from Alpha to Beta earlier this year, bitGPT is preparing for full launch just as interest and attention for AI-enabled Web3 technologies take off. Despite achieving rapid significant adoption, centralized AI model providers present critical challenges, including data privacy risks, restricted accessibility, systemic vulnerabilities, and the emergence of techno-feudalism due to reliance on dominant third-party entities. This centralization compromises user autonomy, undermines digital sovereignty, and fosters a dependency cycle that stifles innovation and equitable access. A growing ecosystem of crypto projects including NEAR Protocol, bittensor, and The Graph have entered the space to introduce Web3’s privacy and security benefits to the ever-growing AI category. Yet usability and adoption challenges continue to limit the industry’s long-term potential. And many of these networks and services still do not connect directly with the end user, meaning users may fail to realize the security and privacy benefits of decentralized options. bitGPT’s breakthrough platform brings to reality the potential for an on-chain economy with its first billion human users, plus billions upon billions of AI Agents. Its platform goes beyond conventional interfaces, offering an intuitive, AI-driven ecosystem for all Web3 activities. By seamlessly integrating cutting-edge technologies with user-centric design, bitGPT provides an unparalleled suite of intelligent tools that simplify and enhance every aspect of blockchain engagement. With bitGPT, users can instantly connect with and transact across any network or service, creating a universally interoperable user experience. Use cases are truly endless, including: Cross-chain Swap Optimization — AI algorithms analyze liquidity across multiple DEXs and chains to find the most efficient swap routes.
Gas Fee Prediction — Machine learning models forecast gas prices, allowing users to optimize transaction timing.
Sentiment Analysis — Real-time analysis of social media, news, and on-chain data to gauge market sentiment.
Trend Prediction — Advanced machine learning models for short and medium-term price trend forecasting.
Alpha Discovery — Identification of potential high-growth opportunities based on on-chain metrics and market data.
Proposal Summarization — AI-generated summaries of complex governance proposals.
Voting Recommendations — Personalized suggestions based on user's previous voting patterns and stated preferences.
Impact Assessment — AI-driven analysis of potential outcomes for different chain governance voting scenarios.
24/7 Market Monitoring — Agents track user-specified metrics and events across multiple chains and platforms. Users can set specific criteria for alerts and research focus.
External Data Integration — Agents can access and analyze data from approved external sources for comprehensive research. In the spirit of Web3 technologies and the wider universe of decentralized networks and services, bitGPT enables the mass transition away from Large Language Models (LLMs) to privacy-preserving Small Language Models (SLMs), ensuring user data protection without compromising functionality. bitGPT allows for on-device AI processing – significantly enhancing security and reducing latency for a seamless user experience – and AI-powered cross-chain operations and market analysis, enabling users to navigate the multi-chain landscape with unprecedented ease. The platform is capable of hosting a virtually unlimited number of AI Agents that can perform any on-chain task automatically and autonomously, adding significant demand for nearly every decentralized project while fueling innovation across the entire crypto universe. After its successful unveiling on the main stage of Binance’s Blockchain Week conference – a demonstration that was followed immediately by Binance founder Changpeng Zhao – the bitGPT team is now focused on organizing its final Beta releases before full launch, including a fully featured mobile app, with a growing list of enterprise and Web3 partners positioned to assist its launch. About bitGPT Backed by a strong team of Web3 and AI industry leaders — including the cofounders of several major crypto projects like SingularityNET — bitGPT represents a revolutionary transformation in blockchain interaction, redefining the user experience through advanced artificial intelligence. Its platform goes beyond conventional interfaces, offering an intuitive, AI-driven ecosystem for all Web3 activities. By seamlessly integrating cutting-edge technology with user-centric design, bitGPT provides an unparalleled suite of intelligent tools that simplify and enhance every aspect of blockchain engagement. The result is an AI-driven platform capable of onboarding the next billion Web3 users as well as kickstarting the AI Agent economy. Learn more — www.BitGPT.network Join the BitGPT community: Telegram Discord X Contact bitGPT info@bitgpt.network This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here **TALLINN, Estonia, November 20th, 2024/Chainwire/--**Following its global unveiling on the main stage of Binance’s Blockchain Week  conference in Dubai, AI platform bitGPT is experiencing sizable growth across every vertical of the project, from active partnership conversations to the number of users currently testing its technology. bitGPT’s X and Telegram communities grew in size by more than 1,000% in the days following its presentation at Binance in which hundreds of onlookers watched as its AI platform was launched and tested for the first time in front of a public audience. X X Telegram Telegram The demonstration replay surpassed a quarter-million views on Binance’s website within hours of being posted – the most popular replay event of the conference. replay replay bitGPT’s live demo and subsequent growth have been covered by several major new outlets, including reporting from Decrypt that predicted bitGPT’s AI platform “will help onboard the next billion Web3 users and kickstart the AI Agent economy.” Decrypt Decrypt https://youtu.be/Lm8hI_ZuiI8 https://youtu.be/Lm8hI_ZuiI8 https://youtu.be/Lm8hI_ZuiI8 https://www.youtube.com/embed/Lm8hI_ZuiI8 https://www.youtube.com/embed/Lm8hI_ZuiI8 https://www.youtube.com/embed/Lm8hI_ZuiI8 Backed by former SingularityNET co-founders and a growing team of AI and Web3 experts, bitGPT bridges the mass acceleration and adoption of generative AI technologies with the entire crypto ecosystem, providing users – both humans and AI agents – with a unified UX to connect and transact across any decentralized network or service using natural language commands. SingularityNET SingularityNET Available in more than 25 languages, its platform overcomes language, literacy, and interface barriers in what appears to be