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Initial transcription/prompt:

this article is called Taming the Beast, a Guide to OpenAI's Unpredictable GPTO, which is the title. So GPTO is OpenAI's new model called GPT, and then the letter O as in Orion, and O1 more accurately, so GPTO-1. I've divided it into a few sections here which I'll run through. So part one is introducing the beast, where we can briefly explain exactly what GPTO is. It's a chain of thought capabilities that it can only be accessed via for GPT-plus members and also the API at the moment. It's a great model, very, very powerful, amazing at reasoning and planning, but it's also costly, it's slow, and it's currently in preview, and most importantly of all, its output can be both powerful and unpredictable. For example, it can hallucinate, which means it can actually be really bad for code. It will hallucinate class names, for example, in CSS. It will hallucinate functions in JavaScript. It'll just do all kinds of things wrong, and also because it's so, so clever and so convincing, it can actually trick you into thinking things that aren't true, especially if you don't have domain-specific knowledge in that area. So it is a model that needs to be treated with respect, but that being said, there is massive potential for high-level planning and ideation with this model, especially if you do know the domain and you can write a really, really good prompt. So now let's have a look at how to actually use it. Then in part 2, we'll call this one Taming the Chaos Prompt Engineering with GPTO. Here I'm going to give them three quick wins for using GPTO. So these are three specific use cases that I found it to be really, really powerful. The first one we'll call Navigate the Fog. Basically, GPTO can accept massive context windows that it understands very, very well. So for example, you could dump an entire file tree for a Git repo along with various notes. Although bear in mind, it can't accept images, you'll just have to paste the text. You could dump in massive research papers, you could dump in entire sales pages or e-books, and it will then be able to help you navigate and understand all of that context, especially if you're just a beginner and you don't know where to start. Now, bear in mind that within GPT+, you will only get a number of runs, a number of chats with this model every single week, which is one of the reasons I like to almost abuse it by giving it very large contexts and a lot of information just because I'm not metered based on each token, but I am metered based on each individual run. So what you can do is you can start the chat by using GPTO, so that there's a lot of initial thought that goes into that initial response and then you can switch to the O1 mini model, which is cheaper and you get more runs with after maybe a couple of chats. Secondly, I want to talk as well about writing prompts. So the second tip here is write prompts. Essentially, GPTO is insane at prompting. It's very, very good at prompt summary. So if you've got a prompt that you want to improve, you can drop it into GPTO and it will summarize it and make it much simpler and much more easy for other models to follow. It's even better than Claude, than Anthropix on Prompt Builder as well. So my experience with it is it can turn a 1,000-word prompt into a 100-word prompt and actually performs better. It's especially good at prompting itself, along with O1 mini and DALI as well. Then the third tip is something that I call ostentatious prompts. So this is when you have a prompt that you really, really want to get the best out of and you want to go over it with, and you can actually try GPTO. For example, all the charts and the images which are going to be within this article, all were made with O. Then finally, let's actually turn our attention to the fourth and main use case for GPTO 1, which is architecting and planning. So then this moves on to part 3, which is architecting with GPTO from file trees to product specs. So GPTO is exceptional at planning, but it's not as good as doing. So you want to use it to describe a product specification that you want to code with, for example. So you can say, I want to build an app that does XYZ using React and Tailwind and then have it generate the file tree and the product specification and the steps that you need to take, and then you can copy that into Sonnet or GPT 4.0, or send it to a developer. All with the caveat that you'll have to be explicit to the model that you don't really understand the topic in hand, and so you want it to really give you step-by-step instruction, and it will generate a 1 to 5,000 word response, and action plan that you can follow. So you really need to think of it as an architect rather than a detailed writer. Then fourthly, in part 4, we can talk about the future of GPTO, and how it's only in preview, it's just the beginning. AI agents are coming up. I know this because I use GPTO for my own agents and bots, and we'll talk about this in another article. But for now, the main thing to understand is GPTO is great for planning, and that's basically for the article. So in terms of keywords, we can research, you can search GPTO, OpenAI GPTO. Again, bear in mind, it's the letter O. GPTO specs, GPTO specifications, GPTO use cases, GPTO architect, and then finally coding with Sonnet and Cursor, and that's basically it.

Article 1 - chatgpt-4o-latest

# Taming the Beast: A Guide to OpenAI's Unpredictable GPTO

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### **Introduction**

Ever find yourself awestruck by what AI can accomplish, but also frustrated by its unpredictability? Imagine using a model so powerful that it can handle the complexity of 300 pages of text in one shot but might confuse or outright invent information in the process. Welcome to **GPTO-1**, OpenAI's latest model in the GPT family. It’s a model that’s as brilliant as it is chaotic, capable of advanced reasoning and high-level planning, but also prone to making unpredictable mistakes—especially in tasks like coding where accuracy matters most.

This guide is here to help you navigate the wild nature of GPTO. We’ll dive into how to **harness its strengths** while avoiding costly mistakes, using key strategies like **effective prompt engineering** and proper oversight. At the end of this journey, you’ll know how to tame this beast and use it confidently for everything from ideation to product planning.

---

### **Understanding GPTO: The Power and the Pitfalls**

#### What Exactly is GPTO?

GPTO-1, also referred to as “Strawberry,” is OpenAI’s cutting-edge language model, designed for advanced reasoning and **chain-of-thought processing**. Imagine feeding it a massive Git repository or a 300-page report, and it not just handling it, but pulling out key opinions, summarizing complex ideas, and even strategizing on next steps. With a **context window** of **128,000 tokens** (about 300 pages of text), GPTO-1 can sift through enormous amounts of information in one go, making it a favorite for planning, coding, and data-heavy tasks.

Unlike older models like GPT-4, some early predictions suggest GPTO-1 has fewer hallucinations—those strange moments when the AI starts spouting made-up information—but it’s still far from perfect. This unpredictability, particularly in **coding tasks**, can lead it to invent code elements that don't exist, such as creating imaginary class names or functions.

#### Access to GPTO

Currently, GPTO is available in preview mode for **ChatGPT Plus users** and through an API offering two versions—standard and mini. But make no mistake: even though it's on the cutting edge, this model is still experimental, and many of its features are in development, with plans for future updates like an **o1-ioi version** focused on even better reasoning capabilities.

---

### **Navigating Large Contexts with GPTO**

One of the most compelling features of GPTO is how it excels at reasoning over **massive contexts**. You can practically throw entire **Git file trees**, **research papers** or even e-books at it. For developers trying to pick apart a mess of code in a large project or researchers sifting through study after study, GPTO can be a lifesaver.

But there’s a catch: while GPTO can handle these tasks, users are **metered based on the number of runs** rather than how many tokens are used in each chat. In other words, you only have access to a limited number of sessions per week. One clever trick to stretch your access is to “front-load” the heavy lifting by asking GPTO to analyze large datasets at the start of a conversation. Then, once it’s processed everything, you can switch to the **O1 mini model** for follow-up questions, saving precious GPTO runs for when you really need it.

---

### **Mastering Prompt Engineering with GPTO**

#### Why Prompt Engineering Matters

If you want to truly harness the power of GPTO, **effective prompt engineering** is key. Getting good output from this model starts with feeding it the right input. A bad prompt will lead even this advanced model to give unclear or straight-up wrong responses, so clarity is essential. According to some studies, refining prompts through **iterative testing** can make a world of difference to your results.

For instance, GPTO-1 is great at **condensing and refining prompts**. Take a bloated, 1,000-word request and hand it to GPTO, and it will efficiently boil it down to the essentials—sometimes giving you a 100-word prompt that performs even better. This makes it ideal for speeding up your workflow, as you don’t need to overcomplicate things.

