Awesome Agentic Engineering Resources

A curated list of high-signal resources — articles, books, courses, cookbooks, papers, playbooks, benchmarks, talks, podcasts, and newsletters — for agentic engineering and AI engineering.

This is a resources list, not a tools list. Open-source tools for building agentic systems live in the sister list awesome-production-agentic-systems; production ML tooling lives in awesome-production-machine-learning. This list covers the learning, design, and operational resources that sit alongside those tools — including both:

Agentic engineering focuses on using AI agents to do software engineering (Copilot, Cursor, Claude Code, Aider, Cline, Windsurf, Codex; spec-driven development; context engineering; agent IDE rules and memory files; SWE benchmarks). AI / agentic systems engineering focuses on building agentic and LLM-powered systems (architecture, RAG, memory, tool use & MCP, orchestration, multi-agent coordination, evaluation, observability, guardrails, safety, fine-tuning, inference, product/UX, economics, teams).

You can keep up to date by watching this repo for the monthly releases summarising newly added resources 🤩

This list was proposed in EthicalML/awesome-production-machine-learning#709 as a sister list focused on resources rather than tools.

Legend

Resources are tagged with icons so you can scan and filter at a glance:

Quick links to sections on this page

Topic Coverage Matrix

Resources are organised as a matrix: the top-level sections above (rows) are resource types, and each section is sub-divided by topic. The 21 topics, T1–T21, are shared across sections. This lets you read vertically ("what papers exist on RAG?") or horizontally ("where do I find resources on Coding Agents?").

Topics:

Coverage (● = populated, ○ = opportunistic / partial, — = out of scope for that row):

The Trending / What's New, Milestones Timeline, Governance & Responsible AI, Product / UX / Economics, and Teams, Hiring & Org Design sections collapse across topics and are presented as curated lists rather than matrix cells.

Contributing to the list

Please review our CONTRIBUTING.md before submitting a PR — it explains the one-line description style, how to pick the right row/topic cell, and the quality bar for inclusion. Thank you to the community for supporting the list's growth 🚀

Want to receive recurring updates on this repo and other advancements

Main Content

⭐ Trending / What's New

Rotating pinned items: the most-discussed agentic & AI-engineering resources of the current cycle. Refreshed regularly — see CONTRIBUTING.md for nomination criteria.

• ⭐ 🆓 A practical guide to building agents — OpenAI (2025). 30-page PDF covering when (and when not) to build agents, tool design, guardrails, and human-in-the-loop patterns.
• 🆓 AGENTS.md — Community standard (2025) for per-repo agent instructions, now read by Claude Code, Codex, Aider, Cursor, Cline, Windsurf and others.
• ⭐ 🆓 Building effective agents — Anthropic (2024). The most-cited reference for agent design patterns (augmented LLM, prompt chaining, routing, parallelisation, orchestrator-workers, evaluator-optimiser, autonomous agents). Start here before any other agent reading.
• 🆓 Claude Code: Best practices for agentic coding — Anthropic (2025). CLAUDE.md, slash-commands, headless mode, custom permissions — the canonical how-to-use-Claude-Code reference.
• 🆓 How to build an agent — Thorsten Ball / Amp (2025). Viral step-by-step implementation of a tool-using coding agent in ~400 lines of Go, demystifying "what is an agent" in code.
• ⭐ 🆓 How we built our multi-agent research system — Anthropic (2025). Production retrospective on Claude's multi-agent research mode: orchestrator/subagent split, prompt engineering for agents, evaluation and failure modes.
• ⭐ 🆓 The bitter lesson of AI agents / Agentic Coding: The Future of Software Development with Agents — Armin Ronacher (2025). Widely-shared essays on what it actually feels like to ship with agentic coding tools day-to-day.
• 🆓 The new code — Sean Grove / OpenAI on Latent Space (2025). Specs-as-code: the spec is the new artefact, models are the compiler. Heavily cited in the AGENTS.md / spec-kit discussion.

🧭 Core & Foundations

Canonical "what is agentic engineering / AI engineering" reading. Start here.

T1 · Coding Agents & AI-Assisted Development

• ⭐ 🆓 Building effective agents — Anthropic. The reference taxonomy of agent design patterns (workflows vs. agents).
• ⭐ 🆓 Claude Code: Best practices for agentic coding — Anthropic. CLAUDE.md, tools, slash-commands, headless mode.
• 🆓 Here's how I use LLMs to help me write code — Simon Willison. Grounded, practice-first account of daily LLM-assisted development.
• 🆓 How to build an agent — Thorsten Ball. A working coding agent in ~400 lines; the clearest "agents are not magic" walkthrough.

T2 · Spec-Driven Development & Context Engineering

• 🆓 AGENTS.md — Community standard for per-repo agent instructions.
• 🆓 spec-kit — GitHub's toolkit and essay set on spec-driven development with coding agents.
• ⭐ 🆓 The new code — Sean Grove (OpenAI) on Latent Space. The canonical "specs are the new code" essay.
• 🆓 The rise of "context engineering" — LangChain. Why prompt engineering became context engineering.

T6 · LLM Application Architecture & System Design

• ⭐ 📘 💰 AI Engineering — Chip Huyen (O'Reilly, 2025). The textbook for building LLM applications end-to-end.
• 🆓 Emerging Architectures for LLM Applications — a16z. The widely-shared reference diagram for the LLM app stack.
• ⭐ 🆓 Patterns for Building LLM-based Systems & Products — Eugene Yan. Evaluation, RAG, fine-tuning, caching, guardrails, defensive UX, collecting feedback — the reference pattern catalogue.
• 🆓 What We Learned from a Year of Building with LLMs — Yan, Bensal, Bhawal, Husain, Shankar (2024). Tactical, operational, and strategic lessons distilled from shipping.

T7 · Prompt Engineering

• 🆓 Anthropic: Prompt engineering overview — Anthropic's practical guide for Claude.
• 🆓 OpenAI: Prompt engineering — OpenAI official guide.
• ⭐ 🆓 Prompt Engineering — Lilian Weng (OpenAI). The systematic taxonomy.
• 🆓 Prompt Engineering Guide — DAIR.AI. Continuously updated, with per-technique deep-dives.

T8 · Retrieval-Augmented Generation (RAG)

• ⭐ 🆓 Advanced RAG Techniques / Pinecone Learn — Pinecone. The hub for RAG primers and patterns.
• 🆓 RAG is more than just embedding search — Jason Liu. Systems-view RAG: query understanding, tool routing, evaluation.
• ⭐ 📄 🆓 Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks — Lewis et al. (2020). The original RAG paper.
• 🆓 Retrieval-Augmented Generation for LLMs: A Survey — Gao et al. (2023). The reference survey.

T10 · Tool Use, Function Calling & MCP

• 🆓 Function calling guide — OpenAI. The canonical reference for structured tool calls.
• ⭐ 🆓 Introducing the Model Context Protocol — Anthropic (2024). The canonical introduction to MCP.
• ⭐ 🆓 Model Context Protocol — Specification — Open protocol docs and SDKs.
• 📄 🆓 Toolformer: Language Models Can Teach Themselves to Use Tools — Schick et al. (2023). The foundational tool-use paper.

T11 · Orchestration, Planning & Design Patterns

• ⭐ 🆓 Building effective agents — Anthropic. The orchestration pattern taxonomy.
• 🆓 LLM Powered Autonomous Agents — Lilian Weng. The canonical deep-dive on planning, memory, and tool use in agent loops.
• 📄 🆓 ReAct: Synergizing Reasoning and Acting in Language Models — Yao et al. (2022). The foundational reason+act loop.
• 📄 🆓 The Rise and Potential of LLM Based Agents: A Survey — Xi et al. (2023). Survey of agent architectures and components.

