AgentBricks
Open-source ML learning platform — build production-grade systems inside a synthetic agent universe
Personal
Problem
Problem
ML education defaults to toy datasets, tutorial code, and isolated concepts that don't translate to production. The gap between "I followed the tutorial" and "I can build a real ML system" is enormous. AgentBricks addresses this by giving learners production-grade infrastructure problems — Kafka streams, real databases, cloud deployment — but fueled by millions of synthetic agents rather than real user data, eliminating privacy concerns while preserving scale.
Approach
Approach
AgentBricks is structured as story-driven modules where each arc presents a realistic business scenario: a synthetic universe of millions of agents behaves like real users — buying, churning, clicking, and converting — and the learner must build the ML system to respond. Modules ship with real infrastructure: Kafka for event streaming, PostgreSQL for storage, Docker Compose for the full stack. Each completed module produces a showcase-ready portfolio project, not a notebook.
Architecture
Architecture
AgentBricks — system diagram
Key Technical Decisions
Key Technical Decisions
Story-driven framing over tutorial problems
Toy problems don't motivate production thinking. When a learner is debugging a recommendation system for 'millions of users generating 50K events/second', they make fundamentally different architectural decisions than when working on the Iris dataset. Narrative context changes what good looks like.
Real infrastructure from day one
Most ML courses use mocked or simulated infrastructure. AgentBricks ships with actual Kafka, actual PostgreSQL, and actual Docker Compose. The learning curve is steeper, but the outcome is a real system the learner can talk through in a job interview.
Results
Results
- ✓Production-grade ML curriculum using real Kafka, PostgreSQL, and Docker infrastructure
- ✓Synthetic agent generator produces millions of realistic behavioral events
- ✓Each completed module generates a portfolio-ready project, not a notebook
- ✓Zero privacy concerns — no real user data involved
Tech Stack
Tech Stack
Links
Links