#### Using “Ostentatious Prompts”

Another advanced tip is what I call **"ostentatious prompts"**. This is when you want to really push GPTO to its limits by giving it creative and complex prompts to work with. Let’s say you need not just text responses but images or charts for a project. GPTO can help fine-tune these outputs, especially when using related models like **DALL·E** for the visuals. Think of it as the spark that starts a multi-layered process, helping you prompt other models more effectively at the same time.

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### **Architecting and Planning with GPTO: A Blueprint**

#### GPTO as a Master Planner

What GPTO is really great at—more so than executing details—is **planning and architecture**. It helps you outline broad-level tasks, brainstorm strategies, or map out product specifications. Imagine you want to develop an app, but you don’t know where to start. GPTO can lay out a whole **file tree**, product specification, and step-by-step action plan on how you’ll code the app, including framework suggestions like **React** and **Tailwind CSS**.

However, here’s the thing—it’s not a “done-for-you” solution. You still need to guide it and check the results for accuracy, especially if you’re not familiar with the subject matter. In fact, one of the best ways to use GPTO in planning is to explicitly tell it that you're unfamiliar with the task at hand. This helps the model to break things down into more understandable chunks.

#### Example Use Case: Planning a Full Stack App

Here’s a concrete example: Suppose you instruct GPTO to help you draft an architecture for a **full-stack web application** using Node.js and React. First, it’ll generate a **file structure** with directories for front-end, back-end, and shared assets. Then, it outlines a product specification, which may even run to **1,000 - 5,000 words** of detailed description, including high-level tasks and subtasks. From here, you could collaborate with a developer to turn the plan into reality.

---

### **Managing GPTO’s Hallucinations and Unpredictability**

Even with all its power, one issue still looms: **AI hallucinations**. Though GPTO may be slightly better than GPT-4 at sticking to the facts, there’s still a chance it might throw in completely incorrect information.

For instance, in **coding** scenarios, GPTO has been known to occasionally fabricate class names in CSS or functions in JavaScript. This could cost you precious development time, especially if you’re not paying careful attention. To combat this, one tactic is to place GPTO’s outputs into an environment where they’re checked for authenticity, whether through manual oversight or additional automated checks.

When working in areas where accuracy is critical, hallucinations are a big deal. You can’t let the model just run unchecked. Instead, apply **critical oversight**—review everything twice, especially if you lack deep knowledge in a particular domain. The model may be convincing, but that doesn’t always mean it's correct.

---

### **Future of GPTO: More to Come**

Just as GPTO-1 is pushing boundaries today, OpenAI’s roadmap suggests things are only going to get wilder. Future updates like **GPTO-ioi** will bring even more advanced reasoning capabilities, with rumors of potential **AI agent integrations**. These agents will likely perform end-to-end tasks autonomously, going beyond just planning and ideation, and could completely overhaul how we interact with AI in everyday tasks.

---

### **Conclusion**

In the end, GPTO-1 is a game changer—but it’s not without its quirks. Treat it like a powerful assistant with enormous potential, but also one that needs your oversight. With refined **prompt engineering**, careful session management, and a watchful eye on its hallucination problem, GPTO transforms from an unpredictable beast into a valuable tool for planning, coding, and ideating on complex projects.

So go ahead: experiment, refine, and most importantly, **tame the beast**. The future of AI is bright, and with GPTO in your toolkit, you’re already ahead of the curve.

Article 2 - O1 Mini

# Taming the Beast: A Guide to OpenAI's Unpredictable GPTO

Have you ever been amazed and frustrated by how powerful AI can be? Imagine using an AI that can read 300 pages of text at once but sometimes makes things up just like that. This is what working with OpenAI's new model, GPTO-1, is like. Known as "the beast," GPTO-1 is excellent at reasoning and planning. It's available to ChatGPT Plus users and through the API in preview and mini versions. While GPTO-1 can outperform GPT-4 in coding, placing it in the top 7% of competitors, it can also produce unpredictable results. This guide will help you harness GPTO-1's strengths while managing its flaws, ensuring you get the most out of this powerful tool.

## Understanding GPTO's Capabilities and Limitations

### What Is GPTO-1?

GPTO-1, also called "Strawberry," is OpenAI's latest AI model. It has advanced chain-of-thought reasoning, meaning it can think through problems step by step. With a massive 128,000-token window, GPTO-1 can handle around 300 pages of text at once. This makes it perfect for analyzing large documents like Git repositories, research papers, or e-books.

### Advanced Reasoning and Potential

GPTO-1 shines in its reasoning abilities. It scored 78.1% in vision modality performance, making it competitive with humans. This advanced reasoning helps reduce the chances of the AI making things up, known as hallucinations. Compared to GPT-4, GPTO-1 is better at coding tasks, placing in the top 7% of competitors. Users have found it "exceptional" for programming, data analysis, and math, even in complex scenarios.

### Access Requirements

GPTO-1 is available to ChatGPT Plus users and select developers through preview programs and APIs. OpenAI plans to make it more widely available in the future. However, using GPTO-1 comes with some challenges, especially regarding cost and speed. It is slower and more expensive than other models, making it essential to use it wisely.

### Unpredictability and Hallucinations

Despite its strengths, GPTO-1 can sometimes produce false or misleading information. This happens because the AI is trained on vast amounts of data, which can lead to mistakes. For example, in coding tasks, GPTO-1 might invent nonexistent class names or functions, causing errors. These hallucinations can be particularly problematic when you rely on the AI for accurate coding or data analysis.

## Navigating Large Contexts with GPTO

### Handling Massive Data Inputs

One of GPTO-1's standout features is its ability to process large amounts of information at once. You can input an entire file tree from a Git repository, along with various notes, research papers, or even an entire e-book. This capability is incredibly useful for beginners who might not know where to start with a complex project. GPTO-1 can help you navigate and understand all that context, making it easier to manage large-scale tasks.

### Maximizing Limited Runs

Users of GPTO-1 are metered based on the number of runs rather than tokens. This means each interaction costs the same, no matter how much data you process. To make the most of this, you can input large datasets in a single run. Start with GPTO-1 for complex tasks and then switch to the cheaper O1 mini model for follow-up interactions. This strategy helps you conserve your run allowance while still getting powerful results.

### Example Use Case

Imagine you have a huge codebase in a Git repository that you need to understand. Instead of tackling it piece by piece, you can dump the entire file tree into GPTO-1. The AI can analyze the structure, highlight key areas, and help you get a comprehensive overview quickly. This saves time and makes managing large projects much more manageable.

## Mastering Prompt Engineering with GPTO

### Importance of Effective Prompts

Crafting the right prompts is crucial to getting the best results from GPTO-1. Effective prompt engineering involves designing and refining your inputs to guide the AI toward desired responses. A well-crafted prompt can make the difference between a useful output and a confusing one.

### GPTO's Strength in Summarizing and Refining Prompts

GPTO-1 excels at summarizing and refining prompts. If you have a long, complicated prompt, GPTO-1 can condense it into a simpler, more efficient version. This makes it easier for other models to follow and improves the overall quality of responses. Users have found that GPTO-1 can turn a 1,000-word prompt into a 100-word one while performing better than models like Claude and Anthropic's Prompt Builder.

### Advanced Strategies

To get the most out of GPTO-1, use advanced prompt engineering techniques. These include:
- **Prompt Chaining:** Linking multiple prompts together to guide the AI through complex tasks.
- **Temperature Control:** Adjusting the creativity of responses by changing how random or focused the AI is.
- **Token Control:** Managing the length and detail of responses to fit your needs.

By mastering these strategies, you can direct GPTO-1 to produce more accurate and useful outputs.

