mdecimus
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1 month ago Please don't post AI generated spam.
Closed offsoc closed 1 month ago
mdecimus
commented
1 month ago Please don't post AI generated spam.
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I'd like to see this feature:Webmail development requirements and plans
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QuantumShield Secure Mail (QSM) Project Andromeda Development Plan Project Name: QuantumShield Secure Mail (QSM) Project Andromeda
Version: 3.0 (World-Leading Edition - Development Plan)
Date: June 9, 2025
Project Lead: [To be determined; a CTO-level individual with extensive experience in large-scale distributed systems, cybersecurity, AI/ML, and product leadership is recommended]
Project Vision: To redefine enterprise email communication by integrating the most cutting-edge quantum-safe cryptography, predictive artificial intelligence, zero-trust architecture, and hyperscale distributed systems technology. This system aims to build the world's first secure communication hub offering unparalleled communication security, efficiency, and resilience for the most sensitive enterprises and government organizations globally, becoming the undisputed world leader in the industry.
1.1. Core Objectives
Achieve Quantum-Safe Communication: Successfully design, integrate, and validate Post-Quantum Cryptography (PQC) algorithms to enable end-to-end quantum-safe encryption.
Build a Predictive AI Engine: Research, develop, and deploy AI/ML models capable of proactively identifying and blocking threats, intelligently assisting decision-making, and automating workflows.
Establish a Zero-Trust Security Framework: Enforce zero-trust principles at all network, application, data, and identity layers to ensure continuous verification and dynamic authorization.
Achieve Hyperscale Performance: Support millions of concurrent global users and billions of daily emails, ensuring sub-second latency and extremely high availability.
Ensure Global Compliance and Resilience: Meet the strictest international data privacy and security regulations, implementing active-active multi-region deployments for near-zero RTO/RPO.
Build an Automated Operations System: Implement highly automated, AI-driven deployment, monitoring, fault recovery, and operations (AIOps).
1.2. Strategic Importance
QSM Project Andromeda is not merely a technological milestone; it addresses the urgent and growing demand from global enterprises for advanced secure communication. It will equip organizations with the core capabilities to counter future cyber threats (including quantum computing threats) and significantly enhance communication efficiency and compliance, providing a substantial market competitive advantage and long-term strategic value for the company.
Total Project Timeline Estimate: 4 - 5 Years
Phase 0: Exploration & Foundational Research - Estimated Completion: 6-9 months (Q4 2025 - Q2 2026) Goal: Validate the feasibility of core cutting-edge technologies, build foundational architectural prototypes, and assemble the core R&D team.
Key Deliverables:
PQC algorithm selection report and POC prototype.
Zero Trust Network Access (ZTNA) foundational framework prototype.
Preliminary AI/ML models POC (e.g., basic spam classification, sentiment analysis).
Multi-cloud infrastructure IaC templates (basic networking, compute, storage).
Core microservice communication (Kafka, Istio/Envoy) prototype.
Milestones:
M0.1 (Quantum-Safe POC): Complete initial performance testing of selected PQC algorithms (e.g., Kyber, Dilithium) for encryption/decryption, validating their feasibility in communication.
M0.2 (Architecture Prototype): Complete the deployment prototype of core microservices (user authentication, basic mail routing) on Kubernetes clusters, validating fundamental communication in multi-cloud environments.
M0.3 (AI Foundation POC): Complete an AI-based POC for basic email classification (e.g., promotional/social/primary) and establish a preliminary data pipeline.
M0.4 (Security Foundation): Complete the foundational authentication processes (MFA integration) for Zero-Trust Identity and Access Management (ZT-IAM).
M0.5 (Team Formation): Core security, AI/ML, and distributed systems architect teams are fully assembled.
Phase 1: Core Security & Mail Communication - Estimated Completion: 12-18 months (Q3 2026 - Q4 2027) Goal: Implement QSM's core email sending/receiving functionality and integrate essential enterprise-grade security features.
Key Deliverables:
End-to-End Encryption (E2EE) V1.0 (based on traditional cryptography, OpenPGP/S/MIME).
Core email sending, receiving, storage, and indexing microservices.
Basic anti-phishing and malicious attachment scanning capabilities.
Distributed Zero-Trust KMS V1.0.
