AI builders September 21 · Full
Agentic Engineering
Build with AI. Ship like an engineer.
Come with an idea, a rough prototype, a problem, or nothing picked yet. Learn how coding setup, authentication, data, deployment, and analytics fit together.
Curriculum and outcomes
Curriculum
- Set up Claude Code or Codex and build the first working flow
- Add authentication and data where the project needs them
- Deploy it and learn how configuration, domains, and secrets work
- Add analytics, test the main workflow, and decide what to improve
What you'll work toward
- A working first version
- A clearer path to deployment
- A build process you can repeat
Product Limited seats
AI PM Accelerator
Ship AI products that actually work.
For product managers who want to move past prompting and lead real AI products: writing specs, defining repeatable quality checks, making model calls, and shipping.
Curriculum and outcomes
Curriculum
- The AI PM mental model: language models, agents, repeatable quality checks, and what’s actually different
- Scoping AI features: PRDs, success metrics, build-vs-buy, and model selection
- Working with AI teams: prompting, red-teaming, and running AI sprints
- Quality and production: define “good enough,” build feedback loops, and present a 90-day AI roadmap
What you'll work toward
- A shipped AI prototype
- An eval framework for it
- A 90-day AI roadmap
Growth Limited seats
AI-Powered GTM
Marketing and growth systems that scale.
For marketers and GTM operators who want AI as infrastructure, not novelty: content pipelines, lead-research agents, and personalization you can actually run.
Curriculum and outcomes
Curriculum
- AI GTM foundations: where AI fits the funnel and how to audit your stack
- Content at scale: pipelines for blog, social, and email; brand voice; AEO and GEO
- AI agents for growth: lead research, competitive intelligence, and outreach
- Personalization and measurement: segmentation, dynamic content, and attribution
What you'll work toward
- A live AI content pipeline
- A lead-intelligence agent
- An AI-augmented GTM playbook
Design Limited seats
Designing AI
UX for products that think.
For designers mastering AI-native experiences: uncertainty and error states, multi-turn flows, trust architecture, and agentic UX. Leave with a portfolio case study.
Curriculum and outcomes
Curriculum
- The AI design paradigm: uncertainty, latency, and probabilistic output
- Conversation and interaction design: multi-turn flows, chat, and multimodal input
- Trust, transparency, and control: explainability, confidence, and human review
- Agentic UX: design delegation, guardrails, and handoffs, then present an end-to-end case study
What you'll work toward
- An AI interaction pattern library
- A trust & transparency framework
- A portfolio-ready case study