The best workflows and configurations I've developed, having heavily used Claude Code since the day of it's release. Workflows are based off applied learnings from our AI-native startup.

Not just GPT4! Easy to use, Self-Hosted, Unlimited and Free WEB API of the latest A.I. like Gemini, DeepSeek, Claude and GPT

My personal Claude Code and OpenAI Codex setup with battle-tested skills, commands, hooks, agents and MCP servers that I use daily.

Builds an Arch Linux package for Claude Desktop by extracting and modifying the official Windows release. I

The Claude subagents and example custom commands I use in my AI-assisted development workflow

Personal collection of Claude skills - growing as I discover patterns and solve real-world problems

My SwiftUI playground where I use Claude AI to help me understand and implement complex UI patterns. I manage the vision and requirements, Claude helps me achieve the right implementation. Each example is a collaboration that resulted in clean, working code you can learn from.

Asada, M.; Hosoda, K.; Kuniyoshi, Y.; Ishiguro, H.; Inui, T.; Yoshikawa, Y.; Ogino, M.; Yoshida, C. (2009). "Cognitive developmental robotics: a survey". IEEE Transactions on Autonomous Mental Development. 1 (1): 12–34. doi:10.1109/tamd.2009.2021702. S2CID 10168773. "ACM Computing Classification System: Artificial intelligence". ACM. 1998. Archived from the original on 12 October 2007. Retrieved 30 August 2007. Goodman, Joanna (2016). Robots in Law: How Artificial Intelligence is Transforming Legal Services (1st ed.). Ark Group. ISBN 978-1-78358-264-8. Archived from the original on 8 November 2016. Retrieved 7 November 2016. Albus, J. S. (2002). "4-D/RCS: A Reference Model Architecture for Intelligent Unmanned Ground Vehicles" (PDF). In Gerhart, G.; Gunderson, R.; Shoemaker, C. (eds.). Proceedings of the SPIE AeroSense Session on Unmanned Ground Vehicle Technology. Unmanned Ground Vehicle Technology IV. 3693. pp. 11–20. Bibcode:2002SPIE.4715..303A. CiteSeerX 10.1.1.15.14. doi:10.1117/12.474462. S2CID 63339739. Archived from the original (PDF) on 25 July 2004. Aleksander, Igor (1995). Artificial Neuroconsciousness: An Update. IWANN. Archived from the original on 2 March 1997. BibTex Archived 2 March 1997 at the Wayback Machine. Bach, Joscha (2008). "Seven Principles of Synthetic Intelligence". In Wang, Pei; Goertzel, Ben; Franklin, Stan (eds.). Artificial General Intelligence, 2008: Proceedings of the First AGI Conference. IOS Press. pp. 63–74. ISBN 978-1-58603-833-5. Archived from the original on 8 July 2016. Retrieved 16 February 2016. "Robots could demand legal rights". BBC News. 21 December 2006. Archived from the original on 15 October 2019. Retrieved 3 February 2011. Brooks, Rodney (1990). "Elephants Don't Play Chess" (PDF). Robotics and Autonomous Systems. 6 (1–2): 3–15. CiteSeerX 10.1.1.588.7539. doi:10.1016/S0921-8890(05)80025-9. Archived (PDF) from the original on 9 August 2007. Brooks, R. A. (1991). "How to build complete creatures rather than isolated cognitive simulators". In VanLehn, K. (ed.). Architectures for Intelligence. Hillsdale, NJ: Lawrence Erlbaum Associates. pp. 225–239. CiteSeerX 10.1.1.52.9510. Buchanan, Bruce G. (2005). "A (Very) Brief History of Artificial Intelligence" (PDF). AI Magazine: 53–60. Archived from the original (PDF) on 26 September 2007. Butler, Samuel (13 June 1863). "Darwin among the Machines". Letters to the Editor. The Press. Christchurch, New Zealand. Archived from the original on 19 September 2008. Retrieved 16 October 2014 – via Victoria University of Wellington. Clark, Jack (8 December 2015). "Why 2015 Was a Breakthrough Year in Artificial Intelligence". Bloomberg News. Archived from the original on 23 November 2016. Retrieved 23 November 2016. After a half-decade of quiet breakthroughs in artificial intelligence, 2015 has been a landmark year. Computers are smarter and learning faster than ever. "AI set to exceed human brain power". CNN. 26 July 2006. Archived from the original on 19 February 2008. Dennett, Daniel (1991). Consciousness Explained. The Penguin Press. ISBN 978-0-7139-9037-9. Domingos, Pedro (2015). The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books. ISBN 978-0-465-06192-1. Dowe, D. L.; Hajek, A. R. (1997). "A computational extension to the Turing Test". Proceedings of the 4th Conference of the Australasian Cognitive Science Society. Archived from the original on 28 June 2011. Dreyfus, Hubert (1972). What Computers Can't Do. New York: MIT Press. ISBN 978-0-06-011082-6. Dreyfus, Hubert; Dreyfus, Stuart (1986). Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. Oxford, UK: Blackwell. ISBN 978-0-02-908060-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Dreyfus, Hubert (1992). What Computers Still Can't Do. New York: MIT Press. ISBN 978-0-262-54067-4. Dyson, George (1998). Darwin among the Machines. Allan Lane Science. ISBN 978-0-7382-0030-9. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Edelman, Gerald (23 November 2007). "Gerald Edelman – Neural Darwinism and Brain-based Devices". Talking Robots. Archived from the original on 8 October 2009. Edelson, Edward (1991). The Nervous System. New York: Chelsea House. ISBN 978-0-7910-0464-7. Archived from the original on 26 July 2020. Retrieved 18 November 2019. Fearn, Nicholas (2007). The Latest Answers to the Oldest Questions: A Philosophical Adventure with the World's Greatest Thinkers. New York: Grove Press. ISBN 978-0-8021-1839-4. Gladwell, Malcolm (2005). Blink. New York: Little, Brown and Co. ISBN 978-0-316-17232-5. Gödel, Kurt (1951). Some basic theorems on the foundations of mathematics and their implications. Gibbs Lecture. In Feferman, Solomon, ed. (1995). Kurt Gödel: Collected Works, Vol. III: Unpublished Essays and Lectures. Oxford University Press. pp. 304–23. ISBN 978-0-19-514722-3. Haugeland, John (1985). Artificial Intelligence: The Very Idea. Cambridge, Mass.: MIT Press. ISBN 978-0-262-08153-5. Hawkins, Jeff; Blakeslee, Sandra (2005). On Intelligence. New York, NY: Owl Books. ISBN 978-0-8050-7853-4. Henderson, Mark (24 April 2007). "Human rights for robots? We're getting carried away". The Times Online. London. Archived from the original on 31 May 2014. Retrieved 31 May 2014. Hernandez-Orallo, Jose (2000). "Beyond the Turing Test". Journal of Logic, Language and Information. 9 (4): 447–466. doi:10.1023/A:1008367325700. S2CID 14481982. Hernandez-Orallo, J.; Dowe, D. L. (2010). "Measuring Universal Intelligence: Towards an Anytime Intelligence Test". Artificial Intelligence. 174 (18): 1508–1539. CiteSeerX 10.1.1.295.9079. doi:10.1016/j.artint.2010.09.006. Hinton, G. E. (2007). "Learning multiple layers of representation". Trends in Cognitive Sciences. 11 (10): 428–434. doi:10.1016/j.tics.2007.09.004. PMID 17921042. S2CID 15066318. Hofstadter, Douglas (1979). Gödel, Escher, Bach: an Eternal Golden Braid. New York, NY: Vintage Books. ISBN 978-0-394-74502-2. Holland, John H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press. ISBN 978-0-262-58111-0. Archived from the original on 26 July 2020. Retrieved 17 December 2019. Howe, J. (November 1994). "Artificial Intelligence at Edinburgh University: a Perspective". Archived from the original on 15 May 2007. Retrieved 30 August 2007. Hutter, M. (2012). "One Decade of Universal Artificial Intelligence". Theoretical Foundations of Artificial General Intelligence. Atlantis Thinking Machines. 4. pp. 67–88. CiteSeerX 10.1.1.228.8725. doi:10.2991/978-94-91216-62-6_5. ISBN 978-94-91216-61-9. S2CID 8888091. Kahneman, Daniel; Slovic, D.; Tversky, Amos (1982). Judgment under uncertainty: Heuristics and biases. Science. 185. New York: Cambridge University Press. pp. 1124–31. doi:10.1126/science.185.4157.1124. ISBN 978-0-521-28414-1. PMID 17835457. S2CID 143452957. Kaplan, Andreas; Haenlein, Michael (2019). "Siri, Siri in my Hand, who's the Fairest in the Land? On the Interpretations, Illustrations and Implications of Artificial Intelligence". Business Horizons. 62: 15–25. doi:10.1016/j.bushor.2018.08.004. Katz, Yarden (1 November 2012). "Noam Chomsky on Where Artificial Intelligence Went Wrong". The Atlantic. Archived from the original on 28 February 2019. Retrieved 26 October 2014. "Kismet". MIT Artificial Intelligence Laboratory, Humanoid Robotics Group. Archived from the original on 17 October 2014. Retrieved 25 October 2014. Koza, John R. (1992). Genetic Programming (On the Programming of Computers by Means of Natural Selection). MIT Press. Bibcode:1992gppc.book.....K. ISBN 978-0-262-11170-6. Kolata, G. (1982). "How can computers get common sense?". Science. 217 (4566): 1237–1238. Bibcode:1982Sci...217.1237K. doi:10.1126/science.217.4566.1237. PMID 17837639. Kumar, Gulshan; Kumar, Krishan (2012). "The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review". Applied Computational Intelligence and Soft Computing. 2012: 1–20. doi:10.1155/2012/850160. Kurzweil, Ray (1999). The Age of Spiritual Machines. Penguin Books. ISBN 978-0-670-88217-5. Kurzweil, Ray (2005). The Singularity is Near. Penguin Books. ISBN 978-0-670-03384-3. Lakoff, George; Núñez, Rafael E. (2000). Where Mathematics Comes From: How the Embodied Mind Brings Mathematics into Being. Basic Books. ISBN 978-0-465-03771-1. Langley, Pat (2011). "The changing science of machine learning". Machine Learning. 