| 010 |
Installing VSCode + GitHub Copilot |
Set up your first AI coding environment |
| 020 |
Installing Cursor |
Set up alternative AI-native IDE |
| 025 |
Downloading Course Materials |
Download and set up the course repository locally to access all training modules and examples in your IDE |
| 030 |
Model Selection |
Choose the right AI model for your task |
| 032 |
Choosing Prompt Language & Freewriting |
Pick the prompt language that maximizes your throughput (usually your native one) and use freewriting to produce long, raw prompts faster than your inner critic can stop you |
| 035 |
Visual Context with Screenshots |
Share visual context with AI using screenshots to get accurate help when interface differs from expectations |
| 040 |
Agent Mode & AI Mechanics |
Understand how AI coding assistants work |
| 050 |
Effective Prompting |
Iterate prompts instead of arguing with AI |
| 055 |
Clarifying Requirements with AI |
Get AI to help you define unclear tasks through questions |
| 056 |
Prompt Engineering Toolkit |
Apply 15 prompt engineering techniques to unlock deeper, more relevant AI responses from a blank chat |
| 057 |
Agent Memory Management |
Use todo lists and external documents to give AI agents persistent memory across complex tasks |
| 058 |
Workspace Kickoff with Iterative Prompt |
Create a kickoff prompt file that grows with UPD blocks as your investigation evolves |
| 060 |
Version Control with Git |
Track changes with baby steps approach |
| 064 |
Debugging AI-Generated Code |
Break the infinite error loop with a systematic debugging workflow for AI-generated code |
| 070 |
Custom Instructions |
Evolution from one-time prompts to instruction architecture |
| 075 |
Shared Instructions & Team Conventions |
Scale personal AI instructions to your whole team via a Git-versioned shared repository |
| 076 |
Advanced Skills Management System |
Set up a centralized AI skills repository with manifest-driven sparse checkout and the skills CLI to manage team-wide AI instructions at scale |
| 080 |
Learning from Hallucinations |
Transform AI output deviations into instruction improvements by delegating fixes to the agent itself |
| 083 |
AI Cost Optimization & Token Economics (optional) |
Understand how tokens are counted, what context windows cost, and how to cut your AI bill without losing quality — primarily for team leads and managers |
| 084 |
Copilot Usage Telemetry — Credit & Token Tracking |
Pull Copilot credit/quota and token-usage statistics from the internal copilot_internal/user API and per-session debug logs — no UI, no screenshots |
| 085 |
Evaluating AI Output Quality |
Score and validate AI responses using a systematic quality framework including hallucination detection |
| 088 |
IDE Workspace as Knowledge Base |
Use your IDE workspace as a lightweight knowledge base — drop documents into a folder and query them through AI chat instead of building a full RAG pipeline |
| 090 |
AI Skills & Tools Creation |
Create reliable AI skills by combining instructions with parameterized tools to eliminate hallucinations |
| 091 |
CLI Snapshot Testing with Docker |
Test any CLI tool by capturing its full output as a golden Markdown snapshot and reviewing changes via git diff |
| 092 |
Kata — Building Calculation Skills |
Practice the full skill-creation loop — describe a calculation, let the agent generate a deterministic Python CLI script, write a SKILL.md, run and verify output, then repeat |
| 100 |
Model Context Protocol (MCP) |
Connect AI to any data source |
| 103 |
CLI: Command Line Interface |
Call REST APIs directly from terminal using curl — bypassing LLM for deterministic, token-efficient tool execution without hallucination risk |
| 104 |
Port Existing Code into Skills |
Port an existing backend service into a portable CLI tool and package it as a reusable AI Skill |
| 105 |
MCP GitHub Integration — Issues Management |
Connect AI to GitHub repositories for automated issue management |
| 106 |
Building Custom MCP Servers with FastMCP |
Build a secure, Python-based MCP server that wraps any REST API using the FastMCP framework |
| 107 |
MCP Image Viewer Tool in PowerShell |
Build a PowerShell MCP server that returns local images as base64 so AI can load and analyze images from the filesystem without manual attachment |
| 108 |
Token & API Key Management |
Manage API keys and secrets safely using .env files and prevent credential leaks to Git |
| 110 |
Development Environment Setup |
Set up Node.js and Docker development environment |
| 120 |
Rapid Prototyping with SpecKit |
Build a spec-driven PoC from idea to working full-stack prototype using SpecKit methodology |
| 125 |
SpecKit for Legacy Projects |
Use SpecKit to analyze an existing codebase, generate structured documentation, and manage the project with spec-driven practices |
| 126 |
AI-Assisted Refactoring |
Clean up AI-generated code with incremental, Git-backed refactoring passes — extract functions, rename, simplify |
| 128 |
Database Schema Design with AI |
Design SQL schemas, generate migrations, and write queries through AI conversation |
| 130 |
AI-Powered QA with Chrome DevTools MCP |
Automate web application testing with AI agent using Chrome DevTools |
| 132 |
AI-Assisted Test Generation & Snapshot Testing |
Generate unit tests with AI and use Git diff as your regression assertion engine |
