Phase 1 MVP
Build career momentum with visible, repeatable progress.
Single-user private mode
Learning Center
Structured progression, not fragmented browsing.
Every path is tuned toward applied AI engineering work and interview leverage.
Python for AI Engineers
Build practical Python fluency for APIs, pipelines, evaluation scripts, and debugging in applied AI systems.
0% complete
beginner14h
Python runtime habits that matter in AI work
Modeling data with dicts, dataclasses, and Pydantic
Async IO for provider calls and ingestion work
LLM App Foundations
Learn the product and systems primitives behind useful LLM applications.
0% complete
intermediate16h
Prompt, context, tools, and memory
Request lifecycle of an LLM feature
Guardrails and structured outputs
RAG Systems
Build retrieval systems that are explainable, measurable, and debuggable.
0% complete
intermediate18h
Why retrieval fails in practice
Chunking and metadata design
Ranking, re-ranking, and citations
AI Agents and Tools
Understand when agentic patterns help and how to keep them safe.
0% complete
intermediate14h
When not to use an agent
Tool definitions and execution boundaries
State machines over vague loops
Evaluation and Observability
Measure behavior before scaling usage and complexity.
0% complete
intermediate12h
What to measure in AI systems
Designing benchmark datasets
Tracing requests end to end
AI Deployment and MLOps
Make AI systems operable beyond the first successful demo.
0% complete
advanced18h
Deployment shapes for AI products
Secrets, environments, and provider configuration
Caching and throughput management
AI Engineer Interview Readiness
Translate execution experience into role-aligned interview performance.
0% complete
intermediate10h
Tell the transition story clearly
System design for applied AI
Python and backend refresh under pressure