the Web3 industry’s most legitimate effort yet to onboard its first billion Web3 users, all while positioning the platform as a central node for kickstarting the AI Agent economy. Developed in stealth for more than two years, bitGPT’s AI platform has already been tested by a wide audience, with a growing community of dedicated users generating valuable data and insights that have been continually used to refine and expand the capabilities of its model. After advancing from Alpha to Beta earlier this year, bitGPT is preparing for full launch just as interest and attention for AI-enabled Web3 technologies take off. Despite achieving rapid significant adoption, centralized AI model providers present critical challenges, including data privacy risks, restricted accessibility, systemic vulnerabilities, and the emergence of techno-feudalism due to reliance on dominant third-party entities. This centralization compromises user autonomy, undermines digital sovereignty, and fosters a dependency cycle that stifles innovation and equitable access. A growing ecosystem of crypto projects including NEAR Protocol, bittensor, and The Graph have entered the space to introduce Web3’s privacy and security benefits to the ever-growing AI category. Yet usability and adoption challenges continue to limit the industry’s long-term potential. And many of these networks and services still do not connect directly with the end user, meaning users may fail to realize the security and privacy benefits of decentralized options. bitGPT’s breakthrough platform brings to reality the potential for an on-chain economy with its first billion human users, plus billions upon billions of AI Agents. Its platform goes beyond conventional interfaces, offering an intuitive, AI-driven ecosystem for all Web3 activities. By seamlessly integrating cutting-edge technologies with user-centric design, bitGPT provides an unparalleled suite of intelligent tools that simplify and enhance every aspect of blockchain engagement. With bitGPT, users can instantly connect with and transact across any network or service, creating a universally interoperable user experience. Use cases are truly endless, including: Cross-chain Swap Optimization — AI algorithms analyze liquidity across multiple DEXs and chains to find the most efficient swap routes. Gas Fee Prediction — Machine learning models forecast gas prices, allowing users to optimize transaction timing. Sentiment Analysis — Real-time analysis of social media, news, and on-chain data to gauge market sentiment. Trend Prediction — Advanced machine learning models for short and medium-term price trend forecasting. Alpha Discovery — Identification of potential high-growth opportunities based on on-chain metrics and market data. Proposal Summarization — AI-generated summaries of complex governance proposals. Voting Recommendations — Personalized suggestions based on user's previous voting patterns and stated preferences. Impact Assessment — AI-driven analysis of potential outcomes for different chain governance voting scenarios. 24/7 Market Monitoring — Agents track user-specified metrics and events across multiple chains and platforms. Users can set specific criteria for alerts and research focus. External Data Integration — Agents can access and analyze data from approved external sources for comprehensive research. Cross-chain Swap Optimization — AI algorithms analyze liquidity across multiple DEXs and chains to find the most efficient swap routes. Gas Fee Prediction — Machine learning models forecast gas prices, allowing users to optimize transaction timing. Sentiment Analysis — Real-time analysis of social media, news, and on-chain data to gauge market sentiment. Trend Prediction — Advanced machine learning models for short and medium-term price trend forecasting. Alpha Discovery — Identification of potential high-growth opportunities based on on-chain metrics and market data. Proposal Summarization — AI-generated summaries of complex governance proposals. Voting Recommendations — Personalized suggestions based on user's previous voting patterns and stated preferences. Impact Assessment — AI-driven analysis of potential outcomes for different chain governance voting scenarios. 24/7 Market Monitoring — Agents track user-specified metrics and events across multiple chains and platforms. Users can set specific criteria for alerts and research focus. External Data Integration — Agents can access and analyze data from approved external sources for comprehensive research. In the spirit of Web3 technologies and the wider universe of decentralized networks and services, bitGPT enables the mass transition away from Large Language Models (LLMs) to privacy-preserving Small Language Models (SLMs), ensuring user data protection without compromising functionality. bitGPT allows for on-device AI processing – significantly enhancing security and reducing latency for a seamless user experience – and AI-powered cross-chain operations and market analysis, enabling users to navigate the multi-chain landscape with unprecedented ease. The platform is capable of hosting a virtually unlimited number of AI Agents that can perform any on-chain task automatically and autonomously, adding significant demand for nearly every decentralized project while fueling innovation across the entire crypto universe. After its successful unveiling on the main stage of Binance’s Blockchain Week conference – a demonstration that was followed immediately by Binance founder Changpeng Zhao – the bitGPT team is now focused on organizing its final Beta releases before full launch, including a fully featured mobile app, with a growing list of enterprise and Web3 partners positioned to assist its launch. About bitGPT Backed by a strong team of Web3 and AI industry leaders — including the cofounders of several major crypto projects like SingularityNET — bitGPT represents a revolutionary transformation in blockchain interaction, redefining the user experience through advanced artificial intelligence. Its platform goes beyond conventional interfaces, offering an intuitive, AI-driven ecosystem for all Web3 activities. bitGPT bitGPT By seamlessly integrating cutting-edge technology with user-centric design, bitGPT provides an unparalleled suite of intelligent tools that simplify and enhance every aspect of blockchain engagement. The result is an AI-driven platform capable of onboarding the next billion Web3 users as well as kickstarting the AI Agent economy. Learn more — www.BitGPT.network www.BitGPT.network www.BitGPT.network Join the BitGPT community: Telegram Telegram Discord Discord X X Contact bitGPT info@bitgpt.network This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Btcwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

