·Tech
The Tailwind AI Paradox: Why Popularity is Dismantling Open Source
Tailwind CSS recently laid off 75% of its engineering team despite being more popular than ever. Discover how AI is breaking the documentation-to-revenue funnel.
LLMs
50 articles
·Artificial Intelligence
Capstone Project: The AI Architect
It's time to put it all together. In this final Capstone Project, you will design a high-stakes AI application from scratch, applying every concept from the 12 modules of this course.
·Artificial Intelligence
Module 12 Lesson 3: The Path to AGI
Are LLMs the end of the road, or just the beginning? In our final lesson of the core curriculum, we explore Artificial General Intelligence (AGI) and the future of human-AI partnership.
·Artificial Intelligence
Module 12 Lesson 2: Personalization and Memory
In the past, AI forgot your name as soon as the session ended. In this lesson, we look at the future of Persistent Memory and the rise of your 'Digital Twin'.
·Artificial Intelligence
Module 12 Lesson 1: Multimodality (Beyond Text)
Language is only the beginning. In this lesson, we explore Multimodality—the shift from Large Language Models to Large Multimodal Models that can see, hear, and speak.
·Artificial Intelligence
Module 11 Lesson 3: Long-Horizon Planning
Why does the AI forget its original goal halfway through a task? In our final lesson of Module 11, we explore 'Long-Horizon Planning' and the limits of AI persistence.
·Artificial Intelligence
Module 11 Lesson 2: Correlation vs. Causation
AI knows what follows what, but does it know why? In this lesson, we learn why LLMs are 'Statistical Parrots' when it comes to cause and effect.
·Artificial Intelligence
Module 11 Lesson 1: The Reasoning Gap (System 1 vs System 2)
Is an LLM actually 'thinking'? In this lesson, we explore the Reasoning Gap using the System 1 vs System 2 framework to understand why AI fails at simple logic while mastering complex prose.
·Artificial Intelligence
Module 10 Lesson 3: Creating a Simple Workflow
How do you go from a prompt to a real app? In our final lesson of Module 10, we learn how to link RAG, Tools, and Memory into a single cohesive AI Agent.
·Artificial Intelligence
Module 10 Lesson 2: Connecting to Tools (Function Calling)
LLMs are smart, but they can't browse the web or calculate math perfectly by themselves. In this lesson, we learn about Function Calling—how LLMs use external tools to get the job done.
·Artificial Intelligence
Module 10 Lesson 1: Prompt Engineering Mastery
How you ask matters just as much as what you ask. In this lesson, we learn the technical art of Prompt Engineering: from Zero-Shot to Chain-of-Thought.
·Artificial Intelligence
Module 9 Lesson 3: Datasets for Fine-Tuning
How much data do you really need to teach an AI a new trick? In our final lesson of Module 9, we learn about the 'Less is More' philosophy of fine-tuning datasets.
·Artificial Intelligence
Module 9 Lesson 2: Full Fine-Tuning vs. PEFT
How do you customize a 70-billion parameter model on a single GPU? In this lesson, we learn about LoRA and PEFT—the breakthroughs that democratized AI fine-tuning.
·Artificial Intelligence
Module 9 Lesson 1: Why Fine-Tune?
General-purpose LLMs are good for many things, but sometimes you need a specialist. In this lesson, we explore the reasons to fine-tune your own version of an LLM.
·Artificial Intelligence
Module 8 Lesson 3: The Alignment Problem
What happens when an AI is 'too good' at its job? In our final lesson of Module 8, we explore the Alignment Problem: the struggle to ensure AI goals match human values.
·Artificial Intelligence
Module 8 Lesson 2: Safety Filters and Guardrails
How does the AI know when to say 'No'? In this lesson, we look at the invisible police force of AI—Safety Filters and Guardrails—that prevent harm while sometimes causing frustration.
·Artificial Intelligence
Module 8 Lesson 1: What Is Bias in LLMs?
LLMs don't have their own opinions, but they do reflect ours. In this lesson, we explore how bias enters the machine and why 'Neutrality' is harder than it sounds.
·Artificial Intelligence
Module 7 Lesson 4: Reducing Hallucinations
How can we make AI reliable enough for a bank or a hospital? In our final lesson of Module 7, we explore the industry best practices for silencing the 'LIAR' in the machine.
·Artificial Intelligence
Module 7 Lesson 3: Detecting Hallucinations
How can you tell if an AI is lying? In this lesson, we learn about Logprobs, Self-Consistency checks, and the 'Stochastic Signature' of a hallucination.
·Artificial Intelligence
Module 7 Lesson 2: Causes of Hallucinations
Why does it happen? Is it a data gap or a logic failure? In this lesson, we break down the three primary causes of LLM hallucinations: Gaps, Blur, and Eagerness.
·Artificial Intelligence
Module 7 Lesson 1: What Hallucinations Are
Why does an AI sometimes lie with total confidence? In this lesson, we define 'Hallucinations' and learn to identify the difference between a creative slip and a factual failure.
·Artificial Intelligence
Module 6 Lesson 3: Why Outputs Change
Why does the AI say things differently every time? In our final lesson of Module 6, we look at the trade-offs between Determinism and Creativity in model responses.
·Artificial Intelligence
Module 6 Lesson 2: Sampling Strategies
Why does an LLM give different answers to the same question? In this lesson, we learn about Temperature, Top-k, and Top-p—the knobs we use to control AI creativity.
·Artificial Intelligence
Module 6 Lesson 1: From Prompt to Output
How does a set of math formulas actually write a story? In this lesson, we look at the 'Inference' phase—the step-by-step process of turning a prompt into a response.
