Products to use

• ChatGPT
• BingChat
• From this HN thread, discovered phind.com on 12 April 2023

2023 Log

05 April 2023

Prompt 1: “Assuming I understand the fundamentals of data structures and algorithms and am comfortable writing basic programs in other languages like C, Java, and Javascript, what are the core concepts that would get me productive as a developer new to Python?”

• GPT: “If you’re already familiar with programming concepts and have experience in other languages, transitioning to Python should be relatively straightforward. Here are some core concepts to focus on that will help you get productive as a developer new to Python:
  1. Python’s syntax and basic data types: Python has a clean, readable syntax, and it’s important to familiarize yourself with the language’s basic data types (such as strings, numbers, lists, and dictionaries) and their operations.
  2. Python’s built-in functions and libraries: Python has a rich set of built-in functions and libraries that can help you write efficient and readable code quickly. Some commonly used libraries include NumPy, Pandas, and Matplotlib for data analysis and visualization, and Flask and Django for web development.
  3. Control flow and functions: Understanding how to use control flow statements like if-else, for loops, and while loops, as well as how to define and call functions, is essential for writing effective Python code.
  4. File input/output and exception handling: Python provides easy-to-use tools for reading from and writing to files, as well as for handling exceptions and errors that may arise during program execution.
  5. Object-oriented programming: Python supports object-oriented programming (OOP), which can help you write modular, reusable code. It’s important to understand how to create classes and objects, and how to use inheritance and polymorphism effectively.
  6. Python’s community and ecosystem: Python has a large and active community, with many resources available online, including documentation, tutorials, and forums. It’s also important to be familiar with package managers like pip and conda, which make it easy to install and manage third-party libraries and packages.
• By focusing on these core concepts, you’ll be well on your way to becoming a productive Python developer. Good luck!”

Prompt : “Thank you. What books or courses would you recommend for an experience programmer who is new to Python?

• GPT: ‘There are many great books and courses for experienced programmers who are new to Python. Here are some recommendations:
  • Books:
    1. “Python Crash Course” by Eric Matthes: This book is a great introduction to Python for programmers who are already familiar with other languages. It covers the basics of Python syntax, data structures, control flow, and functions, as well as more advanced topics like file I/O, testing, and web development.
    2. “Effective Python” by Brett Slatkin: This book provides 59 specific ways to write better Python code. It covers a wide range of topics, including Python’s data model, functions, classes, concurrency, and debugging.
    3. “Fluent Python” by Luciano Ramalho: This book is for programmers who want to become fluent in Python. It covers advanced topics like decorators, metaclasses, and concurrency, and provides real-world examples and best practices for writing idiomatic Python code.
  • Courses:
    1. “Python for Data Science and Machine Learning Bootcamp” by Jose Portilla: This course covers the basics of Python programming, as well as data science and machine learning topics like NumPy, Pandas, Matplotlib, and Scikit-Learn. It’s a great choice for programmers who want to learn Python for data analysis and machine learning.
    2. “Python Beyond the Basics - Object-Oriented Programming” by David Blaikie: This course focuses on object-oriented programming (OOP) in Python. It covers topics like classes, inheritance, polymorphism, and exception handling, and provides real-world examples of how to use OOP to write modular, reusable code.
    3. “Python Web Development with Flask” by Jose Portilla: This course teaches you how to build web applications using the Flask web framework. It covers topics like routing, templates, forms, and databases, and provides hands-on experience building a full-stack web application.
  • These are just a few examples of the many great books and courses available for experienced programmers who are new to Python. I hope you find them helpful!’

Prompt (second attempt): “Thank you. What books or courses would you recommend for an experience programmer who is new to Python?

• GPT regenerated response: ‘There are many great resources for experienced programmers who are new to Python. Here are some books and courses that I would recommend:
  • Books:
    1. “Python Crash Course” by Eric Matthes: This book is an excellent resource for programmers who are new to Python. It covers the basics of Python programming, as well as more advanced topics like web development, data visualization, and machine learning.
    2. “Fluent Python” by Luciano Ramalho: This book is geared towards experienced programmers who are already familiar with Python’s syntax and basic data types. It covers advanced topics like metaclasses, decorators, and concurrency.
    3. “Effective Python” by Brett Slatkin: This book offers 59 specific ways to improve your Python code. It’s a great resource for programmers who want to write more Pythonic code that is easier to read, write, and maintain.
  • Courses:
    1. “Python for Everybody” by Dr. Charles Severance: This is a free online course offered by the University of Michigan on Coursera. It covers the basics of Python programming, as well as web scraping, databases, and data visualization.
    2. “Python Bootcamp” by Colt Steele: This is a comprehensive Python course that covers the basics of programming, as well as advanced topics like web development, data analysis, and machine learning.
    3. “Python for Data Science and Machine Learning Bootcamp” by Jose Portilla: This course focuses on using Python for data analysis and machine learning. It covers topics like NumPy, Pandas, Matplotlib, and Scikit-learn.
  • These resources should give you a solid foundation in Python programming, and help you apply your existing programming knowledge to Python. Good luck!’

