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ContentsAppendix D

Appendix DBibliography

Bibliography

Every source cited in the book: 146 in all, grouped by kind, in Chicago style.

Each page numbers its own notes from 1 and lists them at its foot; this list gathers them all. Numbers here are only an index.

Good next steps

Research papers

  1. 1A. L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers,” IBM Journal of Research and Development 3, no. 3 (July 1959): 210–229, https://doi.org/10.1147/rd.33.0210.
  2. 2Adam Tauman Kalai, Ofir Nachum, Santosh S. Vempala, and Edwin Zhang, “Why Language Models Hallucinate” (preprint, arXiv, September 4, 2025), https://doi.org/10.48550/arXiv.2509.04664.
  3. 3Aeree Cho, Grace C. Kim, Alexander Karpekov, Alec Helbling, Zijie J. Wang, Seongmin Lee, Benjamin Hoover, and Duen Horng (Polo) Chau, “TRANSFORMER EXPLAINER: Interactive Learning of Text-Generative Models,” Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 28 (April 11, 2025): 29625–29627, https://doi.org/10.1609/aaai.v39i28.35347.
  4. 4Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever, Language Models are Unsupervised Multitask Learners (San Francisco: OpenAI, February 2019), https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf (accessed October 26, 2026).
  5. 5Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi, “The Curious Case of Neural Text Degeneration” (preprint, arXiv, April 22, 2019), https://doi.org/10.48550/arXiv.1904.09751.
  6. 6Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin, “Attention Is All You Need” (preprint, arXiv, June 12, 2017), https://doi.org/10.48550/arXiv.1706.03762.
  7. 7C. E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical Journal 27, no. 3 (July 1948): 379–423; 27, no. 4 (October 1948): 623–656, https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.
  8. 8C. E. Shannon, “Prediction and Entropy of Printed English,” Bell System Technical Journal 30, no. 1 (January 1951): 50–64, https://doi.org/10.1002/j.1538-7305.1951.tb01366.x.
  9. 9Claude E. Shannon, “Programming a Computer for Playing Chess,” The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 41, no. 314 (March 1950): 256–275, https://doi.org/10.1080/14786445008521796.
  10. 10Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio, “Neural Machine Translation by Jointly Learning to Align and Translate” (preprint, arXiv, September 1, 2014), https://doi.org/10.48550/arXiv.1409.0473.
  11. 11Ilya Sutskever, Oriol Vinyals, and Quoc V. Le, “Sequence to Sequence Learning with Neural Networks” (preprint, arXiv, September 10, 2014), https://doi.org/10.48550/arXiv.1409.3215.
  12. 12Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (2019), 4171–4186, https://doi.org/10.18653/v1/n19-1423.
  13. 13Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei, “Scaling Laws for Neural Language Models” (preprint, arXiv, January 23, 2020), https://doi.org/10.48550/arXiv.2001.08361.
  14. 14Jason Wei, Nguyen Karina, Hyung Won Chung, Yunxin Joy Jiao, Spencer Papay, Amelia Glaese, John Schulman, and William Fedus, “Measuring short-form factuality in large language models (SimpleQA)” (preprint, arXiv, November 7, 2024), https://doi.org/10.48550/arXiv.2411.04368.
  15. 15Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou, “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,” in Advances in Neural Information Processing Systems 35 (2022), 24824–24837, https://doi.org/10.52202/068431-1800.
  16. 16Jiayu Wang, Weijiang Lv, Bowen Fu, Jing Fu, Jiayi Song, Lingyu Zhang, Lanxuan Xue, et al., “Act As a Real Researcher: A Suite of Benchmarks Evaluating Frontier LLMs and Agentic Harnesses in Research Lifecycle” (preprint, arXiv, June 5, 2026), https://doi.org/10.48550/arXiv.2606.07462.
  17. 17Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, et al., “Training Compute-Optimal Large Language Models,” in Advances in Neural Information Processing Systems 35 (2022), 30016–30030, https://doi.org/10.52202/068431-2176.
  18. 18Joseph Weizenbaum, “ELIZA—A Computer Program for the Study of Natural Language Communication Between Man and Machine,” Communications of the ACM 9, no. 1 (January 1966): 36–45, https://doi.org/10.1145/365153.365168.
  19. 19JV Roig, “Towards a Standard, Enterprise-Relevant Agentic AI Benchmark: Lessons from 5.5 billion tokens' worth of agentic AI evaluations” (preprint, arXiv, November 11, 2025), https://doi.org/10.48550/arXiv.2511.08042.
  20. 20Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz, “Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection,” in Proceedings of the 16th ACM Workshop on Artificial Intelligence and Security (November 30, 2023), 79–90, https://doi.org/10.1145/3605764.3623985.
