From Engineer to AI Engineer in 7 days
Complete stack: agents, MCP, monitoring, context, and deployment.
What You'll Build
An agent for Founder Engineers.
Analyze a codebase
Analyze a codebase, commits, and README files from GitHub repositories using the GitHub API.
Generate content
AI agent decides what type of marketing content to create and generates drafts for your product.
Automate posting
Run on a cron job to schedule weekly content drafts published via MCP.
7-Day Curriculum
Each day builds on the previous, with working code and hands-on tutorials.
Day 1: Setup & LLM Requests
Agents vs workflows fundamentals, TypeScript setup, first LLM request
Day 2: The Agent Loop
Core agent architecture (Think → Act → Observe), GitHub API integration
Day 3: Tool Calling & Actions
OpenAI function calling, tool definitions and execution, content generation
Day 4: Memory & Context Management
Conversation history, context window management, token optimization
Day 5: Connecting to MCP Servers
Model Context Protocol, external tool integration, social media posting
Day 6: Monitoring & Observability
Performance metrics, cost tracking, debugging with Helicone
Day 7: Production Deployment
Vercel deployment, cron scheduling, environment configuration
Why Learn from Helicone?
We've spent years building the infrastructure that powers production AI agents at scale. Now we're sharing what we've learned.
Requests Processed
Tokens Per Month
Users Tracked
“Helicone is essential for debugging our complex agentic flows for AI code reviews. Can't imagine building without it.”
Soohoon Choi
CTO, Greptile
“The most impactful one-line change I've seen applied to our codebase.”
Nishant Shukla
Sr. Director of AI, QA Wolf
Ready to Build Your First AI Agent?
Join the course and start building production-ready AI agents today.