DNS Records Explained for Beginners: A 2025 Guide to Domains, Nameservers, and Email Routing
DNS Records Explained for Beginners walks you through A, MX, CNAME, DMARC and more in plain English. This guide uses simple analogies and practical steps to help you manage domains, nameservers, and email routing with confidence.
Beyond the Hype: Vector Search Limitations and What To Do Instead
This beginner-friendly guide explains Vector Search Limitations, where it shines and where it breaks. It offers practical, ready-to-use strategies—hybrid search, reranking, and smarter embeddings—to avoid slow vector search and inaccurate RAG results.
Git Rebase vs Git Merge: A Beginner's Guide to the Right Workflow
This beginner's guide explains git rebase vs git merge in plain English, with practical examples and practice. Learn when to rebase, when to merge, and how to resolve conflicts for a clean, linear history.
Mastering LLM Temperature: Control Creativity Without Losing the Plot (2025)
Learn how LLM Temperature shapes temperature creativity and coherence. This guide explains how to use temperature, top-p, and sampling to balance precision and imagination, with practical presets for precise, balanced, and creative writing.
Learn LangSmith: The Ultimate Zero-to-Production Guide (2025 Edition)
This beginner-friendly guide takes you from zero to production with LangSmith, covering tracing, evals, LLM observability, and monitoring. Learn to observe, debug, and ship reliable AI apps with copy-paste code and plain-language explanations.
You've Heard of LangChain. Here's What They're Not Telling You.
Confused by all the hype around LangChain? You're not alone. This guide breaks down the entire ecosystem from Agents and Chains to LangGraph and n8n comparisons into simple, easy-to-understand terms. Learn what LangChain is, how it works, and why it's the secret sauce behind today's smartest AI applications.
AI Can Design? Think Again. The Brutal Reality Behind the Hype
AI design tools promise speed but deliver generic, uninspired layouts. This article dives into the reality behind the hype, exploring why human taste, intuition, and creativity remain essential for making a product that truly stands out.
Seen GPT, Gemini, Llama, Claude... Ever Wonder What Actually Makes Them Different?
Seen GPT, Gemini, Llama, Claude... Ever Wonder What Actually Makes Them Different? You see the names everywhere. A new AI model drops and the internet goes wild. But they're not all the same. Think of it like cars. You have sedans, SUVs, and sports cars. They all drive, but you use them for different things. LLMs are similar.
Thinking Deeper, Not Just Wider: A Beginner's Guide to the Hierarchical Reasoning Model
An introduction to the Hierarchical Reasoning Model (HRM), a new, brain-inspired AI architecture. This guide explains why standard LLMs struggle with complex reasoning and how HRM's unique structure enables it to "think deeper," solve complex problems, and learn more efficiently. Perfect for students and AI enthusiasts.
From "Listening" Phones to Self-Driving Cars: Unmasking the AI Brains Called ANNs
It’s a feeling we’ve all had: you talk about something, and an ad for it instantly appears. The technology behind this isn't a mystery, it's an Artificial Neural Network (ANN), a powerful system modeled on the human brain. This article breaks down exactly what ANNs are, how they learn, and how they power everything from your social media feed to the most advanced AI in the world.
Your AI Keeps Forgetting? Here's the Secret Reason Why (It's Called a Context Window)
It's the most common frustration with AI: you're deep in a conversation, and suddenly it forgets key details from the start. The culprit isn't a bug; it's the AI's 'context window' its short term memory. This complete guide demystifies what context windows are in simple terms, why size matters, and how understanding this one concept will stop the AI amnesia and help you get smarter results.
RAFT Explained: How to Fix Inaccurate RAG and Boost Your AI's Performance
A detailed guide breaking down RAFT (Retrieval-Augmented Fine-Tuning), a powerful technique designed to solve a critical flaw in standard RAG systems. Learn how RAFT trains smaller, more efficient models to outperform giants like GPT-4 on domain-specific questions by teaching them how to focus on correct information and ignore distractions. This article covers how it works, when to use it over RAG, performance benchmarks, and real-world applications.
RAG vs. Fine-tuning: Which One Should You Actually Use? (A 2025 Guide)
So you have a powerful LLM, but it knows nothing about your specific data. You hear "RAG" and "Fine-tuning" thrown around as solutions, but they're completely different. This guide cuts through the jargon to show you exactly what they are, how they work, and most importantly, which one is the right choice for your project.