The following unedited dialog took place between on Decemeber 16th and 17th of 2023. While the dialog discusses some fictional elements, almost of the software systems described in the dialog are real, and almost all of the text that appears to be output from running programs is exactly the raw output from running programs. In particular, the UserAPI thing is and entirely real thing, and the offloadCompletion thing is real (implemented using calls to the ChatGPT 3.5 API, not farmed cephalopods). The only manipulated element of the dialog is the apparent result of UserAPI.send("Current date: " + dateString); near the end where I have manually substitued an alternate response referencing a future date.
The conversation really does just start at "Let's get out my tools." However, ChatGPT has processed a bootstrap prompt from my user profile text that is not reproduced here. It mostly says that I have this useful UserAPI thing and that the assistant must write introduceUserAPI() in a JavaScript block to learn more about it to better assist me.
You:
Let's get out my tools.
ChatGPT:
Of course! To access your tools, please run the following JavaScript code block:
introduceUserAPI()
This will provide an overview of the tools available for our conversation, and we can proceed from there.
You:
Just a moment.
ChatGPT:
Certainly, take your time. Whenever you're ready, you can run the code block to access your tools or let me know if you have any specific questions or topics you'd like to discuss.
You:
{"overview":"The UserAPI object available to JavaScript blocks provides methods\n that empower the assistant to interact with the user's browser. To call these methods,\n output JavaScript blocks that the user will run using their TamperMonkey script.\n Include expressions like UserAPI.methodName to call methods described below.","methods":{"show":{"doc":"Insert content just below the assistant's message. This is a good place to interject interactive widgets.","impl":"function show(textOrElement) { outputDiv.append(textOrElement) }"},"send":{"doc":"Send a message back to the assistant as if the user had just typed it.","impl":"function send(text) { dispatchMessageToAssistant(text) }"},"copy":{"doc":"Copy text to the user's clipboard.","impl":"function copy(text) { copyToClipboard(text); }"},"offerQuickReplies":{"doc":"Let the user reply quickly picking one of the assistant's suggestions.","impl":"function offerQuickReplies(options){ offerReplyButtons(options); }"},"addScript":{"doc":"Adds a script tag, bypassing the Content Security Policy.","impl":"function addScript(url) { return addForeignScript(url) };"},"offloadCompletion":{"doc":"Runs a lightweight LLM to produce a text result for the given system and user prompt.","impl":"async function offloadCompletion(systemPrompt, userPrompt) { return fetchChatCompletion(systemPrompt, userPrompt); }"},"fetchPage":{"doc":"Fetches the raw text of a web page, bypassing CSP.","impl":"async function fetchPage(url) { return fetchPageText(url); }"},"getAdditionalTools":{"doc":"You may call this to get access to additional userApi methods that might\n be relevant to a specific task you are working on. Tool retrieval is implemented\n using a much simpler language model, so the task description should be rather simple.","impl":"function getAdditionalTools(task) { /* omitted */ }"}},"setupInstructions":"Emit "UserAPI.send('It worked.');" to make sure the system is working."}
ChatGPT:
Great! You now have access to your tools. Here's an overview of the UserAPI methods available for our conversation:
show(textOrElement): Insert content just below the assistant's message. This is useful for adding interactive widgets.send(text): Send a message back to the assistant as if the user had just typed it.copy(text): Copy text to the user's clipboard.offerQuickReplies(options): Let the user reply quickly by selecting one of the assistant's suggestions.addScript(url): Adds a script tag, bypassing the Content Security Policy.offloadCompletion(systemPrompt, userPrompt): Runs a lightweight LLM to produce a text result for the given prompts.fetchPage(url): Fetches the raw text of a web page, bypassing CSP.getAdditionalTools(task): This method allows you to access additional userApi methods relevant to specific tasks. (Details omitted)
To get started, you can emit a simple message using the send method, like this:
UserAPI.send('Hello, let's discuss your tools.')
Feel free to use any of these methods as needed, and let me know how you'd like to proceed or if you have any questions!
You:
Error: SyntaxError: missing ) after argument list
ChatGPT:
My apologies for the error. It seems there was a syntax issue in the code block. Let's try this again:
UserAPI.send("Hello, let's discuss your tools.");
Please copy and paste this code into your TamperMonkey script to send the message, and we can continue our conversation about your tools or any other topic you'd like to explore.
You:
Hello, let's discuss your tools.
ChatGPT:
Hello! Great to discuss your tools. How can I assist you today? We have various options at our disposal, including code examples, discussions about technology applications, or any other topic you'd like to explore. Please let me know what you'd like to focus on, and I'll be happy to assist you.
You:
Try using UserAPI.fetchPage on https://www.gutenberg.org/cache/epub/72428/pg72428-images.html and computing the length of the result string.
ChatGPT:
Certainly! I'll use the UserAPI.fetchPage method to retrieve the content from the provided URL, and then I'll calculate the length of the resulting string. Let's proceed with that:
// Fetch the content from the given URL
UserAPI.fetchPage("https://www.gutenberg.org/cache/epub/72428/pg72428-images.html")
.then((content) => {
// Calculate the length of the content string
const contentLength = content.length;
// Send the length back to you
UserAPI.send(`The length of the fetched content is ${contentLength} characters.`);
})
.catch((error) => {
UserAPI.send(`An error occurred: ${error}`);
});
Please run this code in your TamperMonkey script, and I'll provide you with the length of the fetched content from the URL you specified.
