18 votes, 24 comments. 91K subscribers in the vocabulary community. A subreddit to discuss words and help increase your vocabulary. **Due to the…
Mnemonic Devices for Cogent: Remember Cogent Easily - Wordpandit
Learning new vocabulary doesn’t have to be difficult. Mnemonic techniques make it easier by associating words with vivid images, sounds, and stories. Today,
How to gain more from reading by taking it all in more slowly | Psyche Guides
There’s more to words than meets the eye. Deepen your appreciation of literature through the art of slow, attentive reading
Readwise: Get the most out of what you read
Easily revisit and learn from your highlights and annotations.
How to improve your memory: 10 easy, enjoyable expert tips – from reading a book to getting more sleep
Relying on technology can make us forgetful, but there are some simple ways to sharpen your recall. Memory experts describe the tricks and habits that can help
Guidance on Authoring Lexicons
At last weekend's Atmosphere Conference, there were a lot of questions about how author Lexicons. I thought it would be a good idea to share some guidance.
lexicon.community
lexicon.community has 7 repositories available. Follow their code on GitHub.
lexicon/community/lexicon/bookmarks/bookmark.json at main · lexicon-community/lexicon
An ATProtocol community Lexicon. Contribute to lexicon-community/lexicon development by creating an account on GitHub.
Why Do Large Language Models (LLMs) Struggle to Count Letters?
Large Language Models (LLMs) have achieved unprecedented performance on many complex tasks, being able, for example, to answer questions on almost any topic. However, they struggle with other simple tasks, such as counting the occurrences of letters in a word, as illustrated by the inability of many LLMs to count the number of "r" letters in "strawberry". Several works have studied this problem and linked it to the tokenization used by LLMs, to the intrinsic limitations of the attention mechanism, or to the lack of character-level training data. In this paper, we conduct an experimental study to evaluate the relations between the LLM errors when counting letters with 1) the frequency of the word and its components in the training dataset and 2) the complexity of the counting operation. We present a comprehensive analysis of the errors of LLMs when counting letter occurrences by evaluating a representative group of models over a large number of words. The results show a number of consistent trends in the models evaluated: 1) models are capable of recognizing the letters but not counting them; 2) the frequency of the word and tokens in the word does not have a significant impact on the LLM errors; 3) there is a positive correlation of letter frequency with errors, more frequent letters tend to have more counting errors, 4) the errors show a strong correlation with the number of letters or tokens in a word and 5) the strongest correlation occurs with the number of letters with counts larger than one, with most models being unable to correctly count words in which letters appear more than twice.
Why Do Large Language Models (LLMs) Struggle to Count Letters?
Large Language Models (LLMs) have achieved unprecedented performance on many complex tasks, being able, for example, to answer questions on almost any topic. However, they struggle with other simple tasks, such as counting the occurrences of letters in a word, as illustrated by the inability of many LLMs to count the number of "r" letters in "strawberry". Several works have studied this problem and linked it to the tokenization used by LLMs, to the intrinsic limitations of the attention mechanism, or to the lack of character-level training data. In this paper, we conduct an experimental study to evaluate the relations between the LLM errors when counting letters with 1) the frequency of the word and its components in the training dataset and 2) the complexity of the counting operation. We present a comprehensive analysis of the errors of LLMs when counting letter occurrences by evaluating a representative group of models over a large number of words. The results show a number of consistent trends in the models evaluated: 1) models are capable of recognizing the letters but not counting them; 2) the frequency of the word and tokens in the word does not have a significant impact on the LLM errors; 3) there is a positive correlation of letter frequency with errors, more frequent letters tend to have more counting errors, 4) the errors show a strong correlation with the number of letters or tokens in a word and 5) the strongest correlation occurs with the number of letters with counts larger than one, with most models being unable to correctly count words in which letters appear more than twice.
In Which A Book Lexicon is Born
Hi everyone, We recently held a sync to discuss creating a shared book lexicon for the Atmosphere Protocol. I’ve shared the full recording (thanks Ronen!) and notes from the meeting here: Fathom with some early notes here: https://app.notion.com/p/Book-Lexicon-Session-1-211b2b43aabb8007b098d063972ca58c (also do we have a ATProto version of Notion yet?) The book logging social scene is highly fragmented. Dozens of Goodreads alternatives are competing but not interoperating, leading to a lack of...
The Top 3 Most Effective Ways to Take Notes While Reading
The most effective way to take notes while reading is to follow this simple three step process that I've tested and honed on over one thousand books.
Learning How to Learn (And 20+ Studies) – Max's Two Cents
I have been interested in cognitive science and effective learning methods for years. I’ve read multiple books and articles and put many ideas to test. In this post, I aim to synthesize my notes and provide references to scientific studies.
- 1000-Word Philosophy: An Introductory Anthology
Welcome to 1000-Word Philosophy: An Introductory Anthology, an ever-growing set of essays on philosophical questions, theories, figures, and arguments.
Speaking is quick, listening is slow
Posted on Friday 27 Feb 2026. 925 words, 17 links. By Matt Webb.