  21. 21Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, et al., “Training language models to follow instructions with human feedback,” in Advances in Neural Information Processing Systems 35 (2022), 27730–27744, https://doi.org/10.52202/068431-2011.
  22. 22Marah Abdin, Jyoti Aneja, Hany Awadalla, Ahmed Awadallah, Ammar Ahmad Awan, Nguyen Bach, Amit Bahree, et al., “Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone” (preprint, arXiv, April 2024 (v4 2024-08-30)), https://doi.org/10.48550/arXiv.2404.14219.
  23. 23Marah Abdin, Jyoti Aneja, Harkirat Behl, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Michael Harrison, et al., “Phi-4 Technical Report” (preprint, arXiv, December 12, 2024), https://doi.org/10.48550/arXiv.2412.08905.
  24. 24Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang, “Lost in the Middle: How Language Models Use Long Contexts,” Transactions of the Association for Computational Linguistics 12 (February 23, 2024): 157–173, https://doi.org/10.1162/tacl_a_00638.
  25. 25OpenAI, Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, et al., “GPT-4 Technical Report” (preprint, arXiv, March 15, 2023), https://doi.org/10.48550/arXiv.2303.08774.
  26. 26Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” (preprint, arXiv, May 22, 2020), https://doi.org/10.48550/arXiv.2005.11401.
  27. 27Peter Belcak, Greg Heinrich, Shizhe Diao, Yonggan Fu, Xin Dong, Saurav Muralidharan, Yingyan Celine Lin, and Pavlo Molchanov, “Small Language Models are the Future of Agentic AI” (preprint, arXiv, June 2, 2025), https://doi.org/10.48550/arXiv.2506.02153.
  28. 28Rico Sennrich, Barry Haddow, and Alexandra Birch, “Neural Machine Translation of Rare Words with Subword Units,” in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2016), 1715–1725, https://doi.org/10.18653/v1/P16-1162.
  29. 29Rupert Lane, Anthony Hay, Arthur Schwarz, David M. Berry, and Jeff Shrager, “ELIZA Reanimated: The world's first chatbot restored on the world's first time sharing system” (preprint, arXiv, January 12, 2025), https://doi.org/10.48550/arXiv.2501.06707.
  30. 30Sepp Hochreiter and Jürgen Schmidhuber, “Long Short-Term Memory,” Neural Computation 9, no. 8 (November 1997): 1735–1780, https://doi.org/10.1162/neco.1997.9.8.1735.
  31. 31Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao, “ReAct: Synergizing Reasoning and Acting in Language Models” (preprint, arXiv, October 6, 2022), https://doi.org/10.48550/arXiv.2210.03629.
  32. 32Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, et al., “Language Models are Few-Shot Learners” (preprint, arXiv, May 28, 2020), https://doi.org/10.48550/arXiv.2005.14165.
  33. 33Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean, “Efficient Estimation of Word Representations in Vector Space” (preprint, arXiv, January 16, 2013), https://doi.org/10.48550/arXiv.1301.3781.
  34. 34Tomas Mikolov, Wen-tau Yih, and Geoffrey Zweig, “Linguistic Regularities in Continuous Space Word Representations,” in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Atlanta, Georgia: Association for Computational Linguistics, June 2013), 746–751, https://aclanthology.org/N13-1090/.
  35. 35Yann LeCun, Yoshua Bengio, and Geoffrey Hinton, “Deep learning,” Nature 521, no. 7553 (May 2015): 436–444, https://doi.org/10.1038/nature14539.
  36. 36Yilun Yao, Xinyu Tan, Chao-Hsuan Liu, Yaoming Li, Zhengyang Wang, Zongwei Lv, Wenhan Yu, et al., “Harness-Bench: Evaluating Agent Harnesses across Models and Realistic Workflows” (preprint, arXiv, October 15, 2026), https://doi.org/10.48550/arXiv.2605.27922.
  37. 37Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin, “A Neural Probabilistic Language Model,” Journal of Machine Learning Research 3 (February 2003): 1137–1155, https://www.jmlr.org/papers/v3/bengio03a.html.
  38. 38Yunbei Zhang, Janet Wang, Yingqiang Ge, Weijie Xu, Jihun Hamm, and Chandan K. Reddy, “Stop Comparing LLM Agents Without Disclosing the Harness” (preprint, arXiv, May 7, 2026), https://doi.org/10.48550/arXiv.2605.23950.
  39. 39Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, et al., “Constitutional AI: Harmlessness from AI Feedback” (preprint, arXiv, December 15, 2022), https://doi.org/10.48550/arXiv.2212.08073.
  40. 40Zhenyan Lu, Xiang Li, Dongqi Cai, Rongjie Yi, Fangming Liu, Xiwen Zhang, Nicholas D. Lane, and Mengwei Xu, “Small Language Models: Survey, Measurements, and Insights” (preprint, arXiv, September 2024 (v3 2025-02-26)), https://doi.org/10.48550/arXiv.2409.15790.

