GPT and LLM Usability...

Nowadays almost everyone is using the GPT and LLM. Some have a unique way to integrate it as a solution into workflow and some using it as a productive tool. In this article I would like to cover some Usability of GPT and LLM.

You can use LLMs like GPT4 to improve your programming skills, especially when dealing with complex topics. By using these models as a reference or starting point, you can quickly grasp the functionality of kernel functions and subsystems, making it easier to delve deeper into the actual code.

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This approach allows you to focus on understanding the underlying concepts rather than getting bogged down in the details of the code itself.

You can benefit from using LLMs like GPT4 to speed up your learning process. By leveraging these models as a prep tool, you can quickly get started with technical concepts and focus on verifying and refining the information yourself. This approach makes learning less tedious and allows you to tackle complex topics more efficiently.

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Remember that LLMs are not meant to replace human effort, but rather to augment it. By using them in this way, you can make the most of their strengths while still developing your critical thinking and problem-solving skills.

You might find it helpful to use LLMs like GPT4 as a tool to accelerate your learning process when working with new technologies. By leveraging these models, you can quickly get the context you need to progress, saving time and reducing frustration.

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This confidence boost can help you feel more interested in approaching challenging problems, knowing that you have a reliable resource at hand to help you overcome obstacles and move forward.

You can benefit from using LLMs like GPT4 to clarify specific parts of technical documentation. By pasting in portions of documentation and asking for clarification, you can avoid potentially uncomfortable online discussions and get the help you need without feeling self-conscious about your knowledge gaps.

You might find that using LLMs can be beneficial for learning and memory, especially when dealing with complex or infrequently used knowledge. As an example, if you're a polyglot like the original commenter, LLMs can help you recall specific phrases or concepts in less-frequently used languages.

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Additionally, the integration of speech-to-text technology has made it easier to interact with computers through voice commands. By leveraging these tools, you can potentially become more efficient and effective in your learning and communication processes.

You might find that using LLMs can be beneficial for debugging issues, especially when dealing with complex technologies. By asking an LLM about a specific issue, you can potentially save time and effort compared to searching documentation, Stack Overflow, or obscure tech forums.

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Even if the LLM doesn't provide the exact answer, it can often help by suggesting possible fixes or pointing you in the right direction. This can make your debugging process more efficient and effective.

You might find that using LLMs in coding has a unique advantage: the ability to directly verify the correctness of the code by running it. This means that you can quickly test and validate the suggestions provided by the LLM, which can save time and effort in the long run.

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If the code doesn't work as expected, providing feedback to the LLM about what went wrong can help improve its suggestions for future use cases.

You might find that using an LLM like a dictionary helps you quickly look up information and evaluate its correctness yourself. This confidence in the accuracy of the LLM's responses can help you focus on more complex and creative aspects of coding, rather than getting bogged down in basic learnings.

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The LLM can also make it fun to create small HTML/JS based tools, providing a good enough prototype that can be molded to your needs. This has led to the creation of several useful scripts and tools over time, which might not have been possible without the aid of the LLM.

You might find it helpful to treat LLMs like "smart friends" when exploring new concepts or seeking general guidance. While relying on them for absolute accuracy can be problematic, using them in a way that suits their strengths can provide valuable insights and help you learn more effectively.

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In certain contexts, the accuracy of an LLM's response might not matter as much as its ability to explain complex ideas in a clear and concise manner. By acknowledging the potential limitations of LLMs and using them in a way that suits their strengths, you can harness their benefits while minimizing the risks associated with their errors.

You might find it helpful to remember that even advanced AI systems like LLMs are not immune to mistakes. By acknowledging this limitation, you can take steps to validate their responses and ensure the accuracy of their output.

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As you work with LLMs, keep in mind that many errors stem from ambiguity or insufficient information. Providing the LLM with robust and extensive context can significantly improve its performance. By taking a proactive approach to validating LLM responses, you can harness the benefits of these powerful tools while minimizing the risks associated with their potential mistakes.

You might find it helpful to consider these two use cases when deciding how to utilize LLMs in your work. By recognizing their strengths and limitations, you can harness the benefits of these powerful tools while minimizing their potential drawbacks.

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Remember that even if an LLM's answer is not correct, the time spent asking it may still be positive EV if it provides a useful starting point or encourages further investigation. You might find it helpful to consider the potential applications of these models in your own life. By recognizing their capabilities and limitations, you can harness their benefits while minimizing any drawbacks.

You might find it helpful to consider the potential applications of these models in your own life. By recognizing their capabilities and limitations, you can harness their benefits while minimizing any drawbacks.

You might find it helpful to view Large Language Models as hint generators when tackling challenging topics. This mindset can help you appreciate the value of these tools and how they can facilitate your learning and problem-solving processes.

The concept of centaur tournaments, where chess computers teamed up with human grandmasters, is a thought-provoking example of how AI can augment human capabilities.

As LLMs continue to improve, they may become even more powerful than humans in certain domains, rendering human contributions less valuable. The story of Deep Blue's victory over Garry Kasparov serves as a reminder that psychological factors can play a significant role in determining the outcome of complex competitions.

The experience of using Large Language Models (LLMs) varies greatly from person to person. Current LLMs may not consistently deliver on serious tasks, unlike what others might claim. Certain types of responses seem to be coalescing around a common cause, which is the issue with using current LLMs for serious tasks. Most people spend 80-90% of their day dealing with mind-numbing procedural work like generating reports no one reads. Lots of this boring common work has been solved six ways from Sunday, so we can lean on LLMs to bootstrap our n+1th solution.

Use cases mentioned in the article come into play when it's not about serious tasks but rather side projects or learning X in Y minutes. Writing shell scripts that mostly work is what LLMs do. Using an LLM daily and having it save time and effort is a personal anecdote that highlights the benefits of these models in certain contexts. Getting stuck in a loop is another common issue that arises at certain levels of complexity.

The cost associated with expensive laptops can vary greatly. Running the 70b model on an M1 Max laptop with 64GiB unified RAM performed well in Q&A tests. The current version of that laptop costs around 4800€ (14-inch MacBook Pro, M3 Max, 64GB of RAM, 1TB of storage).

Enthusiast hobby spending can range from expensive laptops to other forms of luxury items like four-wheelers, jet-skis, gun safes, and the costs of car enthusiast spending.

Day-trading losses and recreational activities like parents' expenses on child sports are also significant forms of enthusiast spending.

The price of wingsuits or getting your pilots license can be considered more extreme hobbies compared to laptop prices.

"Cruisers" (those who prefer to travel by car) and annual-Disney vacationers are other examples of diverse forms of enthusiast spending.

If you're considering investing in a high-end laptop for exploring AI models like LLMs, take into account your own enthusiasm for this technology. It's not just about the cost associated with expensive laptops, but also about how it fits into your overall budget and lifestyle. You might find that this investment is relatively minor compared to other forms of enthusiast spending or even practical investments like buying a used CNC machine from a machine shop.

It's essential to recognize the historical context and the principles of openness that have been established over several decades. While Facebook may seem to be taking steps towards openness from their perspective, it's crucial to consider the broader impact and the value that openness brings to this community. The sacrifice and efforts made by many individuals and organizations should not be taken lightly, and their work should be respected and acknowledged.

The cynical marketing approach of using the term "open" without truly embracing its principles can be misleading and undermine trust within the community. As we move forward, it's essential to hold organizations accountable for their actions and encourage them to genuinely contribute to the advancement of open science and open source.

And thousands more...