ClinchWorks
Explore a subject, work through the curriculum, and choose your next step.
Run, inspect, benchmark, and engineer local LLM inference workflows. Includes detailed lessons, worked calculations, a complete offline reference project, debugging exercises, optional real-tool workflows and separate instructor assessment.
Fine-tune open-source language models efficiently on GPU infrastructure. Actual low-rank matrix optimization plus optional real PEFT and pretrained single-GPU workflows. Includes detailed lessons, worked solutions, failure injection, independent challenges and instructor assessment.
Understand and build interoperable tool/resource integrations using MCP concepts. An offline protocol-contract exercise plus optional real MCP SDK client/server integration. Includes detailed lessons, worked solutions, failure injection, independent challenges and instructor assessment.
Design multiple specialized agents only when decomposition adds measurable value. Bounded multi-worker coordination with synthetic evidence and explicit failure handling. Includes detailed lessons, worked solutions, failure injection, independent challenges and instructor assessment.
Build a complete feed-forward neural network from scratch and understand the full training pipeline. Detailed engineering lessons with worked calculations, complete CPU labs, failure diagnosis, independent challenges and instructor capstone assessment.
Understand and implement the core mechanics of a neural network before using deep learning frameworks. Learn through explicit calculations, executable experiments, guided repairs and independently assessed changes.