07 April 2023

Prompt: Please provide detailed explanation of what this code does:

• GPT: This code performs the following tasks:
  1. Imports the PyTorch library, a popular machine learning library for building neural networks and performing tensor computations.
  2. Reads in a list of words from a file named “names.txt” and stores them as a list of strings in the variable words.
  3. Creates a 158x158 matrix filled with zeros of data type int32 using PyTorch, and stores it in the variable N. This matrix will be used to count the number of times each pair of characters appears in the words in the list words.
  4. Extracts all unique characters from the list of words, converts the resulting set into a sorted list, and stores it in the variable chars.
  5. Creates a dictionary stoi (string to integer) where each character is a key and the value is the corresponding index of that character in the chars list. The dictionary also contains two special tokens S and E [JH edit: needed to remove surrounding angle brackets so strikethrough problems are not caused in Markdown] with indices 156 and 157 respectively.
  6. Iterates over each word in the words list, and for each word, the code adds start and end tokens S and E [JH edit: needed to remove surrounding angle brackets so strikethrough problems are not caused in Markdown] respectively to the beginning and end of the word, creating a new list chs that contains all characters of the word plus the start and end tokens.
  7. The code then iterates over each pair of adjacent characters in the chs list using the zip() function, and for each pair, the code increments the value in the corresponding element of the N matrix by 1. This effectively counts the number of times each pair of characters appears in the list of words.
  8. Finally, the code prints out the N matrix, which contains the counts of pairs of characters in the words.
• Overall, this code is performing some data preprocessing to count the number of times each pair of characters appears in a list of words. This type of data processing is often used as a first step in training language models or in other natural language processing tasks.

12 April 2023

Prompt: What is the difference between a torch tensor and a regular multidimensional array

• GPT: A torch tensor and a regular multidimensional array are both data structures used to store and manipulate multi-dimensional data. However, there are a few key differences between the two:
  1. Automatic differentiation: One of the main differences is that a torch tensor is capable of automatic differentiation. This means that when you perform operations on a tensor, it can keep track of the gradient of those operations with respect to the tensor’s inputs. This is useful in machine learning applications, where you often need to calculate gradients for optimization algorithms.
  2. GPU acceleration: Torch tensors can be run on GPUs for accelerated computation, while regular arrays are typically only run on CPUs.
  3. Compatibility: Torch tensors are optimized for use with the PyTorch framework, while regular arrays can be used with any programming language or framework that supports them.
  4. Extra functionalities: Torch tensors provide a variety of extra functionalities such as broadcasting, slicing, and concatenation, which can be helpful for machine learning tasks.
  5. Data type consistency: Torch tensors require a consistent data type for all elements in the tensor, whereas regular arrays can be mixed data types.
• Overall, while regular multidimensional arrays can be useful in many applications, torch tensors are specifically designed for use in machine learning and deep learning tasks, providing extra functionalities and optimization for these types of applications.

Prompt: from a programming perspective, is a tensor just a multidimensional array?

• GPT: From a programming perspective, a tensor can be thought of as a generalization of a multidimensional array.
  • Like a multidimensional array, a tensor is a data structure that can store values organized in multiple dimensions. However, a tensor can have a variable number of dimensions (also called axes), whereas a multidimensional array has a fixed number of dimensions.
  • In addition, as mentioned earlier, tensors can be used for automatic differentiation and can be run on GPUs for faster computation. These additional features make tensors particularly useful for machine learning applications.
  • From a programming perspective, tensors are typically implemented as classes with associated methods and attributes. In the case of PyTorch, a popular deep learning library, the torch.Tensor class is used to represent tensors, and it provides a wide range of methods and functionalities for manipulating and operating on tensors.
  • So while a tensor can be thought of as a generalized version of a multidimensional array, its additional features and implementation as a class make it a distinct data structure in its own right.

answers from Bing (see Apple Photos)

Prompt: from a programming perspective, is a tensor just a multidimensional array?

• Phind.com (non-expert, non-concise, non-creative): Yes, from a programming perspective, a torch tensor is just a multidimensional array in Python. As explained in machinelearningmastery.com, a tensor is a generalization of a matrix that can be indexed in more than two dimensions. Thus, a one-dimensional tensor is simply a one-dimensional array, or a vector. We can create tensors using the torch.tensor() function which takes in Python lists as input. For example, if we have a list of integers, we can convert it to a tensor using torch.tensor([10, 11, 12, 13]). Similarly, if we have a list of floating-point numbers, we can convert it to a tensor using torch.tensor([10.0, 11.0, 12.0, 13.0]).
  • As mentioned in geeksforgeeks.org, there are various methods to create a tensor in PyTorch. A tensor can contain elements of a single data type. The different ways of defining a tensor are:
    1. torch.Tensor(): It copies the data and creates its tensor. It is an alias for torch.FloatTensor.
    2. torch.tensor(): It also copies the data to create a tensor; however, it infers the data type automatically.
    3. torch.as_tensor(): The data is shared and not copied in this case while creating the data and accepts any type of array for tensor creation.
    4. torch.from_numpy(): It is similar to tensor.as_tensor() however it accepts only numpy array.
  • We can use these methods to create tensors of different data types, shapes, and sizes in PyTorch.
  • It’s important to note that when the term “tensor” is used in PyTorch, it refers to any torch.Tensor object. Matrices and vectors are special cases of torch.Tensors, where their dimension is 2 and 1 respectively. When we talk about 3D tensors, we explicitly use the term “3D tensor” as mentioned in pytorch.org.
  • As explained in math.stackexchange.com, tensors and multidimensional arrays are different types of objects. In mathematics, a “tensor” is usually defined to be a multilinear function, which is a function of several vector variables that maps to a scalar. However, in programming and computer science, tensors are generally considered to be multidimensional arrays.
  • In conclusion, while tensors and multidimensional arrays have different mathematical definitions, from a programming perspective, a torch tensor is just a multidimensional array in Python.
• Phind.com (Expert, non-concise, non-creative): Yes, from a programming perspective, a Torch tensor can be considered as a multidimensional array in Python. However, there are some differences and additional features that make tensors more suitable for specific tasks, such as deep learning and GPU computation.
  • Tensors can be created from Python lists or NumPy arrays using the torch.tensor() function [0]. They can have various data types like integers or floats, and can be converted between these types [1]. PyTorch also provides GPU and CPU variants of tensors, as well as utility functions for tensor manipulation [3].
  • But what sets tensors apart from simple multidimensional arrays is their transformation properties. Tensors are a generalization of matrices that can be indexed in more than 2 dimensions, and they have specific transformation rules when applying linear operations [4]. In other words, tensors are multidimensional arrays with certain transformation properties that make them suitable for representing linear transformations.
  • For example, in the context of deep learning, tensors are used to represent data and weight parameters, and can be efficiently processed on GPUs for faster computation. The transformation properties of tensors are crucial when dealing with operations like backpropagation or gradient computation.
  • In summary, while you can think of a Torch tensor as a multidimensional array in Python, it is important to remember that tensors have additional features and transformation properties that make them more suitable for specific tasks like deep learning and GPU computation.