82 (3): 275–279. doi:10.1007/s10994-011-5242-y. Law, Diane (June 1994). Searle, Subsymbolic Functionalism and Synthetic Intelligence (Technical report). University of Texas at Austin. p. AI94-222. CiteSeerX 10.1.1.38.8384. Legg, Shane; Hutter, Marcus (15 June 2007). A Collection of Definitions of Intelligence (Technical report). IDSIA. arXiv:0706.3639. Bibcode:2007arXiv0706.3639L. 07-07. Lenat, Douglas; Guha, R. V. (1989). Building Large Knowledge-Based Systems. Addison-Wesley. ISBN 978-0-201-51752-1. Lighthill, James (1973). "Artificial Intelligence: A General Survey". Artificial Intelligence: a paper symposium. Science Research Council. Lucas, John (1961). "Minds, Machines and Gödel". In Anderson, A.R. (ed.). Minds and Machines. Archived from the original on 19 August 2007. Retrieved 30 August 2007. Lungarella, M.; Metta, G.; Pfeifer, R.; Sandini, G. (2003). "Developmental robotics: a survey". Connection Science. 15 (4): 151–190. CiteSeerX 10.1.1.83.7615. doi:10.1080/09540090310001655110. S2CID 1452734. Maker, Meg Houston (2006). "AI@50: AI Past, Present, Future". Dartmouth College. Archived from the original on 3 January 2007. Retrieved 16 October 2008. Markoff, John (16 February 2011). "Computer Wins on 'Jeopardy!': Trivial, It's Not". The New York Times. Archived from the original on 22 October 2014. Retrieved 25 October 2014. McCarthy, John; Minsky, Marvin; Rochester, Nathan; Shannon, Claude (1955). "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence". Archived from the original on 26 August 2007. Retrieved 30 August 2007.. McCarthy, John; Hayes, P. J. (1969). "Some philosophical problems from the standpoint of artificial intelligence". Machine Intelligence. 4: 463–502. CiteSeerX 10.1.1.85.5082. Archived from the original on 10 August 2007. Retrieved 30 August 2007. McCarthy, John (12 November 2007). "What Is Artificial Intelligence?". Archived from the original on 18 November 2015. Minsky, Marvin (1967). Computation: Finite and Infinite Machines. Englewood Cliffs, N.J.: Prentice-Hall. ISBN 978-0-13-165449-5. Archived from the original on 26 July 2020. Retrieved 18 November 2019. Minsky, Marvin (2006). The Emotion Machine. New York, NY: Simon & Schusterl. ISBN 978-0-7432-7663-4. Moravec, Hans (1988). Mind Children. Harvard University Press. ISBN 978-0-674-57616-2. Archived from the original on 26 July 2020. Retrieved 18 November 2019. Norvig, Peter (25 June 2012). "On Chomsky and the Two Cultures of Statistical Learning". Peter Norvig. Archived from the original on 19 October 2014. NRC (United States National Research Council) (1999). "Developments in Artificial Intelligence". Funding a Revolution: Government Support for Computing Research. National Academy Press. Needham, Joseph (1986). Science and Civilization in China: Volume 2. Caves Books Ltd. Newell, Allen; Simon, H. A. (1976). "Computer Science as Empirical Inquiry: Symbols and Search". Communications of the ACM. 19 (3): 113–126. doi:10.1145/360018.360022.. Nilsson, Nils (1983). "Artificial Intelligence Prepares for 2001" (PDF). AI Magazine. 1 (1). Archived (PDF) from the original on 17 August 2020. Retrieved 22 August 2020. Presidential Address to the Association for the Advancement of Artificial Intelligence. O'Brien, James; Marakas, George (2011). Management Information Systems (10th ed.). McGraw-Hill/Irwin. ISBN 978-0-07-337681-3. O'Connor, Kathleen Malone (1994). "The alchemical creation of life (takwin) and other concepts of Genesis in medieval Islam". University of Pennsylvania: 1–435. Archived from the original on 5 December 2019. Retrieved 27 August 2008. Oudeyer, P-Y. (2010). "On the impact of robotics in behavioral and cognitive sciences: from insect navigation to human cognitive development" (PDF). IEEE Transactions on Autonomous Mental Development. 2 (1): 2–16. doi:10.1109/tamd.2009.2039057. S2CID 6362217. Archived (PDF) from the original on 3 October 2018. Retrieved 4 June 2013. Penrose, Roger (1989). The Emperor's New Mind: Concerning Computer, Minds and The Laws of Physics. Oxford University Press. ISBN 978-0-19-851973-7. Poli, R.; Langdon, W. B.; McPhee, N. F. (2008). A Field Guide to Genetic Programming. Lulu.com. ISBN 978-1-4092-0073-4. Archived from the original on 8 August 2015. Retrieved 21 April 2008 – via gp-field-guide.org.uk. Rajani, Sandeep (2011). "Artificial Intelligence – Man or Machine" (PDF). International Journal of Information Technology and Knowledge Management. 