Learning Paths

Curated public resources for going deeper on each pillar. Focused on quality over quantity. Each entry includes a brief annotation on why it is worth your time.

Last Updated: March 2026 - Resources are reviewed quarterly. If a link is dead or outdated, open a PR.

Staying Current

These are the resources our team actually uses to keep up with the AI development landscape.

Newsletters

• TLDR AI - Daily digest covering AI business, tech/research, and notable GitHub repos. Curated and concise. (Team recommendation: Jeff)
• Alpha Signal - AI-focused newsletter with research highlights and tool announcements. (Team recommendation: Boban)

Podcasts

• AI Daily Brief - Daily episodes covering AI news plus one deep-dive story. The most current and consistent AI podcast we have found. Also available as audio-only podcast. (Team recommendation: Jeff, Rob)

YouTube

• Theo / t3dotgg - Covers major AI and web development news almost daily. Opinionated but well-informed; use your own judgment. (Team recommendation: Marin)

By Pillar

LLM Foundations (Pillar 0)

• The Illustrated Transformer - The canonical visual explanation of the transformer architecture. Used in courses at Stanford, Harvard, and MIT. Start here for understanding how LLMs actually process input.
• HuggingFace Tokenizer Summary - Technical reference covering BPE, WordPiece, and SentencePiece algorithms. Essential for understanding why token count differs from word count and how that affects cost and quality.
• Understanding Tokens, Embeddings, Vectors, and Similarity - Bridge piece connecting tokens to embeddings to vector similarity. Good companion to the HuggingFace doc for building the full mental model.
• The Illustrated Word2Vec - Visual guide to word embeddings and vector similarity from the same author as the Illustrated Transformer. Foundational for understanding how RAG retrieval works.
• LlamaIndex: Understanding RAG - Well-structured walkthrough of the full RAG pipeline: loading, indexing, storing, querying. Essential for reasoning about retrieval quality when you encounter RAG in client work.
• Anthropic: Building Effective Agents - Anthropic’s guide to agent architecture patterns: prompt chaining, routing, parallelization, orchestrator-workers, evaluator-optimizer. The reference document for understanding how the tools you use daily are structured.

Context Engineering (Pillar 1)

• Claude Code Documentation - Official docs covering CLAUDE.md, rules files, context management, and project configuration. Start here.
• Context Engineering Guide - Theoretical framework for context engineering as a discipline. Covers what goes into the context window, how to structure it, and why placement matters.
• 12-Factor Agents: Own Your Context Window - Practical implementation of context ownership as an engineering discipline. Frames context management as a design decision, not an afterthought.
• awesome-cursorrules - Community collection of rules files. Useful for seeing how other teams structure their AI context.

Planning Before Code (Pillar 2)

• OpenSpec.dev - Spec-first development tool for Claude Code. Follows the specify, clarify, plan, task, implement workflow. Lighter on tokens, tuned for existing codebases.
• SpecKit - Heavier spec-first tool for greenfield projects. Generates comprehensive context through deep codebase research. Token-expensive but catches implicit requirements.

Prompt Engineering (Pillar 3)

• Anthropic Prompt Engineering Documentation - Comprehensive reference covering all prompting techniques from basic to advanced. The primary reference for our daily driver model.
• OpenAI GPT-4.1 Prompting Guide - OpenAI’s model-specific guide (April 2025). GPT-4.1 specific, but the sections on agentic workflows, long context (1M tokens), and instruction following transfer across models. Worth reading for cross-model perspective.
• Prompting Techniques Reference - Well-organized index of all named prompting strategies with explanations: zero/few-shot, chain-of-thought, ReAct, Reflexion, and more. The field’s shared vocabulary in one place.
• Image Prompting Guide - Techniques for multi-modal image prompting. Relevant for UI work, visual debugging, and diagram interpretation.
• Meta-Prompting Masterclass - Deep dive on using LLMs to generate, critique, and refine prompts. Covers the prompt generator, prompt critic, and prompt evolution patterns. Daily-use technique for anyone writing complex prompts.
• Verbalized Sampling - Technique for nudging models away from default word choices by specifying probability. Useful for content generation and getting diverse variations from the same prompt.

The AI as Collaborator (Pillar 4)

• Mozilla AI: Owning Code in the Age of AI - Essential reading on the tension between AI’s code generation speed and human comprehension. Frames the code ownership problem clearly.
• AmazingCTO: AI Code Ownership - Practical framework for code ownership policies when AI is writing significant portions of your codebase. Covers the “you generate it, you own it” principle and migration strategies.
• Programmatic Tool Calling Patterns - Analysis of Sonnet 4.6’s approach to tool execution. Useful for understanding how agentic AI reasons about when and how to use tools.
• OpenAI: Harness Engineering - Interesting read on how engineering teams structure AI-assisted workflows at scale.