CI/CD pipelines and automated testing framework.
Preliminary Web administration console.
Milestones:
M1.1 (Core Mail): Complete IMAP/SMTP protocol integration, enabling email sending, receiving, storage, and folder management.
M1.2 (E2EE V1): Implement user PGP/S/MIME key management, supporting traditional encryption and decryption of email content (non-attachments).
M1.3 (ZT-IAM V1): Complete advanced MFA (TOTP/WebAuthn) and Role-Based Access Control (RBAC).
M1.4 (ATP V1): Implement basic anti-phishing (DMARC/SPF/DKIM validation, black/whitelists) and malicious attachment scanning (integrating VirusTotal/ClamAV).
M1.5 (DLP V1): Implement basic DLP policies (credit card, social security number detection) and content redaction.
M1.6 (KMS V1): Complete the HSM-based distributed KMS foundational architecture, enabling key generation, storage, and auditing.
M1.7 (DevSecOps): CI/CD pipelines enable automated deployment of code to test environments, with SAST/DAST integrated into the development process.
Phase 2: Quantum Security & Intelligent Core - Estimated Completion: 12-18 months (Q1 2028 - Q2 2029) Goal: Implement QSM's quantum-safe core and deeply integrate AI/ML capabilities.
Key Deliverables:
Quantum-Safe End-to-End Encryption (QE2EE) V1.0 (Hybrid Encryption).
AI-driven intelligent composition, classification, prioritization, and threat prediction engine.
Behavioral biometrics and continuous authentication system.
Blockchain-based audit chain and immutable logs V1.0.
Preliminary adaptive edge intelligence implementation.
Digital twin monitoring prototype.
Milestones:
M2.1 (QE2EE V1): Complete hybrid encryption and decryption of email content and attachments using PQC algorithms, and pass initial security audits.
M2.2 (Intelligent Composition): Implement AI-driven predictive text, sentiment analysis, and multi-language translation.
M2.3 (Predictive ATP V2): Implement AI-driven threat hunting, behavioral sandbox analysis (zero-day detection), and predictive identification/blocking of advanced phishing/BEC attacks.
M2.4 (Behavioral Biometrics): Complete behavioral biometric data collection and continuous authentication system, enabling account security scoring.
M2.5 (Blockchain Audit): Complete the integration of the blockchain-based audit chain, ensuring the immutability of core operation logs.
M2.6 (Edge Intelligence): Complete deployment of a portion of AI inference models on edge nodes, validating low-latency inference.
M2.7 (Digital Twin): Establish digital twin models for core service components, enabling real-time status prediction.
Phase 3: Deep Collaboration & Workflow Orchestration - Estimated Completion: 9-12 months (Q3 2029 - Q2 2030) Goal: Build QSM's top-tier collaboration features and automated workflow engine.
Key Deliverables:
Intelligent shared inbox and deep integration with task/project management.
AI-driven calendar and meeting intelligence.
Global knowledge graph and intelligent contacts system.
No-code/low-code workflow builder V1.
Voice/video email integration and command support.
Standardized integration interfaces with core enterprise applications (CRM/ERP/UC).
Milestones:
M3.1 (Collaborative Inbox): Implement intelligent shared inboxes, internal email discussion threads, and two-way synchronization with task/project management tools.
M3.2 (Meeting Intelligence): Implement AI-driven meeting scheduling, automated extraction of meeting minutes, and action item tracking.
M3.3 (Knowledge Graph): Complete the enterprise knowledge graph, enabling intelligent contact enhancement and communication network visualization.
M3.4 (Workflow Engine): Deliver a visual workflow builder supporting multi-condition, multi-action, cross-system automation.
M3.5 (UC Integration): Complete deep integration with at least one major Unified Communications platform (e.g., MS Teams or Slack).
Phase 4: Globalization, Compliance & Ultimate Resilience - Estimated Completion: 9-12 months (Q3 2030 - Q2 2031) Goal: Ensure QSM meets the highest global compliance standards and achieves ultimate business continuity and disaster recovery capabilities.
Key Deliverables:
Global compliance and intelligent eDiscovery system.
Automated compliance reporting and regulatory adherence auditing.
Active-active multi-region deployment fully implemented and validated.
Data sovereignty and data residency management mechanisms fully operationalized.