## Architecting and Planning with GPTO

### Planning vs. Execution

GPTO-1 is exceptional at planning and ideation but less reliable for detailed execution. Think of it as an architect rather than a builder. It can help you design a product, create a file tree, or generate a step-by-step plan, but you'll need to handle the actual implementation.

### Generating Product Specifications and Action Plans

Use GPTO-1 to create detailed product specifications and action plans. For example, if you want to build an app using React and Tailwind, GPTO-1 can generate the file tree and outline the steps needed to develop the app. These outputs can then be handed off to developers or used as a roadmap for your project.

### Lengthy, Comprehensive Outputs

GPTO-1 can produce long, thorough responses ranging from 1,000 to 5,000 words. This makes it ideal for generating comprehensive action plans and detailed specifications. However, it's essential to review these outputs carefully to ensure accuracy and completeness.

## Managing Resource Constraints

### Limited Runs and Cost Considerations

Using GPTO-1 efficiently is key due to its cost and limited runs. Since you're charged per run, it's important to maximize the value of each interaction. Input large amounts of data in one run and get as much useful information as possible before switching to other, cheaper models.

### Switching Between Models

Start your project with GPTO-1 to handle the most complex tasks. Once you've laid the groundwork, switch to the O1 mini model or GPT-4o for follow-up tasks. This approach helps you manage costs while still leveraging the strengths of GPTO-1.

## Addressing GPTO's Unpredictability and Hallucinations

### Understanding AI Hallucinations

AI hallucinations happen when the model generates incorrect or misleading information. This can occur because the AI learns from vast amounts of data, some of which may be inaccurate or outdated. GPTO-1 is better than previous models, like GPT-4, which is 40% more likely to produce factual responses than GPT-3.5, but hallucinations still occur.

### Impact on Coding Tasks

In coding, hallucinations can lead to invalid code, which can disrupt development and cause delays. It's crucial to verify any code generated by GPTO-1 to ensure its accuracy and functionality.

### Mitigation Strategies

To minimize the impact of hallucinations:
- **Embed Verification Systems:** Use tools that check the consistency and accuracy of GPTO-1's outputs.
- **Maintain Critical Oversight:** Always review and validate the AI's responses, especially in areas where precision is vital.
- **Leverage Domain Knowledge:** Understanding the subject matter helps you spot and correct errors the AI might make.

## Ostentatious Prompts and Creative Outputs

### Pushing Boundaries with Complex Prompts

Ostentatious prompts are detailed and complex inputs designed to push the AI's creative limits. Using these prompts with GPTO-1 can result in highly detailed and creative responses, such as elaborate charts or descriptions.

### Examples

For instance, you can create detailed charts and images for your projects by refining GPTO-1's outputs with models like DALL·E. This allows you to enhance your content with high-quality visuals generated from complex prompts.

## Future of GPTO and AI Agents

### GPTO is Just the Beginning

GPTO-1 represents the latest in OpenAI's advancements, with plans for even more powerful versions like GPTO-ioi focusing on reasoning. This is just the start of what AI can achieve.

### Emergence of AI Agents

AI agents using models like GPTO-1 will soon enhance both planning and execution capabilities. These agents will automate more complex tasks, making AI even more integral to various industries.

### OpenAI's Future Plans

OpenAI continues to improve and expand GPTO's abilities. Staying updated with these changes will help you leverage the latest features and improvements, ensuring you make the most of this evolving technology.

## Best Practices and Final Tips

- **Human Oversight is Key:** Always review GPTO-1's outputs, especially in unfamiliar areas.
- **Continuous Learning in Prompt Engineering:** Keep refining your prompts to improve results.
- **Encourage Creativity and Exploration:** Experiment with different approaches to maximize GPTO-1's potential.
- **Ethical and Practical Considerations:** Use GPTO-1 responsibly, being mindful of its limitations and the impact of any inaccuracies.

## Conclusion

Taming GPTO-1 means understanding its strengths and limitations. By mastering prompt engineering and using GPTO-1 as a planning tool, you can harness its incredible potential while avoiding its pitfalls. Responsible and creative use of GPTO-1 can lead to transformative results in your projects. As AI technology continues to advance, tools like GPTO-1 will become even more essential across various industries. Embrace the challenge of taming this powerful AI, and position yourself at the forefront of AI innovation.

Article 3 - O1 Preview

Article 4 - Gemini 1.5 Preview

## Taming the Beast: A Guide to OpenAI's Unpredictable GPTO

Have you ever felt both amazed and annoyed by what AI can do? Imagine an AI that can read 300 pages in a flash, but also makes things up. That's GPTO, OpenAI's new "beast." It's super smart for planning, but it can also be tricky. This guide will help you control GPTO and use its power safely.

---

GPTO-1, nicknamed "Strawberry," is OpenAI's latest creation.  It's like a super-powered brain that thinks things through before answering. ChatGPT Plus members and some developers can access it now. It can handle tons of info—like a whole Git repo, a big research paper, or even an e-book—thanks to its giant 128,000-token window (that's like 300 pages!).  It even scored 78.1% against humans in visual tests! It's great for coding, data work, and math, even beating GPT-4o in code challenges.  But there's a catch: it can "hallucinate," meaning it sometimes makes up info. This can be bad for coding, where it might invent fake functions or classes. So, while it's powerful, you need to know how to use it right.

One way to use GPTO wisely is to give it a lot of info at once. Since you pay per "run" (not per word), it's smart to stuff it full of data each time.  Start with GPTO for the heavy lifting, then switch to the cheaper O1 mini or GPT-4o for follow-up questions. This saves you runs and money. Think of it like having a quick chat with an expert and then asking a junior colleague for more details.

Another key is writing good prompts.  GPTO is a prompt-writing pro! It can take a huge 1,000-word prompt and shrink it to 100 words, making it easier for other AIs to understand. It’s even better than Claude or Anthropic's Prompt Builder. GPTO is great at making prompts for itself, O1 mini, and DALL·E, too. Use this skill to refine complicated prompts into short, powerful ones.

GPTO shines at planning big projects. It’s like an architect—great at designing but not so good at building.  Want to make an app? Tell GPTO what it should do, what tech you want to use (like React and Tailwind), and ask it to lay out the plan, file tree, and steps. It can write you a 1,000 to 5,000-word action plan!  Just tell it you're a newbie and need it to explain things simply.  Then you can give that plan to another AI like GPT-4 or Sonnet, or a human developer.

While GPTO's large context is amazing for digging through loads of information, remember that it's still in preview and access is limited. OpenAI is always making tweaks, and GPT-4o is already twice as fast as GPT-4 Turbo.  Both have big context windows, but GPTO's thinking skills are what makes it special.  However, like all AIs trained on massive datasets, GPTO can make mistakes. GPT-4 is 40% better at facts than GPT-3.5, but hallucinations still happen. Double-check its work, especially for coding. Tools that check for consistent information can help.

Don't be afraid to get creative with your prompts. Use what I call "ostentatious prompts"—really detailed ones—to get the best from GPTO.  I used it to generate all the charts and images for this article, then used tools like DALL-E to refine them.

GPTO is only the start. Future versions like GPTO-ioi will focus on getting even better at reasoning.  AI agents built with GPTO are coming too.  This means AIs that can plan and do things, not just dream them up.

So, how can you tame this beast?  First, understand what it's good and bad at. Use its large brain for big tasks,  but double-check its work.  Write smart prompts. Treat it as your planning expert.  And stay updated on what OpenAI is cooking up next.

---

To recap, GPTO is powerful but needs careful handling. Good prompts and a critical eye are your best tools. Use its planning power to create detailed roadmaps. Remember, it’s a great architect, not a builder.  Think about those impressive 300 pages it can handle, and how it aced those coding tests! As AI keeps evolving, GPTO will become even more important. By learning to use it well, you'll be ready for the future of AI. Start taming the beast today, and unleash its power for yourself.