·Artificial Intelligence
Module 5 Lesson 4: Positional Encoding – The Sense of Order
Transformers see a sentence all at once, which means they are naturally blind to word order. In our final lesson of Module 5, we learn how AI adds the 'GPS of words' to stay organized.
·Artificial Intelligence
Module 5 Lesson 3: Layers and Depth
Why does an LLM need 96 layers? In this lesson, we explore how stacking attention blocks creates a hierarchy of meaning, moving from basic letters to complex abstract logic.
·Artificial Intelligence
Module 5 Lesson 2: Attention Mechanism (Intuitive Explanation)
Why is 'Self-Attention' the most important invention in AI history? In this lesson, we use a simple library analogy to explain how LLMs decide what to focus on.
·Artificial Intelligence
Module 5 Lesson 1: Why Transformers Replaced Earlier Models
Before-Transformer (B.T.) and After-Transformer (A.T.). In this lesson, we learn about the architectural breakthrough that allowed AI to finally understand context at scale.
·Artificial Intelligence
Module 4 Lesson 4: Loss Functions – Measuring Mistakes
How does a model actually 'know' it's getting better? In our final lesson of Module 4, we explore the conceptual magic of the Loss Function.
·Artificial Intelligence
Module 4 Lesson 3: Pretraining vs Fine-Tuning
An LLM isn't 'born' knowing how to be a helpful assistant. It goes through two distinct life stages: Pretraining and Fine-Tuning. Learn why both are critical.
·Artificial Intelligence
Module 4 Lesson 2: Training Data – The Fuel of AI
Where do LLMs get their knowledge? In this lesson, we explore the datasets that power models, the importance of data deduplication, and the risk of 'Data Contamination'.
·Artificial Intelligence
Module 4 Lesson 1: The Core Objective – Next Token Prediction
Why does predicting the next word lead to human-like intelligence? In this lesson, we explore the simple mathematical goal that drives trillions of parameters.
·Artificial Intelligence
Module 3 Lesson 3: Embeddings in Practice
How are businesses actually using those big lists of numbers? In our final lesson of Module 3, we look at semantic search, RECOMMENDATIONS, and the basics of RAG.
·Artificial Intelligence
Module 3 Lesson 2: How Embeddings Are Learned
Embeddings aren't created by humans; they are learned by machines. In this lesson, we look at the intuition behind how LLMs build their conceptual map of the world.
·Artificial Intelligence
Module 3 Lesson 1: What Embeddings Are
How does a computer know that a 'King' is like a 'Queen' but not like a 'Kilometer'? In this lesson, we explore Embeddings: the mathematical heart of AI meaning.
·Artificial Intelligence
Module 2 Lesson 3: Token Limits and Context Windows
Why does the AI forget what you said 20 minutes ago? In our final lesson of Module 2, we explore the 'Context Window' and the hard limits of model memory.
·Artificial Intelligence
Module 2 Lesson 2: Tokenization – The Vocabulary of AI
How do LLMs actually 'read'? They don't see words; they see tokens. Learn the secrets of subword tokenization and why it's the secret sauce of modern AI.
·Artificial Intelligence
Module 2 Lesson 1: Why Computers Cannot Understand Text Directly
Before we learn about tokens, we must understand the fundamental gap between how humans see text and how computers process data: the Numerical Gap.
·Artificial Intelligence
Module 1 Lesson 3: Real-World Uses of LLMs
In our final lesson of Module 1, we look at where LLMs are actually being used to create value, from search and coding to decision support systems.
·Artificial Intelligence
Module 1 Lesson 2: How LLMs Differ from Traditional Software
In this lesson, we explore the fundamental shift in computing: moving from rigid 'If-Then' logic to the fluid, probabilistic nature of Large Language Models.
·Artificial Intelligence
Module 1 Lesson 1: Defining Large Language Models
Welcome to the first lesson of the LLM course! We start by defining what Large Language Models actually are, why they are 'large', and what they can (and cannot) do.
·AI Agents and LLMs
Cost Engineering for AI Systems
AI tokens are the new cloud bill. Learn how to optimize your AI costs through semantic caching, model routing, and prompt compression.
·AI Agents and LLMs
Responsible AI for Builders, Not Policymakers
Stop talking about ethics and start building with safety. Learn the practical engineering guardrails, audit trails, and logging strategies for responsible AI.
·AI Agents and LLMs
From APIs to Platforms: The New AI Stack
The AI tech stack is moving beyond the OpenAI API. Explore the layers of the modern AI platform: vector stores, orchestration, and specialized deployment.
·AI Agents and LLMs
AI Security Risks Every Engineer Should Know
AI is the new attack surface. Learn about prompt injection, data leakage, and model misuse, and how to build production-grade security for your AI systems.
·AI Business
Module 3 Lesson 1: What is Generative AI?
Discover the magic behind the latest AI wave. Learn how Generative AI works, why it feels so 'human', and the business implications of 'The Era of Autocomplete'.
·Generative AI
Module 2 Lesson 1: What Are Large Language Models (LLMs)?
Understanding the scale, training, and significance of models like GPT-4 and Claude.
·AI Agents and LLMs
LLMs Beyond Chat: Real Business Use Cases
Chatbots are just the entry point. Discover how enterprises are using Large Language Models for automated search, summarization, and complex decision support.
·Agentic AI
Module 1 Lesson 1: What Agentic AI Is
The shift from generative to agentic. Understanding the third wave of AI development.
·Agentic AI
Module 2 Lesson 1: Agent Components
Dissecting the agent. Understanding the four pillars: LLM, Memory, Tools, and the Control Loop.