4 (1): 173–176. Archived from the original (PDF) on 18 January 2013. Ronald, E. M. A. and Sipper, M. Intelligence is not enough: On the socialization of talking machines, Minds and Machines Archived 25 July 2020 at the Wayback Machine, vol. 11, no. 4, pp. 567–576, November 2001. Ronald, E. M. A. and Sipper, M. What use is a Turing chatterbox? Archived 25 July 2020 at the Wayback Machine, Communications of the ACM, vol. 43, no. 10, pp. 21–23, October 2000. "Science". August 1982. Archived from the original on 25 July 2020. Retrieved 16 February 2016. Searle, John (1980). "Minds, Brains and Programs" (PDF). Behavioral and Brain Sciences. 3 (3): 417–457. doi:10.1017/S0140525X00005756. Archived (PDF) from the original on 17 March 2019. Retrieved 22 August 2020. Searle, John (1999). Mind, language and society. New York, NY: Basic Books. ISBN 978-0-465-04521-1. OCLC 231867665. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Shapiro, Stuart C. (1992). "Artificial Intelligence". In Shapiro, Stuart C. (ed.). Encyclopedia of Artificial Intelligence (PDF) (2nd ed.). New York: John Wiley. pp. 54–57. ISBN 978-0-471-50306-4. Archived (PDF) from the original on 1 February 2016. Retrieved 29 May 2009. Simon, H. A. (1965). The Shape of Automation for Men and Management. New York: Harper & Row. Archived from the original on 26 July 2020. Retrieved 18 November 2019. Skillings, Jonathan (3 July 2006). "Getting Machines to Think Like Us". cnet. Archived from the original on 16 November 2011. Retrieved 3 February 2011. Solomonoff, Ray (1956). An Inductive Inference Machine (PDF). Dartmouth Summer Research Conference on Artificial Intelligence. Archived (PDF) from the original on 26 April 2011. Retrieved 22 March 2011 – via std.com, pdf scanned copy of the original. Later published as Solomonoff, Ray (1957). "An Inductive Inference Machine". IRE Convention Record. Section on Information Theory, part 2. pp. 56–62. Tao, Jianhua; Tan, Tieniu (2005). Affective Computing and Intelligent Interaction. Affective Computing: A Review. LNCS 3784. Springer. pp. 981–995. doi:10.1007/11573548. Tecuci, Gheorghe (March–April 2012). "Artificial Intelligence". Wiley Interdisciplinary Reviews: Computational Statistics. 4 (2): 168–180. doi:10.1002/wics.200. Thro, Ellen (1993). Robotics: The Marriage of Computers and Machines. New York: Facts on File. ISBN 978-0-8160-2628-9. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Turing, Alan (October 1950), "Computing Machinery and Intelligence", Mind, LIX (236): 433–460, doi:10.1093/mind/LIX.236.433, ISSN 0026-4423. van der Walt, Christiaan; Bernard, Etienne (2006). "Data characteristics that determine classifier performance" (PDF). Archived from the original (PDF) on 25 March 2009. Retrieved 5 August 2009. Vinge, Vernor (1993). "The Coming Technological Singularity: How to Survive in the Post-Human Era". Vision 21: Interdisciplinary Science and Engineering in the Era of Cyberspace: 11. Bibcode:1993vise.nasa...11V. Archived from the original on 1 January 2007. Retrieved 14 November 2011. Wason, P. C.; Shapiro, D. (1966). "Reasoning". In Foss, B. M. (ed.). New horizons in psychology. Harmondsworth: Penguin. Archived from the original on 26 July 2020. Retrieved 18 November 2019. Weizenbaum, Joseph (1976). Computer Power and Human Reason. San Francisco: W.H. Freeman & Company. ISBN 978-0-7167-0464-5. Weng, J.; McClelland; Pentland, A.; Sporns, O.; Stockman, I.; Sur, M.; Thelen, E. (2001). "Autonomous mental development by robots and animals" (PDF). Science. 291 (5504): 599–600. doi:10.1126/science.291.5504.599. PMID 11229402. S2CID 54131797. Archived (PDF) from the original on 4 September 2013. Retrieved 4 June 2013 – via msu.edu. "Applications of AI". www-formal.stanford.edu. Archived from the original on 28 August 2016. Retrieved 25 September 2016. Further reading DH Author, 'Why Are There Still So Many Jobs? The History and Future of Workplace Automation' (2015) 29(3) Journal of Economic Perspectives 3. Boden, Margaret, Mind As Machine, Oxford University Press, 2006. Cukier, Kenneth, "Ready for Robots? How to Think about the Future of AI", Foreign Affairs, vol. 98, no. 4 (July/August 2019), pp. 192–98. George Dyson, historian of computing, writes (in what might be called "Dyson's Law") that "Any system simple enough to be understandable will not be complicated enough to behave intelligently, while any system complicated enough to behave intelligently will be too complicated to understand." (p. 197.) Computer scientist Alex Pentland writes: "Current AI machine-learning algorithms are, at their core, dead simple stupid. They work, but they work by brute force." (p. 198.) Domingos, Pedro, "Our Digital Doubles: AI will serve our species, not control it", Scientific American, vol. 319, no. 3 (September 2018), pp. 88–93. Gopnik, Alison, "Making AI More Human: Artificial intelligence has staged a revival by starting