An image of a blogger who takes himself way too seriously for his own good.
AI is every where. Accept it. Anyway, I had a random thought last night about having a WordPress plugin that allows you to generate images on the fly for your posts. Pictures increase engagement on posts so, what if we just inline Nano Banana directly into Gutenberg?
This morning I built this plugin which is a simple API call to Google’s Gemini AI Studio through a Gutenberg block.
Type your prompt
Choose your model
Hit generate
Insert
Simple!
Nano Banana Image Generator block
Once the image is inserted into the post it turns the block into a standard image block so its as easy to manage as any other image.
I submitted the plugin to the official WordPress repository but it takes a while to get approved. So, if you want to add it to your own WordPress instance feel free to message me and I’ll give you access to the repository!
A few days ago I wrote about building an automated WordPress deployment platform using Terraform and AI. Well, i’m happy to report that the entire platform is live and ready for you to explore and launch your own WordPress website.
Introducing 45Squared’s WordPress deployment platform powered by Ubuntu and Claude. Try it out today at https://ai.45sq.net.
Let’s talk about how this all works.
The front end infrastructure that an end user will see is pretty straightforward. I am utilizing an ECS cluster and NextJS to deliver the end user experience. The second portion of the user experience is handled by an AWS API Gateway to manage all of the user credentials, payment processing, sit launch status. Authentication is handled by AWS Cognito. Hate on it all you want, Cognito works just fine when configured correctly.
Frontend architecture
Behind the scenes, once a user transaction has completed successfully, the website is provisioned using another ECS task. This container runs through a sequence of steps to provision the AWS EC2 instance for the user to utilize. Each tenant instance is running a hardened Ubuntu image that is built using Packer. I will cover this in another post. Throughout the provisioning process, the task is updating the DynamoDB table so that the user gets a live look into how their website is progressing.
Provisioning architecture
Each tenant is given a subdomain as well as the ability to utilize a custom domain name. Each tenant is also given a Cloudfront CDN for global static content distribution. And of course, each tenant receives their own SSL certificate for both their custom domain and their subdomain.
Each site can be managed by SSM which will eventually be linked into an AI agent for management through Slack or another messaging platform.
I don’t intend to use this platform to compete with the large players. 45Squared’s vision has always been to serve the small to medium size businesses who want personalized support while still receiving an amazing product. This platform gives them the ability to quickly launch a website and get their company on the world wide web within 10 minutes.
If you are interested in building out a website using the platform the first few users can receive 50% using code “BETATESTER50”. There are limited redemption so be sure to get going quickly!
I think the promise of AI is to handle the work flows that maybe you don’t have time for. Or, maybe something that’s slightly out of your realm of expertise.
I read an article the other day that had this quote:
I keep waiting for someone to walk into my office and tell me what problems I should be solving with AI. Nobody’s come. (link)
This mind set, in my opinion, is fundamentally incorrect. If you are looking for problems to solve there are a vast number of them and you’ll be drowned in possibility. For me, AI has always been about gaining efficiency or adding capability.
If you haven’t noticed, I have a lot of side projects. I hope some day one of them hits the jackpot and allows me to retire to an island with all my family and friends. It’s unlikely, but hey, I’m allowed to dream.
I realized long ago that I’m not great at marketing. I don’t understand lead generation, i’m not super amazing at SEO (so i built a tool for it https://seoscoreapi.com), I’m not in the least bit artistic (that went to my brother (https://mrbenny.co). But what I am good at is problem solving and process creation.
I’ve been working on a concept of second brain for a while but I realized even a second brain needs to have tools. I started thinking about how to manage all of my side projects and how to interface with them through my preferred platform of Slack. (Sponsor me?).
I’ve come up with a business development agent that can handle the things that I don’t particularly specialize in. On the backbone of Claude Sonnet 4.6, I created an API Gateway that takes input from my Slack instance and can handle a variety of tasks.
SEO – Using my SeoScoreAPI it can handle generating SEO reports
Lead Generation – Using a variety of 3rd party API’s I have it looking for businesses that don’t have websites so I can pitch them some design services. Or, if their SEO is bad I can assist in fixing it
Lead nurturing – From the above leads, i get reminded that “Hey – you should connect with this person”
AWS Monitoring – It has read access into my AWS organization’s bills so that It can tell me if i’m over spending or just give me weekly overviews
WordPress MCP – Each of my managed WordPress instances has the MCP connected to it with read access so that if any of the sites have plugin upgrades, connectivity issues, errors or anything else I can quickly resolve them
The result of this is quite simple, I’ve added another “worker” to my organization that can help me grow. These aren’t necessarily problems I was having. They are simply areas of work that I struggle with and, they have become easier now thanks to AI.
If you think this concept is cool, I’ll be setting up a Terraform module soon over at https://aiopscrew.com
Sign up for the mailing list to be the first one to get the details!
I’ve been doing a lot more with Claude Code and before you shame me for “vibe coding” hear me out.
First – AI might be a bubble. But, I’ve always been a slow adopter. Anything that I have AI do, I can do myself. I just find it pointless to spend hours writing Terraform modules when Claude, or another model, can do it in a few seconds. I’ll post more on my workflow in a later blog.
One of the things that I find tedious is monitoring builds inside of Jenkins. Especially when it comes to troubleshooting. If AI writes the code, it should fix it too right?
I built a new skill for my Claude Code so that it can view and monitor my locally hosted Jenkins instance and automatically handle any issues. The purpose of this is straight forward. Once I approve a commit to the code base my builds are going to automatically trigger. Claude Code needs to make sure that what it wrote is actually deployed.
