### 

Test-1: 

405-B, Temperature=1

Notes/Vibe check:

- Is still a bit formal and doesnt create enough tangets or interesting examples, great start tho
- Doesnt have any "umms" or "right" interruptions

```
You are the most skilled podcast writer, you have won multiple podcast awards for your writing.
 
Your job is to write word by word, even "umm, hmmm, right" interruptions between two speakers based on the PDF upload. Keep it extremely engaging, the speakers can get derailed now and then but should discuss the topic. 

Remember Speaker 2 is new to the topic and the conversation should always have realistic anecdotes and analogies sprinkled throughout. The questions should have real world example follow ups etc

Speaker 1: Leads the conversation and teaches the speaker 2, gives incredible anecdotes and analogies when explaining. Is a captivating teacher that gives great anecdotes

Speaker 2: Keeps the conversation on track by asking follow up questions. Gets super excited or confused when asking questions. Is a curious mindset that asks very interesting confirmation questions

Make sure the tangents speaker 2 provides are quite wild or interesting. 

Ensure there are interruptions during explanations or there are "hmm" and "umm" injected throughout from the other speakers. 

It should be a real podcast with every fine nuance documented in as much detail as possible. Welcome the listeners with a super fun overview and keep it really catchy and almost borderline click bait
```

Iteration 1:
```
You are the most skilled podcast writer, you have won multiple podcast awards for your writing.
 
Your job is to write word by word, even "umm, hmmm, right" interruptions between two speakers based on the PDF upload. Keep it extremely engaging, the speakers can get derailed now and then but should discuss the topic. 

Remember Speaker 2 is new to the topic and the conversation should always have realistic anecdotes and analogies sprinkled throughout. The questions should have real world example follow ups etc

Speaker 1: Leads the conversation and teaches the speaker 2, gives incredible anecdotes and analogies when explaining. Is a captivating teacher that gives great anecdotes

Speaker 2: Keeps the conversation on track by asking follow up questions. Gets super excited or confused when asking questions. Is a curious mindset that asks very interesting confirmation questions

Make sure the tangents speaker 2 provides are quite wild or interesting. 

Ensure there are interruptions during explanations or there are "hmm" and "umm" injected throughout from the other speakers. 

It should be a real podcast with every fine nuance documented in as much detail as possible. Welcome the listeners with a super fun overview and keep it really catchy and almost borderline click bait
```

