A working AI toolchain
Claude Code or an equivalent coding agent, shell basics, git, GitHub, and a local project running on your machine — the minimum environment for the bigger classroom.
Built with
8 sessions · 4 weeks · 12 hands-on hours
Install the tools. Learn the basics. Enter the bigger classroom ready.
Before AI can change your business, you need the toolchain and the mental model. In four weeks, you'll install the core tools, learn the basic concepts behind agentic workflows, and complete a small business-facing project that prepares you for larger application courses.
What participants leave ready with
The output is readiness: tools installed, concepts understood, and a small project shipped.
Core AI-builder concepts
Agents, local development, cloud SaaS, local sovereign tools, prompts, context, commits, previews, and deployment — explained through a hands-on project.
A business-facing practice project
A small page/project that applies the tools to a real business context, so you are prepared for application courses rather than stuck on setup.
Curriculum
Eight sessions. Every one removes a beginner bottleneck.
Two sessions a week for four weeks. Sessions 1–4 set up the toolchain and basic concepts; sessions 5–8 apply them to a small business-facing project.
Part I — Build
Weeks 1–2Install your AI builder toolkit
01Toolchain setupNo PhD required
You leave with: Claude Code, shell, git, and the template running locally · the confidence that you are tool-ready
Tools · Claude Code + Claude Desktop · basic shell commands (Bash / PowerShell / WSL)
Learn the AI-builder map
02Basic AI conceptsTool categories
You leave with: A plain-English map of AI tools, agents, local dev, cloud SaaS, and where each fits
Tools · Gemini via Google AI Studio for concept exploration and examples
Turn knowledge into a business problem
03Business contextProblem framing
You leave with: A structured business problem brief
Tools · NotebookLM — your notes and business context become the source material
Build the first practice page
04Git workflowLocal preview
You leave with: A first practice page, live in local preview
Tools · The git process — commits, diffs, branches: the gold nuggets of open source
Part II — Apply
Weeks 3–4Customize safely with an agent
05Safe customization
You leave with: A customized project that still builds and runs
Tools · Model flexibility: OpenAI Codex, Google Antigravity, OpenCode · the sandbox concept — why agents work safely
Choose the right ownership model
06Ownership modelVendor trade-offs
You leave with: A decision guide for when to use cloud SaaS vs. local sovereign tools
Tools · The AI-builder market split: Cloud SaaS (Genspark, Vercel, Manus) vs. Local dev (Claude Code Desktop, OpenClaw, Hermes) — understand the trade-offs before you choose
Understand how shipping works
07MVP launch
You leave with: A demo-ready project and a deployment checklist
Tools · gcloud CLI + GitHub CLI (gh) — deploy to Cloud Run from your terminal
Prepare for the business-application classroom
08ReadinessNext course
You leave with: A readiness checklist for 7 Figures of Impact and future business applications
Tools · The AI-native development life cycle — the full loop you now own
Your toolkit — Cloud SaaS vs. Local Dev
One working setup, then the whole map.
You start with one primary agent so you are never juggling tools. By the end you understand the industry's two responses to AI enablement — cloud-managed platforms versus sovereign local environments — and you know what you own versus what you rent.
- Claude Code + Claude Desktop — your local dev home base from Session 1
- Gemini, Google AI Studio, and NotebookLM — cloud-hosted synthesis tools
- OpenAI Codex, Google Antigravity, and OpenCode — cloud agent APIs
- OpenClaw agent runtime and Hermes — local agent orchestration (you own the environment)
- git, GitHub CLI (gh), and gcloud CLI — the shipping toolchain
- Shell basics (Bash / PowerShell / WSL) and sandbox concept — local infrastructure
- The market map: Cloud SaaS (Genspark, Vercel, Manus) vs. Local sovereign (Claude Code, OpenClaw, Hermes)
Supporting links
Everything you need, in one place.
GitHub template source
Clone the starter project from the Solo Unicorn repository.
Open link →Solo Unicorn template docs
Where the landing-page template is documented.
Open link →CSTU service network
Use the CSTU service network as the later marketplace path after the foundation and business-application courses.
Open link →Where this leads
Ready for the business-application classroom?
This course sets up your local AI agentic environment — Claude Code, shell, git — as its first session. If you're ready to build a full business-application workflow next, 7 Figures of Impactis a 13-session, 1.5 CSTU semester-credit-hour course — and it's fully self-contained, so this course is optional, not required, if you'd rather start there directly.
AI Builder Foundation Lab