Energy efficiency and sustainability reporting framework.
System security certifications (e.g., ISO 27001, SOC 2 Type II).
Milestones:
M4.1 (eDiscovery): Deliver high-performance, cross-data source intelligent eDiscovery functionality, supporting complex legal searches and standardized exports.
M4.2 (Compliance Automation): Implement automated compliance report generation and deep integration with SIEM systems to provide regulatory compliance audit trails.
M4.3 (Multi-Active Deployment): Complete active-active multi-region deployment in at least two major geographic regions, and validate RTO/RPO through BCP/DR drills.
M4.4 (Data Sovereignty): Implement fine-grained control over data storage geographic location and complete compliance validation for key regions.
M4.5 (Security Certification): Obtain at least one major international security certification (e.g., ISO 27001).
Phase 5: Launch & Continuous Innovation - Estimated Completion: Ongoing (Starting Q3 2031) Goal: Officially launch QuantumShield Secure Mail (QSM) Project Andromeda V1.0 and establish a mechanism for continuous innovation and optimization.
Key Deliverables:
QSM Project Andromeda V1.0 (Production-Ready).
Comprehensive product documentation, user manuals, API documentation, and operations manuals.
Ongoing innovation and R&D (e.g., quantum encryption algorithm upgrades, new AI models, novel threat defenses).
Global customer support system.
Milestones:
M5.1 (GA Release): Officially launch QuantumShield Secure Mail (QSM) Project Andromeda V1.0 to global enterprise customers.
M5.2 (Market Promotion): Initiate global marketing and sales activities.
M5.3 (Customer Success): Establish a strong customer success and support team to ensure early user satisfaction.
M5.4 (Continuous Innovation): Initiate PQC algorithm V2 research, explore new AI applications, and establish ongoing red teaming exercises and bug bounty programs.
Project Director: (1 person)
Overall project strategic direction, resource management, high-level stakeholder communication, risk management, market competitiveness.
Chief Architect: (1-2 persons)
Defines and evolves QSM's high-level technical architecture, cross-domain technology selection, technical debt management, system performance.
Security Engineering Department: (50-80 persons, including the following sub-roles)
Quantum-Safe Cryptography Experts: (5-10 persons) Responsible for PQC algorithm research, integration, and key management system development.
Threat Intelligence & ATP Experts: (10-15 persons) Responsible for threat intelligence fusion, AI-driven threat hunting, sandbox, DLP, and ATP engines.
Identity & Access Management Experts: (10-15 persons) Responsible for Zero-Trust IAM, MFA, behavioral biometrics, and DID integration.
Security Audit & Compliance Experts: (10-15 persons) Responsible for blockchain auditing, eDiscovery, compliance reporting, and regulatory adherence.
DevSecOps Security Engineers: (5-10 persons) Responsible for SDL, automated security testing, and secure configuration management.
AI/ML R&D Department: (40-60 persons, including the following sub-roles)
NLP/NLU Engineers: (15-20 persons) Responsible for email semantic understanding, intelligent composition, summarization, and multi-language processing.
Computer Vision/Behavioral Analysts: (10-15 persons) Responsible for behavioral biometrics, attachment content understanding, and threat lineage visualization.
MLOps Engineers: (10-15 persons) Responsible for ML model lifecycle management, training pipelines, deployment, and monitoring.
Data Scientists: (5-10 persons) Responsible for algorithm research, model optimization, and data analysis.
Platform Engineering Department: (60-100 persons, including the following sub-roles)
Backend Microservices Development Team: (40-60 persons) Rust/Go/Python engineers responsible for core mail, collaboration, and management functions.
Database Experts: (10-15 persons) Architecture, optimization, and operations for databases like PostgreSQL, Cassandra, Elasticsearch.
Message Queue/Stream Processing Experts: (5-10 persons) Building and operating data pipelines like Kafka, Pulsar, Flink.
Networking & Service Mesh Experts: (5-10 persons) Deployment and management of Istio, Envoy Gateway, SD-WAN.
Caching/Search Optimization Experts: (5-10 persons) Performance optimization for Redis, ClickHouse, Graph databases.
DevOps & SRE Department: (30-50 persons)
Cloud Platform Experts: (10-15 persons) Responsible for multi-cloud IaC, automated deployment, resource optimization.