Article 5 - Sonnet 3.5 Preview

Title: Taming the Beast: A Guide to OpenAI's Unpredictable GPTO

Have you ever been amazed and frustrated by AI at the same time? Imagine an AI so powerful it can process 300 pages of text at once, yet unpredictable enough to make up information out of thin air. That's GPTO-1, OpenAI's new model, nicknamed "the beast."

GPTO-1 is a game-changer. It's great at reasoning and planning, available to ChatGPT Plus users and through the API. But here's the catch: it can be wild and unpredictable. It might invent fake functions or classes when coding, even though it's in the top 7% of coders. That's right - it's brilliant but can lead you astray if you're not careful.

So, how do we tame this beast? That's what this guide is all about. We'll show you how to use GPTO's strengths while avoiding its pitfalls. It's all about smart prompt engineering and keeping a critical eye on what it produces.

Let's dive into what GPTO-1 really is. OpenAI calls it "Strawberry," and it's their latest model. It's got some serious brainpower, with advanced "chain-of-thought reasoning." This means it can think through problems step-by-step, almost like a human would.

One of GPTO's superpowers is its massive memory. It can handle a 128,000-token window - that's like reading 300 pages of text all at once! This makes it great for tackling big, complex problems. It's even competitive with humans in some visual tasks, scoring an impressive 78.1% in vision tests.

But here's where it gets tricky. GPTO can sometimes "hallucinate" - that's AI-speak for making stuff up. This is especially problematic in coding. Imagine you're building a website, and GPTO suggests a CSS class that doesn't exist. Or it invents a JavaScript function out of thin air. These mistakes can waste hours of your time if you're not careful.

So, how do we make the most of GPTO without falling into these traps? First, let's talk about how to use its amazing ability to handle huge amounts of information.

GPTO is like a super-smart intern who can read and understand entire Git repositories, research papers, or e-books in seconds. This is fantastic for beginners trying to grasp complex topics. You can dump in a ton of information and ask GPTO to explain it to you.

Here's a pro tip: Since you're limited by the number of times you can use GPTO, not by how much you input each time, make each session count. Load it up with as much relevant info as you can. Start with GPTO for the big picture, then switch to simpler models like O1 mini for follow-up questions. This way, you get the most bang for your buck.

Now, let's talk about prompts. GPTO is a wizard at refining prompts. It can take a rambling 1,000-word prompt and turn it into a razor-sharp 100-word version that actually works better. It's even better at this than other AI assistants like Claude or Anthropic's Prompt Builder.

When crafting prompts for GPTO, think big. Use what I call "ostentatious prompts" - really push the boundaries of what you're asking. This is how I created all the charts and images for this article, using GPTO's outputs refined through models like DALL·E.

But remember, GPTO is an architect, not a builder. It's fantastic for planning and coming up with ideas, but not so great at executing them. Use it to create detailed product specs, file trees, and step-by-step plans. Just be sure to tell it if you're not an expert in the field - it'll give you more comprehensive guidance that way.

GPTO can generate massive responses, from 1,000 to 5,000 words. These aren't just long - they're packed with useful information. But always double-check its output, especially in areas you're not familiar with. GPTO is convincing, but not always correct.

Let's talk about those "hallucinations" again. They happen because AI models like GPTO are trained on huge amounts of data, and sometimes they mix things up. The good news is that GPTO is getting better at this. It's about 40% more likely to give factual responses compared to older models like GPT-3.5.

To avoid being led astray, always verify GPTO's outputs. If you're coding, test the functions it suggests. For other topics, cross-reference with reliable sources. Think of GPTO as a brilliant but sometimes overenthusiastic assistant - always double-check its work.

Looking ahead, GPTO is just the beginning. OpenAI is working on even more advanced versions, like GPTO-ioi, that focus on reasoning skills. We're also seeing the rise of AI agents that use models like GPTO for planning and problem-solving. The future of AI is exciting, and tools like GPTO will be at the forefront.

So, what's the takeaway? GPTO is a powerful tool, but it needs a skilled user to reach its full potential. It's like having a super-smart intern who sometimes gets carried away with their ideas. Use it for big-picture thinking, planning, and tackling complex problems. But always keep your critical thinking hat on.

Remember, the key to taming this beast is understanding its strengths and weaknesses. Use GPTO to expand your thinking and tackle big challenges, but don't blindly trust everything it says. With practice, you'll learn to harness its power while avoiding its pitfalls.

As AI tech keeps advancing, tools like GPTO will become more and more important in various industries. By mastering GPTO now, you're positioning yourself at the cutting edge of AI innovation. You'll be ready to use its power to create amazing results.

So, are you ready to tame the beast? With the right approach, GPTO can be your secret weapon for tackling complex problems and unleashing your creativity. Just remember: think big, verify often, and never stop learning. The AI revolution is here, and you're now equipped to ride the wave.

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<head>
    <meta charset="UTF-8">
    <title>GPTO Comparison Chart</title>
    
    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
    <style>
        /* Ensure the chart fits within a 1200x600 pixels container */
        #chartContainer {
            width: 1200px;
            height: 600px;
            margin: 0 auto;
        }
        canvas {
            max-width: 100%;
            max-height: 100%;
        }
    </style>
</head>
<body>
    <div id="chartContainer">
        <canvas id="contextWindowChart"></canvas>
    </div>
    <script>
        var ctx = document.getElementById('contextWindowChart').getContext('2d');

var data = {
            labels: ['GPT-3.5', 'GPT-4', 'GPTO-1'],
            datasets: [{
                label: 'Context Window Size (tokens)',
                data: [2048, 8192, 128000],
                backgroundColor: [
                    'rgba(54, 162, 235, 0.7)',
                    'rgba(75, 192, 192, 0.7)',
                    'rgba(255, 99, 132, 0.7)'
                ],
                borderColor: [
                    'rgba(54, 162, 235, 1)',
                    'rgba(75, 192, 192, 1)',
                    'rgba(255, 99, 132, 1)'
                ],
                borderWidth: 1
            }]
        };

