Category: Uncategorized

I’ve been thinking about this daily blogging experiment and whether I should continue with it in its current form. This is all very new to me because I’ve never really blogged at a consistency like this before. I’ve created automations that make the process easier, but I’m still running into a problem, because on some days, I’m working on things that just aren’t ready to be shared yet.

So I spent some time thinking about the purpose of this experiment. When I first started, I told myself I wanted to do 365 posts—one small entry every day documenting my journey of experimenting with AI. But now I’m wondering whether daily publishing is always the right format. If I don’t have anything substantial to share, am I just putting content out for the sake of putting content out?

Challenges

The biggest challenge is that not everything I work on can be shared immediately. Sometimes the most interesting thing I’m doing that day is still unfinished, private, or simply not ready to talk about. That makes me question whether I should switch to a more traditional blog format and only post when I have something more substantial to say.

What I Learned

At the same time, I can see a real benefit to blogging daily: it forces me to learn or work on at least one AI-related thing every day. Over the past year and a half, I’ve worked with AI a lot, but not always consistently. Life gets busy, other things take priority, and sometimes I won’t touch an AI project for several days. Then suddenly I’ll have the energy and spend an entire day or weekend immersed in one. I don’t think that pattern is as effective as steady practice. It reminds me of building any habit—learning a skill, exercising, or going to the gym. Consistency matters, especially on the days when you don’t particularly feel like doing it. If I really keep this up for 365 days, the compounding effect on my AI skills could be significant…

What Comes Next

For now, I’m going to continue with the daily blogging experiment. I’m keeping the format open, though. If it starts to feel too forced or becomes too much to maintain, I may eventually return to a non-daily blog. But at this point, I still think the daily habit is a useful way to keep myself learning, experimenting, and moving forward with AI.

I’ve been working on my WordPress plugin, but it’s not ready for a public release yet. There are still a few things I need to tweak, and the process has reminded me that building a plugin for other people is very different from building one just for myself.

What I Did

I’ve continued working on the plugin and thinking through what needs to be improved before it’s ready for other people to use.

Challenges

This is my first outward-facing WordPress plugin. I’ve built a plugin before, but that one was only for my own internal use. This time, I’m creating something for multiple users, which means I have to think much more carefully about the experience beyond my own workflow.

There are more UX gaps to consider, more edge cases, and more details that matter when someone else is going to interact with what I’ve built.

What I Learned

Creating a WordPress plugin for public use is not as simple as just making the core functionality work. There are many things to look into, especially when the plugin has to make sense to people who didn’t build it.

What Comes Next

I still have some tweaking to do before I’m comfortable releasing it publicly. For now, that’s about all I can reveal.

Is blatantly taking an artist’s music a form of disrespect?

The other day, I was relaxing and watching YouTube videos about music, including performances in one of my favorite genres: fusion jazz.

The algorithm recommended a video by an artist I won’t name. The challenge was to take a famous pop song and remake it in any style the artist and band wanted. On the spot, they turned it into a fusion jazz piece.

I watched them build this beautifully complex arrangement with multiple time signatures. It was such a delight to hear. The fact that they could come up with the arrangement and perform it like that on the spot made it even more impressive.

Then I read one of the comments. The commenter basically thanked the artist and said they were going to rip the whole piece, put it into AI, create an album based on it, and make millions.

The struggle within

I still don’t quite know how I feel about AI-generated music as a whole. Song creation is becoming incredibly easy with tools such as Suno AI, and people without a music background can now create polished-sounding tracks. We’re already seeing plenty of AI-generated music on platforms like Spotify and YouTube.

Some of it sounds almost too perfect, like it was mastered in the best studio in the world. Sometimes it even sounds a little robotic. But I also enjoy some AI-generated music. It can be groovy, fun, and even stir up real emotions.

What bothered me was the attitude in that comment. It felt disrespectful and dismissive of the artist’s craft, and of all the effort and time she had spent developing the mastery needed to create something like that.

I think my discomfort is less about AI existing and more about how people choose to use it. There’s a difference between enjoying new creative tools and treating someone else’s hard-earned artistry as raw material you can simply extract, imitate, and profit from.

That comment felt disgusting and distasteful to me because it reduced all that skill and creative effort to something that could just be sucked into a machine and repackaged.

I’m still figuring out exactly where I stand on AI music. I can appreciate what these tools can create while also feeling uneasy about what happens when respect for the original artist disappears.

And even if someone was actually planning to do what that commenter described, maybe some thoughts are better left unposted.

Some months back, I started getting fascinated by Gen AI applications like Gamma. I kept wondering how people actually build SaaS tools like that, and I wanted to understand the workings behind them for myself. So I decided to experiment by creating my own generative AI application.

What I Did

The app is basically an AI content-to-image generator. For now, I call it ImagenArt, although that is just a working name and I may change it later.