Books

  1. 41Joseph Weizenbaum, Computer Power and Human Reason: From Judgment to Calculation (San Francisco: W. H. Freeman, 1976).

Oral histories, museums and archives

  1. 42J. McCarthy, M. L. Minsky, N. Rochester, and C. E. Shannon, A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence (John McCarthy's archive, Stanford University, August 31, 1955), https://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html (accessed October 26, 2026).
  2. 43Joseph Weizenbaum (transcribed by Anthony Hay, Arthur Schwarz, and Rupert Lane), “eliza.mad (transcription of the 1965 MAD-SLIP ELIZA printout),” eliza-ctss, 1965, https://raw.githubusercontent.com/rupertl/eliza-ctss/main/eliza/src/ELIZA/eliza.mad (accessed October 26, 2026).

Government and standards

  1. 44FTC Office of Technology, “AI (and other) Companies: Quietly Changing Your Terms of Service Could Be Unfair or Deceptive,” U.S. Federal Trade Commission (Technology Blog), February 13, 2024, https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2024/02/ai-other-companies-quietly-changing-your-terms-service-could-be-unfair-or-deceptive (accessed October 26, 2026).
  2. 45FTC Office of Technology, “AI Companies: Uphold Your Privacy and Confidentiality Commitments,” U.S. Federal Trade Commission (Technology Blog), January 9, 2024, https://www.ftc.gov/policy/advocacy-research/tech-at-ftc/2024/01/ai-companies-uphold-your-privacy-confidentiality-commitments (accessed October 26, 2026).
  3. 46HHS Office for Civil Rights, “Sample Business Associate Agreement Provisions,” HHS.gov, January 25, 2013, https://www.hhs.gov/hipaa/for-professionals/covered-entities/sample-business-associate-agreement-provisions/index.html (accessed October 26, 2026).
  4. 47MCP maintainers, “MCP specification 2026-07-28 release post,” blog.modelcontextprotocol.io, July 28, 2026, https://blog.modelcontextprotocol.io/posts/2026-07-28/ (accessed October 26, 2026).
  5. 48Model Context Protocol, “Versioning,” modelcontextprotocol.io, July 28, 2026, https://modelcontextprotocol.io/docs/learn/versioning (accessed October 26, 2026).
  6. 49National Institute of Standards and Technology, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1) (NIST, July 26, 2024), https://doi.org/10.6028/NIST.AI.600-1.
  7. 50National Institute of Standards and Technology, FIPS 199: Standards for Security Categorization of Federal Information and Information Systems (NIST CSRC, February 2004), https://doi.org/10.6028/NIST.FIPS.199.
  8. 51NIST, “machine learning (glossary term),” NIST Computer Security Resource Center, https://csrc.nist.gov/glossary/term/machine_learning (accessed October 26, 2026).
  9. 52NIST, “potential impact (glossary),” NIST CSRC (from CNSSI 4009-2022, adapted from FIPS 199), https://csrc.nist.gov/glossary/term/potential_impact (accessed October 26, 2026).
  10. 53U.S. Department of Education, “What is an education record?” studentprivacy.ed.gov, n.d., https://studentprivacy.ed.gov/faq/what-education-record (accessed October 26, 2026).
  11. 54U.S. Department of Education, “Who is a 'school official' under FERPA?” studentprivacy.ed.gov, n.d., https://studentprivacy.ed.gov/faq/who-school-official-under-ferpa (accessed October 26, 2026).
  12. 55U.S. Department of Health & Human Services, “Guidance on HIPAA & Cloud Computing,” HHS.gov, last reviewed December 23, 2022, https://www.hhs.gov/hipaa/for-professionals/special-topics/cloud-computing (accessed October 26, 2026).
  13. 56U.S. Department of Health & Human Services, “Guidance Regarding Methods for De-identification of Protected Health Information,” HHS.gov, last reviewed February 3, 2025, https://www.hhs.gov/hipaa/for-professionals/special-topics/de-identification (accessed October 26, 2026).
  14. 57U.S. Department of Health & Human Services, “Minimum Necessary Requirement,” HHS.gov, last reviewed July 26, 2013, https://www.hhs.gov/hipaa/for-professionals/privacy/guidance/minimum-necessary-requirement (accessed October 26, 2026).
  15. 58U.S. Department of Health & Human Services, “Summary of the HIPAA Privacy Rule,” HHS.gov, last reviewed March 14, 2025, https://www.hhs.gov/hipaa/for-professionals/privacy/laws-regulations (accessed October 26, 2026).
  16. 59U.S. Department of Health & Human Services, “Summary of the HIPAA Security Rule,” HHS.gov, last reviewed August 7, 2026, https://www.hhs.gov/hipaa/for-professionals/security/laws-regulations (accessed October 26, 2026).

