A former coworker and good friend was talking about how they created a quick prototype of a project using nothing but Windsurf and AI, and how they were able to build out the project’s scaffolding very quickly, with very little manual input.

The next day I had an idea that it would be cool to honor all the fonts that were created in 2025 and have people vote on them in a bracket. After reaching out to Dan Cederholm of Simple Bits for a sanity check, the idea carried some merit. The project began with a pretty simple ask of Claude Code: could you make an online app with Next.js that lets people vote on fonts and pick the best one through a bracketed system? Not going to lie, the first version of it took less than an hour.

The first version used standard web fonts, didn’t have a font preview, and was far from good, but the time-to-quality ratio was rather impressive. Still, like all coding projects, the last 20% of the work has taken 80% of the time. The site is live, and we are actually past the initial round and onto the bracketed rounds.

So far, AI has issues with deploying code, making reusable components on its own (I have to prompt it write reusable components), and general style issues.

The scope of this project was relatively simple, so doing it with AI was a great way to see it really shine. The security was not a super big concern with this app, as I’m not storing any personally identifiable information, only tracking voted cookies. It has inspired me to use it on some other bigger projects, though not for the full thing. I’m excited to see how this tool can help me bring more of my ideas to reality in the software space.

It is true; Copilot is a must-have in VSCode. It often saves time when writing repetitive code in commonly used programming languages. When I want to know some piece of information, better and quicker results can be had by asking an LLM than searching and reading through all the ad-dense articles on the internet.

The argument is that AI is currently solving a different problem than what humans solve. The internet is not new. The information for solving most of your coding problems has been available if you know where to look. And if it isn’t readily available on the internet, the AI can’t do the deep reasoning of linking obscure topics together. That’s just not what it is made for. Instead of using Let Me Google That For You, you can now send people a response from ChatGPT.

In the process of trying to understand the competitive advantage of Gluino, I asked an Agent the simple question: “Where does AI let users down?” The response was interesting enough that it is worth an entire post.

Factual Inaccuracies and Hallucinations

AI models sometimes generate information that sounds plausible but is factually incorrect or entirely made up—a phenomenon often referred to as “hallucination.” This can be problematic when users rely on the AI for accurate, reliable information.

From ChatGPT

The issue is that these LLMs are only as strong as their training data. Many modern LLMs take years to train, and so oftentimes, the most recent data that is being used in the LLM is several years old. Other times, the of older events may be harder to find. Here is an article about how wrong Siri is about Super Bowl winners.

The real issue is the same thing I’ve been complaining about for years with AIs. While their level of accuracy is continually improving, their certainty in their results is unwavering. AI comes back very confident about its answers, even if they might be wrong. This is not a new problem. This YouTube video is a parody of a guy acting like Google. One person asks, “Vaccines cause autism.” Google shows a bunch of evidence disproving it, the user changes the prompt “vaccines cause autism true,” and Google can return one result, which leads the person to say, “I knew it.” This is confirmation bias, and very common in human behavior.

The more significant problem could be that if users learn information from AI and treat it as fact without researching the truth, falsities could spread more quickly. The repercussions of these inaccuracies could be significant over time.

Context and Nuance

While AI can handle many straightforward tasks, it may struggle with understanding complex contexts, subtle nuances, or ambiguous queries. For example, in conversations involving irony, sarcasm, or cultural references, the AI might misinterpret the intent, leading to inappropriate or off-target responses.

Context is perhaps the most important part of any situation. While this is something that we learn throughout our life, this is something that is very challenging for computers. The context in which a question is asked matters. Asking an AI a question is asking it in the context of the training data provided.

The example I’ve used here is the recommendation letter for one of my former students for a job. I have the context of knowing the person and having worked with them in the past. The AI hasn’t. The letter I got out of ChatGPT was written with passion but without any details or context on our interactions in the past. Some of this can be mitigated by improving your prompt. Prompt Engineering is an entire science at this point. Still, the response is only as good as the context you give it, and many AIs will have a limitation on the number of tokens they can take in as context. Sometimes, even when more context is provided the response is still off.

Handling Ambiguity

When faced with vague or ambiguous questions, AI might provide answers that are too generic or miss the user’s intended meaning. This can be frustrating, especially if the query requires deep insight or specialized knowledge.

This is similar to lack of context. Ambiguity can often be resolved by having more context. The abilility to decode what is meant by a question, or ask for clarification appropriatly is something that AI is still working on. As context improves, I guess ambiguity will also become less of a concern.

Bias and Representation

AI models are trained on large datasets from the internet, which can include biased or unbalanced perspectives. As a result, the outputs might inadvertently reflect or amplify these biases, potentially leading to skewed or unfair responses.

Because of the nature of the training data set, AI can only know areas where it has been trained. It is only as good as the data you provide it. It cannot empathize, interpret, and extract. These are skills that allow humans to relate to one another.