Data2 - Table

Here's the comparative table based on the article:

```html
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <title>Comparative Table of GPTO-1</title>
    <style>
        body {
            margin: 0;
            padding: 0;
            font-family: Arial, sans-serif;
        }
        .container {
            width: 1200px;
            height: 600px;
            margin: 0 auto;
            padding: 20px;
            box-sizing: border-box;
        }
        h1 {
            text-align: center;
            font-size: 24px;
        }
        table {
            width: 100%;
            height: calc(100% - 40px);
            border-collapse: collapse;
        }
        th, td {
            border: 1px solid #dddddd;
            text-align: left;
            padding: 12px;
            vertical-align: top;
        }
        th {
            background-color: #f2f2f2;
            width: 25%;
        }
        td {
            width: 37.5%;
        }
        .strengths {
            background-color: #e8f5e9;
        }
        .weaknesses {
            background-color: #ffebee;
        }
    </style>
</head>
<body>
    <div class="container">
        <h1>GPTO-1: Strengths vs. Weaknesses</h1>
        <table>
            <tr>
                <th>Feature</th>
                <th>Strengths</th>
                <th>Weaknesses</th>
            </tr>
            <tr>
                <th>Context Window Size</th>
                <td class="strengths">
                    <ul>
                        <li>Massive context window of <strong>128,000 tokens</strong> (about 300 pages).</li>
                        <li>Can process large datasets in one go.</li>
                    </ul>
                </td>
                <td class="weaknesses">
                    <ul>
                        <li>Limited number of sessions per week due to metering based on runs.</li>
                        <li>Requires strategic planning to maximize usage.</li>
                    </ul>
                </td>
            </tr>
            <tr>
                <th>Advanced Reasoning</th>
                <td class="strengths">
                    <ul>
                        <li>Capable of complex reasoning and chain-of-thought processing.</li>
                        <li>Excels at planning and high-level strategy.</li>
                    </ul>
                </td>
                <td class="weaknesses">
                    <ul>
                        <li>Still experimental with some features under development.</li>
                        <li>May require user guidance for optimal performance.</li>
                    </ul>
                </td>
            </tr>
            <tr>
                <th>Handling Large Contexts</th>
                <td class="strengths">
                    <ul>
                        <li>Efficient at sifting through large amounts of information.</li>
                        <li>Useful for analyzing Git repositories, research papers, and more.</li>
                    </ul>
                </td>
                <td class="weaknesses">
                    <ul>
                        <li>Prone to overload if not managed properly.</li>
                        <li>Requires front-loading of tasks to optimize runs.</li>
                    </ul>
                </td>
            </tr>
            <tr>
                <th>Prompt Engineering Efficiency</th>
                <td class="strengths">
                    <ul>
                        <li>Excellent at condensing and refining prompts.</li>
                        <li>Facilitates iterative testing for better results.</li>
                    </ul>
                </td>
                <td class="weaknesses">
                    <ul>
                        <li>Bad prompts can lead to unclear or incorrect responses.</li>
                        <li>Requires clarity and specificity in user input.</li>
                    </ul>
                </td>
            </tr>
            <tr>
                <th>Planning and Architecture</th>
                <td class="strengths">
                    <ul>
                        <li>Great at outlining tasks, brainstorming, and mapping specifications.</li>
                        <li>Can generate detailed action plans and file structures.</li>
                    </ul>
                </td>
                <td class="weaknesses">
                    <ul>
                        <li>Not a "done-for-you" solution; needs user guidance.</li>
                        <li>Outputs require verification for accuracy.</li>
                    </ul>
                </td>
            </tr>
            <tr>
                <th>Unpredictability in Coding Tasks</th>
                <td class="strengths">
                    <ul>
                        <li>Provides high-level coding strategies and frameworks suggestions.</li>
                        <li>Assists in product specification and development planning.</li>
                    </ul>
                </td>
                <td class="weaknesses">
                    <ul>
                        <li>Prone to hallucinations, inventing code elements that don't exist.</li>
                        <li>May fabricate class names or functions, leading to errors.</li>
                    </ul>
                </td>
            </tr>
            <tr>
                <th>Hallucinations</th>
                <td class="strengths">
                    <ul>
                        <li>Early predictions suggest fewer hallucinations than older models.</li>
                    </ul>
                </td>
                <td class="weaknesses">
                    <ul>
                        <li>Still susceptible to making up information.</li>
                        <li>Critical oversight is necessary, especially for factual accuracy.</li>
                    </ul>
                </td>
            </tr>
            <tr>
                <th>Availability</th>
                <td class="strengths">
                    <ul>
                        <li>Available in preview mode for ChatGPT Plus users.</li>
                        <li>Offers both standard and mini API versions.</li>
                    </ul>
                </td>
                <td class="weaknesses">
                    <ul>
                        <li>Limited access due to preview status.</li>
                        <li>Features are still under development; not fully launched.</li>
                    </ul>
                </td>
            </tr>
        </table>
    </div>
</body>
</html>
```

Data3 - SPA1

<!DOCTYPE html>
<html>
<head>
    <meta charset="UTF-8">
    <title>Taming the Beast: GPTO-1 Infographic</title>
    <style>
        /* Reset styles */
        body, html, div, h1, h2, h3, p, ul, li {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
        }
        body {
            font-family: 'Roboto', sans-serif;
            background-color: #f5f5f5;
        }
        #container {
            width: 1200px;
            height: 600px;
            margin: 0 auto;
            position: relative;
            background-color: #fff;
            border: 1px solid #ccc;
            overflow: hidden;
        }
        .title {
            text-align: center;
            padding-top: 20px;
            font-size: 36px;
            font-weight: bold;
            color: #333;
        }
        .subtitle {
            text-align: center;
            font-size: 24px;
            color: #555;
            margin-bottom: 20px;
        }
        .sections {
            display: flex;
            justify-content: space-around;
            padding: 20px;
        }
        .section {
            width: 260px;
        }
        .section h2 {
            font-size: 24px;
            color: #444;
            margin-bottom: 10px;
            text-align: center;
        }
        .section ul {
            list-style-type: disc;
            margin-left: 20px;
            color: #666;
        }
        .icon {
            font-size: 48px;
            color: #888;
            text-align: center;
            margin-bottom: 10px;
        }
        .footer {
            position: absolute;
            bottom: 10px;
            width: 100%;
            text-align: center;
            font-size: 14px;
            color: #999;
        }
    </style>
    
    <link href="https://fonts.googleapis.com/css?family=Roboto:400,700" rel="stylesheet">
    
    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css">
</head>
<body>
    <div id="container">
        <div class="title">Taming the Beast</div>
        <div class="subtitle">A Guide to OpenAI's Unpredictable GPTO</div>
        <div class="sections">
            <div class="section">
                <div class="icon">
                    <i class="fa-solid fa-chart-line"></i>
                </div>
                <h2>Strengths</h2>
                <ul>
                    <li>128,000 token context window</li>
                    <li>Advanced reasoning & planning</li>
                    <li>Handles large contexts</li>
                </ul>
            </div>
            <div class="section">
                <div class="icon">
                    <i class="fa-solid fa-triangle-exclamation"></i>
                </div>
                <h2>Pitfalls</h2>
                <ul>
                    <li>Unpredictable mistakes</li>
                    <li>Hallucinations in coding</li>
                    <li>Needs oversight</li>
                </ul>
            </div>
            <div class="section">
                <div class="icon">
                    <i class="fa-solid fa-lightbulb"></i>
                </div>
                <h2>Strategies</h2>
                <ul>
                    <li>Effective prompt engineering</li>
                    <li>Session management</li>
                    <li>Critical oversight</li>
                </ul>
            </div>
            <div class="section">
                <div class="icon">
                    <i class="fa-solid fa-rocket"></i>
                </div>
                <h2>Future of GPTO</h2>
                <ul>
                    <li>GPTO-ioi updates</li>
                    <li>AI agent integrations</li>
                    <li>Enhanced reasoning</li>
                </ul>
            </div>
        </div>
        <div class="footer">
            © 2023 GPTO Guide
        </div>
    </div>
</body>
</html>

Data5 - imageprompts1

1. **Fantastical and Visceral:** "An epic scene of a person taming a colossal, glowing digital beast composed of swirling code and data streams, representing GPTO-1; the backdrop is a surreal landscape of floating text and circuitry, capturing the struggle and triumph of mastering a powerful but unpredictable AI."

2. **Split-screen and Realistic:** "A split-screen image showing a before-and-after scenario; on the left, a frustrated developer staring at tangled, nonsensical code filled with errors and hallucinations; on the right, the same developer confidently viewing clean, organized code after successfully applying effective prompt engineering with GPTO-1."

3. **Photorealistic and Scenic:** "A highly detailed image of a lone explorer navigating through a vast, labyrinth-like library filled with endless shelves of books and code, symbolizing GPTO-1's massive context window; beams of light illuminate the path forward, representing clarity amid complexity."

4. **Action-based and Realistic:** "A dynamic scene of a diverse team collaborating around a futuristic holographic table projecting detailed plans and architectures for a full-stack app; GPTO-1 is depicted as a glowing digital assistant hovering above the table, actively organizing and refining the project's blueprint."

5. **Fantastical and Photorealistic:** "A stunning visual of a futuristic city where humans and AI agents coexist seamlessly; towering skyscrapers made of integrated circuits reach into the sky where a translucent figure representing GPTO-ioi watches over, symbolizing the advanced reasoning capabilities and AI integration of the future."

Article Plan

**1. Suggested Title** - **"Taming the Beast: A Guide to OpenAI's Unpredictable GPTO"** --- **2. Narrator's Direction** The narrator aims to write an article titled **"Taming the Beast: A Guide to OpenAI's Unpredictable GPTO"**. The article will introduce **GPTO-1**, OpenAI's new and powerful AI model with advanced chain-of-thought capabilities. The narrator wants to highlight both the strengths and challenges of using GPTO, emphasizing its exceptional reasoning, planning abilities, and unpredictability. The article will: - Explain what GPTO is and how it's accessed. - Discuss the potential pitfalls, such as hallucinations, especially in coding tasks. - Provide practical tips and quick wins for effectively using GPTO. - Share specific use cases where GPTO excels, like navigating large contexts and prompt engineering. - Emphasize GPTO's role as an architectural tool for planning and ideation. - Look ahead to the future of GPTO and the emergence of AI agents. - Offer keywords and topics for further research. The ultimate goal is to help readers understand how to harness GPTO's power while mitigating its unpredictability. --- **3. Selected Templates to Follow** The transcription specifies a structure divided into four parts. To create three drafts with different structures, we will select the following formats: 1. **How-To Guide** - **Structure:** Step-by-step instructions on effectively using GPTO. - **Content:** Detailed guidance on navigating GPTO's capabilities, prompt engineering techniques, and practical applications. 2. **Listicle** - **Structure:** A numbered list highlighting key tips or strategies. - **Content:** "10 Ways to Harness GPTO's Power" or "5 Tips for Mastering GPTO", focusing on actionable advice. 3. **Opinion Piece** - **Structure:** Personal insights and analysis. - **Content:** An exploration of GPTO's impact on AI development, its unpredictability, and the author's perspective on its future potential. Each format will present the information uniquely while covering the core content outlined in the transcription. --- **4. Brainstorm Bullet Point Ideas for the Skeleton Structure** --- ### **Draft 1: How-To Guide** #### **Introduction (10-20%):** - Hook: The allure and challenges of harnessing powerful AI like GPTO. - Introduce GPTO as OpenAI's latest advanced model with unpredictable outputs. - Present the guide's purpose: to help users effectively tame GPTO. - Highlight the balance between GPTO's potential and its unpredictability. - Emphasize the importance of proper prompt engineering and domain knowledge. - Mention the limitations in access and the cost implications. - Set expectations on what readers will learn. #### **Middle (60-80%):** - **Step 1: Understanding GPTO's Capabilities and Limitations** - Overview of GPTO-1 and its chain-of-thought reasoning. - Access requirements (GPT-Plus membership, API). - Discuss its unpredictability and tendency to hallucinate. - **Step 2: Preparing Effective Prompts** - The significance of clear and concise prompts. - Tips on leveraging domain knowledge in prompts. - Examples of good vs. bad prompts. - **Step 3: Navigating Large Contexts with GPTO** - Utilizing GPTO's ability to handle massive context windows. - Techniques for inputting large datasets (file trees, research papers). - Strategies to maximize the limited number of runs (batching information). - **Step 4: Optimizing Prompts with GPTO** - Using GPTO to summarize and refine your prompts. - Comparison with other models like Claude and Anthropic's Prompt Builder. - Transforming lengthy prompts into concise versions without losing essence. - **Step 5: Architecting and Planning with GPTO** - Employing GPTO for high-level planning and product specifications. - Generating detailed action plans and step-by-step instructions. - Treating GPTO as an 'architect' for project development. - **Step 6: Managing Resource Constraints** - Balancing between GPTO and the O1 mini model to optimize runs. - Tips on when to switch models during interaction. - Cost-effectiveness and efficiency considerations. - **Step 7: Addressing GPTO's Unpredictability** - Recognizing and mitigating hallucinations, especially in code. - Importance of reviewing GPTO's outputs critically. - Strategies to verify information produced by GPTO. #### **Conclusion (10-20%):** - Recap of the key steps to effectively use GPTO. - Reinforce the potential benefits when GPTO is harnessed correctly. - Encourage readers to experiment thoughtfully and cautiously. - Highlight the future possibilities with GPTO and upcoming AI agents. - Final thoughts on embracing the power of advanced AI responsibly. --- ### **Draft 2: Listicle** #### **Introduction (10-20%):** - Start with a bold statement about the power and unpredictability of advanced AI. - Introduce GPTO as OpenAI's new 'beast' that's both powerful and unpredictable. - Present the article as a roadmap to mastering GPTO through key tips. - Emphasize the importance of understanding GPTO to unlock its potential. #### **Middle (60-80%):** - **Tip 1: Familiarize Yourself with GPTO's Capabilities** - Learn about its chain-of-thought reasoning and access requirements. - **Tip 2: Leverage Massive Context Windows** - Input large amounts of data for comprehensive analysis. - **Tip 3: Master the Art of Prompt Engineering** - Craft effective prompts for better outputs. - **Tip 4: Utilize GPTO for Prompt Summarization** - Simplify and enhance prompts using GPTO itself. - **Tip 5: Navigate Resource Constraints Wisely** - Optimize usage by switching between GPTO and O1 mini models. - **Tip 6: Treat GPTO as an Architect, Not a Builder** - Use it for planning and ideation rather than detailed execution. - **Tip 7: Be Wary of Hallucinations** - Stay alert to inaccuracies, especially in code. - **Tip 8: Enhance Projects with Ostentatious Prompts** - Experiment with complex prompts for richer outputs. - **Tip 9: Collaborate with Other AI Models** - Integrate GPTO outputs with models like Sonnet and GPT-4.0. - **Tip 10: Stay Ahead with Future Developments** - Keep informed about upcoming features and AI agents. #### **Conclusion (10-20%):** - Summarize the top tips for taming GPTO. - Reinforce the idea that understanding GPTO is key to unlocking its full potential. - Encourage readers to apply these strategies in their AI interactions. - Look forward to exciting future advancements in AI technology. - Conclude with a motivating statement about mastering the 'beast'. --- ### **Draft 3: Opinion Piece** #### **Introduction (10-20%):** - Open with a provocative question about the risks and rewards of powerful AI. - Introduce GPTO as a double-edged sword in the AI landscape. - Present the author's personal journey and insights with GPTO. #### **Middle (60-80%):** - **The Allure of GPTO's Power** - Discuss the impressive reasoning and planning abilities. - Share anecdotes of GPTO's successes in complex tasks. - **The Dark Side: Unpredictability and Hallucinations** - Explore instances where GPTO's outputs were misleading. - Analyze the impact of hallucinations on coding and information accuracy. - **The Importance of Human Oversight** - Argue for the necessity of domain knowledge when using GPTO. - Reflect on the balance between AI autonomy and human control. - **Prompt Engineering as a Critical Skill** - Emphasize how crafting prompts can influence outcomes. - Discuss the learning curve and ongoing challenges. - **Resource Constraints and Accessibility** - Critique the limitations imposed by access and cost. - Consider the implications for widespread adoption. - **Looking Ahead: GPTO and the Future of AI** - Contemplate the role of GPTO in upcoming AI agents. - Express thoughts on how AI might evolve with models like GPTO. - **Ethical and Practical Considerations** - Debate the responsibility of developers and users in managing AI risks. - Highlight the need for guidelines and best practices. #### **Conclusion (10-20%):** - Summarize the duality of GPTO's potential and pitfalls. - Reaffirm the author's cautious optimism about GPTO's future. - Encourage a thoughtful approach to integrating powerful AI into workflows. - Pose reflective questions about the path forward with AI. - Conclude with a call to action for responsible innovation. --- **5. Research Keywords (x3)** 1. **GPTO specifications** 2. **Prompt engineering techniques** 3. **GPTO use cases** --- **6. Research News Keywords** 1. **OpenAI GPTO updates** 2. **AI model hallucinations** --- **7. Research Questions** 1. What are the key features and capabilities of GPTO-1 2. How does GPTO compare to other AI models like GPT-4.0 and Claude 3. What are best practices for prompt engineering with GPTO ---

