- https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf
- https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf
- https://www.oneusefulthing.org/p/making-ai-work-leadership-lab-and
- Andrej Karpathy Home
- Andrej Karpathy Blog
- Dr. Furu Wei Home
- Dr. Furu Wei AGI
- Hazy Research - Stanford
- https://vinija.ai/
- https://simonwillison.net/
- https://lilianweng.github.io/
- https://mcginniscommawill.com/
- https://mlu-explain.github.io/
- https://distill.pub/
- https://themlbook.com/
- https://vinija.ai/concepts/index.html
- https://bbycroft.net/llm
- https://thelmbook.com/
- Andrej Karpathy Deep Dive into LLMs
- Guide to Reasoning Models
Stanford Online's Playlists (look for AI collections):
Stanford CS229 Links
Stanford CS25 Links
- Claude Code
- Mastering Claude Code in 30 minutes (Anthropic)
- Amp Code
- OpenCode
- VS Code with GitHub Copilot
- Windsurf
- Cursor
- [RooCode](https://roocode.com/
- Cline
- https://github.com/humanlayer/12-factor-agents
- https://github.com/microsoft/ai-agents-for-beginners
- https://github.com/karpathy/LLM101n
- https://github.com/anthropics/anthropic-cookbook/tree/main/patterns/agents
- https://ampcode.com/how-to-build-an-agent
- https://www.anthropic.com/engineering/building-effective-agents
- https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
- https://github.com/NirDiamant/agents-towards-production
- Hugging Face Agents Course
- Tiny Agents - Python
- Tiny Agents - JS
- https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf
- https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
- https://github.com/punkpeye/awesome-mcp-servers
- https://github.com/punkpeye/awesome-mcp-clients
- https://github.com/punkpeye/awesome-mcp-devtools
- https://engineering.block.xyz/blog/blocks-playbook-for-designing-mcp-servers
- https://modelcontextprotocol.io/introduction
- https://github.com/open-webui/mcpo
- https://github.com/ollama/ollama
- https://github.com/vllm-project/vllm
- Docker Model Runner + Go
- https://github.com/lmstudio-ai
- https://github.com/nomic-ai/gpt4all
- https://docsbot.ai/models
- https://artificialanalysis.ai/
- https://web.lmarena.ai/leaderboard
- https://models.dev
- https://livecodebench.github.io/index.html
- Berkeley Function Calling Leaderboard
| Year | Paper Title | Publication Details | Link |
|---|---|---|---|
| 1993 | Keeping Neural Networks Simple by Minimizing the Description Length of the Weights | NIPS 1993 | |
| 2004 | A Tutorial Introduction to the Minimum Description Length Principle | Online Publication | arXiv |
| 2008 | Machine Super Intelligence | PhD Thesis | Google Drive |
| 2011 | The First Law of Complexodynamics | Blog Post | scottaaronson.blog |
| 2012 | ImageNet Classification with Deep Convolutional Neural Networks | NIPS | |
| 2014 | Quantifying the Rise and Fall of Complexity in Closed Systems: The Coffee Automaton | arXiv | arXiv |
| 2014 | Neural Turing Machines | arXiv | arXiv |
| 2015 | Recurrent Neural Network Regularization | arXiv | arXiv |
| 2015 | The Unreasonable Effectiveness of Recurrent Neural Networks | Blog Post | karpathy.github.io |
| 2015 | Pointer Networks | arXiv | |
| 2015 | Understanding LSTM Networks | Blog Post | |
| 2015 | Deep Speech 2: End-to-End Speech Recognition in English and Mandarin | PMLR | |
| 2015 | Deep Residual Learning for Image Recognition | arXiv | arXiv |
| 2016 | Order Matters: Sequence to Sequence for Sets | arXiv | arXiv |
| 2016 | Multi-Scale Context Aggregation by Dilated Convolutions | arXiv | arXiv |
| 2016 | Neural Machine Translation by Jointly Learning to Align and Translate | arXiv | arXiv |
| 2016 | Identity Mappings in Deep Residual Networks | arXiv | arXiv |
| 2017 | Variational Lossy Autoencoder | arXiv | arXiv |
| 2017 | Kolmogorov Complexity and Algorithmic Randomness | Henry Steinitz | |
| 2017 | Neural Message Passing for Quantum Chemistry | arXiv | arXiv |
| 2017 | A Simple Neural Network Module for Relational Reasoning | arXiv | arXiv |
| 2017 | Attention Is All You Need | arXiv | arXiv |
| 2018 | The Annotated Transformer | Workshop Paper | nlp.seas.harvard.edu |
| 2018 | Relational Recurrent Neural Networks | arXiv | arXiv |
| 2018 | GPipe: Easy Scaling with Micro-Batch Pipeline Parallelism | arXiv | arXiv |
| 2020 | Scaling Laws for Neural Language Models | arXiv | arXiv |
| 2020 | Dense Passage Retrieval for Open-Domain Question Answering | arXiv | arXiv |
| 2020 | Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks | arXiv | arXiv |
| 2023 | Lost in the Middle: How Language Models Use Long Contexts | arXiv | arXiv |
| 2023 | The Perils & Promises of Fact-checking with Large Language Models | arXiv | arXiv |
| 2023 | Zephyr: Direct Distillation of LM Alignment | arXiv | arXiv |
| 2023 | Better & Faster Large Language Models Via Multi-token Prediction | arXiv | arXiv |
I created a NotebookLM audio overview of these papers. Remember that it's AI generated and not a full substitute for reading and understanding the studies:
| Year | Paper Title | Publication Details | Link |
|---|---|---|---|
| 2025 | The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity (Apple) | arXiv | arXiv |
| 2025 | The Illusion of the Illusion of Thinking (Anthropic) | arXiv | arXiv |
| Year | Paper Title | Link | GitHub (if found) |
|---|---|---|---|
| 2020 | T5 | GitHub | |
| 2020 | GPT‑3 | N/A | |
| 2020 | RAG | GitHub | |
| 2022 | Chain-of-Thought Prompting | N/A | |
| 2022 | Constitutional AI | GitHub | |
| 2023 | Gorilla: Large Language Model Connected with Massive APIs | arXiv | N/A |
| 2023 | GPT‑4 Technical Report | N/A | |
| 2023 | Llama 2 | GitHub | |
| 2023 | Instruction Tuning Survey | GitHub | |
| 2023 | Direct Preference Optimization (DPO) | GitHub | |
| 2024 | The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits | arXiv | N/A |
| 2024 | Mixtral of Experts | N/A | |
| 2024 | Learning to Retrieve In‑Context Examples | GitHub | |
| 2024 | xLSTM | GitHub | |
| 2024 | Visual Autoregressive Modeling | N/A | |
| 2024 | Learning Interactive Real‑World Simulators | GitHub | |
| 2024 | Debating with More Persuasive LLMs | GitHub | |
| 2025 | M1: Towards Scalable Test-Time Compute with Mamba Reasoning Models | arXiv | N/A |
| 2025 | PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformers | arXiv | N/A |
| 2025 | Self-Adapting Language Models | arXiv | N/A |
| 2025 | RAG+: Enhancing Retrieval-Augmented Generation with Application-Aware Reasoning | arXiv | N/A |
| 2025 | Advances in LLMs with Focus on Reasoning, Adaptability, Efficiency and Ethics | arXiv | N/A |
| 2025 | Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities. | N/A | |
| 2025 | Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives | arXiv | N/A |
| 2025 | Play to Generalize: Learning to Reason Through Game Play | arXiv | N/A |
| 2025 | Reasoning by Superposition: A Theoretical Perspective on Chain of Continuous Thought | arXiv | N/A |
| 2025 | Reinforcement Pre-Training | arXiv | N/A |
| 2025 | Build the web for agents, not agents for the web | arXiv | N/A |
| 2025 | Large Language Models and Emergence: A Complex Systems Perspective | arXiv | N/A |
| 2026 | Large Language Model Reasoning Failures | arXiv | GitHub |
| 2026 | FullStack-Agent: Enhancing Agentic Full-Stack Web Coding via Development-Oriented Testing and Repository Back-Translation | arXiv | GitHub |
| Year | Paper Title | PDF Link | GitHub Link |
|---|---|---|---|
| 2013 | Efficient Estimation of Word Representations in Vector Space | GitHub | |
| 2014 | Generative Adversarial Networks (GANs) | GitHub | |
| 2015 | ImageNet Large Scale Visual Recognition Challenge | N/A | |
| 2018 | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | GitHub | |
| 2019 | RoBERTa: A Robustly Optimized BERT Pretraining Approach | N/A |
| Year | Paper Title | Link |
|---|---|---|
| 2025 | Future of Work with AI Agents: Auditing Automation and Augmentation Potential across the U.S. Workforce | arXiv |
| 2025 | Don't Pay Attention | arXiv |