Add "How to get started?" page

Author: mkaput

astro.config.ts

@@ -37,10 +37,12 @@ export default defineConfig({
           label: "Getting Started",
           items: [
             { slug: "getting-started/glossary" },
+            { slug: "getting-started/how-to-get-started" },
             { slug: "getting-started/what-can-ai-agents-even-do" },
             { slug: "getting-started/how-to-set-up-a-new-repo" },
             { slug: "getting-started/towards-self-improvement" },
             { slug: "getting-started/first-steps-in-mature-projects" },
+            { slug: "getting-started/towards-self-improvement" },
           ],
         },
         {

src/content/docs/getting-started/how-to-get-started.mdx

@@ -0,0 +1,86 @@
+---
+title: How to get started?
+description: >-
+  A short orientation for this chapter, why hands-on practice matters, and which
+  learning path to take first.
+---
+
+This "Getting Started" chapter is here to give you a concrete sense of what is possible
+with agentic engineering.
+
+We cannot teach you every important technique right away.
+The detailed workflows, constraints, and tradeoffs come in the chapters that
+follow, but for now, the goal is simpler: to give you a small but useful amount
+of practical knowledge and to help you start working with agents in a real
+software project.
+
+## You need a real project
+
+There is no efficient way to learn this subject purely in theory.
+
+If you want to understand agentic engineering, you need to get your hands dirty
+in a living codebase.
+You need to see an agent misunderstand a task, surprise you with a good idea,
+take a wrong turn, recover, and help you ship something anyway.
+That feedback loop is where the real learning happens and appreciation of this
+technology can be born.
+
+Working with LLMs is deeply individual.
+These systems are probabilistic.
+Human thinking is too.
+Pairing the two is never a perfectly standardized process.
+Each person develops their own way of briefing, steering, reviewing, correcting,
+and trusting an agent.
+Over time, everyone builds their own story of how they work with AI.
+
+So yes, this chapter will show you tricks.
+But tricks are not enough.
+You need practice.
+Do not just read the prompts.
+Run them.
+Review the diffs.
+Follow the agent into mistakes.
+Ask it to recover.
+Start a new thread when the current one gets messy.
+Notice what kinds of instructions work well for you and which ones do not.
+
+That is how you begin to build intuition.
+And once you have a bit of real experience,
+the rest of this book will become much more useful.
+
+## Two practical paths
+
+Because hands-on work matters so much, this chapter is organized around two
+practical paths.
+
+### Path A: Start something new
+
+The first path is to start a project from scratch with agentic engineering
+techniques from day one.
+
+This path focuses on using agents early: to explore ideas, scaffold the project,
+shape the first implementation steps, and establish good habits before the
+codebase grows.
+The idea does not matter, and neither does your familiarity with the technology
+stack.
+
+If you want to begin this way, continue with
+[How to set up a new repo?](/getting-started/how-to-set-up-a-new-repo/).
+
+### Path B: Enter a mature project
+
+The other path is to jump into an existing codebase and use agents to move
+faster and make better decisions there.
+If you already have access to a larger or longer-lived codebase, this might be
+a more productive choice.
+
+This path is about using agents to understand unfamiliar systems and make
+surgical changes without losing control of the code.
+It does not matter whether that project is old or new.
+It does not matter whether it is tiny or huge.
+It does not matter whether you knew the project before you became interested in
+AI, or whether you want to learn the project and learn agentic engineering on
+top of it at the same time.
+
+For this path, feel free to jump to
+[First steps in mature projects](/getting-started/first-steps-in-mature-projects/).