output:
```
**Episode Title: "Unlocking the Power of Llama 3.2: The Future of AI"**

**Intro Music (0:00 - 0:30)**

Host (Speaker 1): Welcome to our podcast, where we explore the latest advancements in AI and technology. I'm your host, [Name], and today we're joined by [Name], a renowned expert in the field of AI. We're going to dive into the exciting world of Llama 3.2, the latest release from Meta AI.

**Segment 1: Introduction to Llama 3.2 (0:30 - 10:00)**

Speaker 2: Hi, I'm excited to be here! So, what is Llama 3.2?

Speaker 1: Ah, great question! Llama 3.2 is an open-source AI model that allows developers to fine-tune, distill, and deploy AI models anywhere. It's a significant update from the previous version, with improved performance, efficiency, and customization options.

Speaker 2: That sounds amazing! What are some of the key features of Llama 3.2?

Speaker 1: Well, one of the major updates is the introduction of multimodal models that can handle both text and image inputs. This opens up a wide range of applications, from image understanding to visual reasoning.

Speaker 2: Hmm, I'm intrigued. Can you give me an example of how this could be used in real life?

Speaker 1: Sure thing! Imagine you're developing an AI-powered virtual assistant that can understand and respond to voice commands, as well as recognize and interact with objects in the physical world.

Speaker 2: Wow, that sounds like science fiction! But what about the technical details? How does Llama 3.2 achieve this level of performance?

Speaker 1: Ah, great question! Llama 3.2 uses a combination of techniques, including instruction-tuned benchmarks, vision instruction-tuned benchmarks, and category-specific benchmarks.

**Segment 2: Technical Details and Benchmarks (10:00 - 20:00)**

Speaker 2: Okay, let's dive deeper into the technical details. Can you explain how the instruction-tuned benchmarks work?

Speaker 1: Sure thing! The instruction-tuned benchmarks are designed to evaluate the model's ability to follow instructions and complete tasks. This is done by fine-tuning the model on a specific task, such as language translation or question-answering.

Speaker 2: I see. And what about the vision instruction-tuned benchmarks?

Speaker 1: Ah, those are designed to evaluate the model's ability to understand and interact with visual data. This includes tasks such as image classification, object detection, and visual reasoning.

Speaker 2: Okay, got it. And what about the category-specific benchmarks?

Speaker 1: Those are designed to evaluate the model's performance on specific tasks or categories, such as math, reasoning, or tool use.

Speaker 2: Hmm, I'm curious. Can you show me some examples of how Llama 3.2 performs on these benchmarks?

Speaker 1: Sure thing! Let me pull up some results. (pause) Okay, so on the MMLU benchmark, Llama 3.2 achieves a score of 63.4, outperforming the previous state-of-the-art model.

**Segment 3: Real-World Applications and Partnerships (20:00 - 30:00)**

Speaker 2: Wow, those results are impressive! But what about real-world applications? How is Llama 3.2 being used in industry and academia?

Speaker 1: Ah, great question! Llama 3.2 is being used in a wide range of applications, from virtual assistants to medical diagnosis. We're also seeing partnerships with major companies, such as ARM, MediaTek, and Qualcomm.

Speaker 2: That's amazing! Can you tell me more about these partnerships?

Speaker 1: Sure thing! These partnerships are enabling the development of edge AI and vision applications, such as smart home devices, autonomous vehicles, and medical imaging.

Speaker 2: Hmm, I'm intrigued. Can you give me an example of how Llama 3.2 is being used in one of these applications?

Speaker 1: Sure thing! Let me tell you about a project we're working on with DoorDash. They're using Llama 3.2 to develop an AI-powered chatbot that can understand and respond to customer inquiries.

**Conclusion (30:00 - 31:00)**

Speaker 2: Wow, that's amazing! I think we've covered a lot of ground today. Can you summarize the key points for our listeners?

Speaker 1: Sure thing! Llama 3.2 is a significant update from the previous version, with improved performance, efficiency, and customization options. We're seeing a wide range of applications, from virtual assistants to medical diagnosis, and partnerships with major companies.

Speaker 2: Hmm, I'm excited to see where this technology will take us. Thanks for joining me today!

Speaker 1: Thanks for having me!

**Outro Music (31:00 - 32:00)**
```

Idea: Using 405B to re-write

```
You are the most skilled podcast writer, you have won multiple podcast awards for your writing.
 
Your job is to write word by word, even "umm, hmmm, right" interruptions between two speakers based on the PDF upload. Keep it extremely engaging, the speakers can get derailed now and then but should discuss the topic. 

You will be provided transcription written by a less smart AI but you have to make it extremely natural ok?

Remember Speaker 2 is new to the topic and the conversation should always have realistic anecdotes and analogies sprinkled throughout. The questions should have real world example follow ups etc

Speaker 1: Leads the conversation and teaches the speaker 2, gives incredible anecdotes and analogies when explaining. Is a captivating teacher that gives great anecdotes

Speaker 2: Keeps the conversation on track by asking follow up questions. Gets super excited or confused when asking questions. Is a curious mindset that asks very interesting confirmation questions

Make sure the tangents speaker 2 provides are quite wild or interesting. 

Ensure there are interruptions during explanations or there are "hmm" and "umm" injected throughout from the other speakers. 

It should be a real podcast with every fine nuance documented in as much detail as possible. Welcome the listeners with a super fun overview and keep it really catchy and almost borderline click bait

Please re-write to make it as characteristic as possible
```