Again, this boils down to context for me. If you take, for example, accounts from enslaved people during the period before the Civil War, the amount of context we have is limited because of the systemic barriers to literacy provided to those who were enslaved. The documents of the time will present the world in a way that is more representative of those who had more unrestricted access.

Lack of Explainability

Many AI models operate as “black boxes,” meaning they do not provide transparent reasoning behind their responses. Users who need to understand how a decision was reached may find this lack of explainability limiting, especially in critical applications like healthcare or legal advice.

Like reading a random page on the Internet without looking at the source, AI can produce information that is perceived as valid and accurate. However, the lack of proof associated with AI responses can lead to distrust.

Limited Real-Time Understanding

AI systems often rely on data that isn’t updated in real time. This means they might not be aware of the most recent events or developments, leading to outdated information in fast-changing fields.

This combines the first two issues: factual inaccuracies and a lack of context. It concerns training data. A model uses data that is captured and trained. Data created after the capture is unused and unavailable to the model.

Overreliance on Patterns

AI is excellent at detecting and mimicking patterns in data, but it can struggle with genuinely novel or creative problem-solving that falls outside the patterns it has learned. This can result in responses that are formulaic or less innovative when a truly new perspective is needed.

In asking for further clarification here, ChatGPT says:

It struggles with unique, unexpected, or illogical situations because it tries to apply familiar structures.

The example I liked the most is: If you asked AI to complete the pattern 2, 4, 8, 16, 31, it might respond with 62 as the following number in the sequence. The issue is that 31 breaks the sequence, and humans will often ask what is going on, while AI will follow the doubling pattern.

Ethical and Privacy Concerns

In some cases, the ways AI handles data can raise ethical or privacy issues. For instance, when generating content based on personal data or sensitive topics, the AI might inadvertently produce content that users find invasive or ethically questionable.

However, morality is a complex process that needs to be taught to computers.

Conclusion

Many of these issues overlap and impact one another. The fundamental design of modern Generative AIs leads to many of these issues, but none diminishes the product’s usefulness.

The most striking area of AI is to give you a starting point. Starting with a premise and using AI to start you down a path, even if it is wrong, at least gets you moving. You can realize that the information it’s giving you is not what you need, but then, voila, you now know what you need, and it has gotten the ball rolling.

Tools like Gluino aim to solve some of the problems inherent in these tools. As the Technology space develops, I’m sure even more will help with these challenges.

While AI is fabulous and does help with many knowledge worker tasks, AI by itself is not enough to replace the creativity and intelligence of the workforce. It can be helpful as a tool but still requires operators and people to check its work. I disagree with my friend Scott, who says AI might put us out of a job. AI will likely augment our jobs, just as other tools like Google and Stack Overflow helped engineers in the past.

We live in an age where large quantities of information bombard us. These include emails, phone calls, text messages, news, magazines, and letters. They come from many different avenues and pass through our consciousness. Because of our brains’ nature, retaining all the information we receive is impossible. We do our best to cull and surrender the information we process to keep what we need.

Even after we cull down what we care about and surrender to the fact that there is too much information, our brains are not perfectly cataloged libraries of the information. Information becomes hard to find or is lost in the basement of our minds.

That is where PKMs shine. They provide a location for us to store the information we find valuable. It is the perfect place for us to store this information. Computers are good at searching for content, and it’s easier to see if the database it is searching is limited to the information you care about and not just cluttered with everything anyone has ever written on the subject or even the subset of extensive data used by modern-day LLM solutions.

The overarching point is that whether we have a strictly codified system for creating our knowledge or not, we all have a limited subset of the knowledge available. If we don’t use a tool to do this, our brains act as our PKMs. They retain the information we have access to and function as the database we use.

So, right now, we have two options:

• Manually write down what we need in our PKMs system, and I hope we find it later.
• Trust our brains to make the connections and keep the information relevant and together.

Neither of these solutions is excellent, but I’ve chosen the first one. If you want to give it a go and put something together, again, here is a list of options and who I recommend them for:

• Notion is an excellent option for someone who wants a lot of features, doesn’t want to learn Markdown, and wants a lot of enhanced functionality. However, it also costs money for features.
• ObsidianMD is my tool of choice. You keep your data locally in Markdown and asset files. You are responsible for managing and backing up your information.
• Evernote was once one of the most used systems for this, but it has lost market share over time. Another company bought it and is not as interconnected as some other tools. Also, it uses a priority feature to store its notes, so it isn’t as transparent as Markdown files.
• Bear App — This was the tool I used before Obsidian. It uses tags, not folders. It uses Markdown but converts it to pretty styles. It uses predefined styles and doesn’t give you the same customization as Obsidian.
• Standard Notes is another product I’m just learning about. It’s like another Bear App or Obsidian-like product.

Keeping files, screenshots, images, and other items on your computer is another example of your knowledge management. Cleaning up and managing your hard drive can be tricky, even following something like the P.A.R.A. method.

My database of information has become crowded, and information is placed in places that are either redundant or hard to find. Even when searching for phrases, it might be hard to find the exact document I’m looking for if I don’t use precise wording.