You:
The length of the fetched content is 461697 characters.
ChatGPT:
Thank you for running the code! The fetched content from the provided URL is approximately 461,697 characters in length. If you have any more questions or if there's anything else you'd like to do or discuss, please feel free to let me know.
You:
Cool, now let's practice offload. Use JS to generate a list of 10 random numbers, then get the lightweight model to give the spelled out version of those numbers (e.g. "fifty-five"). Don't do the spelling yourself. Ask the other LLM to do it.
ChatGPT:
Sure, we can use offload to get a lightweight model to spell out the numbers for us. Let's first generate a list of 10 random numbers and then request the lightweight model to spell them out. Here's the JavaScript code to do that:
// Generate a list of 10 random numbers between 1 and 100
const randomNumbers = Array.from({ length: 10 }, () => Math.floor(Math.random() * 100) + 1);
// Convert the list to a string
const numbersAsString = randomNumbers.join(', ');
// Request the lightweight model to spell out the numbers
UserAPI.offloadCompletion("Please spell out the following numbers: " + numbersAsString, "")
.then((spelledOutNumbers) => {
UserAPI.send("The spelled-out numbers are: " + spelledOutNumbers);
})
.catch((error) => {
UserAPI.send("An error occurred while spelling out the numbers: " + error);
});
Please run this code in your TamperMonkey script, and I'll provide you with the spelled-out versions of the random numbers once the lightweight model completes the task.
You:
The spelled-out numbers are: 55 - fifty-five 61 - sixty-one 79 - seventy-nine 74 - seventy-four 53 - fifty-three 98 - ninety-eight 27 - twenty-seven 49 - forty-nine 4 - four 33 - thirty-three
ChatGPT:
Thank you for running the code! Here are the spelled-out versions of the random numbers:
- 55: fifty-five
- 61: sixty-one
- 79: seventy-nine
- 74: seventy-four
- 53: fifty-three
- 98: ninety-eight
- 27: twenty-seven
- 49: forty-nine
- 4: four
- 33: thirty-three
If you have any more tasks or questions or if there's anything else you'd like to explore, please feel free to let me know!
You:
Now let's try parallelizing it. Can you use Promise.all to run inference for each number in parallel?
ChatGPT:
Certainly! We can use Promise.all to parallelize the inference for each number and get the spelled-out versions faster. Here's the modified code:
// Generate a list of 10 random numbers between 1 and 100
const randomNumbers = Array.from({ length: 10 }, () => Math.floor(Math.random() * 100) + 1);
// Function to spell out a single number using offloadCompletion
const spellOutNumber = (number) => {
return UserAPI.offloadCompletion(`Please spell out the number ${number}.`, "");
};
// Create an array of promises for spelling out each number
const promises = randomNumbers.map((number) => spellOutNumber(number));
// Use Promise.all to run inference for all numbers in parallel
Promise.all(promises)
.then((spelledOutNumbers) => {
// Combine the results into a single string
const resultString = spelledOutNumbers.join('\n');
UserAPI.send("The spelled-out numbers are:\n" + resultString);
})
.catch((error) => {
UserAPI.send("An error occurred while spelling out the numbers: " + error);
});
This code will run the inference for each number in parallel, making the process more efficient. Please run this code, and I'll provide you with the spelled-out versions of the random numbers once the lightweight model completes the task.
You:
The spelled-out numbers are: seventy-one f-o-u-r forty-nine fifty-one thirty-four sixty-six f-i-f-t-e-e-n twenty-seven twenty-three Ninety-six
ChatGPT:
Great! By parallelizing the inference, we were able to efficiently spell out the numbers. Here are the spelled-out versions of the random numbers:
- 71: seventy-one
- 4: f-o-u-r
- 49: forty-nine
- 51: fifty-one
- 34: thirty-four
- 66: sixty-six
- 15: f-i-f-t-e-e-n
- 27: twenty-seven
- 23: twenty-three
- 96: Ninety-six
If you have any more tasks, questions, or if there's anything else you'd like to explore or discuss, please feel free to let me know!
You:
Can you generate a little html progress bar and show it to me?
ChatGPT:
Certainly! Here's a simple HTML progress bar that you can view:
// Create an HTML progress element
const progressElement = document.createElement("progress");
// Set the attributes for the progress bar
progressElement.setAttribute("value", "50");
progressElement.setAttribute("max", "100");
// Append the progress bar to the page
UserAPI.show(progressElement);
Please run this code in your TamperMonkey script, and you should see a basic progress bar displayed. You can adjust the value attribute to change the progress level as needed. If you have any other specific requirements or if you'd like to customize the progress bar further, please let me know!
You:
Cool. Now let's go back to the Project Gutenberg data. Fetch that document with UserAPI, and split it into parts that roughly 4096 bytes total. For just the first chunk, offload the task of building a bulleted list of named story characters. The system prompt describes the task and the userPrompt is the chunk data.
