🤖 Show HN: Gemini Pro 3 hallucinates the HN front page 10 years from now
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💬 Ask HN: Should AI-generated replies be banned under HN rules?
This thread captures a growing cultural tension between human and AI-created discourse. It raises important questions about authenticity and the evolving role of generative models in online communities.
A Hacker News discussion questions whether quoting AI-generated text from models like Gemini should be permitted under community guidelines. The author argues that such responses dilute authentic, human-centered conversation and suggests revising the rules to clarify or restrict this behavior.
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⚙️ LLM from Scratch, Part 28 – Training a Base Model on an RTX 3090
An insightful, hands-on deep dive into real-world LLM training beyond major research labs—combining scientific rigor with practical, reproducible experimentation.
Giles Thomas details his experiment training a GPT-2-sized language model from scratch on consumer hardware using Hugging Face’s FineWeb dataset. He covers dataset curation, tokenization, training throughput, mixed-precision techniques, checkpointing, and validation. The post benchmarks his 163M-parameter model against GPT-2 Small and explores trade-offs between dataset quality, compute time, and FLOPs efficiency.
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🧩 Compiler Engineering in Practice, Part 1 – What Exactly Is a Compiler?
A well-written and accessible guide for engineers who want a grounded understanding of compiler construction—balancing conceptual depth with real-world practicality.
This post introduces the fundamentals of compiler design from a practical engineering standpoint. It defines compilers as translators between computational languages, emphasizes reliability to prevent miscompilation, and explores intermediate representations (IR) as the backbone of modern compilers. It shows that with systematic design, even complex compilers can be debugged like regular software.
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🔍 xkcd 1313: Regex Golf (2014)
An educational notebook by Peter Norvig illustrating regex construction and testing—both playful and instructive for anyone exploring pattern-matching logic.
This Jupyter Notebook includes Python functions for testing and validating regular expressions and pattern-matching logic. It defines helper functions such as `subparts`, `dotify`, and `regex_parts`, concluding with verification that all examples pass successfully.
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