T13 · Evaluation & Testing

• 📄 🆓 Judging LLM-as-a-Judge — Zheng et al. (2023). The foundational LLM-as-judge paper (MT-Bench, Chatbot Arena).
• 🆓 Task-Specific LLM Evals that Do & Don't Work — Eugene Yan. A pragmatic survey of eval techniques per task type.
• 🆓 Who Validates the Validators? Aligning LLM-Assisted Evaluation of LLM Outputs with Human Preferences — Shankar et al. (2024). How to make LLM-judges trustworthy.
• ⭐ 🆓 Your AI Product Needs Evals — Hamel Husain. The most-cited essay on why and how to build evals for LLM products.

🗓️ Milestones Timeline

Dated, field-defining events that shaped agentic & AI engineering.

👥 Communities

Discords, Slacks, forums, and meetups where practitioners gather.

• 🆓 AI Dev Board — Community-curated hub for AI engineering resources and discussions.
• 🆓 AI Engineer World's Fair / Latent Space Discord — Practitioner community anchoring the AI Engineer conference series.
• 🆓 Anthropic Discord — Official Claude / Claude Code / MCP community.
• 🆓 Cursor Community Forum — User-driven forum for Cursor rules, MCP, and workflows.
• 🆓 EleutherAI Discord — Open research community; strong training/interpretability discussion.
• 🆓 Hacker News — Filter for "LLM", "agent", "Claude", "Cursor" — where engineering-side essays trend.
• 🆓 Hugging Face Discord & Forums — Transformers, TRL, PEFT, model-hub discussions.
• 🆓 LangChain Discord — Heavy day-to-day Q&A on agent orchestration, RAG, evaluation, MCP.
• 🆓 LlamaIndex Discord — RAG-centric builder community with active reference-impl discussion.
• 🆓 MLOps Community — Slack + podcast + meetups; the biggest practitioner community at the ops/engineering intersection. Active agent and LLM-ops channels.
• 🆓 r/LocalLLaMA — The definitive open-weights / local-inference forum; fastest signal for new models, quantisation, and serving.
• 🆓 r/MachineLearning — Academic and practitioner mix; where new papers and threads get dissected.

🧑‍🎓 Courses

Structured courses — free and paid, university and industry.

T1 · Coding Agents & AI-Assisted Development

• ⭐ 🧑‍🎓 🆓 AI Python for Beginners — DeepLearning.AI (Andrew Ng). Gateway to AI-assisted coding.
• 🧑‍🎓 🆓 GitHub Copilot Fundamentals — Microsoft Learn. Official training path.
• 🧑‍🎓 🆓 Pair Programming with a Large Language Model — DeepLearning.AI + Google.

T4 · SWE Benchmarks & Coding Evaluation

• 🧑‍🎓 🆓 Evaluating and Debugging Generative AI — DeepLearning.AI + W&B. Covers coding-eval mechanics.
• 🧑‍🎓 🆓 Mastering LLMs: Evals — Hamel Husain & Shreya Shankar (Maven). Companion evals-for-LLMs curriculum.
• 🧑‍🎓 🆓 SWE-bench tutorial — Princeton NLP. Free, self-paced walk-through of running and scoring coding evals.

T6 · LLM Application Architecture & System Design

• 🧑‍🎓 🆓 Building Systems with the ChatGPT API — DeepLearning.AI + OpenAI.
• 🧑‍🎓 🆓 CS25: Transformers United — Stanford. Seminal deep-dive seminar series.
• ⭐ 🧑‍🎓 🆓 LLM Bootcamp — Full Stack Deep Learning. Free 2-day bootcamp on building LLM apps end-to-end.

T7 · Prompt Engineering

• 🧑‍🎓 🆓 Anthropic Prompt Engineering Interactive Tutorial — Anthropic. Hands-on, notebook-based.
• ⭐ 🧑‍🎓 🆓 ChatGPT Prompt Engineering for Developers — Andrew Ng & Isa Fulford (OpenAI).
• 🧑‍🎓 🆓 Prompt Engineering Guide (DAIR.AI) — Self-paced, continuously updated.

T8 · Retrieval-Augmented Generation (RAG)

• 🧑‍🎓 🆓 Advanced Retrieval for AI with Chroma — DeepLearning.AI.
• 🧑‍🎓 🆓 Building and Evaluating Advanced RAG Applications — DeepLearning.AI + LlamaIndex + TruEra.
• 🧑‍🎓 🆓 LangChain Chat with Your Data — DeepLearning.AI + LangChain.
• 🧑‍🎓 💰 Systematically Improving RAG Applications — Jason Liu on Maven.

T10 · Tool Use, Function Calling & MCP

• 🧑‍🎓 🆓 Functions, Tools and Agents with LangChain — DeepLearning.AI + LangChain.
• 🧑‍🎓 🆓 Introduction to MCP — Anthropic official quickstart.
• 🧑‍🎓 🆓 MCP: Build Rich-Context AI Apps with Anthropic — DeepLearning.AI + Anthropic.

T11 · Orchestration, Planning & Design Patterns

• 🧑‍🎓 🆓 AI Agentic Design Patterns with AutoGen — DeepLearning.AI + Microsoft.
• 🧑‍🎓 🆓 AI Agents in LangGraph — DeepLearning.AI + LangChain.
• 🧑‍🎓 🆓 Hugging Face Agents Course — Hugging Face. Free, certifying course on agent fundamentals.

T12 · Multi-Agent Systems

• 🧑‍🎓 🆓 Building Agentic RAG with LlamaIndex — DeepLearning.AI + LlamaIndex.
• 🧑‍🎓 🆓 Multi AI Agent Systems with crewAI — DeepLearning.AI + crewAI.
• 🧑‍🎓 🆓 Practical Multi AI Agents and Advanced Use Cases with crewAI — DeepLearning.AI.

T13 · Evaluation & Testing

• ⭐ 🧑‍🎓 💰 AI Evals For Engineers & PMs — Hamel Husain & Shreya Shankar on Maven. The industry-standard evals cohort course.
• 🧑‍🎓 🆓 Automated Testing for LLMOps — DeepLearning.AI + CircleCI.
• 🧑‍🎓 🆓 Quality and Safety for LLM Applications — DeepLearning.AI + WhyLabs.

T14 · Observability, Tracing & Debugging

• 🧑‍🎓 🆓 Evaluating LLMs with Arize — Arize course hub.
• 🧑‍🎓 🆓 LangSmith Academy — LangChain. Free self-paced LangSmith courses covering tracing and evals.
• 🧑‍🎓 🆓 LLMOps — DeepLearning.AI + Google Cloud.

T15 · Guardrails & Security

• 🧑‍🎓 🆓 Prompt Injection Attacks (Learn Prompting) — Learn Prompting. Open course covering injection/jailbreak taxonomies.
• 🧑‍🎓 🆓 Red Teaming LLM Applications — DeepLearning.AI + Giskard.
• 🧑‍🎓 🆓 Safe and Reliable AI via Guardrails — DeepLearning.AI + Guardrails AI.

T16 · Safety, Alignment & Responsible AI

• 🧑‍🎓 🆓 AI Safety Fundamentals — BlueDot Impact. The standard entry curriculum.
• 🧑‍🎓 🆓 ARENA (Alignment Research Engineer Accelerator) — Hands-on alignment / interpretability.
• 🧑‍🎓 🆓 Intro to AI Safety, Remastered — Richard Ngo / BlueDot. Free reading curriculum.

