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. ...

Efficient Cloud Storage with Automated Scaling: A Step-by-Step Guide Research Disclaimer: This guide is based on AWS SDK for Python (boto3) v1.34+, Azure Storage SDK v12.19+, and Google Cloud Storage Python Client v2.14+ official documentation. All code examples use production-tested patterns from official provider documentation. Cloud storage services automatically scale capacity, but cost optimization requires proactive lifecycle management, intelligent tiering, and monitoring. Cloud storage services like AWS S3, Azure Blob Storage, and Google Cloud Storage automatically scale to petabyte levels, but without proper management, costs can spiral out of control. This guide focuses on automated cost optimization through lifecycle policies, intelligent tiering, and monitoring—not capacity scaling (which cloud providers handle transparently). ...

Building NES Games with C: A Practical Guide Note: This guide is based on the CC65 compiler documentation, NESdev Wiki technical specifications, and documented NES development practices. All code examples are derived from CC65 NES library documentation and tested development patterns. The Nintendo Entertainment System (NES), based on the MOS Technology 6502 processor, presents unique constraints that make game development both challenging and educational: 2KB RAM, 32KB maximum PRG-ROM per bank, 8KB maximum CHR-ROM per bank, and a 256x240 pixel display through the Picture Processing Unit (PPU). ...