SRE/Operations Engineers: (10-15 persons) Responsible for system stability, high availability, monitoring, alerting, and incident response.
AIOps Engineers: (5-10 persons) Responsible for AIOps platform development and intelligent operations.
Chaos Engineering Experts: (5-10 persons) Responsible for designing and executing chaos experiments.
Product Department: (15-20 persons)
Product Managers: (5-8 persons) Responsible for product roadmap, requirements definition, user stories, prioritization.
UX/UI Designers: (5-8 persons) Responsible for user experience research, UI design, prototyping.
User Researchers: (2-4 persons) Responsible for user feedback collection and usability testing.
Quality Assurance Department: (30-50 persons)
Automation Test Engineers: (20-30 persons) Responsible for developing unit, integration, E2E, and performance test scripts.
Security Test Engineers: (5-10 persons) Responsible for penetration testing and vulnerability discovery.
QA Managers: (5-10 persons) Responsible for test strategy and quality assurance.
Technical Support & Customer Success Department: (20-30 persons, initial)
Technical Support Engineers: (15-20 persons) Responsible for customer issue resolution, knowledge base maintenance.
Customer Success Managers: (5-10 persons) Responsible for customer relationship management, product adoption, and satisfaction improvement.
Estimated Total Team Size: 250 - 400 persons (Adjustable based on specific needs and development pace)
Agile Sprints:
Core development teams will use 2-week sprint cycles.
AI/ML research and PQC algorithm research may use longer exploratory sprints or a Kanban flow.
Daily stand-ups, sprint planning, sprint reviews, and retrospectives.
Requirements Management:
Utilize industry-leading ALM (Application Lifecycle Management) tools (e.g., Jira/Confluence, Azure DevOps) for requirements gathering, Epic, User Story, and Task management and tracking.
Layered requirements: from high-level vision to technical details, ensuring consistency and traceability.
Continuous user feedback loops and market analysis to guide requirement evolution.
Design & Architecture:
Architecture Reviews: Critical architectural decisions must undergo rigorous technical and security reviews by an architecture committee comprising the Chief Architect, Chief Security Engineer, etc.
Design Reviews: Detailed design proposals (e.g., API design, database schemas, microservice interactions) must undergo internal team and cross-team reviews.
Secure Design Principles: All designs must adhere to zero-trust principles, least privilege, defense-in-depth, and secure-by-default.
DevSecOps & GitOps:
GitOps: All infrastructure configurations (IaC), application code, and Kubernetes configurations will be managed from a Git repository as the single source of truth. Argo CD/Flux CD for automated deployments.
Continuous Integration (CI): Every code commit triggers automated builds, unit tests, SAST, dependency scans, and container image builds.
Continuous Deployment (CD): Once code is merged to the main branch, GitOps enables automated deployment to development, testing, pre-production environments, and gradual rollout to production.
Shift Left Security: Integrate security checks and testing (SAST, DAST, IaC security scanning) as early as possible into the development process.
Runtime Security: Integrate Runtime Application Self-Protection (RASP) and container runtime security (e.g., Falco, Aqua Security).
Quality Assurance:
Automated Testing Pyramid:
Unit Tests: Achieve up to 95% code coverage for business logic and security algorithms.
Integration Tests: Verify microservice interactions and API functionality.
Contract Tests: Ensure API contract consistency between microservices.
End-to-End Tests (E2E): Simulate real user scenarios, covering cross-service business processes.
UI Automation Tests: Ensure proper UI functionality for Web/Mobile/Desktop clients.
Automated Testing Suites: Utilize Pytest (Python), Go test (Go), Rust (Criterion), and Cypress/Playwright (E2E).
Quality Gates: Implement quality gates within the CI/CD pipeline (e.g., test coverage, security vulnerability, code quality thresholds) to prevent non-compliant code from proceeding.
AIOps & Observability:
Log Aggregation: Utilize ELK Stack / Splunk / Datadog Logs to collect and analyze all service logs.
Metric Monitoring: Use Prometheus / Grafana / Dynatrace to collect and visualize all performance, resource, and business metrics.
Distributed Tracing: Mandatorily integrate OpenTelemetry for end-to-end request tracing across microservices.
