ANDAMAS LAB / 25 PRACTICAL PATHS
Choose the work you want to improve. Every Path starts with free Foundations; Advanced is also free during Early Access, with an optional Final Lab included when the Path has one.
EXPLORE ALL PATHS
8 of 25 Paths shown.
COLLECTION / BUILD
Keep the speed of AI-assisted coding while adding the boring disciplines that stop a prototype from becoming a fragile production mystery. Learn repository truth, architecture boundaries, migrations, secrets, reviewable changes, CI, browser verification and controlled deployment.
Use AI to expand testing capacity without confusing generated tests with evidence that the product works. Build risk-based acceptance, deterministic checks, real-browser verification, regression cases and a release gate that connects code quality to the user story.
Put a small AI-enabled application on your own VPS without turning yourself into a full-time infrastructure engineer. Build a minimal Dockerized stack with private database, reverse proxy, secrets, health checks, backups, secure access and a promotion runbook you can actually operate.
COLLECTION / ORCHESTRATE
Turn recurring AI-assisted work into a small operating system with explicit context, authority, handoffs and evidence — so the system keeps working when the conversation gets longer, the tools change or another person has to pick it up.
Build an MCP server that gives an AI client useful, bounded access to real tools and information without handing the model unrestricted application authority. Start from one job, expose the minimum capability, secure the connection and test the protocol as a product boundary.
Move from one overloaded assistant to a small team of specialized agents only where specialization changes the work. Define jobs, context, tools, handoffs, ownership and shared artifacts so collaboration reduces complexity instead of multiplying prompts.
Build the control layer around agents: capability trust, tool permissions, authority/effect classification, human approvals, compatibility, cost admission, lineage, stop conditions and evidence about what actually ran. Governance should make useful agents safer to operate, not bury them in paperwork.
Redesign a small organization around the work AI can genuinely prepare or execute while keeping ownership, source truth and human decision authority explicit. Start from workflows and bottlenecks, then design shared knowledge, agents, automation and operating reviews around measurable business outcomes.