For a Qwen3 pure PyTorch implementation: https://
github.com/rasbt/LLMs-fro
m-scratch/tree/main/ch05/11_qwen3
… For an (coding) assistant on Mac: probably ollama (with gpt-oss or Qwen3-Coder depending on your RAM situation)
CODE
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Qwen3 PyTorch Implementation and Mac Assistant Recommendations
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AI Tools Usability Gap: Non-coders Need Working Solutions
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The replies are interesting. I agree that working with the AI is the easy part! Everything else is hard. But anyone who thinks that any of these tools are intuitive to non-coders hasn't shown them to a non-coder. And they just want a working webpage or an exe file they can use.
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KAT-Dev-32B: 32B Model Achieves 62.4% on SWE-Bench Verified
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KAT-Dev-32B 32B-parameter model for software engineering tasks On SWE-Bench Verified, KAT-Dev-32B achieves comparable performance with 62.4% resolved and ranks 5th among all open-source models with different scales vibe coded a quick chat app in anycoder
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Mojo’s Potential Advantage Through Python-Like Syntax
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Maybe mojo will be have a slight chance given that it’s basically python syntax for the most part
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Pre-training and Fine-tuning Share Same Function in LLM Book
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Fun fact: In my Build A Large Language Model From Scratch book, I reused the pre-training function for the supervised instruction fine-tuning chapter to show this as clearly/intuitively as possible. Only the dataset (structure) changes.
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Read Documentation Properly When Learning New Technology
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If you are learning any new tech, You really need to read the documentation patiently and properly. There is no escape to this. #devtips
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Triton as a summarization tool for CUDA code
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I’d say that Triton kind of is that for CUDA. I.e summarizes code into more compact building blocks.
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Machine Code as Universal Programming Standard: LLM Capabilities
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Hm, if we assume that all programming languages ultimately run machine code, then we already have that scenario where things are standardized. You have Python on the one end, Assembly on the other hand. You could ask LLMs to write machine code today, but it won’t be as good as
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Creating Concise Languages That Map to Popular Targets
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The best you can do for now is create a less verbose language that maps onto a widely used target language. You can then generate paired training examples programmatically so the LLM learns to operate with fewer tokens. (Congratulations, you also just reinvented Triton.)
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LLMs Benefit from Human-Designed Programming Languages
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LLMs are quite capable coders. Now, people say they are limited by programming languages designed for humans. I think it is the opposite: they are enabled by programming languages designed for humans. You cannot just design a new programming language for LLMs because where