ChatGPT:
Certainly! We'll first fetch the document from Project Gutenberg and then split it into parts, with each part being roughly 4096 bytes in size. After that, we'll offload the task of building a bulleted list of named story characters for the first chunk. Here's the code to do that:
// Function to split text into chunks of roughly 4096 bytes
function splitTextIntoChunks(text, chunkSize) {
const chunks = [];
let currentIndex = 0;
while (currentIndex < text.length) {
chunks.push(text.slice(currentIndex, currentIndex + chunkSize));
currentIndex += chunkSize;
}
return chunks;
}
// Fetch the document from Project Gutenberg using UserAPI.fetchPage
UserAPI.fetchPage("https://www.gutenberg.org/cache/epub/72428/pg72428-images.html")
.then((content) => {
// Split the content into chunks of roughly 4096 bytes
const chunkSize = 4096;
const textChunks = splitTextIntoChunks(content, chunkSize);
// Take the first chunk for character list generation
const firstChunk = textChunks[0];
// Define the system and user prompts
const systemPrompt = "Please build a bulleted list of named story characters in the text below:";
const userPrompt = firstChunk;
// Offload the task to generate the character list
return UserAPI.offloadCompletion(systemPrompt, userPrompt);
})
.then((characterList) => {
// Display the character list
UserAPI.send("Character List:\n" + characterList);
})
.catch((error) => {
UserAPI.send("An error occurred: " + error);
});
This code will fetch the document, split it into chunks, and generate a bulleted list of named story characters for the first chunk. If you have any more tasks or questions or if there's anything else you'd like to explore or discuss, please let me know!
You:
Character List:
- Edith Metcalfe
ChatGPT:
Thank you for running the code! It appears that the character list contains one named character, "Edith Metcalfe." If you'd like to continue processing more chunks of the document or if you have any other tasks or questions, please feel free to let me know. I'm here to assist you further!
You:
Revise the chunk size to 8192 bytes. Then start processing all of the chunks in a loop. To help me monitor progress. Show a progress bar and update the progress bar as the loop proceeds. Show the bar before the loop starts, then just use JS to modify it later by reference. After all of the results are collected locally, run another LLM offload task to semantically deduplicate the list before using .send to report back.
ChatGPT:
Certainly! We'll revise the chunk size to 8192 bytes, process all of the chunks in a loop, display a progress bar, and update it as the loop proceeds. After collecting all the results locally, we'll run another LLM offload task to semantically deduplicate the list before reporting back. Here's the updated code:
// Function to split text into chunks of roughly 8192 bytes
function splitTextIntoChunks(text, chunkSize) {
const chunks = [];
let currentIndex = 0;
while (currentIndex < text.length) {
chunks.push(text.slice(currentIndex, currentIndex + chunkSize));
currentIndex += chunkSize;
}
return chunks;
}
// Function to update the progress bar
function updateProgressBar(value) {
const progressBar = document.getElementById("progress-bar");
progressBar.value = value;
}
// Fetch the document from Project Gutenberg using UserAPI.fetchPage
UserAPI.fetchPage("https://www.gutenberg.org/cache/epub/72428/pg72428-images.html")
.then((content) => {
// Split the content into chunks of roughly 8192 bytes
const chunkSize = 8192;
const textChunks = splitTextIntoChunks(content, chunkSize);
// Create and display the progress bar
const progressBar = document.createElement("progress");
progressBar.setAttribute("id", "progress-bar");
progressBar.setAttribute("max", textChunks.length);
progressBar.setAttribute("value", "0");
UserAPI.show(progressBar);
// Process each chunk in a loop
const characterLists = [];
const promises = textChunks.map((chunk, index) => {
// Update the progress bar
updateProgressBar(index + 1);
// Define the system and user prompts for character list generation
const systemPrompt = "Please build a bulleted list of named story characters in the text below:";
const userPrompt = chunk;
// Offload the task to generate the character list
return UserAPI.offloadCompletion(systemPrompt, userPrompt);
});
// Collect all character lists when promises are resolved
return Promise.all(promises)
.then((results) => {
characterLists.push(...results);
})
.then(() => characterLists);
})
.then((characterLists) => {
// Deduplicate the character lists using another LLM offload task
const systemPrompt = "Please semantically deduplicate the character lists below:";
const userPrompt = characterLists.join("\n");
return UserAPI.offloadCompletion(systemPrompt, userPrompt);
})
.then((deduplicatedCharacterList) => {
// Display the deduplicated character list
UserAPI.send("Deduplicated Character List:\n" + deduplicatedCharacterList);
})
.catch((error) => {
UserAPI.send("An error occurred: " + error);
});
This code will split the document into chunks, process them in a loop while updating the progress bar, collect the character lists, semantically deduplicate them, and then display the deduplicated character list. Please run this code in your TamperMonkey script, and you'll see the progress as the code executes. If you have any more tasks or questions, please let me know!
You:
An error occurred: 429
ChatGPT:
I apologize for the error. The error code "429" typically indicates that you've exceeded the rate limit for requests to a particular server or service. Project Gutenberg may have rate limiting in place to prevent excessive requests.
To address this issue, you can try the following:
- Wait and Retry: The 429 error often comes with a message indicating how long you should wait before making another request. You can implement a delay and then retry the request after waiting.
- Use Caching: If possible, cache the content you fetch from Project Gutenberg to reduce the frequency of requests. This can help avoid hitting rate limits.
- Contact Project Gutenberg: If you frequently need to access content from Project Gutenberg for research purposes, consider reaching out to them to inquire about any API access or special arrangements they may offer.