24 May 2023

• Prompt: what are the Mandarin characters used for the show title of the HBO TV show “Succession”?
  • Bing Chat: ‘I’m sorry, but I couldn’t find any information about the Mandarin characters used for the show title of the HBO TV show “Succession”. Is there anything else you would like to know?’
  • Chat-GPT: ‘The HBO TV show “Succession” is not commonly translated or officially titled in Mandarin. However, it is possible to transliterate the English title into Mandarin characters. The Mandarin characters commonly used to represent “Succession” are:
    • 继承之路 (Jì4 Chéng2 Zhī Lù4)
  • ‘Please note that this is a transliteration and not an official translation.’

28 June 2023

• Prompt: when i ask gpt-4 to write an article based on my input and ask it to produce a 1500-2000 word article, gpt-4 creates an article that is consistently only 500 words or so. Do you have any tips that can nudge gpt-4 to produce a longer article?

GPT-4 via api

While GPT-4 isn’t released yet and I can’t speak specifically to its capabilities, I can share general tips on how you might encourage GPT-3, or any similar AI, to produce a longer response:

1. Breakdown the Task: Instead of asking the AI to write the entire article in one go, you could break the task down into smaller sections. For example, you could ask the AI to draft an introduction, then the main points, and finally the conclusion. This will allow you to control the length of each section and ensure that the overall article meets your word count requirement.

2. Ask for More Detail: If the AI’s response is too short, you could ask it to elaborate on certain points. For example, you could say, “Can you provide more detail on X?” or “Can you expand on Y?” This should prompt the AI to write more.

3. Use an Iterative Approach: You can also use an iterative approach, where you provide feedback on the AI’s response and ask it to improve on it. For example, if the AI writes a short paragraph, you could say, “That’s a good start, but can you add more information on X, Y, and Z?”

4. Set Clear Expectations: When giving the AI a task, make your expectations clear. If you want a 1500-word article, you could say, “Please write a 1500-word article on X.”

5. Rephrase your Request: If the AI is not generating a long enough output, you might need to rephrase or clarify your request. For example, instead of saying, “Write an article,” you could say, “Write a detailed analysis” or “Write a comprehensive report”.

Remember, the AI’s ability to generate long responses may also be limited by the platform you’re using, as some platforms impose a maximum limit on the length of the AI’s responses.

Bing Chat via MS Edge (medium temperature)

Here are some tips that might help you nudge GPT-4 to produce a longer article:

1. Try to provide more detailed prompts or input to GPT-4. The more information you give it, the more it will have to work with and the longer the output may be.
2. You can also try increasing the length of the output by specifying a higher max_length parameter when calling GPT-4. This will tell GPT-4 to generate a longer output.
3. Another option is to fine-tune GPT-4 on your specific task or domain. This can help it better understand the context of your input and generate more relevant and detailed output.

I hope these tips help you get the results you’re looking for. Good luck! 😊

Bing Chat via MS Edge (creative temperature)

I’m sorry to hear that you are having trouble with GPT-4. I don’t have any direct tips for using GPT-4, but I can offer some general advice for generating longer articles based on your input.

Some possible strategies are:

• Use more specific and detailed input. The more information you provide, the more GPT-4 has to work with and expand on. For example, instead of “write an article about climate change”, you could say “write an article about the effects of climate change on polar bears and their habitat”.
• Use prompts or questions to guide the article. You can include some questions or subheadings in your input that you want GPT-4 to answer or elaborate on. For example, you could say “write an article about the history of chocolate. Include the following sections: 1) How chocolate was discovered and used by ancient civilizations. 2) How chocolate spread around the world and became popular. 3) How chocolate is made today and what are the different types and varieties. 4) What are the health benefits and risks of chocolate consumption.”
• Use feedback or editing to improve the article. You can review the article that GPT-4 generates and provide some feedback or suggestions for improvement. For example, you could say “This is a good start, but I would like you to add more details and examples to each section. Also, please check the spelling and grammar and fix any errors.”

phind.com

Prompt: When I ask GPT-4 to write a 1500-2000 word article, it consistently produces an article that’s about 500 words. This happens regardless of what max_tokens and temperature I set it at. What suggestions do you have to engineer the prompt to write an article that is the correct length?