AI Platform bitGPT Experiences Significant Growth Following Binance Demonstration

AI platforma bitGPT doživljava značajan rast nakon Binance demonstracije

La plataforma de inteligencia artificial bitGPT experimenta un crecimiento significativo tras la demostración de Binance

پلتفرم هوش مصنوعی bitGPT پس از نمایش بایننس، رشد قابل توجهی را تجربه می کند

Platfòm AI bitGPT fè eksperyans yon kwasans enpòtan apre demonstrasyon Binance

AIプラットフォームbitGPT、Binanceのデモンストレーション後に大幅な成長を記録

AI პლატფორმა bitGPT განიცდის მნიშვნელოვან ზრდას Binance-ის დემონსტრირების შემდეგ

AI platforma bitGPT patyrė reikšmingą augimą po „Binance“ demonstravimo

AI платформ bitGPT нь Binance-ийн үзүүлбэрийн дараа мэдэгдэхүйц өсөлттэй байна

د AI پلیټ فارم bitGPT د بائنانس مظاهرې وروسته د پام وړ وده تجربه کوي

AI Platform bitGPT Zvakaitika Kukura Kwakakosha Kutevera Binance Kuratidzira

แพลตฟอร์ม AI bitGPT ประสบการเติบโตอย่างมีนัยสำคัญหลังการสาธิตของ Binance

AI platformasi bitGPT Binance namoyishidan keyin sezilarli o'sishni boshdan kechirmoqda

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