Ormus Dojo

Lesson 01 Agentic Engineering vs Vibe Coding The mindset that separates people who ship from people who get stuck — and why staying in charge is the whole game Start → Lesson 02 Knowledge Work, Not Just Code The biggest, fastest wins from AI have nothing to do with programming — and they're sitting in your inbox right now Start → Lesson 03 Working Fearlessly: The Git Undo Ladder The safety net that lets you experiment freely — and lets you turn an agent loose without fear of losing work Start → Lesson 04 Reading Errors Without Guessing A wall of red text isn't punishment — it's a map. Read it top to bottom, find the one line that's yours, and the fix is usually right there Start → Lesson 05 Loop Engineering: From Prompt to Loop Stop prompting the agent a hundred times. Design the system that prompts it, checks its work, and runs until the goal is met. Start → Lesson 06 Anatomy of a Loop Every working loop has four building blocks. Get all four right and the loop runs itself; miss one and it burns tokens producing garbage. Start → Lesson 07 Verification & Memory: the heart A loop without verification is just automation that fails quietly. A loop without memory repeats every mistake. Get these two right and the rest is detail. Start → Lesson 08 Guardrails, Queues & Self-Improving Loops Keep a loop safe and cheap, build your first one today, and reach the top of the ladder — loops that improve themselves. Start → Lesson 09 Strategic vs Tactical Programming The agent now does the typing. The distinction that decides whether that helps you or buries you — and why your job moved up a level Start → Lesson 10 Human-in-the-Loop vs AFK When to sit beside the agent and when to send it away — and the four things that must be true before you can walk away Start → Lesson 11 Queues, Not Loops The viral "agentic loop" is really a task queue with away-from-keyboard workers. Borrow the vocabulary computer science already had — it maps straight onto the system you already run. Start → Lesson 12 The Second Brain That Builds Itself Karpathy's "LLM Wiki" pattern: stop re-reading the same sources every time you have a question. Point an agent at a folder and let it grow a linked, self-auditing knowledge base instead. Start → Lesson 13 10 Lessons for Agentic Coding Code got cheap. That doesn't remove the hard part of building software — it relocates it. Where the bottleneck moved, and what to do about it. Start → Lesson 14 The Missing Semester of Agentic Coding MIT's fundamentals course finally added a lecture on agents. The mechanics you already know — here's the toolkit around them: context management, MCP, parallel agents, and where to stop trusting the confident answer. Start → Lesson 15 The Code Agent Orchestra Modules 05-08 taught you to run one loop well. This is the other axis: what changes when you're not running one agent anymore, but a small team of them — each with a scope, a file to own, and a way to check each other's work. Start → Lesson 16 Loop Engineering Is Becoming a Discipline The building blocks now ship inside the tools. What's scarce isn't the ability to build a loop — it's the judgment to gate one. That's the part turning into its own job. Start → Lesson 17 Writing a CLAUDE.md That Actually Gets Read Two lookalike config files, opposite reading order, and one very fixable mistake — burying the instruction that mattered on line 300 Start → Lesson 18 Beyond Vibe Coding What responsible practice looks like once you've accepted AI-assisted coding is real Start → Lesson 19 Context Engineering You can't retrain the model and there's a new one every week. The context window is the one thing you actually control — a live build-off lab on what to put in it, what to leave out, and when. Start → Lesson 20 What a Test Actually Is (and Why It Saves You) A test is a claim about behavior you can re-check for free forever — the cheapest safety net when agents change your code Start → Lesson 21 Hinton's Warning: How Machines Understand, and Why That Should Keep You Up The man who built the learning algorithm behind modern AI explains what LLMs actually are, why digital minds learn millions of times faster than us, and what he saw that made him worry Start → Lesson 22 Debugging by Hypothesis, Not Guessing The four-step loop that turns a mystery into a solved problem — every time, without random thrashing Start → Lesson 23 How Code Actually Reaches Production From your machine to a live URL — what "shipping" actually means, stage by stage Start → Lesson 24 Skills: Procedures vs Abilities Every skill you install is either something you invoke or something the model decides to invoke on its own — and only one of those costs you context on every single turn, forever Start → Lesson 25 Agent Experience (AX): Codebases Agents Thrive In DX has a twin now — and a codebase that's easy for an agent to navigate is what lets a cheaper model do better work for fewer tokens Start → Lesson 26 Harness Over Model You control the harness far more than the model. Why the fundamentals that worked for thirty years still win in the agent era. Start → Lesson 27 Self-Improving Systems: Loops, Reviews, Guardrails If someone keeps stealing your bike, buy a lock. Turn a bug that happened once into a system that can't happen twice. Start →