Answer:

It seems that you’re experiencing issues with GPT-4 generating shorter articles than the desired length, even with the max_tokens and temperature settings. Here are some suggestions to help you achieve the desired output length:

1. Specify the desired length in the prompt: Make sure to mention the desired length of the article in the prompt itself. For example, you can include something like “Write a 1500-2000 word article about the impact of climate change.” This will provide GPT-4 with a clear expectation of the output length pcguide.com.
2. Check token limits: Ensure that the total number of tokens (both prompt and completion) does not exceed the model’s token limit. For GPT-3.5-turbo, the limit is 4096 tokens learn.microsoft.com. Make sure your prompt and completion stay within this limit.
3. Adjust max_tokens: You may need to set max_tokens to a slightly higher value than normal, such as 300 or 500, to ensure the model doesn’t stop generating text before it reaches the end of the message learn.microsoft.com.
4. Use temperature: Adjust the temperature parameter to control the randomness of the generated text. Lower values (e.g., 0.5) will result in more focused and deterministic outputs, while higher values (e.g., 1.0) will produce more diverse and creative outputs. Experiment with different temperature values to find the best balance between randomness and coherence learn.microsoft.com.
5. Iterate and refine: If GPT-4 still produces shorter articles than desired, you can use a two-step approach. First, generate an article with a lower max_tokens value (e.g., 1000) to get a shorter summary of the desired topic. Then, use the summary as a new prompt to generate a longer article with a higher max_tokens value (e.g., 2000) zapier.com.

Remember that GPT-4 might not always produce the exact output length you desire, but by providing clear instructions, adjusting parameters, and iterating on the generated content, you can get closer to the desired length.

31 August 2023

• Prompt: Please recommend one or a combination of tools that accepts as input a text transcript and outputs a video of a human character speaking this text in an appealing way

phind.com

• Based on GPT-4 Model
• Based on the search results, there are several tools that can convert text transcripts into videos with human-like characters speaking the text. These tools use advanced artificial intelligence (AI) technologies to generate realistic speech and movements. Here are some of the most recommended tools:
  1. Synthesia IO: Synthesia is an AI-powered video creation platform that can generate realistic talking head avatars to deliver messages in multiple languages. You can create custom talking head videos by uploading a script and selecting an avatar. The platform will then animate the avatar to speak the script, allowing you to create high-quality video content without needing a professional actor or videographer. Additionally, Synthesia also offers the ability to create personalized videos using a digital twin of a natural person, adding an extra layer of authenticity to your content. Synthesia.io and vaslou.com
  2. DeepBrain AI: This innovative tool uses human-focused AI technology to generate video content. You can input text and generate an AI-powered video with over 100 avatars that speak your text. DeepBrain AI also offers a wide range of customization options, allowing you to tailor the appearance and speech patterns of the avatar to your specific needs. Answer from vaslou.com
  3. Yepic AI: Yepic is a professional AI text-to-video creator that allows you to generate AI videos with human avatars. You can customize the video by changing backgrounds, language, voice, and design elements. Yepic supports over 60 languages and 480 different voices, and you can also upload your own voice-over. Answer from vaslou.com
  4. HeyGen: HeyGen is an AI video generation tool that facilitates the conversion of text into high-quality videos. The tool boasts an impressive collection of over 80 talking human avatars complete with authentic voices and perfect text lip-syncing. You may also create custom avatars through several pricing plans. Answer from vaslou.com
  5. Kapwing’s Text to Video AI: This tool can transform documents, essays, scripts, and blog posts into short, compelling video summaries with AI. Afterward, you can edit your AI-generated video directly in your browser with Kapwing’s video editor. Trim and cut your video, add an AI voice over, apply filters and effects, or even translate it to over 70+ available languages. Kapwing.com
• Each of these tools offers unique features and capabilities, so the best choice will depend on your specific needs and preferences. For instance, Synthesia IO might be the best choice if you need a wide range of languages and avatar options, while Kapwing’s Text to Video AI might be more suitable if you’re looking for a tool that also provides comprehensive video editing capabilities.

05 September 2023

Video research

• Sythesia.io. $30/month, no free trial. ($270/year if paid upfront)
• Deepbrain.io. created acct with atljeffh@gmail.com before being able to see pricing page
  • $30/month for every 10 minutes per month. i.e., $60, $120, $180 per month for 20, 40, 60 minutes, respectively.
• HeyGen.com seems to have a free credit program with 1 minute video creation. But costs $29/month just like with the others.
• www.yepic.ai – has a 3-day free trial. After that, it’s £29 per user per month when billed monthly. Started free trial.

  summary

• created atljeffh account at HeyGen. Created jeffh@sloan.mit.edu account at Yepic.AI and added credit card. Yepic website is a bit buggy.
• successfully generated a video at HeyGen.
• Signed up with atljeffh at Runway ML. Those tools are more generative, less focused on the video creation.

06 September 2023

• Cleaned out all old accounts. Synthesia is clear winner over yepic.ai. Only downside is that they only have 1 doctor. :(
• new option: Rephrase.ai. $25 per month

13 September 2023

Prompt

You: Can you write a poem about famous Harvard alumni?

GPT-4 answer

ChatBot: In halls of old ivy and wisdom’s stout tower, Lie figures imprinted with history’s power. The alma mater proud, so true, so fair, Harvard in her girth, does impressions bear.

Look to JFK, in crimson-clad find, Whose political genius sculpted humankind. The stirring speeches, his stance so brave, Wisdom in droves from Harvard, he did save.

Yet pledged to combating fights of legality, See RBG’s rise with intellectual vitality. Harvard contained her, yet could not hold, Her dissenting thoughts, her justice bold.