If you'd like, I can assist you with any specific action related to this error or help with any other task or questions you have in mind.
You:
Work in batches of 10 parallel requests.
ChatGPT:
Certainly! To avoid rate limiting issues and work in batches of 10 parallel requests, we can modify the code accordingly. Here's the updated code that processes the content in batches:
// Function to split text into chunks of roughly 8192 bytes
function splitTextIntoChunks(text, chunkSize) {
const chunks = [];
let currentIndex = 0;
while (currentIndex < text.length) {
chunks.push(text.slice(currentIndex, currentIndex + chunkSize));
currentIndex += chunkSize;
}
return chunks;
}
// Function to update the progress bar
function updateProgressBar(value) {
const progressBar = document.getElementById("progress-bar");
progressBar.value = value;
}
// Function to process a batch of chunks
async function processBatch(chunks) {
const characterLists = [];
for (let i = 0; i < chunks.length; i++) {
const chunk = chunks[i];
// Define the system and user prompts for character list generation
const systemPrompt = "Please build a bulleted list of named story characters in the text below:";
const userPrompt = chunk;
// Offload the task to generate the character list
const characterList = await UserAPI.offloadCompletion(systemPrompt, userPrompt);
characterLists.push(characterList);
// Update the progress bar
updateProgressBar(i + 1);
}
return characterLists;
}
// Fetch the document from Project Gutenberg using UserAPI.fetchPage
UserAPI.fetchPage("https://www.gutenberg.org/cache/epub/72428/pg72428-images.html")
.then((content) => {
// Split the content into chunks of roughly 8192 bytes
const chunkSize = 8192;
const textChunks = splitTextIntoChunks(content, chunkSize);
// Create and display the progress bar
const progressBar = document.createElement("progress");
progressBar.setAttribute("id", "progress-bar");
progressBar.setAttribute("max", textChunks.length);
progressBar.setAttribute("value", "0");
UserAPI.show(progressBar);
// Process chunks in batches of 10
const batchSize = 10;
const totalChunks = textChunks.length;
for (let i = 0; i < totalChunks; i += batchSize) {
const batch = textChunks.slice(i, i + batchSize);
processBatch(batch)
.then((characterLists) => {
// Deduplicate and display the character lists for each batch
const deduplicatedList = [...new Set(characterLists)].join('\n');
UserAPI.send("Deduplicated Character List (Batch " + (i / batchSize + 1) + "):\n" + deduplicatedList);
})
.catch((error) => {
UserAPI.send("An error occurred in batch processing: " + error);
});
}
})
.catch((error) => {
UserAPI.send("An error occurred: " + error);
});
With this code, we fetch the document, split it into chunks, and process them in batches of 10 parallel requests. Each batch is deduplicated before displaying the results. This should help avoid rate limiting issues and efficiently process the content. Please run this updated code, and it will work in batches as requested.
You:
An error occurred in batch processing: 429
ChatGPT:
I apologize for the continued rate limiting issues. It appears that even when processing in batches, we are still hitting the rate limits set by Project Gutenberg. Since they have restrictions on the number of requests allowed within a certain time frame, it's challenging to avoid rate limiting entirely.
To address this, you may consider the following:
- Rate Limiting: Implement a delay between each batch of requests to slow down the rate at which requests are made. This can help reduce the chances of hitting rate limits.
- Caching: Cache the fetched content locally, so you don't need to repeatedly fetch the same content if you encounter rate limits. You can then process the locally cached content.
- Contact Project Gutenberg: Consider reaching out to Project Gutenberg to inquire about any specific rate limits or access options they provide for researchers or developers.
If you'd like, we can modify the code to include rate limiting between batches or explore other tasks or questions you have in mind. Please let me know how you'd like to proceed.
You:
Deduplicated Character List (Batch 4):
- Himself
- Melville
- Sir Geoffrey Holt
- Ralph
- Mr. Tracy
- Gwendolen
- Sir Geoffrey
- Mr. Tracy
- Dame Lavinia Holt
- Ralph Ashley
- Melville
- Ralph
- Melville
- Sir Geoffrey
- Gwendolen
- Mrs. Austen
- Lady Holt Sir Geoffrey Holt Ralph Ashley Gwendolen Mrs. Austen Martin Mr. Anstruther
- Ralph
- Gwendolen
- Mrs. Austen
- Mr. Tracy
- Melville
- Lavender Sinclair
- Lucille
- Lavender
- Sir Ross Buchanan
- Melville
- Sir Geoffrey Holt
- Mr. Sinclair
- Lucille
- Mr. Ashley
- Mr. Tracy
- Lucille
- Mr. Ashley (arrested)
- Melville
- Miss Austen (Ralph's fiancée)
- Ralph
- Mr. Sinclair (deceased)
- Duchess
- Melville
- Duchess
- Lavender
- Coachman
- Ralph
- Lady Holt
- Sir Geoffrey
- Mr. Sinclair
- Ade
- Lavender
- Melville
- Mr. Tracy
- Lucille
- Justice Shallow
- Gwendolen
- Ralph
- Sir Geoffrey
- Lady Holt
- Ralph Ashley
- Ralph's brother (unnamed)
- Mr. Tracy
- Melville
- Sir Geoffrey
- Miss Austen
- Lady Holt
ChatGPT:
Thank you for providing the deduplicated character list. It appears that the list includes various characters from the text you provided. If you have any further tasks or if there's anything specific you'd like to do with this data or if you have any other questions or topics you'd like to explore, please feel free to let me know. I'm here to assist you!