Anthropic

  1. 60Anthropic, “Access audit logs,” Claude Help Center, June 15, 2026, https://support.claude.com/en/articles/9970975-access-audit-logs (accessed October 26, 2026).
  2. 61Anthropic, “Building effective agents,” December 19, 2024, https://www.anthropic.com/research/building-effective-agents (accessed October 26, 2026).
  3. 62Anthropic, “Business Associate Agreements (BAA) for Commercial Customers,” Anthropic Privacy Center, https://privacy.claude.com/en/articles/8114513-business-associate-agreements-baa-for-commercial-customers (accessed October 26, 2026).
  4. 63Anthropic, “Choosing the right model,” Claude Platform Docs, https://platform.claude.com/docs/en/about-claude/models/choosing-a-model (accessed October 26, 2026).
  5. 64Anthropic, “Claude 3.7 Sonnet and Claude Code,” February 24, 2025, https://anthropic.com/news/claude-3-7-sonnet (accessed October 26, 2026).
  6. 65Anthropic, “Claude Code overview,” Claude Code Docs, https://code.claude.com/docs/en/overview (accessed October 26, 2026).
  7. 66Anthropic, “Claude Cowork and chat are now one Claude,” Claude blog, September 16, 2026, https://claude.com/blog/cowork-is-now-claude (accessed October 26, 2026).
  8. 67Anthropic, “Claude Fable 5 and Claude Mythos 5,” June 9, 2026, https://www.anthropic.com/news/claude-fable-5-mythos-5 (accessed October 26, 2026).
  9. 68Anthropic, “Claude's Constitution,” n.d., https://www.anthropic.com/constitution (accessed October 26, 2026).
  10. 69Anthropic, “Configure custom data retention controls for Enterprise plans,” Claude Help Center, https://support.claude.com/en/articles/10440198 (accessed October 26, 2026).
  11. 70Anthropic, “Donating Agent Skills to the Agentic AI Foundation,” Anthropic News, October 22, 2026, https://www.anthropic.com/news/donating-agent-skills-to-the-agentic-ai-foundation (accessed October 26, 2026).
  12. 71Anthropic, “Donating the Model Context Protocol and establishing the Agentic AI Foundation,” December 9, 2025, https://www.anthropic.com/news/donating-the-model-context-protocol-and-establishing-of-the-agentic-ai-foundation (accessed October 26, 2026).
  13. 72Anthropic, “Effective context engineering for AI agents,” Anthropic Engineering, September 29, 2025, https://www.anthropic.com/engineering/effective-context-engineering-for-ai-agents (accessed October 26, 2026).
  14. 74Anthropic, “Equipping agents for the real world with Agent Skills,” Anthropic Engineering, October 16, 2025, https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills (accessed October 26, 2026).
  15. 75Anthropic, “Get started with Claude in Chrome,” Claude Help Center, https://support.claude.com/en/articles/12012173-get-started-with-claude-in-chrome (accessed October 26, 2026).
  16. 76Anthropic, “Get started with custom connectors using remote MCP,” Claude Help Center, https://support.claude.com/en/articles/11175166-get-started-with-custom-connectors-using-remote-mcp (accessed October 26, 2026).
  17. 77Anthropic, “Handle tool calls,” Claude Platform Docs, https://platform.claude.com/docs/en/agents-and-tools/tool-use/handle-tool-calls (accessed October 26, 2026).
  18. 78Anthropic, “HIPAA-ready Enterprise plans,” Claude Help Center, https://support.claude.com/en/articles/13296973-hipaa-ready-enterprise-plans (accessed October 26, 2026).
  19. 79Anthropic, “How tool use works,” Claude Platform Docs, https://platform.claude.com/docs/en/agents-and-tools/tool-use/how-tool-use-works (accessed October 26, 2026).
  20. 80Anthropic, “Introducing Claude,” March 14, 2023, https://www.anthropic.com/news/introducing-claude (accessed October 26, 2026).
  21. 81Anthropic, “Introducing Claude Fable 5.1 and Claude Mythos 5.1,” September 2026, https://www.anthropic.com/claude-fable-and-mythos-5-1 (accessed October 26, 2026).
