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AI Fundamentals3:00

Part 1: What Is an LLM? Why Your Bot Invents Facts Confidently

An LLM isn't a brain — it's a frozen next-token predictor. That one fact explains every confidently-wrong answer your AI app ships, and what to build around it.

July 20, 20267 min read

Series

The AI-First Developer

Part 0 → 14 · 184 min total

March 24, 202613 min read

Part 0: Part 0 — Your AI Dev Toolkit: The Right Tools, The Right Setup, The Right Stack

Before the mindset. Before the frameworks. Before any of the workflow strategies in this series — you need the right tools configured the right way. Most developers pick the wrong tool, configure it badly, and blame the AI. This is the setup guide nobody wrote.

March 19, 202613 min read

Part 1: Part 1 — The AI-First Mindset: Stop Coding, Start Architecting

You wrote the for loop. The AI wrote 800 lines while you were on the phone. The developers who thrive aren't the fastest coders — they're the ones who learned to stop thinking about how and start thinking about what.

March 19, 202610 min read

Part 2: Part 2 — Green Light / Red Light: The Developer's Framework for AI Delegation

You handed everything to the AI. Three months later, you can't debug without it. The fix isn't using AI less — it's using it strategically. Here's the exact framework for knowing which tasks to delegate and which ones to own.

March 19, 202613 min read

Part 3: Part 3 — The Specification Framework: Write Prompts That Generate Production-Ready Code on the First Try

Vague prompt: 40 minutes of refinement cycles. Precise prompt: production-ready code on the first try. The difference isn't the AI — it's the 5-part specification architecture that eliminates every source of ambiguity before the AI writes a single line.

March 19, 202610 min read

Part 4: Part 4 — The Confident Junior: 4 Ways AI Fails and How to Catch Every One

The AI generated payment processing code with a subtle SQL injection vulnerability. The tests passed. The code review passed. It shipped to production. Understanding how AI fails — with complete confidence — is the skill that separates safe developers from dangerous ones.

March 19, 202612 min read

Part 5: Part 5 — AI Debugging: The 3-Context Framework That Closes Bugs in Minutes

Two developers. Same AI tool. One fixes the bug in 5 minutes. The other spends 40 getting generic suggestions that miss the root cause. The difference isn't the model — it's the 3 pieces of context that unlock AI debugging. Here's the framework and the 4-step workflow.

March 19, 202616 min read

Part 6: Part 6 — The Trust Spectrum: When AI Code Review Should Take 4 Seconds vs 4 Hours

AI generates code in seconds. The professional risk is the instinct to review it just as fast. A bug in formatCurrency costs ten minutes. A bug in verifySession costs your company. This is the framework for calibrating review intensity to blast radius.

March 19, 202612 min read

Part 7: Part 7 — Design First, Code Later: How to Keep Architectural Control When AI Does the Building

You asked AI to build a feature. It gave you working code in the wrong architecture. Then you had to throw it away and start over. The fix isn't better prompting — it's a mindset shift: design the blueprint before the AI lays a single brick.

March 19, 202612 min read

Part 8: Part 8 — The 4 Quality Gates: How to Stop AI-Generated Technical Debt Before It Ships

AI can generate technical debt faster than any team of humans. It creates consistent, compounding anti-patterns across an entire codebase at machine speed. The 4 Quality Gates are the systematic checks that catch it before it lands in your main branch.

March 19, 202610 min read

Part 9: Part 9 — Pick the Right Model Every Time: The Three-Tier Selection Framework

You've been using a sledgehammer for everything. Using your highest-capability model for formatting fixes wastes money and time. Using a fast lightweight model for security-critical code is negligent. Here's the framework for matching the right AI to every coding task.

March 19, 202613 min read

Part 10: Part 10 — Team AI: How to Build a Shared Standard That Scales Across Your Entire Engineering Team

One developer on your team uses AI brilliantly. Another ships bugs 40% faster. Without a shared standard, AI amplifies both good and bad engineering practices simultaneously. Here's how to build an AI standard that makes the whole team better.

March 19, 202611 min read

Part 11: Part 11 — The Dependency Trap: What Happens to Your Skills After 6 Months of AI Coding

After 6 months of daily AI coding, a developer sat down with a blank file and no AI running. A task that should have taken 20 minutes took 2 hours. This is the predictable journey every AI developer faces—and how to come out on the right side.