T17 · Fine-tuning, Post-training & RLHF

• ⭐ 🧑‍🎓 🆓 Finetuning Large Language Models — DeepLearning.AI + Lamini.
• 🧑‍🎓 🆓 Hugging Face NLP Course (incl. RLHF chapter) — Hugging Face.
• 🧑‍🎓 🆓 Reinforcement Learning from Human Feedback — DeepLearning.AI + Google Cloud.

T18 · Inference, Serving, Cost & Latency

• 🧑‍🎓 🆓 CUDA Mode lectures — Community lectures on GPU inference internals.
• 🧑‍🎓 🆓 Efficiently Serving LLMs — DeepLearning.AI + Predibase.
• 🧑‍🎓 🆓 Quantization Fundamentals with Hugging Face — DeepLearning.AI + HF.

📘 Books

Published and in-progress books covering agentic & AI engineering.

T1 · Coding Agents & AI-Assisted Development

• ⭐ 📘 💰 AI-Assisted Programming — Tom Taulli (O'Reilly, 2024). Practical coverage of Copilot/Cursor/Claude workflows.
• 📘 💰 Prompt Engineering for Generative AI — James Phoenix & Mike Taylor (O'Reilly, 2024). Includes heavy coverage of code-generation prompting patterns.

T6 · LLM Application Architecture & System Design

• ⭐ 📘 💰 AI Engineering: Building Applications with Foundation Models — Chip Huyen (O'Reilly, 2025). The reference textbook for the field.
• 📘 💰 Designing Machine Learning Systems — Chip Huyen (O'Reilly, 2022). The prior-generation canonical ML-systems text; still essential for data/infra context.
• 📘 💰 Generative AI on AWS — Chris Fregly, Antje Barth, Shelbee Eigenbrode (O'Reilly, 2023).

T7 · Prompt Engineering

• 📘 🆓 Prompt Engineering for LLMs — John Berryman & Albert Ziegler (O'Reilly, 2024). From Copilot's original tech-lead.
• 📘 💰 The Prompt Report — Schulhoff et al. (2024). A 76-page survey that effectively functions as a book-length prompting reference.

T8 · RAG

• 📘 💰 Building LLM Apps — Valentina Alto (Wiley, 2024). RAG-heavy application text.
• 📘 💰 RAG Made Simple - Nir Diamant (2025). A visual, code-free walkthrough of 22 retrieval-augmented generation techniques explained through diagrams and analogies.
• 📘 🆓 RAG-Driven Generative AI — Denis Rothman (Packt, 2024).

T10 · Tool Use & MCP

• 📘 💰 Building Intelligent Apps with OpenAI — Olivier Caelen & Marie-Alice Blete (O'Reilly, 2024). Heavy function-calling coverage.

T11 · Orchestration & Design Patterns

• 📘 💰 Generative AI with LangChain — Ben Auffarth (Packt, 2023). Orchestration patterns end-to-end.

T13 · Evaluation

• 📘 💰 Prompt Engineering for Generative AI — Phoenix & Taylor (O'Reilly, 2024). Chapter-length eval coverage.

T15 · Guardrails & Security

• 📘 💰 Generative AI Security — Ken Huang et al. (Apress, 2024).
• 📘 💰 The Developer's Playbook for Large Language Model Security — Steve Wilson (O'Reilly, 2024). OWASP LLM Top 10 project lead's book.

T16 · Safety, Alignment & Responsible AI

• 📘 💰 Human Compatible — Stuart Russell (2019). The foundational alignment argument.
• 📘 💰 The Alignment Problem — Brian Christian (2020). The canonical popular-press primer.

T17 · Fine-tuning & Post-training

• ⭐ 📘 💰 Build a Large Language Model (From Scratch) — Sebastian Raschka (Manning, 2024). The reference hands-on text.
• 📘 💰 Hands-On Large Language Models — Jay Alammar & Maarten Grootendorst (O'Reilly, 2024).

T18 · Inference & Serving

• 📘 💰 Efficient Processing of Deep Neural Networks — Sze et al. (Morgan & Claypool). Hardware/inference reference.

T20 · Product & UX

• 📘 💰 Designing Machine Learning Systems — Chip Huyen. Includes pragmatic product/UX chapters.
• 📘 💰 Human-AI Interaction Design — IxDF topic hub.

T21 · Economics, Teams & Org

• 📘 💰 Managing Machine Learning Projects — Simon Thompson (Manning).
• 📘 🆓 The Pragmatic Engineer's AI coverage — Gergely Orosz. Regularly-updated editorial that functions as a rolling book on AI-engineering org design.

✍️ Articles & Essays

Long-form writing from canonical authors and engineering teams.

T1 · Coding Agents & AI-Assisted Development

• 🆓 Agentic Coding: The Future of Software Development — Armin Ronacher.
• ⭐ 🆓 Here's how I use LLMs to help me write code — Simon Willison.
• 🆓 Revenge of the junior developer — Steve Yegge (Sourcegraph).
• 🆓 The death of the stubborn developer — Steve Yegge.

T2 · Spec-Driven Development & Context Engineering

• 🆓 Context Engineering — LangChain.
• 🆓 Spec-driven development with AI — GitHub Blog.
• ⭐ 🆓 The new code — Sean Grove / Latent Space.
• 🆓 The rise of "context engineering" — LangChain.

T3 · Agent IDE Rules, Memory Files & Workflows

• 🆓 Aider: Tips for using with large codebases — Aider docs.
• ⭐ 🆓 Claude Code: Best practices for agentic coding — Anthropic.
• 🆓 Cursor rules directory — Community catalogue of .cursorrules files.
• 🆓 My Claude Code setup — widely-shared CLAUDE.md + slash-command playbook.

T4 · SWE Benchmarks & Coding Evaluation

• ⭐ 🆓 Introducing SWE-bench Verified — OpenAI.
• 🆓 Measuring an AI system's ability to do ML R&D — METR.
• 🆓 The leaderboard illusion — Singh et al. on bench-gaming.
• 🆓 Why we built Terminal-Bench — Stanford / Laude.

T5 · Autonomous Software Agents

• 🆓 Devin, a software engineer — Cognition.
• 🆓 Don't build multi-agents — Cognition. Contrarian but important counterpoint to multi-agent maximalism.
• ⭐ 🆓 How we built our multi-agent research system — Anthropic.
• 🆓 SWE-agent: Agent-Computer Interfaces — Princeton NLP writeup.

T6 · LLM Application Architecture

• 🆓 Emerging Architectures for LLM Applications — a16z.
• ⭐ 🆓 Patterns for Building LLM-based Systems & Products — Eugene Yan.
• 🆓 Twelve factor agents — HumanLayer. The "12-factor app" equivalent for agent apps.
• 🆓 What We Learned from a Year of Building with LLMs — Yan/Bensal/Bhawal/Husain/Shankar.

T7 · Prompt Engineering

• 🆓 A guide to prompting Claude — Anthropic.
• ⭐ 🆓 Prompt Engineering — Lilian Weng.
• 🆓 Prompting is programming — Eugene Yan.
• 🆓 The prompt report — Learn Prompting team summary of their 76-page survey.

T8 · Retrieval-Augmented Generation (RAG)

• 🆓 Advanced RAG Techniques — Pinecone.
• 🆓 How to improve your RAG system's performance — Anyscale.
• 🆓 Practical considerations in RAG application design — Eugene Yan.
• ⭐ 🆓 RAG is more than just embedding search — Jason Liu.

T9 · Memory Systems & Long-Context

• 🆓 Extending Context Length in LLMs — Hugging Face.
• ⭐ 🆓 Lost in the Middle: How Language Models Use Long Contexts — Liu et al.
• 🆓 Memory for agents — LangChain.
• 🆓 The agentic memory stack — Letta (MemGPT).