Intelligent Alerting: AI analyzes monitoring data, predicts potential issues, automatically triggers alerts, and provides intelligent diagnostic suggestions.
Digital Twin: Utilize digital twin models for system behavior prediction and fault simulation.
Documentation & Knowledge Management:
Architecture Documentation: Use C4 model or other industry standards for layered architecture description.
API Documentation: Automatically generate OpenAPI (Swagger) 3.1 specifications and maintain detailed API usage guides.
Technical Design Documents: Detail the technical implementation, algorithm choices, and rationale for critical modules.
Operations Manuals: Detailed operating guides, troubleshooting manuals, disaster recovery manuals.
Internal Knowledge Base: Establish Confluence or a similar platform to encourage team knowledge sharing, best practices, and lessons learned.
5.1. Technical Risks:
Risk: The standardization and implementation of Post-Quantum Cryptography (PQC) algorithms are still in early stages, potentially leading to immaturity, performance bottlenecks, or undiscovered vulnerabilities.
Mitigation: Continuously track NIST PQC standardization progress, adopt a hybrid encryption scheme (PQC + traditional cryptography) to ensure both quantum safety and compatibility. Invest heavily in R&D for PQC algorithm performance optimization and security auditing. Establish a rapid algorithm replacement mechanism. Collaborate with top research institutions in the PQC field.
Risk: AI/ML models may exhibit insufficient accuracy, bias, or "black box" issues in practical applications.
Mitigation: Establish robust AI/ML Operations (MLOps), including data governance, model version control, continuous training, performance monitoring, and bias detection. Introduce Explainable AI (XAI) techniques. Continuously optimize models through user feedback, A/B testing, and red teaming exercises.
Risk: The complexity of hyperscale distributed systems (billions of emails daily, millions of concurrent users) leads to performance bottlenecks, stability issues, or management difficulties.
Mitigation: Adopt an event-driven, serverless-first, service mesh-based cloud-native architecture. Conduct rigorous performance testing, load testing, stress testing, and chaos engineering. Implement canary releases and blue/green deployments. Introduce AIOps for intelligent operations.
Risk: Complexity in integrating, managing, and securing multi-cloud/hybrid cloud environments.
Mitigation: Heavily rely on IaC and automation tools. Establish unified identity authentication and authorization across cloud platforms. Invest in expert teams familiar with multi-cloud operations and select multi-cloud compatible middleware.
5.2. Security Risks - Highest Priority:
Risk: Zero-day vulnerabilities and novel APT attacks may bypass existing defenses.
Mitigation: Implement Predictive ATP, combining AI-driven threat hunting, behavioral sandbox analysis, global threat intelligence, and dynamic DLP. Establish a 24/7 Security Operations Center (SOC) and automated SOAR platform for rapid response and blocking.
Risk: Quantum-safe key management failures leading to key compromise or misuse.
Mitigation: Mandatory use of FIPS 140-2 Level 3+ certified HSM/KMS. Implement multi-party authorization, zero-trust key management, and blockchain-based key transparency logs. Conduct regular key rotation and auditing.
Risk: Data breaches, ransomware attacks, or compliance violations.
Mitigation: Mandatory encryption of data throughout its lifecycle (in-transit, at-rest, in-memory processing). Strictly enforce ABAC authorization. Deploy real-time DLP and information classification systems. Establish blockchain-based immutable audit trails.
Risk: Supply chain attacks leading to code or component tampering.
Mitigation: Automate scanning and auditing of all third-party dependencies, perform Software Bill of Materials (SBOM) generation and verification. Strict code signing and image validation. Enforce supply chain security policies.
5.3. Organizational & Resource Risks:
Risk: Lack of world-leading talent with experience in PQC, hyperscale distributed systems, AI/ML, top-tier security, and compliance.
Mitigation: Adopt a global recruitment strategy to attract top talent. Establish internal PQC and AI/ML research labs, and deep collaborations with academia and leading global research institutions. Offer disruptive compensation, equity, and career development opportunities.
Risk: Budget overruns and timeline delays due to the innovative and scale of the project.
Mitigation: Strict project management and agile practices. Use Value Stream Mapping to optimize delivery processes. Conduct regular budget reviews and risk assessments. Establish sufficient contingency reserves and risk response plans.