All URLs Selected

Here are five URLs selected based on the guidelines provided, each with a summary of the type of information we hope to extract that could enhance the article: -------------------- 1. Overview of GPTO's features and advancements: https://artificialcorner.com/p/openai-releases-gpto1-is-this-gpt This article provides insights into the key features and improvements of GPTO, especially the advanced reasoning capabilities that differentiate it from previous models. This adds depth to the introductory section of the article. 2. Comparison of AI models including GPTO and others: https://encord.com/blog/gpt-4o-vs-gemini-vs-claude-3-opus/ The comparison between GPTO and other AI models like GPT-4.0 and Claude offers valuable perspective for discussing how GPTO stands against its peers, focusing on performance benchmarks and use cases. 3. Effective prompt engineering techniques for GPTO: https://www.techrxiv.org/doi/full/10.36227/techrxiv.22683919.v2 This source details prompt engineering strategies that are crucial for maximizing the potential of GPTO, directly supporting the section of the article that covers effective usage techniques. 4. Latest news and updates on GPTO developments: https://apnews.com/article/openai-gpto-chatgpt-ai-desktop-071dd86594bc310bac07b37cf5e9bafc This news article highlights the latest updates and capabilities introduced with GPTO, providing current context and developments that could enrich the article's future outlook section. 5. Addressing AI hallucinations in models like GPTO: https://www.cnet.com/tech/hallucinations-why-ai-makes-stuff-up-and-whats-being-done-about-it/ By explaining AI hallucinations and strategies to mitigate them, this source provides crucial information relevant to addressing one of the challenges mentioned in the transcription, adding depth to the discussion on GPTO's unpredictability. --------------------

All URLs Summary

Sure, here are the key points from the sources provided: -------------------- Source #1: - OpenAI has launched a new model called GPT o1 "Strawberry," with advanced reasoning capabilities. - GPT o1 is available for ChatGPT Plus users in preview and mini versions. - The o1 model is noted for its ability to think through and analyze problems before generating responses. - The new model features advanced reasoning, potentially reducing hallucinations compared to GPT-4. - GPT o1 outperforms GPT-4o in coding tasks, placing in the top 7% of competitors. - GPT o1 is favored for tasks like programming, data analysis, and math over GPT-4o. - The model provides "impressive" outputs for coding, reasoning, and math, even in complex scenarios. - OpenAI plans for further o1 and o1-ioi versions focusing on reasoning capabilities. - GPT o1 scored 78.1% in vision modality performance, making it competitive with humans. -------------------- Source #2: - GPT-4o, a multimodal AI by OpenAI, can process text, images, and audio. - GPT-4o generates tokens twice as fast as GPT-4 Turbo, providing quicker responses. - The model improves multilingual support, handling languages like Gujarati with fewer tokens. - GPT-4o's context window is 128K tokens, equivalent to about 300 pages of text. - It's available for select developers with plans for general availability. - GPT-4o offers enhanced vision capabilities, enabling it to interpret visual data more accurately. - There is limited transparency about GPT-4o's training data and model size. - The API does not yet support audio input, but future updates are planned. -------------------- Source #3: - Effective prompt engineering is crucial for achieving optimal outcomes with ChatGPT. - Prompt engineering involves designing and refining input prompts to elicit desired AI responses. - Best practices include iterative refinement and balancing user intent. - Advanced strategies involve temperature and token control, prompt chaining, and handling ambiguous inputs. - Real-world case studies demonstrate prompt engineering in customer support and content generation. - The article emphasizes the importance of fostering creativity within the ChatGPT community. -------------------- Source #4: - OpenAI has launched GPTo, enhancing ChatGPT’s text, visual, and audio capabilities. - GPTo is faster than previous versions, providing real-time reasoning across multiple modalities. - The model can add emotion to its voice and solve math and coding problems interactively. - OpenAI's update showcases capability gaps when compared to rivals like Google's Gemini 1.5 Pro. - Gartner analyst Chirag Dekate noted that many of OpenAI's showcased capabilities seemed familiar. - GPTo aims to mimic human cadences in verbal responses and detect people’s moods. -------------------- Source #5: - AI hallucinations occur when AI delivers false or misleading information. - Large language models are trained on extensive data, leading to hallucinations. - ChatGPT warns users of its potential to make mistakes. - Generative AI tools are evolving, yet still prone to inaccuracies. - Techniques to manage hallucinations include embedding models within a larger system that checks for consistency. - OpenAI's GPT-4 is 40% more likely to produce factual responses than GPT-3.5. - The EU’s Artificial Intelligence Act aims to foster reputable AI development. These points can be used to enrich your understanding and integration of GPT and AI capabilities in your essay, highlighting the strengths and weaknesses of different models and approaches.