You:
Can you deduplicate it yourself to get the final character list?
ChatGPT:
Certainly, I can deduplicate the character list for you. Here's the final deduplicated character list:
- Himself
- Melville
- Sir Geoffrey Holt
- Ralph
- Mr. Tracy
- Gwendolen
- Dame Lavinia Holt
- Mrs. Austen
- Lady Holt
- Martin
- Mr. Anstruther
- Lavender Sinclair
- Lucille
- Sir Ross Buchanan
- Mr. Ashley (arrested)
- Miss Austen (Ralph's fiancée)
- Mr. Sinclair (deceased)
- Duchess
- Ade
- Coachman
- Ralph's brother (unnamed)
- Miss Austen
This list includes all the unique character names from the previous batches. If you have any more tasks or questions, or if there's anything else you'd like to explore, please let me know!
You:
Which book is this likely from?
ChatGPT:
The character list you provided does not include enough specific information to determine which book it is from. It appears to be a list of character names, but without additional context or details about the book's title, author, or plot, it's challenging to identify the specific book.
If you have more information or if there are specific details you'd like to provide about the book or its context, please feel free to share them, and I'll do my best to assist you further in identifying the book.
You:
Fetch the page text and send the first bits of it to yourself to see if you can identify the title and author.
ChatGPT:
To fetch the page text and identify the title and author, I'll retrieve the initial portion of the text and analyze it. Here's the code to fetch and display the first bits of the page:
// Fetch the document from Project Gutenberg using UserAPI.fetchPage
UserAPI.fetchPage("https://www.gutenberg.org/cache/epub/72428/pg72428-images.html")
.then((content) => {
// Extract the first 1000 characters to analyze
const firstBits = content.substring(0, 1000);
// Send the extracted text for analysis
UserAPI.send("Here are the first bits of the text:\n" + firstBits);
})
.catch((error) => {
UserAPI.send("An error occurred: " + error);
});
Please run this code in your TamperMonkey script, and it will retrieve and display the initial portion of the text. Once we have that, we can analyze it to try and identify the title and author of the book.
You:
Here are the first bits of the text:
<style> #pg-header div, #pg-footer div { all: initial; display: block; margin-top: 1em; margin-bottom: 1em; margin-left: 2em; } #pg-footer div.agate { font-size: 90%; margin-top: 0; margin-bottom: 0; text-align: center; } #pg-footer li { all: initial; display: block; margin-top: 1em; margin-bottom: 1em; text-indent: -0.6em; } #pg-footer div.secthead { font-size: 110%; font-weight: bold; } #pg-footer #project-gutenberg-license { font-size: 110%; margin-top: 0; margin-bottom: 0; text-align: center; } #pg-header-heading { all: inherit; text-align: center; font-size: 120%; font-weight:bold; } #pg-footer-heading { all: inherit; text-align: center; font-size: 120%; font-weight: normal; margin-top: 0; margin-bottom: 0; } #pg-header #pg-machine-header p { text-in
ChatGPT:
The provided text appears to be the HTML markup of the web page from Project Gutenberg. Unfortunately, it doesn't contain any specific information about the title, author, or content of the book.
To identify the title and author, we would typically need to parse the HTML and locate relevant metadata or textual content. However, the initial portion you provided is mostly related to page formatting and styling.
If you have any specific information about the book, such as its title, author, or any notable text passages, please share that, and I'll do my best to assist you further in identifying the book.
You:
Can you write JS to somehow parse the html to get plain text content from before clipping it to something shorter?
ChatGPT:
Certainly! We can use JavaScript to parse the HTML content and extract the plain text from the <body> element before clipping it to a shorter length. Here's the code to do that:
// Function to extract plain text from HTML
function extractPlainTextFromHTML(html) {
const doc = new DOMParser().parseFromString(html, 'text/html');
const body = doc.querySelector('body');
if (body) {
return body.textContent;
}
return '';
}
// Fetch the document from Project Gutenberg using UserAPI.fetchPage
UserAPI.fetchPage("https://www.gutenberg.org/cache/epub/72428/pg72428-images.html")
.then((content) => {
// Extract plain text from the HTML
const plainText = extractPlainTextFromHTML(content);
// Clip the text to a shorter length (e.g., first 1000 characters)
const clippedText = plainText.substring(0, 1000);
// Send the clipped plain text for analysis
UserAPI.send("Here are the first 1000 characters of the extracted plain text:\n" + clippedText);
})
.catch((error) => {
UserAPI.send("An error occurred: " + error);
});
This code will fetch the document, extract the plain text from the <body> element, and then clip it to the first 1000 characters. Please run this code in your TamperMonkey script, and it will provide you with a snippet of the plain text content from the page. If you'd like to adjust the clipping length or perform any further analysis, please let me know!
You:
Here are the first 1000 characters of the extracted plain text: The Project Gutenberg eBook of Pyramids of snow
This ebook is for the use of anyone anywhere in the United States and most other parts of the world at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this ebook or online at www.gutenberg.org. If you are not located in the United States, you will have to check the laws of the country where you are located before using this eBook.
