Resources

An independent list. Pharmacy Canary is not affiliated with, sponsored by or endorsed by any of these sites or authors, and earns nothing from these links. Links open in a new tab.

O’Reilly Media books

O’Reilly Media ↗ publishes technical books and hosts them, with video courses, on its learning platform. These match the tracks on this site.

• Learning SQL, 3rd ed., Alan Beaulieu ↗
  A gentle start: SELECT, joins, grouping, subqueries and window functions, with exercises.
• SQL for Data Analysis, Cathy Tanimura ↗
  The next step: time series, cohorts, anomaly detection and data cleaning in SQL.
• Python for Data Analysis, 3rd ed., Wes McKinney ↗
  pandas from its creator: loading, cleaning, reshaping and grouping data.
• Fundamentals of Data Visualization, Claus O. Wilke ↗
  How to pick the right chart and make figures that are clear and honest.
• Storytelling with Data, Cole Nussbaumer Knaflic ↗
  Turning a dashboard into a clear message for a manager (Wiley, on O’Reilly).
• AI Engineering, Chip Huyen ↗
  Building apps on foundation models: prompts, RAG, agents and evaluation.

Agent skills and AI engineering repos

Open-source repos of agent skills, prompts and workflows, in the spirit of poteto’s pstack.

• pstack (poteto) ↗
  Skills and playbooks for rigorous, verifiable agent work: plan, test, review, then ship.
• obra/superpowers ↗
  A skills library and method for agents: brainstorm, plan, test-first, then review.
• anthropics/skills ↗
  Public examples of Agent Skills, the SKILL.md format many coding agents now read.
• anthropics/claude-cookbooks ↗
  Notebook recipes for tool use, RAG, evals and agent patterns.
• openai/openai-cookbook ↗
  Worked examples for the OpenAI API, from structured outputs to agents and evals.

Courses

Master.dev ↗ (formerly Frontend Masters) has in-depth video courses on JavaScript, TypeScript, Node and AI. Scott Moss teaches its AI engineering series.

• AI Agents Fundamentals, v2 (Scott Moss) ↗
  Build a CLI agent from scratch: tool calling, agent loops, evals and human approval steps.
• AI Engineering Fundamentals (Scott Moss) ↗
  Eval harnesses, golden datasets, context engineering and RAG on a real app.
• All of Scott Moss’s courses ↗
  Includes agent orchestration and taking an agent from prototype to production.

Math

• Professor Leonard: Precalculus (YouTube playlist) ↗
  Free, full-length lectures on college algebra and trigonometry. A solid base for the Math track.

People to follow in AI

Five accounts on X that share useful, practical AI and AI engineering content.

• Lauren Tan (@poteto) ↗
  Creator of pstack and on the React Compiler core team. Practical posts on rigorous, verifiable agent workflows.
• Andrej Karpathy (@karpathy) ↗
  Clear explanations of how LLMs work and how to use them well, from someone who helped build them.
• Simon Willison (@simonw) ↗
  Hands-on notes almost daily on new models, prompt injection and building tools with LLMs.
• swyx (@swyx) ↗
  Popularized the term “AI engineer”. Covers the field, its tools and the Latent Space podcast.
• Chip Huyen (@chipro) ↗
  Author of AI Engineering (listed above). Thoughtful takes on evals and production AI systems.

Community

• Hacker News ↗
  A daily feed of tech news and discussion, good for keeping up with data and AI tools.