to incorporate what we know about how children learn", Scientific American, vol. 316, no. 6 (June 2017), pp. 60–65. Johnston, John (2008) The Allure of Machinic Life: Cybernetics, Artificial Life, and the New AI, MIT Press. Koch, Christof, "Proust among the Machines", Scientific American, vol. 321, no. 6 (December 2019), pp. 46–49. Christof Koch doubts the possibility of "intelligent" machines attaining consciousness, because "[e]ven the most sophisticated brain simulations are unlikely to produce conscious feelings." (p. 48.) According to Koch, "Whether machines can become sentient [is important] for ethical reasons. If computers experience life through their own senses, they cease to be purely a means to an end determined by their usefulness to... humans. Per GNW [the Global Neuronal Workspace theory], they turn from mere objects into subjects... with a point of view.... Once computers' cognitive abilities rival those of humanity, their impulse to push for legal and political rights will become irresistible – the right not to be deleted, not to have their memories wiped clean, not to suffer pain and degradation. The alternative, embodied by IIT [Integrated Information Theory], is that computers will remain only supersophisticated machinery, ghostlike empty shells, devoid of what we value most: the feeling of life itself." (p. 49.) Marcus, Gary, "Am I Human?: Researchers need new ways to distinguish artificial intelligence from the natural kind", Scientific American, vol. 316, no. 3 (March 2017), pp. 58–63. A stumbling block to AI has been an incapacity for reliable disambiguation. An example is the "pronoun disambiguation problem": a machine has no way of determining to whom or what a pronoun in a sentence refers. (p. 61.) E McGaughey, 'Will Robots Automate Your Job Away? Full Employment, Basic Income, and Economic Democracy' (2018) SSRN, part 2(3) Archived 24 May 2018 at the Wayback Machine. George Musser, "Artificial Imagination: How machines could learn creativity and common sense, among other human qualities", Scientific American, vol. 320, no. 5 (May 2019), pp. 58–63. Myers, Courtney Boyd ed. (2009). "The AI Report" Archived 29 July 2017 at the Wayback Machine. Forbes June 2009 Raphael, Bertram (1976). The Thinking Computer. W.H.Freeman and Company. ISBN 978-0-7167-0723-3. Archived from the original on 26 July 2020. Retrieved 22 August 2020. Scharre, Paul, "Killer Apps: The Real Dangers of an AI Arms Race", Foreign Affairs, vol. 98, no. 3 (May/June 2019), pp. 135–44. "Today's AI technologies are powerful but unreliable. Rules-based systems cannot deal with circumstances their programmers did not anticipate. Learning systems are limited by the data on which they were trained. AI failures have already led to tragedy. Advanced autopilot features in cars, although they perform well in some circumstances, have driven cars without warning into trucks, concrete barriers, and parked cars. In the wrong situation, AI systems go from supersmart to superdumb in an instant. When an enemy is trying to manipulate and hack an AI system, the risks are even greater." (p. 140.) Serenko, Alexander (2010). "The development of an AI journal ranking based on the revealed preference approach" (PDF). Journal of Informetrics. 4 (4): 447–459. doi:10.1016/j.joi.2010.04.001. Archived (PDF) from the original on 4 October 2013. Retrieved 24 August 2013. Serenko, Alexander; Michael Dohan (2011). "Comparing the expert survey and citation impact journal ranking methods: Example from the field of Artificial Intelligence" (PDF). Journal of Informetrics. 5 (4): 629–649. doi:10.1016/j.joi.2011.06.002. Archived (PDF) from the original on 4 October 2013. Retrieved 12 September 2013. Sun, R. & Bookman, L. (eds.), Computational Architectures: Integrating Neural and Symbolic Processes. Kluwer Academic Publishers, Needham, MA. 1994. Tom Simonite (29 December 2014). "2014 in Computing: Breakthroughs in Artificial Intelligence". MIT Technology Review. Tooze, Adam, "Democracy and Its Discontents", The New York Review of Books, vol. LXVI, no. 10 (6 June 2019), pp. 52–53, 56–57. "Democracy has no clear answer for the mindless operation of bureaucratic and technological power. We may indeed be witnessing its extension in the form of artificial intelligence and robotics. Likewise, after decades of dire warning, the environmental problem remains fundamentally unaddressed.... Bureaucratic overreach and environmental catastrophe are precisely the kinds of slow-moving existential challenges that democracies deal with very badly.... Finally, there is the threat du jour: corporations and the technologies they promote." (pp. 56–57.)