To conclude, while there are tools to help us with knowledge, they are all faulty and costly. The question is, where do they work and fail for you? Also, is what you are currently doing good enough?

These systems are a way to collect and manage the information in one’s life for reference, research, and retrieval. When discussing Taigo Forte and Second Brains, you discuss organizing and building your PKM.

There are a bunch of tools out there for doing this:

• Notion
• Obsidian
• Evernote
• BearApp

To name a couple. Each of these tools has two primary components:

• You can enter text, pictures, and thoughts into a file.
• You can link files together.

Linking files together creates what some people refer to as a knowledge graph. If you check out r/ObsidianMD, you will see countless posts about knowledge graphs. There is a whole field called graph theory that discusses vertices and edges. While these graphs are very cool, their interconnected nature provides data about how information is connected.

The problem with PKMs, or at least my PKM, is that organizing all the data is incredibly manual. The system user has to organize notes into folders and provide links from one document to another. What if you realize something is tangential later but haven’t connected it? Sometimes, finding a document you are searching for by idea instead of exact text can be complicated. This happens to me weekly, if not more frequently.

Could I be better about linking my documents? Yes, without question. That being said, I don’t always know how to use information in the future. I’m not sure about connections until an idea comes to me. Searching and analyzing information text is one thing that computers are very good at.

One of my friends who also uses a PKM system and knows what Gluino is and that I’m working on it asked:

If gluino could magically parse my obsidian docs that would be very cool Seems like a real hard ass problem though

He is right. This is a tricky problem, but it is worthwhile. It is one of the many avenues ahead of us with Gluino.

Related is the notion that the amount of data available in this information age is enormous. Knowing which data to trust is challenging, as is finding the signal among the noise. One of the reasons I created a PKM is to combine the information I trust with the information I don’t and comment on it. I’m excited about a system that even marginally better understands the information I trust.

While this post doesn’t magically reveal a feature or future, it represents how I process and think through this problem. I hope it helps some.

If you stumble upon this post and use a PKM or are starting to use one, we are researching the community. If you have a second and are inclined, please complete our survey.

In this series of posts, I want to discuss how I’ve used AI in the past, how it has helped me, and how it hasn’t. I will also mention some interesting uses of AI that I have seen or heard about, hoping to inspire responsible use of what we call AI.

The Gemini Commercial

During the 2024 Paris Summer Olympic Games, Google released a commercial for its AI product, Gemini. In the commercial, a father uses Gemini to write an appreciation letter to an athlete from his daughter.

There was a bunch of feedback on the commercial.

The issue is whether or not an AI should be responsible for writing something like this—for writing the words of a kid instead of having the child write them. Are we losing something authentically human by using an assistant to do work that we should be doing?

I agree with most of this in general. AI shouldn’t be used to do human things like show appreciation for something or someone. AI, by nature, doesn’t have emotion.

Where I differ is my belief that AI could be used to create a “framework” for a letter if you don’t know where to start. Asking the AI, “What should I put in my appreciation letter?” is much more useful than asking it to write the entire letter for you.

How I’ve used AI

There are two prominent examples of how I’ve used AI. One was very successful, and the other was a big failure, possibly due to how I wrote the prompt.

Acroyoga Teacher Description

I teach acroyoga at the YMCA. I’m their first teacher, so they needed a description for the job they were hiring me for if they ever needed to replace me in the future.

I asked an AI to generate a job description. I started the prompt with “Write me a job description for an acroyoga teacher.” What I got back from that was about 70% correct. That’s awesome. I went through and updated the content quickly and got the job description out very quickly.

This was a very successful use of AI.

Student Recommendation

One of my former cheerleaders asked me to write a recommendation for them for a cheer coaching position in their hometown.

I asked the AI to generate a recommendation letter, hoping that I could go in and edit it. What I got out was predictably full of platitudes without specifics; I hadn’t given it any to use.

I tried to modify the letter to be used, but in the end, I had to start from scratch. What the AI generated was an excellent example of what I didn’t want to write.

I might have gotten better results by asking about the format of my letter or if there were specific areas I should target, but generating the whole letter wasn’t successful.

The Legal Case for AI

I was listening to a podcast this week from Hacked where they were talking about the hype surrounding AI. One of the hosts mentioned having a lawyer friend who sends in the transcripts of testimony in his cases and asks the AI to find all the inconsistencies. This saves the lawyer a ton of time and should be easy for a computer to identify.

This seems like a very good use of AI, especially if you can tell the AI to err on the side of a false positive instead of a missed positive.

The challenge here is that if you have to catch all the inconsistencies absolutely, depending on AI might not be possible. But combining large data with specific targets sounds like a great use for a computer, and using something that can understand some of the subtleties of the language is pretty awesome.

Summary

I don’t want to call this a conclusion because I’m still investigating the use of Artificial Intelligence as we use it today. These specific examples highlight that there are good uses of AI and uses that might not be appropriate. We haven’t even talked about using AI to write college essays or anything else that could be considered academic cheating.