| 134 |
AI Code Security Review |
Find and fix OWASP vulnerabilities in AI-generated code with a reusable security review instruction |
| 140 |
Advanced MCP Integration in POC |
Create custom MCP server to control your application from IDE |
| 150 |
GitHub Coding Agent Delegation |
Delegate tasks to GitHub coding agent for autonomous implementation |
| 152 |
AI-Assisted Code Review |
Review AI-generated pull requests effectively with structured AI-assisted feedback and a team review protocol |
| 155 |
CI/CD Pipeline with AI Agents |
Automate testing and quality gates with GitHub Actions for AI-generated code |
| 160 |
Bulk File Processing with AI |
Learn three approaches for processing multiple files with AI agents |
| 165 |
Elitea Platform MCP Integration |
Connect to Elitea platform and manage AI agents through MCP from VS Code |
| 168 |
Elitea Remote MCP — HTTP Integration |
Configure Remote MCP servers on Elitea platform to connect cloud services via HTTP without local installation |
| 170 |
DIAL API Key and cURL Access |
Obtain DIAL API key and test connection to AI models using simple cURL requests |
| 175 |
CodeMie CLI Setup & IDE Integration |
Install, authenticate, and configure CodeMie CLI to run AI coding agents (Claude Code, Gemini, OpenCode) through your company's enterprise proxy, and connect them to VS Code or Cursor |
| 180 |
DIAL Integration with Python and Langchain |
Set up Python virtual environment, install langchain framework, and build a working AI application that connects to DIAL |
| 185 |
Prompt Templates for Dynamic Queries |
Use Langchain prompt templates to create reusable, parameterized AI queries |
| 187 |
Structured Output & JSON Mode |
Get reliable JSON from any LLM using Pydantic schemas and Langchain output parsers |
| 190 |
RAG: Document Question Answering |
Build Retrieval-Augmented Generation systems that search your documents, find relevant context, and generate accurate answers |
| 193 |
LangGraph & Advanced Agent Frameworks |
Build stateful multi-agent pipelines with conditional routing, persistence, and human-in-the-loop using LangGraph |
| 195 |
Multi-Agent Orchestration |
Coordinate specialised AI agents (coder, reviewer, tester) for higher-quality results than single-agent work |
| 196 |
Reverse Engineering Project Knowledge |
Extract project conventions, architecture rules, and instructions from issue+diff pairs using the text-triangle principle |
| 197 |
Onboarding New Team Members with AI |
Set up AI-powered codebase Q&A so new joiners answer "how does X work?" in minutes, not days |
| 200 |
AI for Data Analysis & Reporting |
Analyse any CSV dataset and generate executive reports through AI conversation — no data science skills required |
| 220 |
AI Study Buddy — Learning New Tech |
Use AI as a structured learning partner to rapidly understand any unfamiliar technology through the Feynman method |
| 230 |
Creating Training Modules from Articles |
Transform any article or resource into a structured, course-quality training module using AI agent |
| 240 |
Engineering Career Evolution with AI |
Reframe AI disruption as a career accelerator — map your engineering strengths to the agent-led workflow and build a personal evolution plan |
| 250 |
Export Chat Session |
Extract, preserve, and share AI chat sessions from VS Code as portable HTML files |
| 260 |
Translate Any Website with AI Agent |
Use Chrome DevTools MCP to translate any website into any language via AI agent commands |
| 300 |
DMtools — Agent Skill & Automation Catalog |
Install a third-party agent skill, explore a catalog of 152+ automation tools across 16 integrations |
| 350 |
OpenClaw — AI Personal Assistant Platform |
Install OpenClaw, connect an AI model, and interact with a personal AI assistant through a local Gateway, dashboard, and chat channels |
| 400 |
Installing mcpyrex — MCP Python Toolbox |
Install mcpyrex (open-source MCP server with 30+ Python/Langchain tools) into a dedicated workspace and run your first deterministic tool |
| 465 |
Cookie Grabber — Secure API Calls |
Access cookie-protected internal APIs from the AI agent via an encrypted CLI pipeline — without exposing cookies to the model or storing them in plaintext |
| 470 |
Jira CLI Access via MCPyrex Python Script |
Build a Python CLI for Jira REST API access, create a scoped API token with expiry and IP restriction, use VPN for protection, and teach the AI agent to drive it via a skill.md descriptor |
| 500 |
AI Workflow Decision Guide |
Know exactly which AI tools to use, when to use them, and how to combine them for any daily development task |
| 600 |
Microsoft Teams AI Chat Summarizer |
Build a Dockerized Python app that authenticates to Entra ID, reads Teams chats via Microsoft Graph, summarizes with an LLM, and posts back to a dedicated chat |
| 620 |
Microsoft Teams Meeting Transcription |
Convert Teams meeting .docx transcripts to clean text with optional speaker anonymization, ready for LLM summarization |
| 650 |
WinAPI MCP Toolbox |
Install a self-contained Python MCP server that exposes Windows automation tools (screenshots, mouse, keyboard, clipboard, processes) so the agent can drive your desktop |
| 900 |
Course Feedback & Completion |
Reflect on your learning journey, provide structured feedback on the training course, and send it to the course author for continuous improvement |