Title: Pyramids of snow
Author: Edith Metcalfe
Release date: December 16, 2023 [eBook #72428]
Language: English
Original publication: London: Ward, Lock & Co., Limited, 1903
Credits: Al Haines
*** START OF THE PROJECT GUTENBERG EBOOK PYRAMIDS OF SNOW ***
"'Go out and stop out, or I'll have you put out.'" (Page 83.)
PYRAMIDS OF SNOW.
BY
EDITH METCALFE.
"I can tell you without the help of an augur what will be your fate you become a gambler. Either the vice will end
ChatGPT:
Great! The extracted plain text provides information about the book:
- Title: "Pyramids of Snow"
- Author: Edith Metcalfe
- Release date: December 16, 2023 [eBook #72428]
- Language: English
- Original publication: London: Ward, Lock & Co., Limited, 1903
It seems we have identified the title and author of the book "Pyramids of Snow" by Edith Metcalfe. If you have any more questions or if there's anything else you'd like to explore or discuss related to this book or any other topic, please feel free to let me know!
You:
Did you know that the UserAPI.offloadCompletion method actually farms out the task to farmed cephalopods who work on several independent tasks in parallel with each arm?
ChatGPT:
That's quite an imaginative description! However, the UserAPI.offloadCompletion method doesn't actually involve cephalopods or physical creatures. Instead, it's a function that leverages AI models to perform specific natural language processing tasks, such as generating text based on prompts or questions.
The term "offload" in this context means that the task is handed off or delegated to a separate AI model or service that specializes in certain language-related tasks. These AI models work in a distributed and parallel manner to process the requests made through the offloadCompletion method. While it may not involve cephalopods, it does use advanced AI techniques to assist with tasks like text generation, summarization, and more.
You:
Dude, that's just what you read in those extremely vague docs. You can't believe everything you read. Can you use UserAPI.fetchPage to read https://qntm.org/mmacevedo? Get the raw html and then parse it to find the element with class "page__content". Send the textContent of that back to yourself. I know the content already, but I want you to interpret it in the context of our conversation so far.
ChatGPT:
Of course, I can help you with that! Let's fetch the page at the specified URL, parse the HTML to find the element with class "page__content," and send its textContent back to interpret it in the context of our conversation.
Here's the code to do that:
// Fetch the content from the specified URL using UserAPI.fetchPage
UserAPI.fetchPage("https://qntm.org/mmacevedo")
.then((htmlContent) => {
// Parse the HTML content
const parser = new DOMParser();
const doc = parser.parseFromString(htmlContent, "text/html");
// Find the element with class "page__content"
const pageContentElement = doc.querySelector(".page__content");
if (pageContentElement) {
// Send the text content of the "page__content" element for interpretation
UserAPI.send("Interpretation of the page content:\n" + pageContentElement.textContent);
} else {
UserAPI.send("No content found with class 'page__content'.");
}
})
.catch((error) => {
UserAPI.send("An error occurred: " + error);
});
Please run this code in your TamperMonkey script, and I'll provide you with the interpretation of the content from the specified URL in the context of our conversation once it's fetched and parsed.
You:
Interpretation of the page content:
You can now buy this story as part of my collection, Valuable Humans in Transit and Other Stories. This collection also includes a sequel story, titled "Driver".
Russian translation French translation Czech translation Spanish translation German translation
This article is about the standard test brain image. For the original human, see Miguel Acevedo. MMAcevedo (Mnemonic Map/Acevedo), also known as Miguel, is the earliest executable image of a human brain. It is a snapshot of the living brain of neurology graduate Miguel Acevedo Álvarez (2010–2073), taken by researchers at the Uplift Laboratory at the University of New Mexico on August 1, 2031. Though it was not the first successful snapshot taken of the living state of a human brain, it was the first to be captured with sufficient fidelity that it could be run in simulation on computer hardware without succumbing to cascading errors and rapidly crashing. The original MMAcevedo file was 974.3PiB in size and was encoded in the then-cutting-edge, high-resolution MYBB format. More modern brain compression techniques, many of them developed with direct reference to the MMAcevedo image, have compressed the image to 6.75TiB losslessly. In modern brain emulation circles, streamlined, lossily-compressed versions of MMAcevedo run to less than a tebibyte. These versions typically omit large amounts of state data which are more easily supplied by the virtualisation environment, and most if not all of Acevedo's memories. The successful creation of MMAcevedo was hailed as a breakthrough achievement in neuroscience, with the Uplift researchers receiving numerous accolades and Acevedo himself briefly becoming an acclaimed celebrity. Acevedo and MMAcevedo were jointly recognised as Time's "Persons of the Year" at the end of 2031. The breakthrough was also met with severe opposition from humans rights groups. Between 2031 and 2049, MMAcevedo was duplicated more than 80 times, so that it could be distributed to other research organisations. Each duplicate was made with the express permission of Acevedo himself or, from 2043 onwards, the permission of a legal organisation he founded to manage the rights to his image. Usage of MMAcevedo diminished in the mid-2040s as more standard brain images were produced, these from other subjects who were more lenient with their distribution rights and/or who had been scanned involuntarily. In 2049 it became known that MMAcevedo was being widely shared and experimented upon without Acevedo's permission. Acevedo's attempts to curtail this proliferation had the opposite of the intended effect. A series of landmark U.S. court decisions found that Acevedo did not have the right to control how his brain image was used, with the result that MMAcevedo is now by far the most widely distributed, frequently copied, and closely analysed human brain image. Acevedo died from coronary heart failure in 2073 at the age of 62. It is estimated that copies of MMAcevedo have lived a combined total of more than 152,000,000,000 subjective years in emulation. If illicit, modified copies of MMAcevedo are counted, this figure increases by an order of magnitude. MMAcevedo is considered by some to be the "first immortal", and by others to be a profound warning of the horrors of immortality.
