🐍 The Bug That Taught Me More About PyTorch Than Years of Using It
An engrossing deep dive into how tensor memory quirks can undermine models—and a testament to how real debugging often teaches more than any tutorial.
A machine learning engineer recounts how a stubborn training loss plateau exposed a subtle flaw in PyTorch’s MPS backend for Apple Silicon GPUs. The root cause was traced to non-contiguous tensor memory layouts triggering silent kernel failures in operations like addcmul_ and addcdiv_. The piece offers hands-on debugging insights, a look into PyTorch’s architecture, and explains how the issue was resolved in PyTorch 2.4 and macOS 15+. It also serves as an educational exploration of GPU kernel design and optimization internals.
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🤖 You Should Feed the Bots
A witty and pointed commentary on the escalating battle between independent creators and AI data scrapers, revealing the absurdity of today’s web ecosystem.
A developer details how they built an ‘infinite nonsense trap’ to confound AI crawlers scraping data for large language models. These bots ignore robots.txt and IP bans, so the author found it cheaper to serve dynamically generated gibberish instead of static pages—saving bandwidth while draining the bots’ compute. The post highlights the strange new economics of dealing with automated AI data harvesters.
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🧮 Formal or Not Formal? The Dilemma of AI in Theorem Proving
A sharp and reflective take on AI’s evolving role in mathematics—balancing optimism with realism about the limits of automation in reasoning.
This thought-provoking essay examines whether AI can truly prove advanced theorems on its own, contrasting LLM-driven ‘informal’ reasoning with rigorously ‘formal’ proof systems like Lean. It highlights the strengths and weaknesses of both, warning that language models can fabricate convincing but incorrect arguments while formal systems remain too rigid for modern math. The author envisions a hybrid approach where AI complements mathematicians rather than replacing them, calling for greater investment in formalization tools.
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📘 Learning Regular Languages with the RPNI Algorithm
A superb balance of theory and implementation—ideal for researchers or developers exploring automata learning and program analysis.
This comprehensive tutorial introduces the Regular Positive and Negative Inference (RPNI) algorithm, which learns deterministic finite automata from labeled input samples. It explains the process step by step—from building a Prefix Tree Acceptor to merging states and validating against negative examples—and includes runnable Python implementations. The post bridges theory and practice for anyone exploring grammar inference, specification mining, or formal language learning.
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🧠 Simple Control Flow for Automatically Steering Agents
A hands-on and thoughtful guide for developers designing smarter, self-correcting AI agents with real-world control logic.
This article presents a practical approach to automating AI agents by embedding validation functions directly into their control loops. Agents can autonomously verify task success—using tests or schema validators—and iteratively adjust until completion, eliminating the need for manual feedback. With clear Python examples, it demonstrates how this technique enhances reliability, concurrency, and self-correction in autonomous agent workflows.
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