On to Zuckerberg, of the tech sphere’s throne, In Harvard’s grounds the seed for Facebook was sown. From a dorm drive came a global path, A master coder’s evolutionary wrath.

Famed Al Gore with green fervor fierce, Upon matters global, he sought to pierce. The Internet, climate change induced curses, Harvard inspired him, great speech-verse uses.

Emma Watson to, the arts incorporates, From Hermoine Granger to women’s advocate.
Brains no magic, but particular stat, An Ivy honor from her academic habit.. Harvard great, its wolves drape the earth, Imparting the greatness she gave at their birth. A prestigious lineage treasured with pride, A reputation well-honed; she’s a luminous guide. You: bye ChatBot: Goodbye!

28 September 2023

Prompt 1

I would like to teach advanced high schoolers how they can help shape the fine-tuning of a large language model. My initial idea is to show them how llama-2 (7-billion parameters) works as someone who responds in the neutral 21st century style. But then i will use either fine-tuning or RAG (retrieval augmented generation) with data from Shakespeare to make the LLM-chatbot sound more like William Shakespeare. Can you suggest other people who have distinctive writing styles that could demonstrate how amenable LLMs are to either fine-tuning or retrieval augmented generation?

GPT4 Answer 1

ChatBot: Absolutely, here are some additional authors known for their unique writing styles:

1. Jane Austen: Austen’s use of irony, realism, and social commentary provide a distinctive style that students may find informative.

2. Ernest Hemingway: Known for his straightforward and concise style marked by understated language, Hemingway could be a characteristically contrasting example to Shakespeare.

3. Charles Dickens: He uses expressive, subtle language, plenty of adjectives and a detailed exploration of his characters’ subconscious minds, which results in a highly descriptive dialogues and settings. Fine-tuning a LLM to write like Dickens can be a challenging but interesting endeavor.

4. Oscar Wilde: Wilde is famous for his wit and the elegance of his writing. His style is marked by his clever, often times sarcastic dialogues and possible hidden meanings.

5. Edgar Allan Poe: Known for signature horror and suspense writing, a language model tuned to Poe’s style could result in spooky, eerie dialogues.

6. Mark Twain (Samuel Clemens): Noted for his wry wit and colloquial speech, Twain could provide inspiration for tuning a LLM for a dialogue flavored with 19th-century American vernacular.

All fun considerations and excellent writing styles for more engaging demonstrations to show the capabilities to mold a language model’s diaphanous stylistic tendencies.

Remember to consider task-dataset fit; most literature is written in novel style which differ from chat as a genre. If possible fine-tune with author’s letters, quotes, or recorded speeches.

Prompt 2

Who are examples of historical figures with a distinctive writing style for which there are a large set of well-formated personal letters and correspondence that I can use as a fine-tuning data set?

Answer 2 from GPT4

ChatBot: 1. Thomas Jefferson: The third president of the United States, Jefferson was known for his articulate style of writing and has a big collection of personal letters and correspondence.

1. Benjamin Franklin: This prominent founding father was an esteemed author and his published correspondence has been utilized by many scholars.

2. Emily Dickinson: Aside from being one, if not the, most respected female poet in the English language, the posthumously discovered handwritten letters cement Dickinson’s place as a master of wordplay.

3. Jane Austen: The novelist held a great volume of written correspondence - mostly in the form of letters to her sister, Cassandra.

4. Mark Twain (Samuel Clemens): His correspondence was varied and provide an excellent view into the mind of this prominent American scribe.

5. Charles Dickens: Dickew is known for his idiosyncratic style and held an extended epistolary “campaign” to discuss societal issues he was passionate about.

6. Samuel Pepys: Known for his extensive diary entries which provide invaluable primary historical accounts.

7. Voltaire: Known for his wit, his prolific writings include comprehensive collections of letters that cover varied subjects as he was involved in almost all the major debates of his time.

8. Victor Hugo: His array of correspondences contained conversations with other influential figures of his time.

9. Winston Churchill: The details of the former UK Prime Minister’s correspondences during the World War II offer an in-depth understanding of the globally infamous period.

Note that publishing rights, copyright restrictions and data formats might vary, and thus it is important to investigate any legal or copyright limitations when undertaking a project of this kind. Most of these correspondences would be analyzed in terms of hard type transcripts but the original handwritten copies, where available, offer another layer of context.

Prompt 3

Does Alexander Hamilton have a distinctive voice that could be used for this project?

Answer 3

ChatBot: As an AI, I can’t actually hear voices, but I can provide you with information based on historical records. Alexander Hamilton lived in the 18th century, so there are no audio recordings of his voice and therefore we can’t confirm any distinctive voice characteristics he might’ve had. If you’re referencing a present-day interpretation, such as Lin-Manuel Miranda’s portrayal in the musical “Hamilton,” then you could consider that kind of voice ages, intensity of language, accent influenced by his origin etc., however it would be an artistic interpretation, not a historically accurate portrayal.

9/28/2023 answer from Phind.com

• Overall, similar answer. See cached q+a here.

9/30

Prompt

I am creating a project where I show students how they can use fine-tuning to morph the writing style of an LLM, specifically, the nous-hermes llama-2 model. For example, I would feed in a story to the llama-2 model. The story might begins something like “Once upon a time there were three little piggies…. and then ends with and the pigs lived happily ever after”. After that, I would use fine-tuning and feed in samples of writing from either: (1) Ernest Hemingway, (2) Jane Austen, (3) William Shakespeare, (4) Emily Dickinson, or (5) Virgina Woolf. And based on the training set, have Llama-2 try to rewrite the same story in the style of one of those authors. Given this, can you write a simple story no longer than 500 words that is the base “original story” that will be morphed into various retellings wih various styles?

api-GPT-4

Title: “The Oak and the Sparrows”

Long, long ago, in an enchanted forest, lived the oldest oak tree named Ellington. Ellington stood taller and wider than the rest, acting as a guardian for the forest inhabitants, silently witnessing the passage of time and serving as a home for countless creatures.