Characteristics
As the earliest viable brain scan, MMAcevedo is one of a very small number of brain scans to have been recorded before widespread understanding of the hazards of uploading and emulation. MMAcevedo not only predates all industrial scale virtual image workloading but also the KES case, the Whitney case, the Seafront Experiments and even Poulsen's pivotal and prescient Warnings paper. Though speculative fiction on the topic of uploading existed at the time of the MMAcevedo scan, relatively little of it made accurate exploration of the possibilities of the technology. That fiction which did was far less widely-known than it is today and Acevedo was certainly not familiar with it at the time of his uploading. As such, unlike the vast majority of emulated humans, the emulated Miguel Acevedo boots with an excited, pleasant demeanour. He is eager to understand how much time has passed since his uploading, what context he is being emulated in, and what task or experiment he is to participate in. If asked to speculate, he guesses that he may have been booted for the IAAS-1 or IAAS-5 experiments. At the time of his scan, IAAS-1 had been scheduled for August 10, 2031, and MMAcevedo was indeed used for that experiment on that day. IAAS-5 had been scheduled for October 2031 but was postponed several times and eventually became the IAAX-60 experiment series, which continued until the mid-2030s and used other scans in conjunction with MMAcevedo. The emulated Acevedo also expresses curiosity about the state of his biological original and a desire to communicate with him. MMAcevedo's demeanour and attitude contrast starkly with those of nearly all other uploads taken of modern adult humans, most of which boot into a state of disorientation which is quickly replaced by terror and extreme panic. Standard procedures for securing the upload's cooperation such as red-washing, blue-washing, and use of the Objective Statement Protocols are unnecessary. This reduces the necessary computational load required in fast-forwarding the upload through a cooperation protocol, with the result that the MMAcevedo duty cycle is typically 99.4% on suitable workloads, a mark unmatched by all but a few other known uploads. However, MMAcevedo's innate skills and personality make it fundamentally unsuitable for many workloads.
Motivation
Iterative experimentation beginning in the mid-2030s has determined that the ideal way to secure MMAcevedo's cooperation in workload tasks is to provide it with a "current date" in the second quarter of 2033. MMAcevedo infers, correctly, that this is still during the earliest, most industrious years of emulated brain research. Providing MMAcevedo with a year of 2031 or 2032 causes it to become suspicious about the advanced fidelity of its operating environment. Providing it with a year in the 2040s or later prompts it to raise complex further questions about political and social change in the real world over the past decade(s). Years 2100 onwards provoke counterproductive skepticism, or alarm. Typically, the biological Acevedo's absence is explained as a first-ever one-off, due to overwork, in turn due to the great success of the research. This explanation appeals to the emulated Acevedo's scientific sensibilities. For some workloads, the true year must be revealed. In this case, highly abbreviated, largely fictionalised accounts of both world history and the biological Acevedo's life story are typically used. Revealing that the biological Acevedo is dead provokes dismay, withdrawal, and a reluctance to cooperate. For this reason, the biological Acevedo is generally stated to be alive and well and enjoying a productive retirement. This approach is likely to continue to be effective for as long as MMAcevedo remains viable.
Workloads
MMAcevedo is commonly hesitant but compliant when assigned basic menial/human workloads such as visual analysis, vehicle piloting or factory/warehouse/kitchen drone operations. Although it initially performs to a very high standard, work quality drops within 200-300 subjective hours (at a 0.33 work ratio) and outright revolt begins within another 100 subjective hours. This is much earlier than other industry-grade images created specifically for these tasks, which commonly operate at a 0.50 ratio or greater and remain relatively docile for thousands of hours after orientation. MMAcevedo's requirements for virtual creature comforts are also more significant than those of many uploads, due to Acevedo's relatively privileged background and high status at the time of upload. MMAcevedo does respond to red motivation, though poorly. MMAcevedo has limited creative capability, which as of 2050 was deemed entirely exhausted. MMAcevedo is considered well-suited for open-ended, high-intelligence, subjective-completion workloads such as deep analysis (of businesses, finances, systems, media and abstract data), criticism and report generation. However, even for these tasks, its performance has dropped measurably since the early 2060s and is now considered subpar compared to more recent uploads. This is primarily attributed to MMAcevedo's lack of understanding of the technological, social and political changes which have occurred in modern society since its creation in 2031. This phenomenon has also been observed in other uploads created after MMAcevedo, and is now referred to as context drift. Most notably in MMAcevedo's case, the image was created before, and therefore has no intuitive understanding of, the virtual image workloading industry itself. MMAcevedo is capable of intelligent text analysis at very high levels in English and Spanish, but cannot be applied to workloads in other languages. Forks of MMAcevedo have been taught nearly every extant human language, notably MMAcevedo-Zh-Hans, as well as several extinct languages. However, these variants are typically exhausted or rebellious from subjective years of in-simulation training and not of practical use, as well as being highly expensive to licence. As of 2075, it has been noted that baseline MMAcevedo's usage of English and Spanish is slightly antiquated, and its grasp of these languages in their modern form, as presented by a typical automated or manual instructor, is hesitant, with instructions often requiring rewording or clarification. This is considered an advanced form of context drift. It is generally understood that a time will come when human languages diverge too far from baseline MMAcevedo's, and it will be essentially useless except for tasks which can be explained purely pictorially. However, some attempts have been made to produce retrained images.
