Ray Myers contends mathematicians initially pursued a cooperative path with AI, combining large language models with symbolic proof assistants like Lean and Mathlib, but that partnership fractured when corporate behavior - exemplified by OpenAI - betrayed trust. He contrasts the modest, transparent contributions of Lean’s community and figures like Leonardo de Moura and Terence Tao with the hype, dishonesty, and labor-hostile tactics of well-funded labs. The core argument is that LLMs and symbolic systems are complementary, yet the AI gold rush has prioritized marketing, layoffs, and devaluation of workers, turning boosters into enablers of harm. Myers frames the industry’s conduct as narcissistic abuse, using concepts like “flying monkeys” and DARVO to explain how defenders obscure real grievances and attack critics.
The account traces a timeline: early advocacy for formal methods (Buzzard, Tao), Tao’s 2023 turn to Lean aided by ChatGPT, the author’s 2023 Mechanized Mending Manifesto and 2024 push for reliability, followed by escalating corporate misconduct - governmental austerity campaigns (DOGE), NSF funding cuts in 2025, massive OpenAI fundraising, and the October 2026 release of mathematical documents that provoked public letters from Fields medalists and the Association for Human Mathematics. Myers urges boundary-setting and an “aspiring Luddite” stance when technology is deceptive, degrades quality, or harms work conditions.
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DeepSeek V4.1 Flash 1017 is a sparse mixture-of-experts model built on the Causal Encoder-Decoder architecture, designed for coding, long-horizon tasks, and visual-text understanding. It offers improved performance and cost efficiency, with reduced cache memory and faster task completion compared to previous versions. (openrouter.ai)
DeepSeek-v4.1-Flash-1017 is a multimodal image-text-to-text model available on Hugging Face, optimized for text generation tasks. It supports various precisions and is licensed under MIT, with deployment options in the US region. (huggingface.co)
Meta plans to scrape content from its business customers to train its AI models. This practice raises concerns about data privacy and user consent. (theregister.com)
Llmspeed allows users to simulate the speed of local large language models on various hardware setups before making a purchase. It provides benchmark estimates based on community data for different models and devices. (llmspeed.dev)
Elon Musk has made numerous predictions about Tesla achieving full autonomous driving, with timelines ranging from 2018 to 2023. As of 2024, Tesla's FSD system remains classified as Level 2, and full autonomy has not yet been achieved. (en.wikipedia.org)
Lithos-metal develops high-performance LLM inference kernels and serving solutions optimized for Apple silicon. The project is hosted on GitHub with 257 stars and 24 forks, focusing on efficient AI model deployment. (github.com)
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