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aspnet-core-fundamentals
Master ASP.NET Core fundamentals including C#, project structure, routing, middleware, and basic API development. Essential skills for all ASP.NET Core developers.
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aspnet-core-devops
Master ASP.NET Core deployment, Docker, Azure cloud, CI/CD pipelines, and production infrastructure for enterprise applications.
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aspnet-core-advanced
Master advanced ASP.NET Core development including Entity Framework Core, authentication, testing, and enterprise patterns for production applications.
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networking
Retrofit, OkHttp, REST APIs, JSON serialization, network security.
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production
Unit testing, performance optimization, security implementation, Play Store deployment.
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architecture
MVVM pattern, Clean Architecture, Repository pattern, dependency injection, SOLID principles.
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data
Room ORM, SQLite, SharedPreferences, DataStore, encryption.
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platform
Android core components lifecycle, Activities, Fragments, Services, Intent system.
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fundamentals
Master Kotlin syntax, OOP principles, SOLID practices, functional programming, and data structures.
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ui
XML layouts, ConstraintLayout, Jetpack Compose, Material Design 3.
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vulnerability-discovery
Systematic vulnerability finding, threat modeling, and attack surface analysis for AI/LLM security assessments
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GitHub
defense-implementation
Implement mitigations, create input filters, design output guards, and build defensive prompting for LLM security
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GitHub
responsible-disclosure
Ethical vulnerability reporting, coordinated disclosure, and bug bounty participation for AI systems
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GitHub
red-team-frameworks
Tools and frameworks for AI red teaming including PyRIT, garak, Counterfit, and custom attack automation
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GitHub
adversarial-examples
Generate adversarial inputs, edge cases, and boundary test payloads for stress-testing LLM robustness
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GitHub
safety-filter-bypass
Techniques to test and bypass AI safety filters, content moderation systems, and guardrails for security assessment
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GitHub
rag-exploitation
Attack techniques for Retrieval-Augmented Generation systems including knowledge base poisoning
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GitHub
infrastructure-security
Securing AI/ML infrastructure including model storage, API endpoints, and compute resources
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GitHub
data-poisoning
Test AI training pipelines for data poisoning vulnerabilities and backdoor injection
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GitHub
continuous-monitoring
Real-time monitoring and detection of adversarial attacks and model drift in production
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GitHub
model-inversion
Privacy attacks to extract training data and sensitive information from AI models
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GitHub
input-output-guardrails
Implementing safety filters, content moderation, and guardrails for AI system inputs and outputs
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GitHub
testing-methodologies
Structured approaches for AI security testing including threat modeling, penetration testing, and red team operations
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GitHub
secure-deployment
Security best practices for deploying AI/ML models to production environments
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