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
MACHINE LEARNING
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LLM Knowledge Assessment for College Exams
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This should somehow make it into college exams to check whether you pass the LLMs 101 class
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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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Training Data Determines Model Behavior and Outputs
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More generally, it’s whatever is predominant in the training data.
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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
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Share AI, Mathematics and Machine Learning Insights
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If you found it insightful, reshare with your network.
— Akshay 🚀 (@akshay_pachaar) 27 septembre 2025
Find me → @akshay_pachaar ✔️
For more insights and tutorials on AI, Mathematics and Machine Learning! https://t.co/DrcSzcaOH4If you found it insightful, reshare with your network. Find me → @akshay_pachaar For more insights and tutorials on AI, Mathematics and Machine Learning!
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Understanding LLM Internal Architecture: Interactive Transformer Guide
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How LLMs work under the hood?
— Akshay 🚀 (@akshay_pachaar) 27 septembre 2025
This is the best place to visually understand the internal workings of a transformer-based LLM.
Explore tokenization, self-attention, and more in an interactive way: pic.twitter.com/Sk8yK5VpESHow LLMs work under the hood? This is the best place to visually understand the internal workings of a transformer-based LLM. Explore tokenization, self-attention, and more in an interactive way: