Why and When Choose Claw Free Opus Over ChatGPT?

Claw-Free: The GPT-4 Killer?

• Claw-Free is a new large language model that claims to outperform GPT-4 in benchmarks, as per reports from Anthropic. It has also garnered positive feedback in real-world usage, challenging the dominance of GPT-4.

• Comparisons between Claw-Free and GPT-4 are inevitable due to the latter's status as the leading large language model. Despite GPT-4's exceptional performance and popularity, Claw-Free is emerging as a strong competitor in the field.

• The key points of Claw-Free's appeal to users include its specifications, usability, and performance in various applications. Its usability in content creation assistance and idea generation has been a focus of evaluation, shedding light on its practical value in day-to-day use.

• The author's extensive testing of Claw-Free across niche use cases and daily workflows revealed its potential to serve specific automations and complex tasks. The comparison with GPT-4 and the assessment of foundational models highlight the nuanced considerations for adopting Claw-Free.

• Claw-Free's emergence as a challenger to GPT-4 raises questions about the future landscape of large language models, prompting users to consider the potential benefits and trade-offs in transitioning from GPT-4 to Claw-Free.

Claw-Free: The GPT-4 Killer?

Overview of Claw Free Opus Model

• The Claw Free Opus is a flagship model released by chat.LMS for free use on their website.

• It is designed to compete with other models like gpt-4 and offers high-quality outputs for users.

• The pricing for Claw Free Opus is around $20 per month, but users can also test it for free on chat.LMS website.

• The website allows users to compare it with gpt-4 and run prompts to get outputs from both models, making it a valuable tool for evaluation.

• The company aims to create a leaderboard for chatbots with VC funding and frequent updates, providing a reliable way to evaluate different models.

• However, access to Claw Free Opus is not available in Europe, and the best model is behind a paywall of $20 per month.

Overview of Claw Free Opus Model

GPT Context Window and API Comparison

• The comparison focuses on the context windows and API performance of GPT. The current context window for GPT is 200k, while inside chat GPT it has a 32k context window. However, using the 128k context window of the gb4 API may result in some information being lost in the middle.

• A benchmark test called 'needle in a haystack' is used to evaluate the performance of the context window. The test involves hiding a line within a very long document to maximize the context, and then prompting the model to retrieve that specific piece of information. The test results in a graph that visualizes the model's ability to retrieve hidden information.

• While the web interface lacks certain features compared to chat GPT, such as code interpreter, image generation, voice input/output, and custom instructions, the core strength of the product lies in its ability to provide well-performing answers to basic prompts like essay writing or research topics.

• The performance of the context window and API is subjectively assessed by a power user who extensively experiments with the product.

GPT Context Window and API Comparison

Analyzing the Impact of Context on AI Output

• Providing context in AI prompts results in custom-tailored and more relevant outputs. By expanding the context, such as including screenshots or custom instructions, users can expect incredible results.

• An example is given where custom instructions and a screenshot of recent YouTube videos are included with a basic prompt. The result is an impressive list of video ideas that resonate with the user's feelings and intuition.

• While data can inform the decision-making process, the ultimate choice in selecting videos is driven by intuition and a 'feeling' that a particular idea makes sense for creation.

Analyzing the Impact of Context on AI Output

Analyzing the Content of the Chat GPT Conversation

• The conversation revolves around the chat GPT model and its capabilities in understanding context and memory during interactions.

• The speaker expresses interest in creating hands-on tutorials and engineering prompts related to AI ethics and governance, rather than historical content.

• The speaker emphasizes the focus on generative AI specifically chat GPT, and indicates a preference for topics that align with the context provided.

• There is a clear distinction between the relevance of certain content to the speaker's YouTube channel and the context provided in the custom instructions.

• The speaker outlines a preference for creating content related to the current developments in AI and its practical applications, rather than the history of AI.

Analyzing the Content of the Chat GPT Conversation

The Power of CLA in Image Interpretation

• CLA (Constrained Language Adaptation) technology has proven to be exceptional in its ability to interpret and process images.

• Compared to GPT-4V, CLA outperforms in vision capabilities, demonstrating its superior effectiveness in image analysis.

• In contrast to other models, CLA is designed as a multimodal system from the ground up, leading to a fundamentally different and more accurate approach to image processing.

• When tested with complex images, CLA's performance surpassed that of other technologies, showcasing its precision in image interpretation and the absence of significant errors.

• The utilization of CLA in automations and work processes has demonstrated its value, particularly in its capacity to eliminate errors that may affect critical tasks.

The Power of CLA in Image Interpretation

The Power of Image-Based Prompts and Prompt Generators

• Using image-based prompts has been found to be highly efficient, especially for quick, one-time tasks that don't require extensive engineering of the prompt.

• The simplicity and efficiency of using images along with custom instructions have proven to be a key tool for maintaining productivity and efficiency.

• In addition to image-based prompts, customized prompt generators have been highlighted as valuable resources, particularly in the context of a free newsletter and a GPD resource.

• The prompt generators offer customizable prompt formulas for different professions, allowing for repeated use with variations based on specific instructions and needs.

The Power of Image-Based Prompts and Prompt Generators

Advanced AI Prompt Engineering Comparison

• The speaker's favorite method for finding new tasks for chat GPT is to run custom instructions at the bottom of the prompt.

• They have tested the performance of GPT-3 and GPT-4 multiple times and found that both models work equally well for their purposes.

• However, the output of GPT-4 is limited, providing around 22 prompts, while CLA has more token outputs, giving it an advantage in this aspect.

• The speaker emphasizes that they have a workflow to improve on the generated prompts based on specific contexts, and the result is a more detailed and actionable generation from CLA compared to GPT-4.

• Based on their experience, the speaker concludes that if someone is using a large language model for prompt engineering, CLA is significantly better than GPT-4.

Advanced AI Prompt Engineering Comparison

Comparison of GPT and CLA prompts

• Generations right we also covered this on the channel and I gave you the prompt for that you can create these photo realistic images that are incredible because all I do in this prompt at the end I say a cat with a hat and then it flashes it out and it really gives you rich detail which then allows you to easily customize this turns out there's no difference whatsoever between the chat GPT prompt and the CLA free prompt as you can see here first one is chipt second one is CLA essentially the same thing so there it doesn't matter but if you're generating prompts for large language models I did find that it does matter now look this might depend on your workflow and your prompting but I'm just trying to compare apples to apples I've been developing some of these prompt since quite a while and I've been surprised by how many perform bettering claw just right off the bat now look not to Hype it up too much there was actually a few use cases where it actually completely failed now for example here's another prompt that I found on Reddit very simple Sams 50 books in his room he reads five of them how many books are left in his room well Claud Frey seems to think it's 45 books but he just read the books he didn't remove them they're still in the room so it should be 50 chip got this right first try and then beyond that I run a bunch of other tests like creating palindromes or code generation I don't know Apples to Apples it honestly it feels too early for me to have an opinion on that I mean both failed with palen drums with the code generation really depends on what you're generating what package you're working in I don't have a real opinion on that yet again it does win on all the benchmarks but then at this point they can't be trusted too

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Comparison of GPT and CLA prompts

The Impact of Benchmark Questions and AI Ethics in the Industry

• The industry heavily relies on benchmark questions to evaluate AI models. While it's claimed that benchmark questions are not included in the training data, the lack of public access to the training data raises concerns about the validity of this claim.

• There has been a significant improvement in AI models' capabilities, as evidenced by the transformation from struggling with simple tasks like generating a snake game to now being able to perform such tasks effectively. This improvement is largely driven by the awareness that such tasks will be tested and showcased by influencers and creators.

• A specific prompt developed by Synaptic Labs and Professor Synapse has been commended for its effectiveness in enhancing chatbot experiences. The prompt involves asking clarifying questions to create specific characters tailored to individual needs, marking a significant advancement in AI interaction.

• Concerns have been raised regarding the ethical implications of AI development. While the focus is on creating safe and ethical AI, there is a debate about the potential use of AI for task automation, R&D strategy, and the overall intent of the AI industry.

The Impact of Benchmark Questions and AI Ethics in the Industry

Limitations of Persona Modeling and Creative Writing with AI

• One limitation of the AI model is its inability to support persona modeling, where users instruct the AI to act as a specific persona. This restriction is in place to prevent jailbreaking, making it challenging for users to apply persona modeling in their interactions with the AI.

• Custom instructions and the AI Advantage approach, which utilizes 24 building blocks to represent different aspects of persona, still work effectively. These approaches do not rely on traditional role playing and are universally applicable across various language models (LLMs).

• The AI's performance in creative writing is subjective, with the initial impression being that it is comparable to or potentially worse than GPT-4 in content creation. While GPT-4 acts more as a director and takes responsibility in content planning, Claud AI simply generates text without exceptional input.

Limitations of Persona Modeling and Creative Writing with AI

Using AI for Idea Generation and Contextual Input

• The writer expresses high standards for content creation and states that they would not use the AI-generated scripts directly. However, they find the AI tool excellent for ideation and brainstorming.

• They plan to continue using both the AI tool and another platform for different use cases, relying on the AI tool for prompt improvement and contextual input with images.

• The writer predicts that the AI tool will compete with OpenAI's offerings, indicating the potential impact it may have on users' preferences.

• The writer invites readers to share their experiences with the AI tool, prompting discussions on when each tool is preferred for specific use cases.

Using AI for Idea Generation and Contextual Input

Conclusion:

In conclusion, the emergence of Claw-Free as a challenger to GPT-4 raises questions about the future landscape of large language models. This blog sheds light on the nuanced considerations for adopting Claw-Free and its impact on the AI industry.