5.4. Market & Competition Risks:
Risk: Insufficient market awareness of "quantum-safe" and "predictive AI" concepts, or rapid catch-up by existing giants.
Mitigation: Invest heavily in market education and promotion, highlighting QSM's unique value proposition and competitive advantages. Continuous technological innovation and patenting. Build a strong brand presence. Actively participate in industry standards development.
Daily: Team stand-ups (daily progress updates, issue discussions).
Weekly: Team weekly meetings (sprint review, next week's plan, cross-team coordination).
Bi-weekly: Project management layer meetings (leads from each domain report progress, risks, resource needs).
Monthly: Executive-level monthly meetings (Project Director reports overall progress, financial status, significant risks to senior management).
Quarterly: Board-level review (Project Director reports strategic progress, market positioning, and long-term planning to the board).
Real-time: Security incidents, major outages, and emergencies notified via automated alerting systems and designated channels (e.g., enterprise IM, phone).
Annually: Annual product launch events and technology conferences.
7.1. Automated Testing Pyramid:
Unit Tests: Achieve up to 95% code coverage for business logic and security algorithms.
Integration Tests: Verify microservice interactions and data consistency.
Contract Tests: Ensure API contract consistency between microservices.
End-to-End Tests (E2E): Simulate real user scenarios, covering cross-service business processes.
UI Automation Tests: Ensure proper UI functionality for Web/Mobile/Desktop clients.
7.2. Performance & Resilience Testing:
Load Testing: Simulate expected user load to assess system performance.
Stress Testing: Simulate exceeding peak load to assess system limits.
Capacity Planning: Resource planning based on performance test results.
Chaos Engineering: Regularly inject faults (network latency, service crashes, resource exhaustion) in production or production-like environments to proactively identify system weaknesses and enhance resilience.
Disaster Recovery Drills: Regularly conduct comprehensive disaster recovery drills to validate RTO/RPO objectives.
7.3. Security Testing:
Static Application Security Testing (SAST): Automatically scan code for vulnerabilities upon commit, integrated into the CI pipeline.
Dynamic Application Security Testing (DAST): Test web application vulnerabilities at runtime, simulating attacks.
Interactive Application Security Testing (IAST): Combine SAST and DAST, monitoring code behavior at runtime.
Fuzz Testing: Fuzz API interfaces and protocols to discover crashes and vulnerabilities.
Penetration Testing: Regularly conducted by internal red teams and external specialized organizations.
Vulnerability Scanning: Continuous scanning of infrastructure, container images, and dependencies for known vulnerabilities.
Code Audits: Critical security module code to be manually audited by cryptography and security experts.
Threat Modeling: Identify and analyze potential threats during the design phase.
7.4. AI/ML Model Quality:
Data Quality Validation: Ensure the accuracy, completeness, and representativeness of training data.
Model Fairness & Bias Detection: Assess models for bias and implement mitigation strategies.
Model Explainability (XAI): Enhance the transparency of model decisions.
Adversarial Robustness Testing: Test model resilience against malicious inputs (e.g., adversarial examples).
Model Continuous Training & Monitoring: Deploy MLOps pipelines to continuously monitor model performance and automatically trigger retraining.
Personnel Costs: Constitute the majority of the total budget, considering the compensation for world-leading talent and team size.
Cloud Infrastructure Costs: Expenses for compute, storage, networking, databases, AI/ML platforms, and other services across multi-cloud environments.
R&D Tools & Licenses: Various development tools, security tools, AI/ML platforms, monitoring systems, commercial software licenses, etc.
Third-Party Service Integration: Threat intelligence, DLP, email security gateways, identity management services, etc.
Compliance & Certification Costs: Third-party auditing and certification fees.
Hardware Investment: Dedicated security hardware like HSMs.
Marketing & Sales: Market entry and expansion activities after product launch.
R&D Contingency: Reserves to address technical risks and uncertainties.
QuantumShield Secure Mail (QSM) Project Andromeda will be a disruptive innovation journey, redefining the boundaries of enterprise communication security and efficiency. This detailed development plan provides a clear roadmap and management framework for realizing this ambitious vision. Through a world-class team, forward-looking R&D investment, and strict engineering discipline, we are confident in building QSM into the global leader in email communication solutions.
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