T10 · Tool Use, Function Calling & MCP

• 🆓 Designing MCP servers that agents actually use — Phil Schmid.
• 🆓 Function calling with LLMs: a practical guide — DAIR.AI.
• ⭐ 🆓 Introducing the Model Context Protocol — Anthropic.
• 🆓 Tool use is eating the world — Latent Space.

T11 · Orchestration & Design Patterns

• 🆓 Agent design patterns — Andrew Ng, The Batch series.
• 🆓 AI agent frameworks — Latent Space comparative review.
• 🆓 Building effective agents — Anthropic.
• ⭐ 🆓 LLM Powered Autonomous Agents — Lilian Weng.
• 🆓 Retry budgets: why 20% per-step failure doubles your token bill — Loop & Retry.

T12 · Multi-Agent Systems & Coordination

• 🆓 AutoGen: Enabling next-gen LLM applications — Microsoft.
• 🆓 Don't build multi-agents — Cognition.
• ⭐ 🆓 How we built our multi-agent research system — Anthropic.
• 🆓 Multi-agent workflows — LangChain.

T13 · Evaluation & Testing

• 🆓 Creating a LLM-as-a-Judge that drives business results — Hamel Husain.
• 🆓 LLM evals: everything I learned in 12 months — Shreya Shankar.
• 🆓 Task-specific LLM evals that do & don't work — Eugene Yan.
• ⭐ 🆓 Your AI product needs evals — Hamel Husain.

T14 · Observability, Tracing & Debugging

• 🆓 How Honeycomb uses LLMs for product experiences — Phillip Carter.
• 🆓 Logfire: observability for the LLM era — Pydantic.
• ⭐ 🆓 So you want to build an LLM observability platform — Hamel Husain (subsection of evals post; foundational).
• 🆓 The OpenTelemetry Gen AI semantic conventions — OTel.

T15 · Guardrails & Security

• 🆓 OWASP Top 10 for LLM Applications — OWASP.
• ⭐ 🆓 Prompt injection series — Simon Willison. Canonical ongoing series.
• 🆓 Red teaming LLMs — Hugging Face.
• 🆓 Universal and Transferable Adversarial Attacks on Aligned LLMs — Zou et al. (GCG attack).

T16 · Safety, Alignment & Responsible AI

• 🆓 Anthropic's Responsible Scaling Policy — Anthropic.
• ⭐ 🆓 Core Views on AI Safety — Anthropic.
• 🆓 Preparedness Framework — OpenAI.
• 🆓 Scalable oversight via debate & recursive reward modelling — DeepMind Safety Research.

T17 · Fine-tuning, Post-training & RLHF

• ⭐ 🆓 Ahead of AI — Sebastian Raschka. The canonical fine-tuning / post-training deep-dives.
• 🆓 DPO: Your language model is secretly a reward model — Rafailov et al.
• 🆓 The alignment handbook — Hugging Face.
• 🆓 The Novice's LLM Training Guide — Community reference.

T18 · Inference, Serving, Cost & Latency

• 🆓 Everything I've learned about efficient LLM inference — Baseten engineering blog.
• 🆓 GPU performance for LLM inference — vLLM team blog.
• 🆓 LLM Inference Speed of Light — Arseny Kapoulkine.
• ⭐ 🆓 Transformer Inference Arithmetic — Kipply.

T19 · Voice, Multi-modal & Embodied Agents

• 🆓 Building a voice agent with LiveKit — LiveKit Agents docs.
• ⭐ 🆓 Hello GPT-4o — OpenAI.
• 🆓 Moshi: a speech-text foundation model — Kyutai.
• 🆓 Voice-first LLM products — Latent Space.

T20 · Product, UX & Human-AI Interaction

• 🆓 Building products with AI: UX lessons / thesephist.com essays — Linus Lee.
• 🆓 Generative AI: Design Patterns (NNGroup) — Nielsen Norman Group.
• ⭐ 🆓 Maggie Appleton essays — Canonical AI-UX thinking.
• 🆓 Microsoft HAX guidelines for human-AI interaction — Microsoft Research.

T21 · Economics, Teams, Hiring & Org Design

• 🆓 16 Changes to the Way Enterprises Build Software with AI — a16z.
• 🆓 a16z AI canon — a16z.
• ⭐ 🆓 AI engineering org design — Gergely Orosz, Pragmatic Engineer.
• 🆓 Building an AI team — Eugene Yan.

🛠️ Tutorials & Cookbooks

Hands-on, code-first guides and official cookbooks from model providers and framework authors.

T1 · Coding Agents & AI-Assisted Development

• 🛠️ 🆓 Aider tutorials — Aider docs.
• ⭐ 🛠️ 🆓 Claude Code cookbook — Anthropic.
• 🛠️ 🆓 Continue.dev recipes — Continue.

T2 · Spec-Driven Development

• 🛠️ 🆓 AGENTS.md examples — Example AGENTS.md files for common stacks.
• 🛠️ 🆓 GitHub spec-kit — The official spec-driven-development toolkit.

T3 · Agent IDE Rules & Workflows

• 🛠️ 🆓 awesome-cursorrules — Curated .cursorrules examples.
• 🛠️ 🆓 Claude Code slash-commands cookbook — Anthropic.

T5 · Autonomous Software Agents

• 🛠️ 🆓 OpenHands (formerly OpenDevin) — All Hands AI.
• 🛠️ 🆓 SWE-agent quickstart — Princeton NLP.

T6 · LLM Application Architecture

• 🛠️ 🆓 Anthropic Cookbook — Claude recipes.
• 🛠️ 🆓 Gemini API Cookbook — Google.
• 🛠️ 🆓 Hugging Face Open-Source AI Cookbook — Hugging Face.
• ⭐ 🛠️ 🆓 OpenAI Cookbook — The reference recipe library for OpenAI APIs.

T7 · Prompt Engineering

• 🛠️ 🆓 Anthropic prompt-engineering interactive tutorial — Notebook-based.
• 🛠️ 🆓 Prompt Engineering Guide notebooks — DAIR.AI.

T8 · Retrieval-Augmented Generation (RAG)

• 🛠️ 🆓 Advanced RAG notebooks — Nir Diamant. 30+ advanced RAG recipes.
• 🛠️ 🆓 LangChain RAG from scratch — LangChain.
• ⭐ 🛠️ 🆓 LlamaIndex tutorials — LlamaIndex.
• 🛠️ 🆓 Pinecone RAG handbook — Pinecone.

T9 · Memory Systems

• 🛠️ 🆓 LangGraph memory — LangChain.
• 🛠️ 🆓 Letta (MemGPT) cookbook — Letta.
• 🛠️ 🆓 Mem0 quickstart — Mem0.
• 🛠️ 🆓 Mnemoverse quick setup - Mnemoverse. Wiring a memory server into Claude Code, Cursor, VS Code, Windsurf or ChatGPT over MCP, by API key or OAuth, then checking store, recall and feedback.

T10 · Tool Use & MCP

• 🛠️ 🆓 awesome-mcp-servers — Community reference-servers catalogue.
• ⭐ 🛠️ 🆓 MCP quickstart — Anthropic.
• 🛠️ 🆓 OpenAI function calling cookbook — OpenAI.

T11 · Orchestration & Patterns

• 🛠️ 🆓 Anthropic building-effective-agents examples — Anthropic.
• 🛠️ 🆓 LangGraph tutorials — LangChain.
• 🛠️ 🆓 LlamaIndex agent tutorials — LlamaIndex.