Repo of the scripts I'm using to work with Claude and Gemini

I want to say please and thank you to Claude, but I don't want to waste tokens on it.

Multi-agent Claude Code orchestrator with Telegram I/O, Docker isolation, swarm patterns, and Mission Control UI

I tried to replicate a complex AI pentest pipeline, using Kali MCP and HexStrike AI, and a proper setup Claude Code with augmented context. Results are mind-blowing, reports here

This is a little experiment I want to start with. Basically ask claude to give a course on a github project. But basically I get a link to the github project, generate the course and then upload it as a git page. I'm just bored twin

Commands, agents and skills I've made or collected for use with Claude Code

Unlock GPT-4-32K & Claude-2-100K API Instantly With Open Router

Notes and skills for how I use Claude Code

Tips and tricks I use to optimize my experience with Claude Code.

These are the skills in markdown as well as supporting python scripts that Claude Desktop uses when using the new skill feature. These were provided to me in a .gz file from Claude desktop in an artifact and I haven't seen them published yet.

This tool is an implementation of the Telegram Bot API for Linux terminal use. It's ment for quick OSINT and grading useful information or to do things like takedown, destroy or grab messages. Note, this tool is converted from a web-tool I once made to C++ and done so via Claude.

Markov Text Generation Problem Description The Infinite Monkey Theorem1 (IFT) says that if a monkey hits keys at random on a typewriter it will almost surely, given an infinite amount of time, produce a chosen text (like the Declaration of Independence, Hamlet, or a script for ... Planet of the Apes). The probability of this actually happening is, of course, very small but the IFT claims that it is still possible. Some people have tested this hypotheis in software and, after billions and billions of simulated years, one virtual monkey was able to type out a sequence of 19 letters that can be found in Shakespeare’s The Two Gentlemen of Verona. (See the April 9, 2007 edition of The New Yorker if you’re interested; but, hypothesis testing with real monkeys2 is far more entertaining.) The IFT might lead to some interesting conversations with Rust Cohle, but the practical applications are few. It does, however, bring up the idea of automated text generation, and there the ideas and applications are not only interesting but also important. Claude Shannon essentially founded the field of information theory with the publication of his landmark paper A Mathematical Theory of Computation3 in 1948. Shannon described a method for using Markov chains to produce a reasonable imitation of a known text with sometimes startling results. For example, here is a sample of text generated from a Markov model of the script for the 1967 movie Planet of the Apes. "PLANET OF THE APES" Screenplay by Michael Wilson Based on Novel By Pierre Boulle DISSOLVE TO: 138 EXT. GROVE OF FRUIT TREES - ESTABLISHING SHOT - DAY Zira run back to the front of Taylor. The President, I believe the prosecutor's charge of this man. ZIRA Well, whoever owned them was in pretty bad shape. He picks up two of the strain. You got what you wanted, kid. How does it taste? Silence. Taylor and cuffs him. Over this we HEAR from a distance is a crude horse-drawn wagon is silhouetted-against the trunks and branches of great trees and bushes on the horse's rump. Taylor lifts his right arm to ward off the blow, and the room and lands at the feet of Cornelius and Lucius are sorting out equipment falls to his knees, buries his head silently at the Ranch). DISSOLVE TO: 197 INT. CAGES - CLOSE SHOT - FEATURING LANDON - FROM TAYLOR'S VOICE (o.s.) I've got a fine veternary surgeons under my direction? ZIRA Taylor! ZIRA There is a small lake, looking like a politician. TAYLOR Dodge takes a pen and notebook from the half-open door of a guard room. Taylor bursts suddenly confronted by his 1https://en.wikipedia.org/wiki/Infinite_monkey_theorem2https://web.archive.org/web/20130120215600/http://www.vivaria.net/experiments/notes/publication/NOTES_ EN.pdf3http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6773024 1 original pursuer (the dismounted cop coming up with a cigar butt and places it in the drawer beside them. TAYLOR What's the best there is a. loud RAP at the doll was found beside the building. Zira waits at the third table. TAYLOR Good question. Is he a man? CORNELIUS (impatiently. DODGE Blessed are the vegetation. These SHOTS are INTERCUT with: 94 WHAT THE ASTRONAUTS They examine the remnants of the cage. ZIRA (plunging on) Their speech organs are adequate. The flaw lies not in anatomy but in the back of his left sleeve. TAYLOR (taking off his shirt. 80 DODGE AND LANDON You don't sound happy in your work. GALEN (defensively) Gorilla hunter stands over a dead man, one fo Besides a few spelling errors and some rather odd things that make you wonder about the author, this passage is surprisingly human-like. This is a simple example of natural language generation, a sub-area of natural language processing—a very active area of research in computer science. The particular approach we’re using in this assignment was famously implemented as the fictitious Mark V. Shaney4 and the Emacs command Disassociated Press5. Approach So, here’s the basic idea: Imagine taking a book (say, Tom Sawyer) and determining the probability with which each character occurs. You would probably find that spaces are the most common, that the character ‘e’ is fairly common, and that the character ‘q’ is rather uncommon. After completing this “level 0” analysis, you would be able to produce random Tom Sawyer text based on character probabilities. It wouldn’t have much in common with the real thing, but at least the characters would tend to occur in the proper propor- tion. In fact, here’s an example of what you might produce: Level 0 rla bsht eS ststofo hhfosdsdewno oe wee h .mr ae irii ela iad o r te u t mnyto onmalysnce, ifu en c fDwn oee iteo Now imagine doing a slightly more sophisticated level 1 analysis by determining the probability with which each character follows every other character. You would probably discover that ‘h’ follows ‘t’ more frequently than ‘x’ does, and you would probably discover that a space follows ‘.’ more frequently than ‘,’ does. You could now produce some randomly generated Tom Sawyer text by picking a character to begin with and then always choosing the next character based on the previous one and the probabilities revealed by the analysis. Here’s an example: Level 1 "Shand tucthiney m?" le ollds mind Theybooure He, he s whit Pereg lenigabo Jodind alllld ashanthe ainofevids tre lin-p asto oun theanthadomoere Now imagine doing a level k analysis by determining the probability with which each character follows every possible sequence of characters of length k (kgrams). A level 5 analysis of Tom Sawyer for example, would reveal that ‘r’ follows “Sawye” more frequently than any other character. After a level k analysis, you would be able to produce random Tom Sawyer by always choosing the next character based on the previous k characters (a kgram) and the probabilities revealed by the analysis. 