All Articles(6)
GaaS: Why Agentic AI as a Service Is Killing the SaaS Seat Model
SaaS charged per seat because humans used software. GaaS charges per outcome because AI replaces the human. Most engineers can describe GaaS. Almost none can explain why its architecture, failure modes, and billing model are fundamentally different — and why interviewers are now testing exactly that.
Why MCP Servers Are Replacing Traditional AI Agents
Most candidates explain MCP as "a protocol for tools." That's wrong enough to fail the interview. Here's why MCP exists, what prompt-chained agents actually break, and how interviewers test this.
LLM Quantization Explained: FP32 to INT4, RAM Math & Hardware Reality
Most engineers know quantization "reduces model size." Few can explain the exact byte math, the accuracy tradeoff curve, or why your 24GB GPU still can't run LLaMA-2 70B in INT4.
VECTORLESS RAG: Why Tree-Based Retrieval Beats Vector Databases (And When It Doesn't)
VECTORLESS RAG: Why Tree-Based Retrieval Beats Vector Databases (And When It Doesn't)
The GenAI Learning Path for Data Scientists in May 2026 (And the Ordering Trap That Kills Most Candidates)
A practical GenAI interview-first roadmap for data scientists in 2026, focused on failure-mode reasoning and correct topic ordering.
Why Your RAG System Retrieves the Right Chunks but Still Gives Wrong Answers (GenAI Interview Trap)
Your RAG pipeline retrieves the right context. The answer is literally in the chunks. The model still gets it wrong. This is the most dangerous RAG failure mode — and a core GenAI interview trap.