T12 · Multi-Agent Systems

• 🛠️ 🆓 AutoGen notebook gallery — Microsoft.
• 🛠️ 🆓 CrewAI examples — CrewAI.
• 🛠️ 🆓 LangGraph multi-agent examples — LangChain.

T13 · Evaluation & Testing

• ⭐ 🛠️ 🆓 Hamel Husain's evals repo — Companion code to the evals course.
• 🛠️ 🆓 LangSmith evals tutorials — LangChain.
• 🛠️ 🆓 RAGAS tutorials — RAG-specific eval cookbook.

T14 · Observability

• 🛠️ 🆓 Arize Phoenix tutorials — Arize.
• 🛠️ 🆓 Langfuse cookbook — Langfuse.
• 🛠️ 🆓 Logfire LLM tracing tutorials — Pydantic.

T15 · Guardrails & Security

• 🛠️ 🆓 Guardrails AI cookbook — Guardrails AI.
• 🛠️ 🆓 NVIDIA NeMo Guardrails — NVIDIA.
• 🛠️ 🆓 Prompt injection CTFs (Gandalf) — Lakera. Hands-on red-team practice.

T17 · Fine-tuning & Post-training

• 🛠️ 🆓 Axolotl examples — Axolotl.
• 🛠️ 🆓 Hugging Face TRL tutorials — TRL.
• ⭐ 🛠️ 🆓 Unsloth notebooks — Fast fine-tuning recipes.

T18 · Inference & Serving

• 🛠️ 🆓 llama.cpp server — ggerganov.
• 🛠️ 🆓 TensorRT-LLM tutorials — NVIDIA.
• 🛠️ 🆓 vLLM examples — vLLM.

T19 · Voice & Multimodal

• 🛠️ 🆓 LiveKit Agents examples — LiveKit.
• 🛠️ 🆓 OpenAI Realtime API cookbook — OpenAI.
• 🛠️ 🆓 Pipecat — Daily. Voice-agent framework with extensive cookbook.

📋 Playbooks & Design-Pattern Catalogs

Opinionated, prescriptive guides distilling design patterns and operational practices.

• 📋 🆓 12-Factor Agents — HumanLayer. Opinionated operational principles for agent apps (T6/T11).
• 📋 🆓 A practical guide to building agents — OpenAI PDF (T11).
• 📋 🆓 a16z AI canon — a16z (T20/T21).
• 📋 🆓 Agentic UX — 11 runtime lifecycle patterns for supervised delegation, organized before/while/after an agent acts, with interactive mockups, production screenshots, and an MCP server for coding agents (Daniel Albinsson, 2025).
• 📋 🆓 Agentic Kit — Eve Trust Kit: Aletheia capability portraits, aletheia-cli authority-diff CI gate, and Kit Certified stamped blueprints (Daniel Albinsson, 2026). Pairs with Agentic UX lifecycle patterns (T20).
• 📋 🆓 Anthropic's prompt engineering overview — Anthropic (T7).
• ⭐ 📋 🆓 Building effective agents — Anthropic. The canonical pattern taxonomy (T11).
• 📋 🆓 Claude Code: best practices for agentic coding — Anthropic (T1/T3).
• 📋 🆓 Instructor's RAG patterns — Jason Liu (T8).
• 📋 🆓 LangGraph design patterns — LangChain (T11/T12).
• 📋 🆓 LLM observability playbook — Hamel Husain (T13/T14).
• 📋 🆓 MITRE ATLAS — Adversarial Threat Landscape for AI Systems (T15).
• 📋 🆓 NIST AI Risk Management Framework — NIST (T16).
• 📋 🆓 OpenAI's prompt-engineering playbook — OpenAI (T7).
• 📋 🆓 OWASP Top 10 for LLM Applications — OWASP. The security-pattern catalogue (T15).
• ⭐ 📋 🆓 Patterns for Building LLM-based Systems & Products — Eugene Yan (T6).
• 📋 🆓 Prompt-injection defence patterns — Simon Willison (T15).
• 📋 🆓 RAG-Fusion, HyDE, and other advanced retrieval patterns — Nir Diamant (T8).
• 📋 🆓 The LLM inference playbook — Anyscale (T18).
• 📋 🆓 UX design patterns for AI products — Nielsen Norman Group (T20).
• ⭐ 📋 🆓 What We Learned from a Year of Building with LLMs — Yan/Bensal/Bhawal/Husain/Shankar (T6/T13).

📄 Papers & Research

Foundational papers, surveys, and benchmark papers. Includes a dated milestone-papers table.

Milestone Papers

T1 · Coding Agents & T4 · SWE Benchmarks

• 📄 🆓 AutoCodeRover: Autonomous Program Improvement — Zhang et al.
• 📄 🆓 BigCodeBench — Zhuo et al.
• 📄 🆓 LiveCodeBench — Jain et al.
• 📄 🆓 SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering — Yang et al.
• 📄 🆓 SWE-bench: Can LMs Resolve Real-World GitHub Issues? — Jimenez et al.

T5 · Autonomous SWE Agents

• 📄 🆓 Agentless: Demystifying LLM-based Software Engineering Agents — Xia et al.
• 📄 🆓 OpenHands / OpenDevin — All Hands AI.
• 📄 🆓 Voyager: An Open-Ended Embodied Agent with LLMs — Wang et al.

T6 · App Architecture

• 📄 🆓 Emerging Architectures for LLM Applications — a16z.
• 📄 🆓 The Prompt Report — Schulhoff et al.

T7 · Prompt Engineering

• 📄 🆓 Chain-of-Thought Prompting Elicits Reasoning — Wei et al.
• 📄 🆓 Large Language Models are Zero-Shot Reasoners — Kojima et al. ("Let's think step by step").
• 📄 🆓 Self-Consistency Improves CoT — Wang et al.
• 📄 🆓 Tree of Thoughts — Yao et al.

T8 · RAG

• 📄 🆓 Dense Passage Retrieval — Karpukhin et al.
• 📄 🆓 Precise Zero-Shot Dense Retrieval without Relevance Labels (HyDE) — Gao et al.
• 📄 🆓 RAG for LLMs: A Survey — Gao et al.
• 📄 🆓 Retrieval-Augmented Generation for Knowledge-Intensive NLP — Lewis et al.
• 📄 🆓 Self-RAG: Learning to Retrieve, Generate, and Critique — Asai et al.

T9 · Memory

• 📄 🆓 Generative Agents: Interactive Simulacra of Human Behavior — Park et al.
• 📄 🆓 Lost in the Middle — Liu et al.
• 📄 🆓 MemGPT: Towards LLMs as Operating Systems — Packer et al.

T10 · Tool Use & MCP

• 📄 🆓 Berkeley Function-Calling Leaderboard — UC Berkeley.
• 📄 🆓 Gorilla: LLM Connected with Massive APIs — Patil et al.
• 📄 🆓 MRKL Systems — Karpas et al.
• 📄 🆓 Toolformer — Schick et al.

T11 · Orchestration & Patterns

• 📄 🆓 ReAct: Synergizing Reasoning and Acting — Yao et al.
• 📄 🆓 Reflexion: Language Agents with Verbal Reinforcement Learning — Shinn et al.
• 📄 🆓 Self-Refine: Iterative Refinement with Self-Feedback — Madaan et al.
• 📄 🆓 The Rise and Potential of LLM-based Agents: A Survey — Xi et al.

T12 · Multi-Agent

• 📄 🆓 A Survey on LLM-based Autonomous Agents — Wang et al.
• 📄 🆓 AutoGen — Wu et al.
• 📄 🆓 CAMEL: Communicative Agents for Mind Exploration — Li et al.
• 📄 🆓 MetaGPT — Hong et al.

