Dhruva Karkada's paper on arXiv (2602.15029) showed that LLM embeddings of the 12 months project onto a circle with an approximately circulant Gram matrix, a result the author of the post bet a beer h…
AMD detailed model weight profiles that break down how many parameters go to embeddings, attention, and dense layers, explaining why the amd/Llama-3.1-8B-Instruct-FP8-KV checkpoint is 9.08 GB rather t…
A developer's blog post traces the evolution of language models from rule-based systems to modern LLMs, framing each breakthrough as a bug fix. The post highlights key milestones such as n-gram models…
GloVe, FastText, and word2vec are all word embedding methods that produce dense vector representations of words, but they differ in their underlying approaches: GloVe uses a weighted least-squares reg…
Google researchers led by Tomas Mikolov published word2vec in 2013, a method that made high-quality dense word embeddings cheap to train at billion-token scale on a single machine, transforming them f…
Here is a factual summary of the article: The article describes a solver for Semantle, a Wordle variant that scores guesses based on semantic similarity rather than letter matching. The solver uses a…