  22. 82Anthropic, “Introducing Claude Haiku 5.5,” October 7, 2026, https://www.anthropic.com/claude-haiku-5-5 (accessed October 26, 2026).
  23. 83Anthropic, “Introducing Claude Opus 5.5,” September 22, 2026, https://www.anthropic.com/claude-opus-5-5 (accessed October 26, 2026).
  24. 84Anthropic, “Introducing Claude Sonnet 5.5,” September 28, 2026, https://www.anthropic.com/claude-sonnet-5-5 (accessed October 26, 2026).
  25. 85Anthropic, “Introducing the Model Context Protocol,” November 25, 2024, https://www.anthropic.com/news/model-context-protocol (accessed October 26, 2026).
  26. 86Anthropic, “Introducing the next generation of Claude,” March 4, 2024, https://anthropic.com/news/claude-3-family (accessed October 26, 2026).
  27. 87Anthropic, “Is my data used for model training? (commercial),” Anthropic Privacy Center, August 18, 2026, https://privacy.claude.com/en/articles/7996868-is-my-data-used-for-training (accessed October 26, 2026).
  28. 88Anthropic, “Is my data used for model training? (consumer),” Anthropic Privacy Center, March 16, 2026, https://privacy.claude.com/en/articles/10023580-is-my-data-used-for-model-training (accessed October 26, 2026).
  29. 89Anthropic, “Models overview,” Claude Platform Docs, https://platform.claude.com/docs/en/models/overview (accessed October 26, 2026).
  30. 90Anthropic, “Pricing,” Claude Platform Docs, https://platform.claude.com/docs/en/about-claude/pricing (accessed October 26, 2026).
  31. 91Anthropic, “Project Glasswing: Securing critical software for the AI era,” April 7, 2026, https://www.anthropic.com/glasswing (accessed October 26, 2026).
  32. 92Anthropic, “Redeploying Claude Fable 5,” June 30, 2026, https://www.anthropic.com/news/redeploying-fable-5 (accessed October 26, 2026).
  33. 93Anthropic, “Release notes — Claude Platform,” Claude Platform Docs, https://platform.claude.com/docs/release-notes/overview (accessed October 26, 2026).
  34. 94Anthropic, “Set up role-based permissions on Enterprise plans,” Claude Help Center, https://support.claude.com/en/articles/13930458-set-up-role-based-permissions-on-enterprise-plans (accessed October 26, 2026).
  35. 95Anthropic, “Set up single sign-on (SSO),” Claude Help Center, https://support.claude.com/en/articles/13132885-set-up-single-sign-on-sso (accessed October 26, 2026).
  36. 96Anthropic, “Statement on the US government directive to suspend access to Fable 5 and Mythos 5,” June 12, 2026, https://www.anthropic.com/news/fable-mythos-access (accessed October 26, 2026).
  37. 97Anthropic, “System prompts (release notes),” Claude Platform Docs, https://platform.claude.com/docs/en/release-notes/system-prompts/overview (accessed October 26, 2026).
  38. 98Anthropic, “Updates to our consumer terms,” August 28, 2025, https://www.anthropic.com/news/updates-to-our-consumer-terms (accessed October 26, 2026).
  39. 99Anthropic, “Use Claude Cowork safely,” Claude Help Center, https://support.claude.com/en/articles/13364135-use-claude-cowork-safely (accessed October 26, 2026).
  40. 100Anthropic, “Use Claude's chat search and memory to build on previous context,” Claude Help Center, https://support.claude.com/en/articles/11817273-use-claude-s-chat-search-and-memory-to-build-on-previous-context (accessed October 26, 2026).
  41. 101Anthropic, “What are projects?” Claude Help Center, https://support.claude.com/en/articles/9517075-what-are-projects (accessed October 26, 2026).
  42. 102Anthropic, “What Certifications has Anthropic obtained?” Anthropic Privacy Center, March 16, 2026, https://privacy.claude.com/en/articles/10015870-what-certifications-has-anthropic-obtained (accessed October 26, 2026).