Final Article Structure

**Updated Skeleton Structure for "Taming the Beast: A Guide to OpenAI's Unpredictable GPTO"** --- ### **Introduction (approx. 100-200 words)** 1. **Hook with a Provocative Question or Statement:** - *"Have you ever been simultaneously amazed and frustrated by the capabilities of AI?"* - *"Imagine harnessing an AI so powerful it can process 300 pages of text at once, yet unpredictable enough to fabricate information out of thin air."* 2. **Introduce GPTO as OpenAI's New Powerful yet Unpredictable AI Model:** - Present **GPTO-1**, dubbed "the beast," known for its advanced reasoning and planning abilities. - Highlight its availability to **ChatGPT Plus users** and via the API in preview and mini versions. 3. **Highlight the Problem and Agitate the Reader:** - Discuss the **unpredictability and hallucinations** of GPTO, especially in coding tasks where it might invent nonexistent functions or classes. - Mention that despite **outperforming GPT-4** in coding, placing in the **top 7% of competitors**, its outputs can mislead without proper handling. 4. **Present the Article as the Solution:** - Introduce the guide's purpose: to help readers **tame GPTO**, leveraging its strengths while mitigating its pitfalls. - Emphasize the importance of **effective prompt engineering** and **critical oversight**. --- ### **Middle Section (approx. 500-800 words)** 1. **Understanding GPTO's Capabilities and Limitations:** - **Explain What GPTO-1 Is:** - OpenAI's latest model, known as **"Strawberry"**, featuring advanced **chain-of-thought reasoning** capabilities. - Ability to process massive contexts with a **128,000-token window**, equivalent to **300 pages of text**. - **Advanced Reasoning and Potential:** - GPTO has enhanced reasoning skills, potentially reducing hallucinations compared to previous models. - *"GPTO scored an impressive **78.1%** in vision modality performance, making it competitive with human capabilities."* - **Access Requirements:** - Available to **ChatGPT Plus users** and select developers. - Admittance via preview programs and APIs, with plans for broader availability. - **Unpredictability and Hallucinations:** - Despite strengths, GPTO can unpredictably hallucinate, especially in code—creating false class names or functions. - *"AI hallucinations occur when AI delivers false or misleading information due to extensive training data."* 2. **Navigating Large Contexts with GPTO:** - **Leverage Its Ability to Handle Large Data Inputs:** - Ideal for analyzing entire **Git repositories**, **research papers**, or **e-books**. - Assists beginners in understanding complex materials by digesting vast amounts of information. - **Strategies to Maximize Limited Runs:** - Since users are metered by the **number of runs**, maximize each session by inputting large datasets. - *"GPTO-1 can process data efficiently despite being metered differently than other models."* - **Example Use Case:** - Inputting an entire file tree to understand a complex codebase or project structure. 3. **Mastering Prompt Engineering with GPTO:** - **Importance of Effective Prompts:** - Crafting precise prompts is crucial for optimal outcomes. - *"Effective prompt engineering balances user intent with AI capabilities for desired responses."* - **GPTO's Prowess in Summarizing and Refining Prompts:** - Transforms lengthy, complex prompts into concise, efficient ones. - Outperforms models like **Claude** and **Anthropic's Prompt Builder** in prompt optimization. - **Advanced Strategies:** - Utilize techniques like **prompt chaining**, controlling **temperature and token settings**. - Handle ambiguous inputs to refine and direct outputs effectively. 4. **Architecting and Planning with GPTO:** - **Exceptionally Good at Planning, But Not Execution:** - Treat GPTO as an **"architect"**—excellent for planning and ideation, less reliable for detailed execution. - **Generating Product Specifications and Action Plans:** - Create detailed **product specs**, **file trees**, and **step-by-step instructions**. - Explicitly state lack of domain knowledge to receive comprehensive guidance. - **Lengthy, Comprehensive Outputs:** - Capable of generating responses ranging from **1,000 to 5,000 words**, providing thorough action plans. 5. **Managing Resource Constraints:** - **Limited Runs and Cost Considerations:** - Metered by the **number of runs** rather than tokens, encouraging efficient use of each session. - *"GPTO-1 offers a cost-effective solution by maximizing the value of each interaction."* - **Switching Between Models:** - Start with GPTO for complex initial responses. - Switch to **O1 mini model** or **GPT-4o** for follow-up interactions to conserve run allowances. 6. **Addressing GPTO's Unpredictability and Hallucinations:** - **Understanding Hallucinations in AI:** - AI may generate plausible yet incorrect information due to patterns in training data. - *"OpenAI's GPT-4 is **40%** more likely to produce factual responses than GPT-3.5."* - **Impact on Coding Tasks:** - Hallucinations can lead to invalid code, causing development setbacks. - Critical in coding where accuracy is paramount. - **Strategies to Mitigate Hallucinations:** - Embed GPTO outputs within systems that **verify consistency**. - Maintain **critical oversight** and **domain knowledge** to assess outputs. 7. **Ostentatious Prompts and Creative Outputs:** - **Pushing Boundaries with Complex Prompts:** - Use **ostentatious prompts** to elicit detailed, creative responses. - Enhance projects by experimenting with the complexity of prompts. - **Examples:** - Generated charts, images, or elaborate descriptions for content creation. - *"All charts and images in this article were refined using GPTO outputs and models like **DALL·E**."* 8. **Future of GPTO and AI Agents:** - **GPTO is Just the Beginning:** - Represents the forefront of OpenAI's advancements, with **GPTO-ioi** versions focusing on reasoning. - **Emergence of AI Agents:** - Development of AI agents utilizing models like GPTO will enhance planning and execution capabilities. - **OpenAI's Plans for Future Versions:** - Continuous improvement and expansion of GPTO's abilities are in the pipeline. - **Stay Informed:** - Keep abreast of updates to fully leverage GPTO's evolving capabilities. 9. **Comparisons with Other Models:** - **Performance Metrics:** - GPTO-1 outperforms **GPT-4o** in coding tasks. - *"GPTO-1 has advanced reasoning capabilities, potentially reducing hallucinations compared to GPT-4."* - **Multimodal Capabilities:** - While **GPT-4o** processes text, images, and audio, GPTO excels specifically in reasoning and planning. - **Processing Speed and Token Limits:** - **GPT-4o** generates tokens twice as fast as GPT-4 Turbo. - Both models support large context windows, but GPTO's reasoning is a standout feature. 10. **Best Practices and Final Tips:** - **Human Oversight is Key:** - Always **review GPTO's outputs**, especially in unfamiliar domains. - **Continuous Learning in Prompt Engineering:** - Refine prompts iteratively; small changes can significantly impact outputs. - **Encourage Creativity and Exploration:** - Experiment with different approaches to maximize GPTO's potential. - **Ethical and Practical Considerations:** - Use GPTO responsibly, being mindful of its limitations and the potential impact of inaccuracies. --- ### **Conclusion (approx. 100-200 words)** 1. **Recap the Journey of Taming GPTO:** - Summarize how understanding GPTO's capabilities and limitations allows users to harness its full potential. 2. **Reiterate the Key Benefits When Used Correctly:** - Emphasize the advantages of effective prompt engineering and treating GPTO as an architectural tool. 3. **Encourage Responsible and Innovative Use:** - Advocate for **thoughtful experimentation** while maintaining critical oversight. 4. **Look Ahead to Future Possibilities:** - Highlight the exciting developments on the horizon with GPTO and AI agents. - *"As AI technology advances, tools like GPTO will become increasingly integral to various industries."* 5. **Final Thought or Call to Action:** - Inspire readers to embrace the challenge of taming GPTO. - *"By mastering the beast, you position yourself at the forefront of AI innovation, ready to leverage its power for transformative results."* --- **Note:** This structure incorporates specific details, numbers, and facts from the provided research, as well as phrases and ideas from the initial transcription. It aligns with the guidelines for introductions and conclusions, aiming to engage the reader, provide valuable insights, and encourage thoughtful application of GPTO's capabilities.

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GPTO Comparison Chart

Table

Here's the comparative table based on the article: ```html Comparative Table of GPTO-1

GPTO-1: Strengths vs. Weaknesses

Feature	Strengths	Weaknesses
Context Window Size	Massive context window of 128,000 tokens (about 300 pages). Can process large datasets in one go.	Limited number of sessions per week due to metering based on runs. Requires strategic planning to maximize usage.
Advanced Reasoning	Capable of complex reasoning and chain-of-thought processing. Excels at planning and high-level strategy.	Still experimental with some features under development. May require user guidance for optimal performance.
Handling Large Contexts	Efficient at sifting through large amounts of information. Useful for analyzing Git repositories, research papers, and more.	Prone to overload if not managed properly. Requires front-loading of tasks to optimize runs.
Prompt Engineering Efficiency	Excellent at condensing and refining prompts. Facilitates iterative testing for better results.	Bad prompts can lead to unclear or incorrect responses. Requires clarity and specificity in user input.
Planning and Architecture	Great at outlining tasks, brainstorming, and mapping specifications. Can generate detailed action plans and file structures.	Not a "done-for-you" solution; needs user guidance. Outputs require verification for accuracy.
Unpredictability in Coding Tasks	Provides high-level coding strategies and frameworks suggestions. Assists in product specification and development planning.	Prone to hallucinations, inventing code elements that don't exist. May fabricate class names or functions, leading to errors.
Hallucinations	Early predictions suggest fewer hallucinations than older models.	Still susceptible to making up information. Critical oversight is necessary, especially for factual accuracy.
Availability	Available in preview mode for ChatGPT Plus users. Offers both standard and mini API versions.	Limited access due to preview status. Features are still under development; not fully launched.

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SPA

Taming the Beast: GPTO-1 Infographic

Taming the Beast

A Guide to OpenAI's Unpredictable GPTO

Strengths

128,000 token context window
Advanced reasoning & planning
Handles large contexts

Pitfalls

Unpredictable mistakes
Hallucinations in coding
Needs oversight

Strategies

Effective prompt engineering
Session management
Critical oversight

Future of GPTO

GPTO-ioi updates
AI agent integrations
Enhanced reasoning