🧠 LaTeX, LLMs, and the Beauty of Boring Technology
A reflective take on the harmony between modern AI and timeless engineering tools — a reminder that innovation often thrives on stability rather than novelty.
This essay explores how large language models (LLMs) enhance long-standing, reliable tools like LaTeX. Trained on vast historical data, LLMs make LaTeX easier to use — from symbol lookup to debugging and diagram generation — reducing the learning curve compared to newer alternatives such as Typst.
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📘 Computational Complexity (2023)
An accessible and well-structured guide to the foundations of algorithmic complexity — great for learners seeking to grasp core computational principles.
This paper introduces key ideas in computational complexity, including function growth and Big-O notation, showing how algorithms scale with input size. Examples such as binary tree search demonstrate different time complexities, emphasizing the importance of efficiency analysis in computer science.
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📜 The FSF Takes On Large Language Models
A nuanced exploration of how the open-source community is rethinking licensing in the AI age — pragmatic yet cautious about the long-term legal ripple effects of LLMs.
During the 2025 GNU Tools Cauldron, the FSF’s Licensing and Compliance Lab discussed the legal and ethical implications of large language models (LLMs) in open-source software. Topics included copyrightability of AI outputs, non-free training datasets, and potential GPL violations. Proposed measures involve labeling AI-generated code and disclosing model and prompt data.
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