Builder Academy
Learn the basics. Apply with purpose. Learn AI basics, understand the tools behind an app, or practice a research task. Choose a path, read a short lesson, then try its linked guide or lab.
Choose the path that fits your goal. For AI learning, start with four short lessons. For building, begin with the frontend or delivery path. AI basics is not a prerequisite for every path.
Start the AI path Start the frontend path Start the delivery path Choose a starting path Enterprise evaluation and buying process No AI or coding prerequisite; begin with the framework orientation
Build a fictional POC plan, mutual action plan, executive readout and buying-readiness packet.
Start enterprise evaluation and buying process → Start with AI basics No experience needed
Understand AI, protect your inputs, write a clear prompt and check its claims.
Start start with ai basics → Build and release safely Start with the linked programming and Git references if you are new to code
Connect delivery planning, Cursor, dependencies, integrations and cost controls.
Start build and release safely → Understand the frontend No coding prerequisite
Explain how structure, components, styling and accessibility fit together.
Start understand the frontend → Build a research workflow Start with AI and prompting basics
Frame a GTM research task, protect sensitive inputs and verify its output.
Start build a research workflow → Verify an AI output A question or output you want to check
Separate supported claims from assumptions and compare models with evidence.
Start verify an ai output → Explore guides, labs and planned modules Short lessons are available in the catalog below. Guides are longer reading; simulations use fictional examples. Planned modules are outlines, not completed lessons.
Available · guided simulation
Follow one click Trace an account request, diagnose denied access, and resume practice on this device.
Partly available · guided tutorials
Programming Foundations Two guided tutorials and a technology explorer. Eight modules are planned.
Available · simulation
Git Workflow Essentials Practice branches, merge choices and failing checks with fictional scenarios.
Available · simulation
Practice playground Source Detective, Prompt Studio and the Model & Tool Selector.
Planned · Connected Systems
Thirteen topic outlines and seven future lab ideas. These are not guided courses or working labs.
View the systems roadmap Short lessons 38 Available to read
Reviewed 0 / 38 Self-reported · this page only
Workflows 5 Prompts and tasks
01 / Learn 02 / Apply AI 03 / Build skills Choose a lessonDatabases: branching, backups, and platform decisions Choose a sales framework for the job Qualify the problem and map the decision Build a value hypothesis and executive brief Choose deployment and prepare the architecture Design and run a decision-ready POC Coordinate a mutual action plan Navigate procurement, MNDA and commercial terms Coordinate security and privacy reviews Prepare a commercial proposal and negotiate responsibly Deliver the executive readout and inspect buying readiness Transition to production or close out responsibly How AI agents become engineers CMS fundamentals: architecture, modeling, and selection Reading code, files, JSON and YAML LLMs, models, inference, and AI gateways Frontend building blocks: from framework to experience Evaluate AI models with evidence Build and test your Design Lab Control integrations and cloud spend Write an evidence-based product request From idea to verified release Cursor modes, rules, and safe handoffs Build a private GTM workspace with Better Auth Measure product use without collecting private activity MCP versus REST APIs: how integrations work NDA vs MNDA, DPA, MSA: enterprise deal reviews Security assessments from startups to enterprises AI, models and assistants From rules to AI agents Write prompts that work Grounding and source quality Privacy, security and human oversight GTM: account intelligence workflow From prompts to reusable skills Package registries, npm and lockfiles Security hardening: standards to release gates Secure CSV, AI research, and account export Learning track All Foundations Prompting Responsible AI GTM Builder 01 · Builder · 35 min Databases: branching, backups, and platform decisions 02 · GTM · 20 min Choose a sales framework for the job 03 · GTM · 20 min Qualify the problem and map the decision 04 · GTM · 20 min Build a value hypothesis and executive brief 05 · GTM · 20 min Choose deployment and prepare the architecture 06 · GTM · 20 min Design and run a decision-ready POC 07 · GTM · 20 min Coordinate a mutual action plan 08 · GTM · 20 min Navigate procurement, MNDA and commercial terms 09 · GTM · 20 min Coordinate security and privacy reviews 10 · GTM · 20 min Prepare a commercial proposal and negotiate responsibly 11 · GTM · 20 min Deliver the executive readout and inspect buying readiness 12 · GTM · 20 min Transition to production or close out responsibly 13 · Builder · 20 min How AI agents become engineers 14 · Builder · 35 min CMS fundamentals: architecture, modeling, and selection 15 · Builder · 25 min Reading code, files, JSON and YAML 16 · Foundations · 20 min LLMs, models, inference, and AI gateways 17 · Builder · 35 min Frontend building blocks: from framework to experience 18 · Builder · 18 min Evaluate AI models with evidence 19 · Builder · 18 min Build and test your Design Lab 20 · Builder · 16 min Control integrations and cloud spend 21 · GTM · 20 min Write an evidence-based product request 22 · Builder · 45 min From idea to verified release 23 · Builder · 30 min Cursor modes, rules, and safe handoffs 24 · Builder · 25 min Build a private GTM workspace with Better Auth 25 · Builder · 20 min Measure product use without collecting private activity 26 · Builder · 25 min MCP versus REST APIs: how integrations work 27 · GTM · 30 min NDA vs MNDA, DPA, MSA: enterprise deal reviews 28 · GTM · 35 min Security assessments from startups to enterprises 29 · Foundations · 8 min AI, models and assistants 30 · Foundations · 10 min From rules to AI agents 31 · Prompting · 12 min Write prompts that work 32 · Responsible AI · 10 min Grounding and source quality 33 · Responsible AI · 10 min Privacy, security and human oversight 34 · GTM · 15 min GTM: account intelligence workflow 35 · Builder · 15 min From prompts to reusable skills 36 · Builder · 15 min Package registries, npm and lockfiles 37 · Builder · 25 min Security hardening: standards to release gates 38 · Builder · 25 min Secure CSV, AI research, and account export
Beginner · Foundations · 8 minutes
Available · Short lesson
AI, models and assistants Learn the difference between AI, machine learning, LLMs, and products such as ChatGPT and Claude.
Before you start No earlier Academy lesson required.
Think of it like this The model is an engine; the app is the vehicle with controls, navigation and optional tools.
What you should remember AI is a broad field, not just chatbots. Apps may offer different models and tools. Fluent output is not proof of accuracy. Try it yourself Ask an assistant to explain a topic for a novice, then identify two claims to independently check.
Knowledge check Which describes ChatGPT or Claude as an app?
Mark as reviewed Your reading review is self-reported. Quiz answers and practice are separate; neither is marked passed by this button.
All lessons catalog · Lesson 29 of 38
← Previous: Security assessments from startups to enterprises Next: From rules to AI agents →
Explore practice labs Educational prototype · No login, model API calls or persistent progress in this version. Review privacy requirements before using internal data. Sourced references are linked in lessons; model-specific comparisons require evaluation. 