Articles & Tutorials
149 technical articles covering AI, Machine Learning, Cybersecurity, Data Science, and Programming. Practical guides, tutorials, and deep dives — written for developers.
65 articles in Cybersecurity · LLMs
Post-Quantum Cryptography Explained
Key Takeaway --> Quantum computers will break RSA, ECC, and Diffie-Hellman. NIST finalized three post-quantum cryptography standards in Au…
How HTTPS and TLS Actually Work
Key Takeaway --> HTTPS is HTTP running over TLS. The TLS handshake performs three critical functions in a single exchange: negotiating enc…
Feature Engineering in the Age of AI
Key Takeaway --> Classical feature engineering uses domain expertise to create interpretable features. Embeddings capture semantic meaning…
RAG Security: Protecting Vector Stores and Preventing Data Leakage
Key Takeaway --> RAG systems create unique security challenges because they connect AI models to your data. Protecting vector stores requi…
From RAG Prototype to Production: Building Reliable AI Knowledge Systems
Key Takeaway --> Moving a RAG system from prototype to production requires far more than better prompts. Production readiness demands secu…
Build a Private Course Assistant with RAG
A course assistant needs more than just RAG — it needs access control (students see only their course), citation tracking (answers linked t…
Build a Private Local AI Assistant on Your Own Computer
You can build a complete AI assistant that runs entirely on your computer. No data leaves your machine. No API costs. No vendor lock-in. Ju…
Local AI vs Cloud AI: Privacy, Cost, Performance and Control
Neither local AI nor cloud AI is universally superior. The best choice depends on your privacy requirements, budget, hardware, and use case…
GGUF Explained: The Practical Guide to Local LLM Model Files
GGUF (GPT-Generated Unified Format) is the standard file format for running LLMs locally. It packages model weights, metadata, and tokenize…
LLM Quantization Explained: 4-bit vs 8-bit Models
Quantization reduces model precision to save memory and increase speed. A 7B parameter model shrinks from 28 GB (FP32) to 3.5 GB (INT4) whi…
Running LLMs on CPU: What Actually Matters?
CPU inference speed depends primarily on memory bandwidth and model size—not CPU cores. A well-quantized 7B model on a modern CPU can gener…
Why Choose a Local Runtime?
Ollama, llama.cpp and LM Studio are the three leading local AI runtimes. Ollama excels at developer workflow, llama.cpp at maximum performa…