MrComputerScience.com
Blog For Marketing And Tech Nerds
This is the core of the site. The place where theory meets code, and where ideas stop being abstract and start becoming usable. These articles reflect years of building systems, breaking them, fixing them, and learning what actually matters once the tutorials end and the constraints show up.
This section contains some of my best guides on artificial intelligence, programming, data science, machine learning, deep learning, software systems, and the practical foundations of modern computer science. You will find long form explanations, experiments, and applied reasoning across Python, Java, Rust, algorithms, performance, and AI driven systems. The focus is not just on how things work in principle, but how they behave in real projects under real constraints.
A lot of people understand the surface level theory and still struggle to connect it to production code. That gap is exactly what this collection is designed to close. I write for people who want to know why models fail, why code slows down, why abstractions leak, and how to think clearly when the documentation stops helping.
If you want durable understanding instead of cargo cult patterns, scroll down. Everything here is meant to be applied, stress tested, and reused long after trends move on.
LLM application quality degrades silently. Prompt changes, model updates, retrieval drift, and data distribution shift all degrade outputs without raising an exception. The only way to know your app …
Continue Reading about How Do You Measure Whether Your LLM App Is Getting Worse? →
When a large language model predicts the next token, it runs your entire input through dozens of transformer layers, each one computing attention scores that determine which previous tokens influence …
Continue Reading about What Actually Happens When an LLM Predicts the Next Token? →
Dijkstra's algorithm finds the shortest path but explores nodes in every direction equally, which is expensive on a road network with millions of nodes. A* uses a heuristic to bias the search toward …
Continue Reading about Why Does Google Maps Use A* Instead of Dijkstra for Routing? →
A watering hole attack compromises a site or resource that your target population visits, rather than attacking targets directly. For sophisticated threat actors targeting technology companies, …
Continue Reading about What Is a Watering Hole Attack and Why Are Developer Tools the Target? →
Incident response playbooks written before 2024 do not address vector store contamination, model weight integrity, prompt injection forensics, or agent audit log preservation. When an AI system is …
Continue Reading about Why Your Incident Response Plan Doesn’t Cover AI Systems →
Yes. Current voice cloning technology produces convincing voice replicas from samples as short as three seconds using commercial tools. Thirty seconds produces high-quality clones indistinguishable …
Continue Reading about Can Attackers Clone Your Voice From a 30-Second Sample in 2026? →