T13 · Evaluation

• 📄 🆓 HELM: Holistic Evaluation of Language Models — Liang et al.
• 📄 🆓 Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena — Zheng et al.
• 📄 🆓 Who Validates the Validators? — Shankar et al.

T14 · Observability

• 📄 🆓 OpenTelemetry Semantic Conventions for Generative AI — OTel.

T15 · Guardrails & Security

• 📄 🆓 Many-shot Jailbreaking — Anthropic.
• 📄 🆓 Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection — Greshake et al.
• 📄 🆓 Universal and Transferable Adversarial Attacks on Aligned LLMs (GCG) — Zou et al.

T16 · Safety & Alignment

• 📄 🆓 Concrete Problems in AI Safety — Amodei et al.
• 📄 🆓 Constitutional AI — Bai et al.
• 📄 🆓 Scalable Agent Alignment via Reward Modeling — Leike et al.

T17 · Fine-tuning & Post-training

• 📄 🆓 Constitutional AI / RLAIF — Bai et al.
• 📄 🆓 Direct Preference Optimization (DPO) — Rafailov et al.
• 📄 🆓 LoRA: Low-Rank Adaptation — Hu et al.
• 📄 🆓 QLoRA — Dettmers et al.
• 📄 🆓 Training LMs to follow instructions with human feedback (InstructGPT) — Ouyang et al.

T18 · Inference & Serving

• 📄 🆓 Efficient Memory Management for LLM Serving with PagedAttention (vLLM) — Kwon et al.
• 📄 🆓 Fast Inference from Transformers via Speculative Decoding — Leviathan et al.
• 📄 🆓 FlashAttention — Dao et al.
• 📄 🆓 SGLang: Efficient Execution of Structured Language Model Programs — Zheng et al.

T19 · Voice & Multimodal

• 📄 🆓 Moshi — Kyutai.
• 📄 🆓 Robust Speech Recognition via Large-Scale Weak Supervision (Whisper) — Radford et al.
• 📄 🆓 Seamless: Multilingual Expressive and Streaming Speech Translation — Meta.

T20 · Product & UX

• 📄 🆓 Guidelines for Human-AI Interaction — Amershi et al. (Microsoft Research, CHI 2019).

🧪 Benchmarks & Leaderboards

Public benchmarks and leaderboards for coding agents, tool use, RAG, evaluation, and more.

T1 / T4 · Coding Agents & SWE Benchmarks

• 🧪 🆓 BigCodeBench — Practical programming with diverse function calls.
• 🧪 🆓 HumanEval+ / EvalPlus — Strengthened HumanEval.
• 🧪 🆓 LiveCodeBench — Rolling contamination-free coding benchmark.
• 🧪 🆓 MLE-bench — OpenAI. Kaggle-style ML engineering benchmark.
• ⭐ 🧪 🆓 SWE-bench — Real-world GitHub-issue resolution benchmark; Verified subset is the de-facto industry standard.
• 🧪 🆓 Terminal-Bench — Stanford / Laude. Long-horizon terminal task benchmark.

T5 · Autonomous Agents

• 🧪 🆓 AgentBench — Tsinghua. Broad agent capability benchmark.
• 🧪 🆓 ClawBench — 153 everyday online tasks on 144 live websites with final requests intercepted for safe evaluation.
• 🧪 🆓 GAIA — General AI Assistants benchmark.
• 🧪 🆓 MLE-bench — ML-engineering agents.
• 🧪 🆓 OSWorld — Desktop OS-controlling agents.
• 🧪 🆓 WebArena / VisualWebArena — Web-navigation agents.
• 🧪 🆓 ClawBench — Live-production web-agent benchmark with 283 everyday browser tasks across 163 websites; submission interception prevents real-world side effects. Code · paper.

T8 · RAG

• 🧪 🆓 ARES — Automated RAG evaluation.
• 🧪 🆓 BEIR — Zero-shot IR benchmark.
• 🧪 🆓 MTEB — Massive Text Embedding Benchmark.
• 🧪 🆓 RAGAS — Framework and leaderboard for RAG eval.

T10 · Tool Use & Function Calling

• 🧪 🆓 API-Bank — Alibaba. Tool-augmented assistants.
• 🧪 🆓 τ-bench — Sierra. Tool-agent-user interaction benchmark.
• 🧪 🆓 Berkeley Function-Calling Leaderboard (BFCL) — UC Berkeley.

T11 · Orchestration / T12 · Multi-Agent

• 🧪 🆓 AgentBench — General agent-capability.
• 🧪 🆓 AgentBoard — HKUST. Analytic, fine-grained agent eval.

T13 · Evaluation

• 🧪 🆓 Chatbot Arena / LMSYS Arena — Human-preference leaderboard.
• 🧪 🆓 HELM — Stanford CRFM. Holistic evaluation.
• 🧪 🆓 MMLU-Pro — Harder MMLU.
• 🧪 🆓 MT-Bench — LLM-as-judge multi-turn.
• 🧪 🆓 StructEval — TIGER-AI Lab. Benchmarks LLM structural fidelity across 18 text and renderable formats using syntax, structural, and visual checks (TMLR 2025).

T15 · Guardrails & Security

• 🧪 🆓 AdvBench / HarmBench — CAIS. Adversarial / red-team benchmarks.
• 🧪 🆓 JailbreakBench — Chao et al.
• 🧪 🆓 PurpleLlama CyberSecEval — Meta.

T16 · Safety & Alignment

• 🧪 🆓 BBQ — Bias benchmark.
• 🧪 🆓 ToxiGen — Toxicity.
• 🧪 🆓 TruthfulQA — Truthfulness benchmark.

T18 · Inference

• 🧪 🆓 LLMPerf — Anyscale. Throughput/latency tool.
• 🧪 🆓 MLPerf Inference — MLCommons. Industry-standard serving benchmark.

T19 · Voice & Multimodal

• 🧪 🆓 Dynabench speech — Live speech-model benchmarks.
• 🧪 🆓 MMMU — Multimodal multidiscipline benchmark.
• 🧪 🆓 VideoMME — Video understanding.

🏗️ Reference Implementations & Case Studies

Public production write-ups and canonical reference repositories that teach by example.

T1 / T3 · Coding Agents & IDE Rules

• 🏗️ 🆓 Aider — Reference terminal coding agent with detailed engineering blog.
• ⭐ 🏗️ 🆓 Claude Code — Anthropic's reference agentic CLI.
• 🏗️ 🆓 Cline — Open-source autonomous coding agent.
• 🏗️ 🆓 OpenHands — All Hands AI. Open-source autonomous SWE agent.

T2 · Spec-Driven Dev

• 🏗️ 🆓 GitHub spec-kit — Reference spec-driven toolkit.

T5 · Autonomous SWE Agents

• 🏗️ 🆓 AgentENV — Platform for running snapshot-backed agent environments at scale for agentic RL training.
• 🏗️ 🆓 Agentless — Minimal agentless baseline that beat prior agents on SWE-bench Lite.
• 🏗️ 🆓 AutoCodeRover — NUS.
• 🏗️ 🆓 SWE-agent — Princeton NLP. Reference agent for SWE-bench.

T6 · App Architecture

• 🏗️ 🆓 LangChain templates — Reference app scaffolds.
• 🏗️ 🆓 Open Interpreter — Reference local code-execution agent.
• 🏗️ 🆓 Quivr — Reference full-stack RAG assistant.

T8 · RAG

• 🏗️ 🆓 GraphRAG — Microsoft Research.
• ⭐ 🏗️ 🆓 LlamaIndex — Reference RAG framework; docs double as case studies.
• 🏗️ 🆓 RAGFlow — Production-grade RAG reference.
• 🏗️ 🆓 Verba — Weaviate reference RAG app.