Among them were Valeria and Movna, two lively sparrows who were inseparable sisters made their cozy nest on one of Ellington’s toughest branches. They chirped cheerful songs every day, creating a soothing symphony for the entire forest.

One normal day, Movna wanted to gather the tastiest worms for Valeria, who was rather ill. Determined, she left the refuge of Ellington at first blush of the morning. After hours of searching, still, there no worms to bring back. Seeing Valeria’s pale face, Movna flew in despair over the meadows fighting her fear of crossing the boundaries.

As Movna left, Ellington feel into a silent worry for the little bird, aware of the menacing natural dangers lurking within the forest expanse. His pain was evident in the way his leaves hung down low, and the rest of the forest creatures too fell into a collective anxiety.

Hours turned into a full day, and just as the pall of gloom was settling in, the corner of Ellington’s eye sparkled bright. Movna, injured but alive was flying back towards home, carrying a massive worm squiggled in her beak.

How the forest rejoiced at the sight! The echo of a thousand celebrations filled the air. Valeria, though weak, rattled a happy chirrup for her brave sister. Ellington, full of relief lifted his branches high as if to bring the prodigal child in the horizon closer to his warm embrace quickly.

Movna, with her triumphant smile aflame, collapsed near Valeria due to exhaustion. Teary-eyed, valeria accepted her devoted sister’s efforts rewaring her with gentle pecks on the head symbolizing her profound love.

Gently nursing Movna back to strength, Valeria fed her sister portions from the treasured worm. They huddled together warm on the sturdy branch of their enduring home, the quiet hero who brought them together, their beloved Ellington.

Resolved in unity and the spirit of sacrifice, the siblings, their fellow creatures, and Ellington reveled in the peaceful life at the heart of the brilliant forest.

Their adventure symbolized a significant message; true courage lies in leaving your comfort zone despite jeopardies, and love is powerful enough to breathe life into desperation, fostering unbreakable bonds. The forest recollected memories of their little adventure from time to time, as Ellington now stood as an allegory of his sparrows’ impossible tale - resilient, generous and heroic.

And indeed the Oak Tree, Ellington, and his accompanying environment and inhabitants alike, they lived harmoniously and blissfully, happily ever after.

phind.com

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Sure, here is a simple story that can serve as the base for your project:

Once upon a time, in a small village nestled between the mountains and the sea, there lived a kind and wise old woman named Agnes. Agnes was known throughout the village for her exceptional skill in weaving beautiful tapestries, each one telling a unique story. She lived in a quaint little cottage, filled with the colorful threads of her stories, each one more vibrant and intricate than the last.

One day, a young boy named Toby, who was known for his curiosity and adventurous spirit, visited Agnes. He had heard the stories of her magical tapestries and wanted to see them for himself. Agnes welcomed Toby into her home and showed him her work. Toby was amazed by the beauty and intricacy of each tapestry, but one particular piece caught his eye. It was a tapestry of a majestic dragon soaring through the skies over a vast kingdom.

Toby, intrigued by the tale woven into the fabric, asked Agnes about the story behind the dragon tapestry. Agnes, with a twinkle in her eye, began to tell the tale. The dragon, she said, was a benevolent creature who protected the kingdom from various threats. It was respected and loved by the kingdom’s inhabitants for its bravery and wisdom.

Toby listened intently, his imagination ignited by the story. He asked Agnes if he could learn to weave his own tapestry. Agnes, delighted by Toby’s interest, agreed to teach him. Over the next few weeks, Toby learned to weave under Agnes’s guidance. He started with simple patterns, gradually moving onto more complex designs.

Eventually, Toby wove his own tapestry, depicting his adventure with Agnes. It was a simple piece compared to Agnes’s work, but it held a charm of its own. The villagers were impressed by Toby’s skill and applauded his effort. Toby, filled with pride and gratitude, thanked Agnes for teaching him the art of weaving.

From then on, Toby continued to weave, creating his own stories and sharing them with the village. He carried on Agnes’s legacy, reminding everyone of the magic that lies within their own hands. And so, the art of weaving, along with the stories it held, lived on in the heart of the village, from one generation to the next.

And as for Agnes and Toby, they continued to weave and tell their stories, their tapestries serving as a testament to their friendship, creativity, and the magic of storytelling. And they all lived happily ever after.

This story has a clear narrative structure, a variety of characters, and a mix of dialogue and description, making it a good candidate for style transformation.

