MrComputerScience.com
Blog For Marketing And Tech Nerds
AI is everywhere, which makes clarity rare. This category strips away the hype and focuses on what learning systems actually are, what they can do, and where they inevitably break.
This section contains some of my best guides on artificial intelligence, machine learning foundations, optimization, generalization, scaling laws, and the limits of data driven systems. I write about AI as a computer scientist, not a marketer, grounded in math, systems constraints, and empirical reality.
You will find discussions of why models generalize, when they do not, and why bigger is not always better. I explore symbolic versus statistical approaches, failure modes, hallucinations, alignment problems, and the infrastructure realities beneath modern AI.
If you want durable understanding instead of breathless headlines, this category is where AI becomes intelligible again.
The test was not a benchmark suite or a collection of LeetCode problems. It was a real feature: a rate-limited async job queue with retry logic, dead letter handling, and a Redis backend, implemented …
Continue Reading about I Tested 5 AI Coding Assistants on the Same Real Python Project →
Vector embeddings represent text as points in high-dimensional space where semantic similarity maps to geometric proximity. Retrieval augmented generation uses this property to find relevant documents …
Continue Reading about Why Do AI Systems Use Vector Embeddings and How Does RAG Work? →
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 →
AI coding assistants suggest packages that do not exist, reproduce vulnerable code patterns from training data, and can be manipulated through poisoned context to recommend attacker-controlled …
Continue Reading about Why AI Code Assistants Are the New Supply Chain Attack Surface →
Shadow AI is employees using personal ChatGPT, Claude, and Gemini accounts for work tasks involving sensitive data, outside any corporate oversight, DLP policy, or audit trail. It is happening at …
Continue Reading about What Is Shadow AI and Why Is It Your Biggest Security Blind Spot? →
Both frameworks let you build Java LLM applications. Spring AI integrates cleanly with the Spring ecosystem and suits teams already running Spring Boot in production. LangChain4j is more flexible and …
Continue Reading about Spring AI vs LangChain4j: Which Is Actually Production Ready? →