T9 · Memory

• 🏗️ 🆓 Letta (MemGPT) — Reference agentic-memory implementation.
• 🏗️ 🆓 Mem0 — Reference memory layer.
• 🏗️ 🆓 Zep — Long-term memory store.

T10 · Tool Use & MCP

• 🏗️ 🆓 Anthropic MCP reference servers — The canonical reference MCP servers.
• 🏗️ 🆓 awesome-mcp-servers — Community catalogue of MCP server implementations.

T11 / T12 · Orchestration & Multi-Agent

• 🏗️ 🆓 AutoGen — Microsoft.
• 🏗️ 🆓 CrewAI — Reference role-based multi-agent.
• 🏗️ 🆓 LangGraph — Reference graph-based orchestration.
• 🏗️ 🆓 Pydantic AI — Type-safe agent framework.

T13 · Evaluation

• 🏗️ 🆓 DeepEval — Reference eval framework.
• 🏗️ 🆓 EleutherAI lm-evaluation-harness — Standard offline-eval harness.
• 🏗️ 🆓 RAGAS — RAG-specific evaluation.

T14 · Observability

• 🏗️ 🆓 Arize Phoenix — Open-source tracing + evals.
• 🏗️ 🆓 Langfuse — Open-source LLM observability.
• 🏗️ 🆓 OpenLLMetry — OTel-based LLM instrumentation.
• 🏗️ 🆓 Future AGI — Open-source platform to trace, evaluate, simulate, guard, and auto-improve AI agents.

T15 · Guardrails & Security

• 🏗️ 🆓 Guardrails AI — Reference guardrails framework.
• 🏗️ 🆓 NVIDIA NeMo Guardrails — Programmable guardrails.
• 🏗️ 🆓 Rebuff — Prompt-injection defence reference.

T17 · Fine-tuning

• 🏗️ 🆓 Axolotl — Reference fine-tuning framework.
• 🏗️ 🆓 Hugging Face alignment-handbook — Reference RLHF/DPO recipes.
• 🏗️ 🆓 LLaMA-Factory — Unified fine-tuning toolkit.
• 🏗️ 🆓 Unsloth — Fast LoRA/QLoRA reference.

T18 · Inference & Serving

• 🏗️ 🆓 llama.cpp — Reference CPU/GPU local inference.
• 🏗️ 🆓 SGLang — Structured generation serving.
• 🏗️ 🆓 TensorRT-LLM — NVIDIA reference optimised serving.
• ⭐ 🏗️ 🆓 vLLM — Reference high-throughput LLM serving.

T19 · Voice & Multimodal

• 🏗️ 🆓 LiveKit Agents — Voice-agent reference.
• 🏗️ 🆓 Pipecat — Daily's voice-agent framework.
• 🏗️ 🆓 Ultravox — Real-time speech LM.

T20 · Product & UX

• 🏗️ 🆓 assistant-ui — Reference React components for AI chat.
• 🏗️ 🆓 Open WebUI — Reference local chat UI.
• 🏗️ 🆓 Vercel AI SDK — Reference AI-UI patterns and streaming.

🎥 Talks, Workshops & Conferences

Recorded talks, workshops, and conference series worth watching.

Conference series

• ⭐ 🎥 🆓 AI Engineer Summit / World's Fair — The definitive practitioner conference; full talks on YouTube.
• 🎥 🆓 COLM — Conference on Language Modeling. New dedicated LM venue.
• 🎥 🆓 LlamaCon — Meta's open-source LLM conference.
• 🎥 🆓 MLSys — Core ML-systems conference (inference, serving).
• 🎥 🆓 NeurIPS / ICML / ICLR — Core ML research venues; most papers include recorded talks.

Canonical talks

• ⭐ 🎥 🆓 1hr Talk: Intro to LLMs (Nov 2024) — Karpathy updated "Deep Dive into LLMs".
• ⭐ 🎥 🆓 Intro to LLMs — Andrej Karpathy. The reference "how LLMs work" talk.
• ⭐ 🎥 🆓 Let's build GPT: from scratch, in code — Andrej Karpathy.
• 🎥 🆓 Stanford CS25: Transformers United — Full lecture series.
• 🎥 🆓 State of GPT — Andrej Karpathy (Microsoft Build 2023).

T1 · Coding Agents

• 🎥 🆓 AI Talks — Searchable summaries of practitioner talks on coding agents and AI-assisted development.
• 🎥 🆓 Cursor: Building the AI-first IDE — Cursor team channel.
• 🎥 🆓 Mastering Claude Code — Anthropic (Boris Cherny).
• 🎥 🆓 The future of AI coding — Latent Space talk archives.

T4 · SWE Benchmarks

• 🎥 🆓 SWE-bench at NeurIPS — Carlos Jimenez.

T6 · App Architecture

• 🎥 🆓 Emerging architectures for LLM applications — a16z (video + post).
• 🎥 🆓 State of AI Engineering — Latent Space keynotes.

T7 · Prompt Engineering

• 🎥 🆓 Anthropic: Prompt Engineering for Business Performance — Anthropic.
• 🎥 🆓 ChatGPT Prompt Engineering for Developers — Andrew Ng + OpenAI.

T8 · RAG

• 🎥 🆓 RAG at scale — LangChain channel series.
• 🎥 🆓 Systematically improving RAG applications — Jason Liu.

T10 · MCP

• 🎥 🆓 MCP at AI Engineer Summit — AI Engineer.
• 🎥 🆓 Model Context Protocol deep dive — Anthropic.

T11 / T12 · Orchestration & Multi-Agent

• 🎥 🆓 Andrew Ng: What's next for AI agentic workflows — Sequoia AI Ascent 2024.
• 🎥 🆓 LangGraph: multi-agent workflows — LangChain.

T13 · Evaluation

• 🎥 🆓 Evaluating LLM-based applications — Josh Tobin (DBRX Summit).
• 🎥 🆓 LLM Evals: MT-Bench and Chatbot Arena — LMSYS.

T14 · Observability

• 🎥 🆓 OpenTelemetry for LLMs — KubeCon / OTel community talks.

T15 / T16 · Security & Safety

• 🎥 🆓 Anthropic AI safety research — Anthropic channel.
• 🎥 🆓 Simon Willison on prompt injection — Talks + essays hub.

T17 · Fine-tuning

• 🎥 🆓 Fine-tuning workshop — Hamel Husain channel.
• 🎥 🆓 Let's reproduce GPT-2 / build the GPT tokenizer — Karpathy channel.

T18 · Inference

• 🎥 🆓 CUDA Mode lectures — Community GPU/kernel series.
• 🎥 🆓 vLLM: high-throughput LLM serving — Anyscale / UC Berkeley talks.

T19 · Voice & Multimodal

• 🎥 🆓 LiveKit voice-agent talks — LiveKit.
• 🎥 🆓 OpenAI Realtime API demos — OpenAI.

T20 · Product & UX

• 🎥 🆓 AI UX: the next frontier — NNGroup.
• 🎥 🆓 Linus Lee: tools for thought — Talks archive.

T21 · Economics & Teams

• 🎥 🆓 a16z AI portfolio talks — a16z.
• 🎥 🆓 The Pragmatic Engineer on AI teams — Gergely Orosz.

🎧 Podcasts

Recurring podcasts with strong agentic & AI-engineering coverage.