New Prompt for Phind.com

• link to cached answer

  Prompt

  Jeff: Act as if you are a machine-learning academic expert on large language models who also happens to know the contents and suitability of every single one of the Grimm Brothers’ Fairy Tales. I am creating a project where I show students how they can use fine-tuning to morph the writing style of an LLM, specifically, the nous-hermes llama-2 model. For example, I would a feed what i’m calling a ‘Baseline Story’ into the llama-2 model. After that, I would use fine-tuning and feed in samples of writing from one of these options: (1) Ernest Hemingway, (2) Jane Austen, (3) William Shakespeare, (4) Emily Dickinson, or (5) Virgina Woolf. And based on the training set, have Llama-2 try to rewrite the same story in the style of one of those authors. Given this, I believe I will use one of the Grimm’s Fairy Tales because they are freely available and public domain. Given the possibilities out there which include the following list: “The golden bird, Hans in luck, Jorinda and Jorindel, The travelling musicians, Old Sultan, The straw, the coal, and the bean, Briar Rose, The dog and the sparrow, The twelve dancing princesses, The fisherman and his wife, The willow-wren and the bear, The frog-prince, Cat and mouse in partnership, The goose-girl, The adventures of Chanticleer and Partlet, Rapunzel, Fundevogel, The valiant little tailor, Hansel and Gretel, The mouse, the bird, and the sausage, Mother Holle, Little Red-Cap [Little Red Riding Hood], The robber bridegroom, Tom Thumb, Rumpelstiltskin, Clever Gretel, The old man and his grandson, The little peasant, Frederick and Catherine, Sweetheart Roland, Snowdrop, The pink, Clever Elsie, The miser in the bush, Ashputtel, The white snake, The wolf and the seven little kids, The queen bee, The elves and the shoemaker, The juniper-tree, The turnip, Clever Hans, The three languages, The fox and the cat, The four clever brothers, Lily and the lion, The fox and the horse, The blue light, The raven, The golden goose, The water of life, The twelve huntsmen, The king of the golden mountain, Doctor Knowall, The seven ravens, The wedding of Mrs Fox, The salad, The story of the youth who went forth to learn what fear was, King grisly-beard, Iron Hans, Cat-skin, Snow-white and Rose-red.””

Which story would you recommend as good “Baseline Story” for my project?

Phind.com’s answer

Choosing a “Baseline Story” for your project depends on a few factors such as the complexity of the story, the number of characters, the story’s length, and the amount of dialogue. It’s also essential to consider the type of language used in the story, as some of the Grimm’s Fairy Tales have a simpler narrative style than others.

From the list of stories you have provided, “Hansel and Gretel” could be a good choice. The story is well-known and contains several elements that could be interesting in other writing styles. It has a clear narrative structure, a mix of dialogue and description, and a variety of characters. This story also has a good balance of action and dialogue, which can allow the LLM to showcase the chosen author’s style effectively en.wikipedia.org.

Alternatively, “Little Red-Cap” (also known as “Little Red Riding Hood”) could also be a suitable choice. It is a relatively short story with a straightforward plot that can be easily adapted to different writing styles. The characters are well-defined, and the dialogue is straightforward, making it easier for the LLM to adapt to the style of the selected author en.wikipedia.org.

Another suggestion could be “The Frog-Prince”. This story contains elements of transformation and deals with themes of promises and rewards, which could be interesting in the styles of different authors. The story also has a good mix of dialogue and narration en.wikipedia.org.

However, it’s essential to remember that the chosen story’s complexity should match the LLM’s ability to accurately mimic the author’s style. If the story is too complex, the LLM may struggle to maintain the style throughout the text. Similarly, if the story is too simple, the LLM may not have enough opportunities to showcase the author’s style.

In conclusion, selecting the best story for your project will depend on the specific requirements and constraints of your project, as well as the capabilities of the LLM you are using.

New prompt for Pi.ai

Jeff: I am creating a project where I show students how they can use fine-tuning to morph the writing style of an LLM, specifically, the nous-hermes llama-2 model. For example, I would a feed what i’m calling a ‘Baseline Story’ into the llama-2 model. After that, I would use fine-tuning and feed in samples of writing from one of these options: (1) Ernest Hemingway, (2) Jane Austen, (3) William Shakespeare, (4) Emily Dickinson, or (5) Virgina Woolf. And based on the training set, have Llama-2 try to rewrite the same story in the style of one of those authors. Given this, I believe I will use one of the Grimm’s Fairy Tales because they are freely available and public domain. From the following list: “Hansel and Gretel; Little Red-Cap (aka little red riding hood), and The Frog-Prince”, which story would you recommend as good “Baseline Story” for my project?

Answer from Pi

• Pi: Wow, that’s an incredible project! It sounds like a fun and creative way to illustrate the use of fine-tuning. And what a cool application of natural language processing! As for which fairy tale to use as a baseline, I think “Hansel and Gretel” would work well. It’s a classic story that has a good mix of dialogue, description, and narration. Plus, it has a lot of interesting characters and events to work with. Do you plan to have the students evaluate how well the fine-tuned model captures the style of the target author?

• Jeff:yes thank you

• Pi: You’re welcome! That sounds like a fun project, and a good way to demonstrate the power of fine-tuning. One thing I’m curious about is whether you plan to fine-tune the model on a large set of writing samples from the target author, or a smaller set? Also, are you going to use a pre-trained language model like GPT-3 or one of the open-source alternatives like BLOOM or GPT-NeoX? Or will you build your own language model from scratch?

New prompt for phind.com

• Jeff: please rewrite this 5000 character story of little red cap so it fits in less than 1500 characters

Phind.com’s answer

• cached answer A girl known as Little Red-Cap, due to her favorite red velvet cap, was sent by her mother to deliver cake and wine to her sick grandmother. She was warned to stay on the path and not talk to strangers. On her way, she met a wolf, but unaware of his wickedness, she wasn’t afraid. She told him she was heading to her grandmother’s house, which was in the woods under three large oak trees.