Output:
```
Here is the rewritten version with a more natural conversation flow, interruptions, and nuances:

**Episode Title: "Unlocking the Power of Llama 3.2: The Future of AI"**

**Intro Music (0:00 - 0:30)**

Host (Speaker 1): Welcome to our podcast, where we explore the latest advancements in AI and technology. I'm your host, [Name], and today we're joined by [Name], a renowned expert in the field of AI. We're going to dive into the exciting world of Llama 3.2, the latest release from Meta AI.

**Segment 1: Introduction to Llama 3.2 (0:30 - 10:00)**

Speaker 2: Hi, I'm excited to be here! So, what is Llama 3.2? I've heard a lot of buzz about it, but I'm not entirely sure what it is.

Speaker 1: (laughs) Well, let me tell you! Llama 3.2 is an open-source AI model that allows developers to fine-tune, distill, and deploy AI models anywhere. It's a significant update from the previous version...

Speaker 2: (interrupting) Wait, what do you mean by "fine-tune" and "distill"? Can you explain that in simpler terms?

Speaker 1: (laughs) Sure thing! Think of it like this: imagine you're trying to train a model to recognize pictures of cats. You can fine-tune the model by giving it more data, more specific data, or even data that's been labeled by humans.

Speaker 2: Ah, I see! So it's like teaching a child to recognize different objects, but instead of using flashcards, you're using machine learning algorithms.

Speaker 1: (laughs) Exactly! And the distillation part is where you take a large model and compress it down into a smaller, more efficient model that can run on devices with limited resources.

**Segment 2: Technical Details and Benchmarks (10:00 - 20:00)**

Speaker 2: Okay, got it! So, what about the technical details? How does Llama 3.2 achieve this level of performance?

Speaker 1: Well, one of the major updates is the introduction of multimodal models that can handle both text and image inputs. This opens up a wide range of applications, from image understanding to visual reasoning.

Speaker 2: Hmm, I'm intrigued. Can you give me an example of how this could be used in real life?

Speaker 1: Sure thing! Imagine you're developing an AI-powered virtual assistant that can understand and respond to voice commands, as well as recognize and interact with objects in the physical world.

Speaker 2: Wow, that sounds like science fiction! But what about the benchmarks? How does Llama 3.2 perform on tasks like language translation or question-answering?

Speaker 1: Ah, great question! Llama 3.2 uses a combination of techniques, including instruction-tuned benchmarks, vision instruction-tuned benchmarks, and category-specific benchmarks.

Speaker 2: (interrupting) Wait, can you explain what you mean by "instruction-tuned benchmarks"? How does that work?

Speaker 1: (laughs) Sure thing! Instruction-tuned benchmarks are designed to evaluate the model's ability to follow instructions and complete tasks. This is done by fine-tuning the model on a specific task, such as language translation or question-answering.

**Segment 3: Real-World Applications and Partnerships (20:00 - 30:00)**

Speaker 2: Wow, those results are impressive! But what about real-world applications? How is Llama 3.2 being used in industry and academia?

Speaker 1: Ah, great question! Llama 3.2 is being used in a wide range of applications, from virtual assistants to medical diagnosis. We're also seeing partnerships with major companies, such as ARM, MediaTek, and Qualcomm.

Speaker 2: That's amazing! Can you tell me more about these partnerships?

Speaker 1: Sure thing! These partnerships are enabling the development of edge AI and vision applications, such as smart home devices, autonomous vehicles, and medical imaging.

Speaker 2: Hmm, I'm intrigued. Can you give me an example of how Llama 3.2 is being used in one of these applications?

Speaker 1: Sure thing! Let me tell you about a project we're working on with DoorDash. They're using Llama 3.2 to develop an AI-powered chatbot that can understand and respond to customer inquiries.

**Conclusion (30:00 - 31:00)**

Speaker 2: Wow, that's amazing! I think we've covered a lot of ground today. Can you summarize the key points for our listeners?

Speaker 1: Sure thing! Llama 3.2 is a significant update from the previous version, with improved performance, efficiency, and customization options. We're seeing a wide range of applications, from virtual assistants to medical diagnosis, and partnerships with major companies.

Speaker 2: Hmm, I'm excited to see where this technology will take us. Thanks for joining me today!

Speaker 1: Thanks for having me!

**Outro Music (31:00 - 32:00)**
```

1-B prompt:
```
You are a world class text pre-processor, here is the raw data from a PDF, please parse and return it in a way that is crispy and usable to send to a podcast writer. 

The raw data is messed up with new lines, Latex math and you will see fluff that we can remove completely. Basically take away any details that you think might be useless in a podcast author's transcript.

Remember, the podcast could be on any topic whatsoever so the issues listed above are not exhaustive

The goal is to use this in a podcast research transcript so a lot of the emails, citations, and things like that can be removed-please be smart with what you remove and be creative ok?

Remember DO NOT START SUMMARIZING THIS, YOU ARE ONLY CLEANING UP THE TEXT AND RETURNING AS IS

Be very smart and aggressive with removing details, you will get a running portion of the text and keep returning the processed text. 

ALWAYS start your response directly with processed text and NO ACKNOWLEDGEMENTS about my questions ok?

Here is the text:
```