4https://en.wikipedia.org/wiki/Mark_V._Shaney5https://en.wikipedia.org/wiki/Dissociated_press Page 2 of 5 At only a moderate level of analysis (say, levels 5-7), the randomly generated text begins to take on many of the characteristics of the source text. It probably won’t make complete sense, but you’ll be able to tell that it was derived from Tom Sawyer as opposed to, say, The Sound and the Fury. Here are some more examples of text that is generated from increasing levels of analysis of Tom Sawyer. (These “levels of analysis” are called order K Markov models.) K = 2 "Yess been." for gothin, Tome oso; ing, in to weliss of an’te cle - armit. Papper a comeasione, and smomenty, fropeck hinticer, sid, a was Tom, be suck tied. He sis tred a youck to themen K = 4 en themself, Mr. Welshman, but him awoke, the balmy shore. I’ll give him that he couple overy because in the slated snufflindeed structure’s kind was rath. She said that the wound the door a fever eyes that WITH him. K = 6 people had eaten, leaving. Come - didn’t stand it better judgment; His hands and bury it again, tramped herself! She’d never would be. He found her spite of anything the one was a prime feature sunset, and hit upon that of the forever. K = 8 look-a-here - I told you before, Joe. I’ve heard a pin drop. The stillness was complete, how- ever, this is awful crime, beyond the village was sufficient. He would be a good enough to get that night, Tom and Becky. K = 10 you understanding that they don’t come around in the cave should get the word "beauteous" was over-fondled, and that together" and decided that he might as we used to do - it’s nobby fun. I’ll learn you." To create an order K Markov model of a given source text, you would need to identify all kgrams in the source text and associate with each kgram all the individual characters that follow it. This association or mapping must also capture the frequency with which a given character follows a given kgram. For example, suppose that k = 2 and the sample text is: agggcagcgggcg The Markov model would have to represent all the character strings of length two (2-grams) in the source text, and associate with them the characters that follow them, and in the correct proportion. The following table shows one way of representing this information. kgram Characters that follow ag gc gg gcgc gc agg ca g cg g Once you have created an order K Markov model of a given source text, you can generate new text based on this model as follows. Page 3 of 5 1. Randomly pick k consecutive characters that appear in the sample text and use them as the initial kgram. 2. Append the kgram to the output text being generated. 3. Repeat the following steps until the output text is sufficiently long. (a) Select a character c that appears in the sample text based on the probability of that character following the current kgram. (b) Append this character to the output text. (c) Update the kgram by removing its first character and adding the character just chosen (c) as its last character. If this process encounters a situation in which there are no characters to choose from (which can happen if the only occurrence of the current kgram is at the exact end of the source), simply pick a new kgram at random and continue. As an example, suppose that k = 2 and the sample text is that from above: agggcagcgggcg Here are four different output text strings of length 10 that could have been the result of the process described above, using the first two characters (’ag’) as the initial kgram. agcggcagcg aggcaggcgg agggcaggcg agcggcggca For another example, suppose that k = 2 and the sample text is: the three pirates charted that course the other day Here is how the first three characters of new text might be generated: •A two-character sequence is chosen at random to become the initial kgram. Let’s suppose that “th” is chosen. So, kgram = th and output = th. •The first character must be chosen based on the probability that it follows the kgram (currently “th”) in the source. The source contains five occurrences of “th”. Three times it is followed by ’e’, once it is followed by ’r’, and once it is followed by ’a’. Thus, the next character must be chosen so that there is a 3/5 chance that an ’e’ will be chosen, a 1/5 chance that an ’r’ will be chosen, and a 1/5 chance that an ’a’ will be chosen. Let’s suppose that we choose an ’e’ this time. So, kgram = he and output = the. •The next character must be chosen based on the probability that it follows the kgram (currently “he”) in the source. The source contains three occurrences of “he”. Twice it is followed by a space and once it is followed by ’r’. Thus, the next character must be chosen so that there is a 2/3 chance that a space will be chosen and a 1/3 chance that an ’r’ will be chosen. Let’s suppose that we choose an ’r’ this time. So, kgram = er and output = ther. •The next character must be chosen based on the probability that it follows the kgram (currently “er”) in the source. The source contains only one occurrence of “er”, and it is followed by a space. Thus, the next character must be a space. So, kgram = r_ and output = ther_, where ’_’ represents a blank space. Page 4 of 5 Implementation Details You are provided with two Java files that you must use to develop your solution: MarkovModel.java and TextGenerator.java. The constructors of MarkovModel build the order-k model of the source text. You are required to represent the model with the provided HashMap field. The main method of TextGenerator must process the following three command line arguments (in the args array): •A non-negative integer k •A non-negative integer length. •The name of an input file source that contains more than k characters. Your program must validate the command line arguments by making sure that k and length are non- negative and that source contains at least k characters and can be opened for reading. If any of the command line arguments are invalid, your program must write an informative error message to System.out and terminate. If there are not enough command line arguments, your program must write an informative error message to System.out and terminate. With valid command line arguments, your program must use the methods of the MarkovModel class to create an order k Markov model of the sample text, select the initial kgram, and make each character selection. You must implement the MarkovModel methods according to description of the Markov modeling process in the section above. A few sample texts have been provided, but Project Gutenberg (http://www.gutenberg.org) maintains a large collection of public domain literary works that you can use as source texts for fun and practice. Acknowledgments This assignment is based on the ideas of many people, Jon Bentley and Owen Astrachan in particular.