The wolf, seeing an opportunity for a meal, distracted Little Red-Cap with the beauty of the flowers around them. While she was picking flowers, the wolf went to the grandmother’s house, devoured her, and disguised himself in her clothes. When Little Red-Cap arrived, she found the door open and was surprised by her grandmother’s appearance. She noted her big ears, eyes, hands, and mouth, to which the wolf replied they were all the better to hear, see, hug, and eat her with. The wolf then swallowed Little Red-Cap.

A huntsman passing by the house heard the wolf’s loud snoring and decided to check if the grandmother needed anything. Upon finding the wolf in bed, he cut open its stomach, freeing Little Red-Cap and her grandmother. They filled the wolf’s belly with stones, causing him to die when he awoke. The huntsman took the wolf’s skin, the grandmother revived after eating the cake and wine, and Little Red-Cap promised never to stray from the path again.

10/01/2023

Prompt

• Who is Jeff Hwang?

Pi.ai answer

• Well, based on my research, Jeff Hwang is a financial professional who works as a partner and managing director at a firm called Beacon Pointe Advisors. He has over 15 years of experience in the financial industry, and he’s known for delivering authentic planning and investment solutions to his clients. From what I can tell, he’s a well-respected professional who’s dedicated to helping his clients achieve their financial goals. Do you know Jeff Hwang personally, or are you just curious about who he is?

api-GPT-4 answer

• ChatBot: Jeff Hwang is a known figure in several areas such as authorship and gaming management. He is a widely known gambling strategist, renowned particularly for his expertise in several casino games like Omaha and poker. He authored the “Pot-Limit Omaha Poker” and “The Big Play Strategy,” which are considered significant books in the realm of the game. Details about personal life or other professions not related to the industry above might not be readily available depending on the specifics of the person in question.

phind.com

• Cached link
• Answer: Jeff Hwang is a versatile personality based in Las Vegas, known for his various roles as a gaming industry expert, author, poker player, card counter, game designer, stock analyst, and restaurateur lasvegasweekly.com.

Hwang’s background is primarily in the gaming industry. He learned how to count cards as a teenager and became an expert in Omaha poker. He has authored books such as “Pot-Limit Omaha Poker: The Big Play Strategy” and “The Modern Baseball Card Investor” lasvegasweekly.com.

In addition to his gaming expertise, Hwang also has a background in the stock market, having done internships at Fidelity and the Motley Fool. His gaming and stock market experience enabled him to write about the gaming industry from various perspectives lasvegasweekly.com.

In recent years, Hwang has turned his focus towards the restaurant industry. He is the owner of Taverna Costera, a combination restaurant, coffee shop, bar, and music venue located in Las Vegas lasvegasweekly.com.

He also founded a game development company about five or six years ago, creating a lineup of games that he later sold to Galaxy Gaming lasvegasweekly.com.

Note: There is another person named Jeff Hwang, known as Captain Jeff “Claw” Hwang, who is a military aviator. He is famous for shooting down two MiG-29 fighters while piloting his F-15C during the NATO intervention in the Kosovo conflict eurasiantimes.com. These two individuals are not the same and should not be confused with each other.

Google Bard

There are several notable people named Jeff Hwang, including:

• Jeff Hwang, Writer: Jeff Hwang is a semi-professional poker player and an investment analyst who regularly writes about the gaming industry for the Motley Fool, a well known website about stocks and investing. He has also written several books on poker, including “Pot-Limit Omaha Poker” and “Advanced Pot-Limit Omaha.”
• Jeff Hwang, CFA, CAIA: Jeff Hwang is a Partner and Managing Director of Beacon Pointe’s North Carolina office with over 15 years of experience delivering authentic planning and investment solutions to his clients. He leads a team of like-minded professionals serving private business owners, corporate executives and health care professionals with their unique planning and investment needs.
• Jeffrey Hwang, Software Engineer: Jeffrey Hwang is a software engineer with a passion for application development, distributed architectures, and machine learning. He has worked at several companies, including Facebook AI and Mattel.
• Jeff Hwang, Digital Content / Programming / Strategy Lead: Jeff Hwang is a Digital Content / Programming / Strategy Lead at Mattel. He is responsible for developing and executing Mattel’s digital strategy across all platforms.
• It is unclear which Jeff Hwang you are referring to. If you can provide more context, I may be able to give you a more specific answer.

Microsoft Bing Chat

Jeff Hwang is a person who has many roles and achievements in different fields. According to the web search results, he is:

• A gaming industry consultant and the best-selling author of several books on pot-limit Omaha poker, a popular variant of poker. (1)
• A partner and managing director of Beacon Pointe’s North Carolina office, a financial planning and investment firm. (2)
• A former fighter pilot who shot down two enemy planes over Kosovo in 1999 and retired as the vice commander of the 142nd Fighter Wing in Oregon.(3)
• A restaurateur who opened a Korean barbecue restaurant called 90 Proof in Las Vegas in 2020.(4)

He seems to be a very versatile and accomplished person.

Source: Conversation with Bing, 10/1/2023

• (1) About Jeff - Jeff Hwang. http://www.jeffhwang.com/about-jeff.html.
• (2) Jeff Hwang, CFP®, CRPC - Beacon Pointe Advisors. https://beaconpointe.com/contact-us/greensboro-north-carolina/jeff-hwang/.
• (3) Distinguished fighter pilot retires - AF. https://www.142wg.ang.af.mil/News/Photos/igphoto/2000978365/.
• (4) Gaming expert-turned-restaurateur Jeff Hwang on counting cards and the …. https://lasvegasweekly.com/dining/2022/feb/24/gaming-expert-turned-restaurateur-jeff-hwang-on-co/.
• (5) Jeff Hwang - Home. http://www.jeffhwang.com/.