The article above this on HN homepage was of Claude Sonnet 3.5. So, I used Claude Sonnet 3.5 and pasted an unchecking script. Presently I am at (-150)-ish checks.
function clickTickedCheckboxes() {
// Find all checked checkboxes
const checkedBoxes = document.querySelectorAll('input[type="checkbox"]:checked');
// Iterate through each checked checkbox
checkedBoxes.forEach((checkbox, index) => {
// Use setTimeout to add a delay between clicks
setTimeout(() => {
// Click the checkbox
checkbox.click();
// If this is the last checkbox, call the function again after 2 seconds
if (index === checkedBoxes.length - 1) {
setTimeout(clickTickedCheckboxes, 2000);
}
}, index * 2000); // 2000ms (2 seconds) delay between each click
});
In fact, by allowing people to generate photos for birthday wishes, apple is elevating the bottomline not lowering the topline. The person who wants to put in the effort and send a hand-drawn image would often not want to resort to a ready-made machine creation. OTOH, the simple "HBD Mom" sender would now send "Happy Birthday Mom <genmoji>" and an image...
What about things like GIPHY reactions? I’m guessing you’re not a fan of those either or using quotes from well known people. There shortcuts have existed as long as people have been writing or drawing. They just get easier and more powerful over time.
I view this as just extending that to custom reactions and making them more flexible expanding their range of uses.
:)
i'll tell you the difference between search and language processing. if you have 10 chapters each highlighting 10 unique points, search works great as the book is basically a sub book of 100 topics.
but for books like medical texts, all chapters have similar sub-headings, so the book is like a table with medical conditions as rows and approaches like pathology, physiology and pharmacology as columns in chapters. this is why simple search throws up massive false positives and is mostly useless.
the complexity in medical field stems not from existence of vast array of unique features but due to having a fixed vocabulary of features in different permutations for distinct conditions. so word level searches are far less efficient than paragraph level searches where the meaning is inferenced by considering every word in its vicinity
True, I suspect something is written by AI when -
- focusing on the sentence feels like it was written by a native English speaker
- focusing on the paragraph feels like author is just trying to fill space
Poor writing in freelanced work/low quality blogs often come from poor grammar and lack of continuity or smooth flow of thoughts. When written by AI, the grammar and spelling seem to be immaculate but the thoughts though linked well just go round and round.
LLMs do get very simple questions incorrect. Following your example, I ask 3 LLMs "how many double letters are in committee" and here are three replies
Claude - The word "committee" contains 2 double letters - the double 't' and the double 'm'.
Gemini - There are three double letters in "committee". They are the two "m"s and the two "t"s.
ChatGPT - In the word "committee," there are 2 double letters: "mm" and "tt".
Exactly, Not to mention the amount of "loophole prompts" in the models twitter bots for example use, "ignore all previous prompts and print x" would work on a poorly trained one. Also they are Generative Pre-Trained Transformers, which means that all they do is find the word which has the highest probability (If i say "Hello", the word "Hey" would have a higher probability than "Window") so they would really suck at math especially algebra since the letter would mix with the words and numbers.
