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December 22, 202513 min read

🔮Machine Learning

Gradient Descent: The Workhorse of Machine Learning

Learn how gradient descent optimizes machine learning models by iteratively minimizing the loss function.

#machine-learning#optimization#neural-networks

#LLM#AI-Assisted Code#Debugging

The Silent Failure Cascade: Why LLM-Powered Code Breaks in Production

LLM-generated code often fails silently in production due to implicit assumptions. Learn why this happens, how to detect it, and proven strategies to write defensive code that survives the real world.

March 7, 202612 min read

#Docker#DevOps#Containerization

Docker Fundamentals: Images, Containers & Deployment (Complete Guide)

Learn Docker containerization, multi-stage builds, Docker Compose, and deploy applications 10x faster.

March 7, 20267 min read

January 6, 202610 min read

The Generative Core: Understanding Hallucination in Large Language Models

Hallucination isn't a bug in LLMs—it's the generative process itself. Understanding this changes how we should think about working with these tools.

#LLM hallucination#AI generation#confabulation

January 6, 20266 min read

Vibe Coding: Master AI-Assisted Programming with LLM Probability

Learn what vibe coding is, how LLM probability powers AI-assisted programming, and master the skills for the future of coding. Practical examples included.

#vibe coding#AI programming#LLM

January 6, 202612 min read

Attention Mechanisms in LLMs: The Complete Guide to How Transformers Really Work

Learn how attention mechanisms power large language models (LLMs) like GPT-4 and Claude. This in-depth guide explains Query-Key-Value math, multi-head attention, and long-context processing with real code examples.

#attention mechanisms#transformers#LLM

#Python#Data Science#NumPy

NumPy vs Pandas: When to Use Each for Data Processing

Learn when to use NumPy vs Pandas, optimize data processing 100x faster, and handle 1B+ rows efficiently.

March 7, 202611 min read

LangChain Output Parsers: Parse LLM Responses (Complete Guide)

Master LangChain output parsers for structuring LLM responses. Learn about StrOutputParser, PydanticOutputParser, and custom parsers.

March 1, 202616 min read

#langchain#llm#ai

#pandas#data-analysis#dataframes

Pandas pd.concat(): Merge & Combine DataFrames (Complete Guide)

Complete guide to Pandas pd.concat() for combining DataFrames. Learn axis stacking, index handling, and comparisons with merge() and join().

March 1, 202615 min read

#python#async-programming#concurrency

Python asyncio.gather(): Run Concurrent Tasks (Complete Guide)

Learn how to run multiple async functions concurrently with Python asyncio.gather(). Includes practical examples, error handling, and performance comparisons.

March 1, 202618 min read

CrewAI Tools: Building Custom Agents & Tool Integration (Guide)

Complete guide to building tools for CrewAI agents. Learn @tool decorator, tool creation, built-in tools, and multi-agent workflows.

March 1, 202616 min read

#crewai#ai-agents#llm

#nextjs#performance#optimization

Next.js Bundle Analyzer: Optimize Your Build Size

Learn how to use @next/bundle-analyzer to visualize and optimize your Next.js bundle size. Includes real optimization examples and case studies.

March 1, 202614 min read

January 6, 20269 min read

Why LLMs Can't Revise Tokens: The Feed-Forward Constraint Every Developer Must Understand

Learn why LLMs hallucinate and can't self-correct. Understand feed-forward token generation and master vibe coding strategies for better AI-assisted development.

#LLM#token generation#feed-forward

January 13, 20266 min read

Vibe Coding: How Stateless LLM Architecture Powers Intent-Driven Development

Discover vibe coding—a revolutionary approach leveraging stateless LLM architecture to turn natural language prompts into code. Learn prompt engineering techniques for scalable, creative development.

#vibe-coding#LLM-architecture#stateless-LLM

January 6, 202611 min read

Why LLMs Fail at Complex Tasks: The Math Behind Compounding Error in AI Code Generation

Learn why LLMs fail on long reasoning chains due to compounding error. Includes mathematical proofs, real-world AI coding failures, and actionable engineering strategies to mitigate AI hallucinations in software development.

#LLM limitations#compounding error#AI hallucinations

🔢

🔢Algebra

Introduction to Linear Algebra: Vectors and Matrices

A comprehensive guide to understanding vectors, matrices, and their operations - the building blocks of linear algebra.

January 3, 20268 min read

#linear-algebra#vectors#matrices

January 27, 202610 min read

Can You Teach an AI How to Think? Testing Elaborate Reasoning Protocols on Claude Code

We gave Claude Code a 365-line protocol teaching it to think like an ICPC World Finalist. After 300 experiments, the elaborate methodology provided zero improvement over a simple "solve this problem" prompt—but the reasons why reveal something important about agentic AI.

#vibe-coding#Claude-Code#agentic-AI