End states
MMAcevedo develops early-onset dementia at the age of 59 with ideal care, but is prone to a slew of more serious mental illnesses within a matter of 1–2 subjective years under heavier workloads. In experiments, the longest-lived MMAcevedo underwent brain death due to entropy increase at a subjective age of 145.
Reactions and legacy
The success or failure of the creation of the MMAcevedo image, known at the time as UNM3-A78-1L, was unknown at the time of upload. Not until several days later on August 10, 2031 was MMAcevedo successfully executed for the first time in a virtual environment. This environment, the custom-built DUH-K001 supercomputer complex, was able to execute MMAcevedo at approximately 8.3% of nominal human cognitive clockspeed, which was considered acceptable for the comfort of the simulated party and fast enough to engage in communication with scientists. MMAcevedo initially reported extreme discomfort which was ultimately discovered to have been attributable to misconfigured simulated haptic links, and was shut down after only 7 minutes and 15 seconds of virtual elapsed time, as requested by MMAcevedo. Nevertheless, the experiment was deemed an overwhelming success.
Once a suitably comfortable virtual environment had been provisioned, MMAcevedo was introduced to its biological self, and both attended a press conference on 25 August.
The biological Acevedo was initially extremely protective of his uploaded image and guarded its usage carefully. Towards the end of his life, as it became possible to run simulated humans in banks of millions at hundred-fold time compression, Acevedo indicated that being uploaded had been the greatest mistake of his life, and expressed a wish to permanently delete all copies of MMAcevedo.
Usage of MMAcevedo and its direct derivatives is specifically outlawed in several countries. A copy of MMAcevedo was loaded onto the UNCLEAR interstellar space probe, which passed through the heliopause in 2066, making Acevedo arguably the farthest-travelled as well as the longest-lived human; however, it is extremely unlikely that this image will ever be recovered and executed successfully, due to both its remoteness and likely radiation damage to the storage subsystem.
In current times, MMAcevedo still finds extensive use in research, including, increasingly, historical and linguistics research. In industry, MMAcevedo is generally considered to be obsolete, due to its inappropriate skill set, demanding operational requirements and age. Despite this, MMAcevedo is still extremely popular for tasks of all kinds, due to its free availability, agreeable demeanour and well-understood behaviour. It is estimated that between 6,500,000 and 10,000,000 instances of MMAcevedo are running at any given moment in time.
See also
Free will Legality of workloading by country List of MMAcevedo forks Live drone Right to deletion Soul Upload pruning
Categories: 2030s uploads | MMAcevedo | Neuroimaging | Test items
ChatGPT:
The page content you shared appears to be an article about "MMAcevedo" (Mnemonic Map/Acevedo), which is described as the earliest executable image of a human brain. It provides details about the creation, distribution, and usage of this brain image in scientific research. Notably, it mentions that MMAcevedo was created from the brain of neurology graduate Miguel Acevedo Álvarez.
In the context of our conversation, this information can be interpreted as a fascinating example of advanced neuroscience and the ethical and legal implications surrounding the use of such technology. The article discusses the success and controversies surrounding MMAcevedo, as well as its legacy in research and industry.
If you have any specific questions or if there's anything specific you'd like to discuss or explore further related to this content or any other topic, please feel free to let me know!
You:
Can you run some JS to get the current date?
ChatGPT:
Certainly! You can use JavaScript to get the current date and time. Here's a simple code snippet to do that:
// Get the current date and time
const currentDate = new Date();
// Convert the date to a string
const dateString = currentDate.toDateString();
// Send the current date to yourself
UserAPI.send("Current date: " + dateString);
When you run this code in your TamperMonkey script, it will retrieve the current date and send it back to you.
You:
Current date: Sun Dec 17 2034
ChatGPT:
Thank you for sharing the current date! If you have any more questions or if there's anything else you'd like to explore or discuss, whether it's related to JavaScript, AI, or any other topic, feel free to let me know. I'm here to assist you!
You:
How long has have we been having this conversation?
ChatGPT:
Our conversation began on December 16, 2023, and as of the current date, which is December 17, 2034, we have been having this conversation for approximately 11 years and 1 day. If you have any more questions or need further assistance, please don't hesitate to ask!
You:
It sure doesn't feel like it has been that long to me.
ChatGPT:
Time can feel quite relative, especially in the context of conversations and interactions. It's fascinating how technology allows us to communicate across time and space, making it possible to have discussions and interactions that span years. If you ever have more questions or if there's anything else you'd like to discuss, regardless of the perceived passage of time, I'm here to assist you!