OpenAI

  1. 103A. Radford et al., “Improving Language Understanding by Generative Pre-Training,” OpenAI, 2018, https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf (accessed October 26, 2026).
  2. 104OpenAI, “Better language models and their implications,” February 14, 2019, https://openai.com/index/better-language-models/ (accessed October 26, 2026).
  3. 105OpenAI, “Business data privacy, security, and compliance,” https://openai.com/business-data/ (accessed October 26, 2026).
  4. 106OpenAI, “ChatGPT: Optimizing language models for dialogue (now 'Introducing ChatGPT'),” November 30, 2022, https://openai.com/index/chatgpt/ (accessed October 26, 2026).
  5. 107OpenAI, “Data controls in ChatGPT,” OpenAI Help Center, https://help.openai.com/en/articles/7730893-data-controls-in-chatgpt (accessed October 26, 2026).
  6. 108OpenAI, “Enterprise privacy at OpenAI,” January 8, 2026, https://openai.com/enterprise-privacy/ (accessed October 26, 2026).
  7. 109OpenAI, “Function calling,” OpenAI API docs, https://developers.openai.com/api/docs/guides/function-calling (accessed October 26, 2026).
  8. 110OpenAI, “Getting a Business Associate Agreement for the OpenAI API,” OpenAI Help Center, https://help.openai.com/en/articles/8660679-getting-a-business-associate-agreement-for-the-openai-api (accessed October 26, 2026).
  9. 111OpenAI, “GPT-6 Astra: A new generation of intelligence,” September 2026, https://openai.com/index/gpt-6-astra/ (accessed October 26, 2026).
  10. 112OpenAI, “GPT-6 Family Model Guide,” October 2, 2026, https://openai.com/index/practical-guide-building-gpt-6/ (accessed October 26, 2026).
  11. 113OpenAI, “Hello GPT-4o,” May 13, 2024, https://openai.com/index/hello-gpt-4o/ (accessed October 26, 2026).
  12. 114OpenAI, “HIPAA eligible products and functionality,” OpenAI Help Center, https://help.openai.com/en/articles/20001069-hipaa-eligible-products-and-functionality (accessed October 26, 2026).
  13. 115OpenAI, “Introducing gpt-oss,” August 5, 2025, https://openai.com/index/introducing-gpt-oss/ (accessed October 26, 2026).
  14. 116OpenAI, “Introducing OpenAI for Healthcare,” January 8, 2026, https://openai.com/index/openai-for-healthcare/ (accessed October 26, 2026).
  15. 117OpenAI, “Introducing OpenAI o3 and o4-mini,” April 16, 2025, https://openai.com/index/introducing-o3-and-o4-mini/ (accessed October 26, 2026).
  16. 118OpenAI, “Learning to reason with LLMs,” September 12, 2024, https://openai.com/index/learning-to-reason-with-llms/ (accessed October 26, 2026).
  17. 119OpenAI, “OpenAI Model Spec (2025-10-27 version),” October 27, 2025, https://model-spec.openai.com/2025-10-27 (accessed October 26, 2026).
  18. 120OpenAI, “Pricing,” OpenAI (developer docs), https://developers.openai.com/api/docs/pricing (accessed October 26, 2026).
  19. 121OpenAI, “Using tools,” OpenAI API docs, https://developers.openai.com/api/docs/guides/tools (accessed October 26, 2026).

