Curation methodology

April 18, 2026 · View on GitHub

This document explains how resources on this list are selected, placed and maintained so that contributors and readers share a common mental model.

What belongs on this list

In scope: resources that help you learn, design, build, evaluate and operate agentic and LLM-powered systems — articles, books, courses, cookbooks, tutorials, papers, talks, podcasts, newsletters, benchmarks, reference implementations, and case studies.

Out of scope: pure tools, libraries or SaaS products. Those belong in the two sister lists:

A resource repo may appear here only if its primary value is teaching (cookbooks, reference implementations, annotated examples) rather than being a library you depend on.

Quality bar

Every entry must be:

  1. Substantive — a full guide, course, book, paper, talk, or cookbook. Not a tweet, not a 300-word marketing post.
  2. Canonical — referenced by at least one authoritative third-party source (course syllabus, survey, canonical author's reading list, well-known practitioner's post).
  3. Primary — the original source rather than an aggregator repost.
  4. Durable — on a host likely to still exist in 2 years. Avoid Medium paywalls, dead personal domains and URL shorteners.
  5. Not duplicative — substantially different from other entries in the same cell.

Matrix-first placement

Resources are placed in a single strongest-fit cell in the resource-type × topic matrix documented in the README. Cross-referencing is reserved for the Trending, Milestones Timeline, and Core & Foundations sections.

When in doubt, pick the topic the reader would most plausibly be searching for when they'd want this resource, and place it in the resource-type that matches its primary format.

One-line descriptions

Each entry is a single bullet of the form:

- [icons] [**Title**](url) — Author / Org. One sentence explaining what you'll learn or why it matters.
  • Icons come from the Legend (⭐🆓💰📘🧑‍🎓🎥🎧📄🛠️📋🧪🏗️📰).
  • The sentence is informational, not marketing. "A short course on RAG that covers retrieval, re-ranking and evaluation" is good; "Unlock the power of AI!" is bad.
  • Prefer ⭐ sparingly — typically 1–2 editors' picks per cell.

Curation cadence

  • Weekly: review open PRs, triage issues.
  • Monthly: review the Trending section, promote evergreen entries to their permanent cells, rotate in new candidates.
  • Quarterly: full pass against the thoroughness checklist (see below).
  • Continuously: lychee link-check workflow flags broken links; awesome-lint and markdownlint enforce structural hygiene.

Thoroughness checklist (per section)

Before merging a section update we verify:

  • Each populated cell has ≥ 3–4 entries.
  • No cell over-indexes on a single vendor (≥ 2 distinct authors/orgs where possible).
  • Each entry has a verified provenance trail (≥ 1 authoritative citation).
  • Mix of classic + current (≤ 12 months) material where the topic is fast-moving.
  • All links resolve (local lychee run).
  • One-line descriptions are substantive.
  • At least one external "AI canon" reference was cross-checked for omissions.
  • No pure tool repos.

Research inputs

Candidate discovery draws from:

  • Canonical practitioner blogs (Anthropic / OpenAI / Google DeepMind / Meta AI engineering blogs; Chip Huyen, Eugene Yan, Hamel Husain, Shreya Shankar, Lilian Weng, Simon Willison, Sebastian Raschka, Jason Liu, Phillip Carter, swyx, Armin Ronacher, Steve Yegge, Maggie Appleton, Linus Lee).
  • Conference proceedings (NeurIPS, ICML, ICLR, EMNLP, ACL, COLM, MLSys, CHI, USENIX Security).
  • arXiv and Semantic Scholar citation graphs.
  • GitHub trending with stars:>1000 filters for reference implementations.
  • Neighbouring awesome lists (cross-referenced rather than copy-pasted).
  • Community signal (r/LocalLLaMA, r/MachineLearning, Hacker News, MLOps Community Slack, Latent Space Discord).

How to propose a change

See CONTRIBUTING.md. File an issue (using the templates) for discussion before large restructuring PRs.