I have been using it almost daily for the past few months because I need images for the daily blog posts I do for my church. Before this, I used stock image libraries or prompted ChatGPT and Gemini until I got something that felt right. That process was slow. I first had to figure out what kind of image I wanted, then keep nudging the AI, and there were usually several steps before I got something usable.

ImagenArt takes a piece of written prose—anything from a few lines to a full article or even a rough collection of thoughts—understands what it is about, generates three image options, and suggests which one it thinks best fits the article. I also baked in some styling so the output has a more consistent feel.

Even though ChatGPT and Gemini have improved a lot since I started this project, and can now get pretty close to what I need, my app has still been saving me time every day. I no longer have to dig through stock libraries, prompt endlessly, or deal with removing watermarks from generated images.

Challenges

The image generation itself became smoother, but the overall workflow still had friction. After generating an image in my app, I still had to download it to my computer, upload it into the WordPress media library, open the right post, attach the image, save it, and go through a few more clicks before I was done.

I had been thinking for a while about building a WordPress plugin to solve exactly that pain point, but other projects kept getting in the way. Today I decided to just get it over and done with. I fired up my IDE, worked with my agents, and before too long I had a plugin running in my local environment.

The plugin changes the workflow completely. The image can be generated inside WordPress, saved directly into the media library, and attached to the post automatically. No downloading, re-uploading, or extra clicking around.

What I Learned

I was actually a bit disappointed after building the original image generation app. There are already so many similar tools in the market, and people can just use ChatGPT, Gemini, or more powerful image platforms. I started wondering whether my little experiment would end up as something I simply threw away.

But after building the WordPress plugin and testing the full workflow, I got excited about it again. I could immediately see myself using it because it solves a very specific problem in the way I already work.

That is probably my biggest takeaway from Day 3: it is all about the workflows. You can have the best feature or the most impressive piece of software, but if the workflow around it is full of friction, users still have to work too hard. The more friction you remove, the smoother and more useful the whole experience becomes.

Sometimes the real value is not in creating a completely new capability. It is in making an existing task much easier to complete.

What Comes Next

The plugin is still being tested locally, but I am happy with where it is heading. The next step is to keep refining it and see how well it holds up in my actual day-to-day workflow. For now, I am just glad that an experiment I was close to dismissing has turned into something I can genuinely see myself using.

Today I wanted to try creating this blog automation. It didn’t actually start out that way. It started with a simple problem: I have things I want to share, but creating a post, uploading it, and sharing it with the world takes more time and effort than I want to spend on it.

This isn’t my main job. I have tons of other things to do and many projects I’m working on simultaneously. I wanted a simple way to record what I’m doing each day, both as a challenge for myself to see my progress and so anyone who’s interested can follow along as I learn to create using AI.

What I Did

I decided to see if I could automate the process. With the help of AI, I created a workflow that should turn what I dictate in ChatGPT into a blog post automatically.

Initially, I thought the process would send my dictation through Make.com before pushing it to WordPress. But while brainstorming with AI, I found that I could eliminate that layer entirely and connect the process more directly.

This is the first time I’m testing it. If it works correctly, I can dictate what I want to share, have the blog post created for me, then review it and publish it if I’m happy with it—or make changes first if I want to amend anything.

Challenges

The main challenge was that there are so many ways to do this. I didn’t want to go too deep into complex automation workflows with tools like n8n or Make.com. That felt like too much for a very simple blog that I want to keep as low-maintenance as possible.

Another consideration was maintenance overhead. I could have built this as a React app, designed it nicely, connected it to my backend, and gone down that route. That was one of the possibilities I considered while brainstorming, but I didn’t want to create unnecessary maintenance for such a simple project and experiment. I already have tons of other things to do, so keeping this simple was important.

I also explored publishing this on Medium or Substack. One issue for me was ownership. If I publish on those platforms, I don’t really own the platform itself. With my own WordPress site—and, in the future, my own custom domain—it feels much more like it’s truly mine.

More fundamentally, Medium and Substack didn’t have the native connectors I needed to connect them directly with ChatGPT. That would have made the workflow more complicated, which went against the whole point of keeping this experiment simple. So in the end, I decided not to go that route.

What I Learned

One of my biggest learnings from this simple project is that sometimes it’s more difficult to simplify something than it is to add to it. Adding layers and connections can be easy because eventually you can make everything work. The harder part is knowing exactly what you want from a system and then ruthlessly simplifying it until it works beautifully with as few layers as possible.

That’s what happened here. I initially expected Make.com to sit between my dictation and WordPress, but after brainstorming with AI, I realized I could remove that layer entirely. There’s something beautiful about seeing a system work with minimal complexity.

It also has practical benefits. Every extra layer is another potential point of failure and another place where security issues can arise. Fewer layers mean fewer break points, less maintenance, and a simpler system to secure.

So the goal isn’t to build the most advanced automation. It’s to build the simplest system that does what I actually need and makes sharing my progress easy enough that I’ll keep doing it.

What Comes Next

Now I get to find out whether this workflow actually works. If this post appears as a draft ready for me to review, then that’s a pretty good start to Day 1.