Every Bluesky account has a DID: 24 random characters nobody chose. Sometimes they spell something by accident. This checks you and everyone you follow for the longest dictionary word hiding in one.
Words of Bluesky
Find the Bluesky accounts that write like an account you know, and the accounts that use a word far more than most.
Bluesky User Statistics
Enter a Bluesky username to see stats on posts, likes, reposts, and follows. View hashtag, mention, link, and word usage, along with their counts.
Smush
A daily word game. Each letter gets 5 uses. Except the gold one, which is always required.
OUTLIER · Daily Global Word Game Scored on Rarity
The daily global word game where rare words win. Same grid worldwide. Can you find what everyone else misses?
I got banned from Bluesky last weekend, let's all have a talk about it - Multi-hyphenate
about user experience and atprotocol and silicon valley
Wordle - A daily word game
Guess the hidden word in 6 tries. A new puzzle is available each day.
GlimpSky - Bluesky Profile Viewer, Likes, and Regex Search
Explore public Bluesky posts, likes, followers, and account activity. Filter by date, author, and regex with interactive analytics. No login required.
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.
Verified Account Tracker (@verified.evil.gay)
⚠️ ⚠️ ⚠️ !!! >>> NOT AFFILIATED WITH BLUESKY <<< !!! ⚠️ ⚠️ ⚠️ A bot tracking newly verified accounts on Bluesky. Check out the lists tab for full lists on verified accounts. ran by: @mmatt.net https://github.com/mmattbtw/bsky-verified-account-tracker
We should stop calling it as Bluesky "account". It's Bluesky "profile", not "account". I'm not saying people should learn about the whole #atproto business to use Bluesky, but at least they should know that their "account" is part of larger world and not tied to just Bluesky.
38 Influential Posters
Getting lots of questions about login. When you login to bsky38.com you're really logging into your Bluesky account on their webpage. We never see your password. Bluesky can let other websites use it as authentication, similar to "Login with Google." The screen found here bsky.social/account
