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Field notes from Stable Panther
OWASP Web and Mobile Security
Spent the last few months diving deep into OWASP guidelines for web and mobile security. Here’s what actually matters when you’re building real applications. Web Security That Works Input validation isn’t optional. Every single input needs validation. Not just “check if it’s a string” validation - proper validation that understands what the data should look like and rejects everything else. SQL injection is still everywhere. Even with ORMs, you can still mess this up.
Read more…PyTorch: From Research to Reality
PyTorch has become the go-to framework for deep learning research. But moving from research notebooks to real-world applications requires understanding performance optimization, deployment strategies, and operational considerations. This guide covers essential tips for building robust PyTorch applications that actually work. Performance Optimization 1. Model Optimization Model Quantization: import torch.quantization as quantization # Dynamic quantization for inference model = torch.quantization.quantize_dynamic( model, {torch.nn.Linear}, dtype=torch.qint8 ) # Static quantization for better performance model.qconfig = torch.
Read more…Reinforcement Learning: The Hard Truth
Everyone’s talking about reinforcement learning like it’s the future of AI. It’s not. Here’s what actually happens when you try to make RL work in the real world. Why RL Fails (Most of the Time) The sample efficiency problem. RL needs millions of samples to learn anything useful. In the real world, you can’t afford to let your robot crash into walls a million times just to learn how to walk.
Read more…SciPy in the Real World
SciPy powers everything from aerospace simulations to medical imaging. Here’s how it’s actually used in real engineering and scientific applications. Aerospace: Flight Dynamics and Control Orbital mechanics calculations. NASA uses SciPy for trajectory optimization, calculating orbital transfers, and mission planning. The scipy.optimize module handles complex multi-objective optimization problems that would be impossible to solve analytically. Aircraft stability analysis. Boeing and Airbus use SciPy for analyzing aircraft stability, calculating control surface effectiveness, and optimizing flight control systems.
Read more…Effective Security Testing
Most security testing finds the same old vulnerabilities. Here’s how to find the bugs that actually matter. Start with threat modeling What are you trying to protect? Who might attack it? How? Threat modeling helps you focus your testing on the risks that actually matter. Test the authentication flow Login, logout, session management, password reset. These are where most security issues happen. Test every path through your authentication system. Input validation is everywhere SQL injection, XSS, command injection.
Read more…Computer Vision Beyond the Demo
Computer vision demos look amazing. Real-world systems are harder. Here’s what actually works. Lighting matters more than algorithms Your model works great in perfect lighting. Real world lighting is messy. Test in the actual environment where you’ll deploy. Consider lighting as part of your system design. Edge cases are everywhere That object detection model? It works great on the demo images. Now try it on blurry photos, weird angles, and objects it’s never seen.
Read more…Feature Engineering: The ML Secret Weapon
Good features beat fancy algorithms. Here’s how to engineer features that actually matter. Start with domain knowledge What do you know about the problem? Use that knowledge to create features. A good feature engineer knows the domain better than the algorithm. Create interaction features Sometimes the magic is in combinations. Age × income. Time of day × day of week. Let your features talk to each other. Handle categorical variables properly One-hot encoding isn’t always the answer.
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