March 19, 202614 min read

Part 12: Part 12 — 100% Ownership: The Ethics, Responsibility, and Long Game of AI-Assisted Development

The AI wrote it — that's not an excuse. The moment you hit commit, every line becomes yours. Here's the complete framework for taking full ownership, avoiding the bias and security traps AI reliably creates, and building a career that compounds as AI handles more of everything else.

March 24, 202610 min read

Part 13: Part 13 — AI-Powered Testing: Write Tests That Actually Test Something

The AI wrote your tests. They all pass. Your code coverage is 94%. Then production breaks. The problem isn't the AI—it's that most AI-generated tests are hollow. Here's how to write tests that genuinely protect your code.

March 24, 202615 min read

Part 14: Part 14 — Taming Legacy Code with AI: From Archaeology to Modern Architecture

200,000 lines of code. No documentation. The original developer left 18 months ago. Before AI, this would take weeks of archaeology. With AI, you can understand, document, and safely modify a codebase you've never seen—in days.

Series

AI Fundamentals

Part 1 → 14 · 165 min total

March 12, 202611 min read

Part 1: Part 4 — The Artificial Neuron: The Tiny Decision Machine Behind Every AI

Your brain has 86 billion neurons. ChatGPT has trillions. Today we isolate just one digital brain cell and watch it make a decision — in Python.

Part 2: Part 5 — How AI Actually Learns: The Training Loop Explained

AI isn't programmed — it's trained. Four steps, repeated millions of times: guess, measure the mistake, find who's responsible, fix it. Here's exactly how that works.

April 7, 202611 min read

March 12, 202611 min read

Part 3: Part 6 — From Zero to ChatGPT: The 4 Learning Types That Built Modern AI

ChatGPT didn't just learn — it learned in four completely different ways. Discover how Supervised, Unsupervised, Reinforcement, and Self-Supervised learning combine in a secret 3-step pipeline to turn raw text into a helpful, safe, and eloquent AI.

Part 4: Part 1 — Tokens: Why Your Language Costs More Than English When You Use AI

The AI doesn't read your words — it reads numbers. And those numbers aren't equal across languages. Here's what tokens are, why they cost money, and why non-English speakers pay more.

March 7, 202612 min read

Part 5: Part 8 — Token-by-Token: Why AI Generates Text One Word at a Time (And Why It Costs 4x More)

ChatGPT doesn't think its answer in advance — it predicts one token at a time in a recursive loop. Understanding this changes how you design prompts, control costs, and build responsive AI applications.

March 12, 202612 min read

Part 6: Part 2 — Embeddings: How AI Understands the World Without Knowing a Single Word

If AI doesn't read words, how does it know that 'Apple' the fruit is different from 'Apple' the company? The secret is in a 10,000-dimensional galaxy of numbers called Embeddings.

March 12, 202612 min read

Part 7: Part 3 — How AI Finds Your Answer in 30 Million Documents in Under a Second

The old internet searched for words. The new internet searches for meaning. Here's the math that killed keyword search — and powers everything from TikTok to RAG.

March 12, 202612 min read

Part 8: Part 7 — The Transformer: The Architecture That Accidentally Changed the World

In 2017, Google researchers published a paper called 'Attention Is All You Need' — and didn't patent it. That decision launched ChatGPT, Claude, Gemini, and the entire modern AI industry. Here is the complete story of the architecture that changed everything.

Part 9: Part 9 — Context Window: The Invisible Limit That Shapes Every AI Conversation

Every LLM has a context window — a hard limit on how much it can 'see' at once. Understanding what fills it up, why models forget things, and the 'Lost in the Middle' effect will change how you architect every AI feature you build.

March 12, 202615 min read

Part 10: Part 14 — RAG: Give Your AI a Memory

ChatGPT confidently recommended a smart glasses model discontinued two years ago. The fix doesn't require retraining anything. RAG — Retrieval Augmented Generation — lets any LLM answer from YOUR data, in real time, without a single GPU.

March 12, 202611 min read

Part 11: Part 12 — RLHF: How Humans Taught AI to Be Helpful

GPT-3 was brilliant but chaotic — it would answer 'How do I bake a cake?' with a story about a Belgian chocolate factory in 1923. RLHF is the secret process that transformed raw AI intelligence into the helpful assistant you use every day.

Part 12: Part 13 — Prompt Engineering: The Art of Talking to AI

The model isn't getting dumber. Your prompts are getting lazier. Learn the 5 techniques that separate vague, frustrating AI interactions from precise, powerful ones — including the exact RAG prompt template used in production systems.

