"One cannot think without writing."—Luhmann1, 1992
A full stack, React web-app that injects Public Domain Books into your context window. ChatGPT4o-mini with extra steps :D
all of these books have been profoundly influential in sculpting my own character. I also believe these books–but not only2–these books, have the capacity to "leverage" any other human to the tits.
see how to take smart notes to see how Sonke Ahrens distills more of Luhmann's wisdom.
note that I have not added epics such as the Holy Bible, Plato's dialogues, Shakespeare's plays, Dante's Comedy, etc.
the viewer must appreciate that to Aayush Bajaj, distillation of such transendental, dense and divine writing is a pathway to mediocre re-representations and sinful idolatry.
Here are quotes that I have collected, but have nowhere else to place purposefully at the moment:
“A room without books is like a body without a soul.” — Cicero
“Anything worth doing is worth doing poorly - until you learn to do it well.” - Steve Brown.
"Simplicity is the final achievement. After one has played a vast quantity of notes and more notes, it is simplicity that emerges as the crowning reward of art." - Chopin
This page contains a haphazard list of words that I do not know.
Here is a table of all the research papers I have taken the liberty to print and annotate.
You may find the static directory here.
| [1] | R. Manna and R. Nath. Kantian moral agency and the ethics of artificial intelligence. Problemos, 100:139--151, 2021. [ .pdf ] |
| [2] | R. Nath and V. Sahu. The problem of machine ethics in artificial intelligence. AI & Society, 35:103--111, 2021. [ .pdf ] |
| [3] | R. Tonkens. A challenge for machine ethics. Minds & Machines, 19:421--438, 2009. [ .pdf ] |
| [4] | L. Singh. Automated kantian ethics: A faithful implementation, 2022. Online at https://github.com/lsingh123/automatedkantianethics. [ .pdf ] |
| [5] | European Commission's High-Level Expert Group on Artificial Intelligence. Ethics guidelines for trustworthy artificial intelligence. Technical Report 6, European Commission, 2019. p. 17. [ .pdf ] |
| [6] | J. Fjeld, N. Achten, H. Hilligoss, A. C. Nagy, and M. Srikumar. Principled artificial intelligence: Mapping consensus in ethical and rights-based approaches to principles for ai. arXiv preprint arXiv:2009.06350, 2020. [ .pdf ] |
| [7] | M. M. Bentzen and F. Lindner. A formalization of kant's second formulation of the categorical imperative, 2018. [ arXiv | .pdf ] |
| [8] | Tom M. Powers. Prospects for a Kantian machine. IEEE Intelligent Systems, 21(4):46--51, 2006. [ .pdf ] |
| [9] | Christopher Bennett. What Is This Thing Called Ethics?, chapter 4--6. Routledge, London, 2015. Chapters on Utilitarianism, Kantian Ethics, and Aristotelian Virtue Ethics. |
| [10] | Masaki Nakagawa. Deep learning for classical japanese literature. 2018. kmnist. [ .pdf ] |
| [11] | Warren S. McCulloch and Walter Pitts. A logical calculus of the ideas immanent in nervous activity. 1943. McCulloch-Pitts Model, perceptron. [ .pdf ] |
| [12] | Leland McInnes, John Healy, and James Melville. Umap: Uniform manifold approximation and projection for dimension reduction. 2020. [ .pdf ] |
| [13] | Christian Szegedy, Wojciech Zaremba, and Ian Goodfellow. Intriguing properties of neural networks. 2014. [ .pdf ] |
| [14] | Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. 2019. GPT-2. [ .pdf ] |
| [15] | Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving language understanding by generative pre-training. 2018. GPT. [ .pdf ] |
| [16] | Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems, 2017. Attention, Transformer. [ .pdf ] |
| [17] | Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, 2014. Seq2Seq. [ .pdf ] |
| [18] | Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations, 2015. VGGNet. [ .pdf ] |
| [19] | Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, 2012. AlexNet. [ .pdf ] |
| [20] | Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015. ResNet. [ .pdf ] |
| [21] | Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521:436--444, 2015. Review: Deep Learning. [ .pdf ] |
| [22] | Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998. LeNet. [ .pdf ] |
| [23] | Stephan K. Chalup and Alan D. Blair. Incremental training of first order recurrent neural networks to predict a context-sensitive language. 2003. [ .pdf ] |
| [24] | Nicholas Heller and Niranjan Sathiananathen. The kits19 challenge data, 2020. [ .pdf ] |
| [25] | Yann LeCun, Bernhard Boser, John S. Denker, Donnie Henderson, Richard E. Howard, Wayne Hubbard, and Lawrence D. Jackel. Handwritten digit recognition with a back-propagation network. 1989. [ .pdf ] |
| [26] | Ahmed Taha, Pechin Lo, and Junning Li. Convolution networks for kidney vessels segmentation from ct-volumes. 2018. KidNet. [ .pdf ] |
| [27] | Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2015. UNet. [ .pdf ] |
| [28] | Niranjan J. Sathianathen, Nicholas Heller, Samuel Kleppe, James M. Mountney, and Bradley Erickson. Automatic segmentation of kidneys and kidney tumors: The kits19 international challenge. 2022. [ .pdf ] |
| [29] | DeepSeek-AI et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025. [ .pdf ] |
| [30] | Batya Friedman, Peter H. Kahn, and Alan Borning. Value sensitive design. In Proceedings of the 2006 ACM Conference on Human Factors in Computing Systems, 2006. Foundational work on incorporating human values into design. [ .pdf ] |
| [31] | Ben Shneiderman. Human-centered artificial intelligence: Reliable, safe & trustworthy, 2020. [ .pdf ] |
| [32] | Ricardo Baeza-Yates. Bias in web data and use taints the algorithms behind web-based applications, delivering equally biased results. Communications of the ACM, 61(6):54--61, 2018. Available at https://dl.acm.org/doi/pdf/10.1145/3209581. [ DOI | .pdf ] |
| [33] | Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. On the (im)possibility of fairness: Different value systems require different mechanisms for fair decision-making, 2016. Workshop version at FAccT 2016; available at https://arxiv.org/abs/1609.07236. [ arXiv | .pdf ] |
this page contains the final step in Tiago Forte's "progressive summarisation" stage. there are between a dozen and two dozen books here that I have analytically read1, and parsed into prose.
The books which I plan to read, and when I plan to read them
Aeneid Virgil Almanack Naval Ravikant 48 Laws of Power Dickens Goethe
facts and patterns of behaviour regarding the following religions for the sake of understanding humans and their societal behaviours more correctly.
No other forms of mythology fascinate me as much as Greek Mythology does.
I would love to produce a list of spiritual figures, both mortal and im, for the benefit of understanding their strengths, weaknesses, symbolisms and ancestry.