Guardrails and Quality (Pillar 5)

• Google Cloud: Five Best Practices for Using AI Coding Assistants - Google’s recommendations for integrating AI coding tools into team workflows. Practical and well-grounded.
• 12 Factor App - The classic software engineering best practices. Following these makes your codebase more AI-friendly by default (clean structure, explicit dependencies, config in environment).

Verification and Security (Pillar 6)

• OWASP Top 10 for LLM Applications - Industry-standard framework for LLM security risks. Covers prompt injection, data leakage, and insecure output handling. Required reading for anyone shipping AI-integrated systems.
• Anthropic: Measuring Agent Autonomy - Research on Claude Code’s agent behavior in practice. Key takeaway: models should recognize their own uncertainty.
• Latent Space: Are Code Reviews Dead? - Thought-provoking analysis of how AI changes code review. Not necessarily gospel, but a useful framework for thinking about where human review still matters most.

Workflow and Tooling (Pillar 7)

• Function Calling vs MCP: What You Need to Know - Accessible introduction to function calling and how MCP builds on it. Good starting point if you’re new to how LLMs interact with external tools.
• Martin Fowler: Function Calling with LLMs - Thorough deep dive on function calling architecture, including security considerations and design patterns. Fowler’s treatment is the most complete single resource on this topic.
• MCP Protocol - The Model Context Protocol specification. Understanding MCP is essential for extending AI capabilities with external tools.
• MCP Apps: Bringing UI to MCP Servers - MCP Apps let tools return interactive UI components (dashboards, forms, visualizations) that render directly in the conversation. This is where MCP stops being just a data pipe and becomes a full application layer. Understanding MCP Apps changes how you think about what your AI tools can do.
• MCP Apps Specification - The technical spec and examples for building MCP servers that serve interactive UIs. Includes starter templates for React, Vue, Svelte, and vanilla JS.

Continuous Evolution (Pillar 8)

• The Complete Guide to Building Skills for Claude - Anthropic’s guide to creating reusable skills. Worth reading both for using existing skills and for building custom ones for your team.
• RTK (Reduce Token Konsumption) - Tool that reduces token usage on common shell commands by 70%+. Practical for managing costs on heavy Claude Code usage.
• IPE Newsletter: The AI Stack War - Analysis of closed vs. open AI platforms. Useful context for understanding where the tooling ecosystem is heading.

Evaluation and Measurement (Pillar 9)

• promptfoo - Open-source prompt evaluation framework. Define test cases, run them against multiple prompts or models, compare results. Our recommended eval tool for testing rules files, prompt variations, and model selection.
• promptfoo Documentation - Getting started guide for setting up your first eval suite. Covers assertions, test cases, and CI integration.
• Verbalized Sampling - Technique for generating diverse output variations by specifying probability parameters. Useful for comparing output quality across prompt structures.

Data Hygiene and IP (Pillar 10)

• GitGuardian 2026 State of Secrets Sprawl - Definitive data on secret leak rates in AI-assisted development. AI-assisted commits leak secrets at roughly double the baseline rate. Also documents 24,000+ secrets exposed in MCP configuration files.
• OWASP Top 10 for LLM Applications - Sensitive information disclosure ranks #2 on their list. The full framework covers both input-side and output-side data risks.
• CrowdStrike: Data Leakage as AI’s Plumbing Problem - Analysis of input-side data leakage risks in AI coding tools. Covers how developers inadvertently share source code with hardcoded API keys and proprietary algorithms through coding assistants.

Knowing When NOT to Use AI (Pillar 11)

• Simon Willison: Hallucinations in Code Are the Least Dangerous Form of LLM Mistakes - Counterintuitive argument that obvious hallucinations are easy to catch. The real danger is code that runs correctly but implements the wrong logic. Essential framing for the “looks right, is wrong” failure mode.
• Ten Simple Rules for AI-Assisted Coding in Science - Academic paper with practical rules that apply well beyond scientific computing. Covers problem preparation, context management, testing, and quality assurance with AI. Strong on verification discipline and knowing when AI is the wrong tool.

Cross-cutting Frameworks

Resources that span multiple pillars and provide broader context for AI-assisted development.

• Google DORA 2025: State of AI-Assisted Software Development - Data-driven analysis of how AI tools affect team performance based on ~5,000 respondents. Key finding: AI amplifies what is already there. Strong teams get stronger, struggling teams get worse. Useful for calibrating expectations.
• NIST AI Risk Management Framework - Federal framework for managing AI risks in software systems. Heavy reading, but essential context if you work in regulated industries or government contracts.
• Thoughtworks Technology Radar - Quarterly assessment of emerging technologies including AI-assisted development tools and practices. Good signal-to-noise ratio on what is production-ready vs. what is still experimental.