Part 13: Part 10 — Hallucination: Why AI Lies With Complete Confidence (And How to Stop It)

An AI chatbot invented a refund policy that cost a company $650. Lawyers filed AI-generated case citations that didn't exist. AI confidently fabricates because it can't say 'I don't know' — here's the root cause, the four danger tiers, and the five solutions that actually work.

March 14, 202618 min read

Part 14: Responsible AI: From Google's 7 Principles to Bias Detection Code and CI/CD Fairness Gates

Build responsible AI using Google's proven framework: 7 core principles, 4 hard limits, the issue spotting process, Python bias detection with demographic parity and equalized odds, and GitHub Actions CI/CD gates that block biased models from shipping.

Series

AI Agents

Part 1 → 1 · 12 min total

October 7, 202612 min read

Agent Engineering

Part 1: What Makes an AI Agent

Most AI products sold as agents are stateless chatbots in disguise. Here is the control loop and architecture that separates autonomous agents from single-shot LLM wrappers.

Series

Agentic Coding

Part 1 → 2 · 26 min total

October 12, 202614 min read

Agentic Coding

Part 1: How to Build Production Software With AI Without Drowning in Technical Debt

AI coding agents generate functional code faster than a team can verify it. Here is how to classify the failures, draw the right system boundaries, and run a verification workflow that keeps a codebase maintainable.

Agentic Coding

Part 2: Structuring Your Coding Agents: Stop Picking Frameworks, Pick Control Flow

October 13, 202612 min read

AI Insights

4 articles · newest first

Beyond the Hype: What 121,000 Developers and Autonomous Agents Tell Us About AI's Real Impact on Software Engineering

92.6% of developers use AI monthly. 26.9% of production code is now AI-authored. Yet productivity gains have plateaued at 10%. Here's the full picture — the data, the shift in operating model, the risks nobody talks about, and what it actually means to be a software engineer right now.

April 4, 202618 min read

March 17, 202612 min read

Why AI Makes Programmers More Valuable — Not Less

Every week another headline says AI is coming for developers. But look at who is actually using these tools. Developers aren't running from AI. They're building with it — faster, bigger, alone. Here's the structural reason why.

The One Thing AI Can't Replace

Twenty years in the industry, and I still found myself caught in the same trap — just in a different city. This is what stepping back actually taught me about AI, expertise, and the only thing that never goes out of date.

March 4, 20265 min read

November 27, 20254 min read

Is AI Making You a Worse Developer?

Using AI can make you way more productive, but it also comes with a risk. If we're not careful, we might get too used to letting the AI do the thinking and slowly lose our own problem-solving skills.

AI Engineering

6 articles · newest first

March 15, 202620 min read

Cut Your LLM Bills in Half: Building a ModernBERT-Based Prompt Compressor (Zero Extra API Calls)

We built a prompt compressor using only ModernBERT attention scores: no extra LLM calls, no black box, ~45–58% token savings on real prompts (measured, code included). Here's how it works, the complete code, honest benchmarks, and when to use it over LLMLingua-2.

March 14, 202614 min read

AI Deep Research: From a Single Vague Question to a Structured, Cited Research Report

Standard AI gives you a paragraph. Deep Research gives you a structured multi-source report with cited evidence — automatically. Learn the DEEP framework and 7 proven prompt templates that separate generic AI responses from professional research outputs.

March 14, 202615 min read

Build a Local AI Agent: The 4-Step Agentic Loop That Runs Everything Offline

I built an AI agent that detects my location, checks the weather, finds national parks, and gives me opinionated hiking recommendations — all running locally with zero cloud APIs. This is the 4-step agentic loop that powers every modern AI agent, and how to build one from scratch.

March 14, 202625 min read

Build an Autonomous Research Agent with Claude: Web Search, PDF Downloads, and Conversational Memory

Learn to build a fully autonomous AI research agent using the Anthropic Messages API with web search via Tavily, PDF downloading, multi-turn memory, and a Streamlit UI. Step-by-step from zero to production.

March 14, 202620 min read

Interpretable AI: Attention Maps, Sparse Autoencoders, and Steering Vectors Explained with Code

Three hands-on interpretability techniques for understanding what language models think: attention visualization with circuitsvis, sparse autoencoders for hidden concept discovery, and steering vectors for behavior control—all with runnable code.