Model Context Protocol guide
How connected AI tools use external data, tools, and services.
Articles on applied AI, data science, product systems, and engineering
A crawlable index of practical notes by Yangming Li, covering LLM systems, NLP, MLOps, statistical learning, workflow automation, experimentation, and product thinking.
Applied AI and ML
Autonomous AI workflows with n8n
A technical guide to agentic automation systems and workflow design.
CMU Advanced NLP course notes
Study notes on foundational and advanced natural language processing topics.
Random Forest explained
An ensemble learning guide with practical machine learning context.
PyTorch review
A compact review of tensors, shapes, broadcasting, reshaping, and distributions.
Trustworthy machine learning
Notes on transparency, fairness, privacy, robustness, and accountability.
Data platforms and MLOps
MLOps essential skills
Model management and deployment foundations for production ML work.
Databricks lakehouse guide
Delta Lake, MLflow, Unity Catalog, and data platform implementation notes.
Docker for machine learning
Using Docker for reproducible data science and ML environments.
Kubernetes guide
Container orchestration concepts and implementation patterns.
Product and experimentation
High-impact value propositions
A practical framework for defining and validating product value.
What makes products successful?
Notes on user needs, stickiness, and product experience.
A/B test engineering guide
Feature gates, experiments, and reliable launch measurement.
Jira for agile project management
How issue tracking and agile workflows support technical delivery.