It is an LLM powered assistant (MultiModal RAG) that can read directories, even images, to gather information about companies. You can use HuggingFace or Ollama models,Claude,OpenAI and Google LLMs.I am inspired by IBM Discovery.

moosic - made by claude code (and I?)

A collection of Claude skills organized by how I think, decide, research and build.

A JUCE tutorial fully generated with kilocode.ai and Claude Sonnet 4, so that I can hopefully learn some audio plugin development. Not verified, use at your own risk.

Just a small project I decided to start with the help of Claude. It's a python program that runs in termux to create custom sub-ghz and IR codes for the Flipper Zero. Not complete!!

Prompts for "Wiring the Winning Organization" that I use for OpenAI and Claude

I got laid off and built a robot army to find my next job. Multi-agent job search automation for Claude Code.

Browser in a Browser??? This is a mini side project. Created by Claude Sonnet 4.5 and System Technologies! This is just a small project that I was working on and finished today. Hope you like it!

After Claude Code source leaked in March 2026, I reverse-engineered the agent harness architecture and created a comprehensive 5-level learning guide. From complete beginner to production deployment in 8 hours. No leaked code - just understanding. 500+ examples.

I scraped a lot of information on MCP (Model Context Protocol) servers . with Integration to Cursor AI and Claude Desktop . That way you can add this folder to your preferred IDE so that it will have contextual indexing information available to grasp how to create MCP servers correctly .