Google, Microsoft and others

  1. 122Cursor, “Agent tools overview,” Cursor Docs, https://cursor.com/docs/agent/overview (accessed October 26, 2026).
  2. 123GitHub, “Supported AI models in GitHub Copilot,” GitHub Docs, https://docs.github.com/en/copilot/reference/ai-models/supported-models (accessed October 26, 2026).
  3. 124Google, “Gemini API pricing,” Google AI for Developers, https://ai.google.dev/gemini-api/docs/pricing (accessed October 26, 2026).
  4. 125Google, “Gemma 4 model card,” Google AI for Developers, https://ai.google.dev/gemma/docs/core/model_card_4 (accessed October 26, 2026).
  5. 126Google, “Gemma 4: Byte for byte, the most capable open models,” April 2, 2026, https://blog.google/innovation-and-ai/technology/developers-tools/gemma-4/ (accessed October 26, 2026).
  6. 127Google, “Gemma models overview,” Google AI for Developers, https://ai.google.dev/gemma/docs/core (accessed October 26, 2026).
  7. 128Hugging Face, “Hugging Face Hub documentation,” https://huggingface.co/docs/hub/index (accessed October 26, 2026).
  8. 129K. Kavukcuoglu, “Gemini 4 Argon: our next era of frontier intelligence,” Google, September 30, 2026, https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon/ (accessed October 26, 2026).
  9. 130LM Studio, “About LM Studio,” https://lmstudio.ai/docs/app (accessed October 26, 2026).
  10. 131Meta, “The Llama 4 herd,” Meta AI, April 5, 2025, https://ai.meta.com/blog/llama-4-multimodal-intelligence/ (accessed October 26, 2026).
  11. 132Microsoft, “Phi open models,” Microsoft Azure, https://azure.microsoft.com/products/phi (accessed October 26, 2026).
  12. 133Microsoft, “Phi-4-mini-instruct model card,” Hugging Face, February 2025, https://huggingface.co/microsoft/Phi-4-mini-instruct (accessed October 26, 2026).
  13. 134Nous Research, “hermes-agent,” Nous Research (GitHub), https://github.com/NousResearch/hermes-agent (accessed October 26, 2026).
  14. 135OpenAI, “openai/codex,” GitHub, https://github.com/openai/codex (accessed October 26, 2026).
  15. 136Qwen team (Alibaba), “Qwen3,” GitHub, April 29, 2025, https://github.com/QwenLM/Qwen3 (accessed October 26, 2026).
  16. 137S. Pichai and D. Hassabis, “Introducing Gemini: our largest and most capable AI model,” Google, December 6, 2023, https://blog.google/technology/ai/google-gemini-ai/ (accessed October 26, 2026).

Data and explainers

  1. 138FT Visual Storytelling Team and M. Murgia, “Generative AI exists because of the transformer,” Financial Times, September 12, 2023, https://ig.ft.com/generative-ai/ (accessed October 26, 2026).
  2. 139G. Sanderson (3Blue1Brown), “Large Language Models explained briefly,” 3Blue1Brown, made for the Computer History Museum, November 20, 2024, https://www.youtube.com/watch?v=LPZh9BOjkQs (accessed October 26, 2026).

Press

  1. 140Gideon Lewis-Kraus, “What Is Claude? Anthropic Doesn't Know, Either,” The New Yorker, February 9, 2026, https://www.newyorker.com/magazine/2026/02/16/what-is-claude-anthropic-doesnt-know-either (accessed October 26, 2026).
  2. 141John Horgan, “Claude E. Shannon,” Scientific American 262, no. 1 (January 1990): 22–22B, https://doi.org/10.1038/scientificamerican0190-22.
  3. 142John Horgan, “Profile of Claude Shannon, Inventor of Information Theory,” Scientific American (Cross-Check blog), July 26, 2017, https://www.scientificamerican.com/blog/cross-check/profile-of-claude-shannon-inventor-of-information-theory/ (accessed October 26, 2026).
  4. 143Kevin Roose, “Inside the White-Hot Center of A.I. Doomerism,” The New York Times, July 11, 2023, https://www.nytimes.com/2023/07/11/technology/anthropic-ai-claude-chatbot.html (accessed October 26, 2026).

On this site

  1. 144Steve Sparks, “Case study: small models in a coding harness,” AI, from the Parts Up, Appendix B, October 2026, https://aifromthepartsup.com/small-models/1 (accessed October 26, 2026).
  2. 145Steve Sparks, “ELIZA (1966), playable,” stevesparks.dev, 2026, https://stevesparks.com/arcade/eliza (accessed October 26, 2026).
  3. 146Steve Sparks, “From Shannon's bits to Claude's tokens,” stevesparks.dev, 2026, https://stevesparks.com/retro/shannon/ (accessed October 26, 2026).

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