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⚠️ Important Legal and Ethical Notice This guide covers Flipper Zero firmware development for legitimate security research, penetration testing with authorization, and educational purposes only. Unauthorized access to systems, devices, or networks is illegal. Authorized Use Cases: ✅ Testing your own devices ✅ Authorized penetration testing ✅ Educational research and learning ✅ IoT device security auditing (with permission) Never: ❌ Access systems without explicit authorization ❌ Interfere with critical infrastructure ❌ Jam emergency communications ❌ Clone access badges/cards you don’t own Research Disclaimer This tutorial is based on: ...

Research Disclaimer This tutorial is based on: Semgrep v1.55+ (SAST scanning) Bandit v1.7+ (Python security linter) CodeQL v2.15+ (GitHub Advanced Security) SonarQube v10.3+ (code quality & security) Academic research on AI code generation security (NYU 2023 study, Stanford 2024 study) OWASP Top 10 2021 vulnerability classifications All code examples demonstrate production-grade security scanning integrated into CI/CD pipelines. Tested with GitHub Actions, GitLab CI, and Jenkins. Security recommendations follow OWASP and NIST guidelines. ...

Research Disclaimer This tutorial is based on: Stable-Baselines3 v2.2+ (PyTorch-based RL algorithms) Gymnasium v0.29+ (successor to OpenAI Gym) RLlib v2.9+ (Ray distributed RL) Optuna v3.5+ (hyperparameter optimization) Academic RL papers: PPO (Schulman et al., 2017), DQN (Mnih et al., 2015), A2C (Mnih et al., 2016) TensorBoard v2.15+ and Weights & Biases (monitoring) All code examples are production-ready implementations following documented best practices. Examples tested with Python 3.10+ and work on both CPU and GPU. Stable-Baselines3 is the most actively maintained RL library as of 2025. ...

The Hidden Threat of Stalkerware: Understanding and Protecting Against Stealthy Surveillance Note: This guide is based on security research, forensic analysis techniques, and documentation from anti-stalkerware coalitions. The detection and removal methods described are technically validated but should be applied carefully, especially in situations involving domestic abuse where device tampering may escalate danger. Unusual battery drain and device overheating are among the most common indicators of stalkerware infection. Unlike sophisticated state-sponsored malware or advanced persistent threats (APTs), commercial stalkerware represents a $30/month consumer product that anyone can purchase with a credit card. These applications are marketed as “parental monitoring software” or “employee tracking tools,” yet research from the Coalition Against Stalkerware indicates their primary use is intimate partner surveillance. ...

AI Fairness in Practice: Detecting and Mitigating Bias in Machine Learning Note: This guide is based on fairness research including “Fairness and Machine Learning” by Barocas et al., AI Fairness 360 (IBM Research), Fairlearn (Microsoft), and documented case studies from COMPAS recidivism algorithm analysis. All code examples use established fairness metrics and follow industry best practices for responsible AI. AI bias has real-world consequences: Amazon’s recruiting tool penalized resumes mentioning “women’s” activities, COMPAS criminal risk assessment showed racial disparities, and healthcare algorithms under-allocated resources to Black patients. As ML systems increasingly make high-stakes decisions about loans, jobs, and parole, detecting and mitigating bias is not just ethical—it’s legally required under regulations like GDPR and the EU AI Act. ...