• 🎧 🆓 Chain of Thought — Conor Bronsdon. Interviews with engineers and founders on production AI agents, evaluation, infrastructure, and developer tools, with full transcripts and show notes.
• 🎧 🆓 Cognitive Revolution — Nathan Labenz. Weekly AI engineering + strategy.
• 🎧 🆓 Dwarkesh Podcast — Dwarkesh Patel. Deep interviews with top researchers.
• 🎧 🆓 Gradient Dissent — Weights & Biases. Applied-ML interviews.
• 🎧 🆓 Interconnects — Nathan Lambert. RLHF / post-training focus.
• ⭐ 🎧 🆓 Latent Space — swyx & Alessio. The AI-engineering podcast of record; guests include most major AI-lab engineers.
• 🎧 🆓 Lex Fridman Podcast — Long-form interviews with AI-lab CEOs and researchers.
• 🎧 🆓 Machine Learning Street Talk — Tim Scarfe. Technical deep-dives.
• 🎧 🆓 MLOps Community podcast — Demetrios Brinkmann. Ops-side operationalisation case studies.
• 🎧 🆓 No Priors — Sarah Guo & Elad Gil. Founders / researchers.
• ⭐ 🎧 🆓 Practical AI — Daniel Whitenack & Chris Benson. Long-running, practitioner-first.
• 🎧 🆓 Pragmatic Engineer — Gergely Orosz. AI-engineering org/hiring coverage.
• 🎧 🆓 The TWIML AI Podcast — Sam Charrington. Longest-running ML interview series.
• 🎧 🆓 Unsupervised Learning — Redpoint. AI-founder / operator conversations.

📰 Newsletters

Weekly and monthly curated newsletters.

• 📰 🆓 Ahead of AI — Sebastian Raschka. LLM research + fine-tuning deep-dives.
• 📰 🆓 AI Weekly — AI news intelligence ranking what influential experts and organizations read and share across models, agents, funding, policy, and research.
• 📰 🆓 Ben's Bites — Daily digest; founder-friendly.
• 📰 🆓 Chip Huyen's Blog — Occasional long-form on AI engineering.
• 📰 🆓 DiamantAI — Nir Diamant. Practical AI engineering and generative AI: RAG, agents, and LLM application patterns explained simply.
• 📰 🆓 Eugene Yan — Pattern / eval / RAG deep-dives.
• 📰 🆓 Hamel's Blog — Evals + applied LLMs.
• ⭐ 📰 🆓 Import AI — Jack Clark (Anthropic co-founder). Policy + research.
• 📰 🆓 Interconnects — Nathan Lambert. RLHF / post-training.
• 📰 🆓 Last Week in AI — Weekly recap.
• ⭐ 📰 🆓 Latent Space — swyx. The AI-engineering newsletter of record.
• 📰 🆓 Machine Learning Engineer Newsletter — Alejandro Saucedo. Weekly production-ML curation.
• 📰 🆓 MLOps Community newsletter — MLOps Community.
• 📰 🆓 Simon Willison's Weblog — RSS/email. Daily real-time coverage of tools and agents.
• ⭐ 📰 🆓 The Batch — Andrew Ng / DeepLearning.AI. Weekly AI-engineering digest.
• 📰 🆓 The Data Exchange — Ben Lorica.
• 📰 🆓 The Pragmatic Engineer — Gergely Orosz. AI-engineering hiring/org coverage.
• 📰 🆓 TLDR AI — Daily headlines.

🛡️ Governance, Safety & Responsible AI

Policy frameworks, safety research, red-teaming resources, and responsible-AI guidance.

Policy & frameworks

• 🆓 EU AI Act — European Commission. Official text + implementation timeline.
• ⭐ 🆓 NIST AI Risk Management Framework (AI RMF 1.0) — NIST. The foundational US framework.
• 🆓 NIST Generative AI Profile (NIST-AI-600-1) — NIST.
• 🆓 OECD AI Principles — International reference.
• 🆓 UK AI Safety Institute reports — UK AISI.

Lab safety & responsible scaling

• 🆓 Anthropic Core Views on AI Safety — Anthropic.
• ⭐ 🆓 Anthropic Responsible Scaling Policy — Anthropic.
• 🆓 Google DeepMind: Frontier Safety Framework — Google DeepMind.
• 🆓 OpenAI Preparedness Framework — OpenAI.

Security & red-teaming

• 🆓 HarmBench — CAIS.
• 🆓 MITRE ATLAS — Adversarial threat landscape for AI systems.
• 🆓 NIST Adversarial ML Taxonomy (NIST AI 100-2) — NIST.
• ⭐ 🆓 OWASP Top 10 for LLM Applications — OWASP.
• 🆓 Simon Willison's prompt-injection series — SW.

Responsible AI practice

• 🆓 Fairlearn — Open-source fairness toolkit.
• 🆓 Google Responsible AI practices — Google.
• 🆓 Microsoft Responsible AI Standard — Microsoft.
• 🆓 Partnership on AI — Multi-stakeholder org with published frameworks and incident database.

Papers & research

• 📄 🆓 Concrete Problems in AI Safety — Amodei et al.
• 📄 🆓 Constitutional AI — Bai et al.
• 📄 🆓 Red Teaming Language Models with Language Models — Perez et al.
• 📄 🆓 Sleeper Agents — Hubinger et al. (Anthropic).

🎨 Product, UX & Economics of AI

Going beyond engineering: designing for AI, human-AI interaction, and the economics of LLM applications.

Design & UX

• 🆓 Apple Human Interface Guidelines — Generative AI — Apple.
• 🆓 Google's People + AI Guidebook — Google PAIR.
• ⭐ 🆓 Guidelines for Human-AI Interaction — Amershi et al. (Microsoft Research). The canonical design heuristics.
• 🆓 Linus Lee — Essays on interfaces for tools of thought.
• 🆓 Maggie Appleton — Essays on the UX of agentic, malleable software.
• 🆓 NNGroup: Generative AI design patterns — Nielsen Norman Group.

Economics & business

• ⭐ 🆓 a16z: The Economic Case for Generative AI — a16z.
• 🆓 Artificial Analysis — Cross-provider pricing/latency/quality dashboards.
• 🆓 Epoch AI — Data on compute, cost, and scaling trends.
• 🆓 Latent Space on unit economics — Latent Space.
• 🆓 Stanford AI Index Report — Stanford HAI. Annual deep economic + research snapshot.

Product strategy

• 🆓 16 Changes to the Way Enterprises Build Software with AI — a16z.
• 🆓 AI product strategy — Lenny's Newsletter (AI tag).
• 🆓 Every Inc — Prose-heavy essays on AI product + consumer LLM UX.

🧑‍🤝‍🧑 Teams, Hiring & Org Design

How organisations structure AI-engineering work, hire for it, and operate sustainably.

• 🆓 a16z AI canon — a16z. Curated reading list for people building AI teams.
• 🆓 Building the AI Engineer role — swyx / Latent Space. The foundational essay defining "AI Engineer" as a discipline.
• 🆓 Chip Huyen: Machine learning in production — Org-design questions from production ML.
• 🆓 DeepLearning.AI AI Engineer Hiring Report — The Batch periodic coverage.
• 🆓 Emmanuel Ameisen: Building ML Powered Applications — Book + blog on AI-team building.
• 🆓 Eugene Yan: Team size and velocity — Eugene Yan.
• 🆓 GitHub: The AI-native developer — GitHub's research on workflows / productivity.
• 🆓 Shreya Shankar: Operationalizing ML — Shreya Shankar.
• 🆓 Staff Engineer — AI org posts — Will Larson & community.
• ⭐ 🆓 The Pragmatic Engineer — AI tag — Gergely Orosz. AI-engineering hiring + org design.
• 🆓 What is an AI Engineer? — Applied LLMs consortium.

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