Inside of the markdown file you’ll find examples of how the skill can be used including:
List
View
Start/Stop
These are all imperative features so that the AI can handle the pipelines accordingly. This has resulted in a significant increase in the time it takes me code and deliver a project immensely. I also don’t have to copy and paste logs back to the AI for it to troubleshoot.
For you doomers out there – This hasn’t removed me from my job. I still act as the infrastructure architect, the software architect, the primary human tester, the code reviewer and MUCH more.
Anyway, I’ll be publishing more skills so be sure to star the repository and follow along by subscribing to the newsletter!
I think one of the biggest time sucks is getting a vague alert or issue and not having a clue on where to start with troubleshooting.
I covered this in the past when I built an agent that can review your AWS bill and find practical ways to save money within your account. This application wasn’t event driven but rather a container that you could spin up when you needed a review or something you could leave running in your environment. If we take a same read-only approach to building an AWS Agent we can have have a new event driven teammate that helps us with our initial troubleshooting.
The process flow is straight forward:
Given a Cloudwatch Alarm
Send a notification to SNS
Subscribe a Lambda function to the topic (this is our teammate)
The function utilizes the AWS Nova Lite model to investigate the contents of the alarm and utilizes its read only capabilities to find potential solutions
The agent sends its findings to you on your preferred platform
For my environment I primarily utilize Slack for alerting and messaging so I built that integration. Here is an architecture diagram:
When the alarm triggers we should see a message in Slack like:
The AI is capable of providing you actionable steps to either find the root cause of the problem or in some cases, present you with steps to solve the problem.
This workflow significantly reduces your troubleshooting time and by reducing the troubleshooting time it reduces your downtime.
So, if this is something you are interested in deploying I have created a Terraform module so you can quickly deploy it into your own environment to reduce your troubleshooting steps!
I think the AI mostly selected correctly this week. Unfortunately it wasn’t enough. We fell short by about 5 points. Going into Monday night we needed a massive game from George Kittle as the rest of the team performed very poorly. He delivered all the way until the 4th quarter where he likely twisted his ankle and was done for the game as the 49ers were up 2 scores.
Here are the results:
Josh Allen might have had a foot injury early in the game but stayed in for the entire game. The Bills simply didn’t throw the football. TreVeyon Henderson got absolutely demolished and left the game with a probable concussion. Josh Jacobs was questionable going into the game and cleared to play. The Packers simply didn’t play him.
With that loss we are eliminated from the playoffs and will be playing next week for 3rd place. Still a decent finish for our first year utilizing AI.
Looking Ahead
Through the off season I want to continue to work on the overall architecture of this agent and system. Ideally, I want to have the custom model built for next season and build an API around that to help us make better predictions.
Other action items:
Find a way to load news stories and story lines for determinations
Manage injuries/waivers better
Handle live NFL standings (teams eliminated from playoffs might play differently than teams fighting for a spot)
I also would love to be able to expose all of this publicly so that anyone reading can build their own applications around my predictions.
So I was wrong in last weeks post! Our playoffs started this week. In my league all the teams go to the playoffs and if you lose then there is a loser’s bracket.
Our AI run team was seeded at number 3. We were in a three way tie for first place and we ended the “regular season” with 2054.76 points. The leader had 2114.12. So, we weren’t far off the front!
Anyway, enough of that. You all just want to know the outcome. Here is our point totals from our first round in the playoffs:
I ran the AI on Saturday and it suggested pulling out Josh Jacobs in favor of TreVeyon Henderson. This ended up getting us an extra 10 points. Josh Jacobs still put up 24.2 points this week. Everyone played really well this week except Sam Darnold. I’m not sure if his hot streak is over or what is going on with him but its been rough. Christian Watson took a nasty hit in his game and left early but he is expected to be just fine.
So, did we win? We sure did! We’re on to the next round of the playoffs and we’re going to be up against Jahmyr Gibbs so we have to hope for our best performance of the season next week. Here is the currently proposed roster:
We have a lot of injuries and questionable players so I expect this to change. We picked up the Bills defense as they play Cleveland and they should have a good time against that struggling offense.
As we look to the off season I hope to build up my API website https://gridirondata.com and start training the model that we will use for next year. I have been working on the overall workflow and looking into how I can have both the scrapers running in the cloud and in my homelab so that I can easily work with the data locally and not incur a lot of cloud cost.
Stay tuned for more Fantasy Football news next week!
Happy Wednesday. Victory Wednesday that is! Our AI selected correctly this week and we snuck in a tough win that was finalized on Sunday night.
Unfortunately, we lost Zach Ertz on the way. A really nasty low hit took him out for the year. Here is the final scores for our lineup:
Josh Allen came up huge for us. Breece Hall was useless and the Commanders defense might as well have never stepped out on the field. But, a win is a win! We are now in a 3 way tie for first place but will likely take the third seed into the playoffs given our total fantasy points.
Here is the lineup for week 15: We’ve had to make some changes from waivers and I’m hoping the AI selected correctly. We are heading into the part of the season where teams are going to be fighting for playoff spots. I hope that it is taking that into account as it made the waiver picks.
We have some highly projected players this week. What do you think? Will we be able to pull off another win this week?
Well, unfortunately we took a big loss and are now in a three way tie for first place. Here are the actual results:
I think the biggest hit was how poorly Josh Allen played. What is interesting is that I was reviewing his passed performance against Houston and he has had his worst outings of his career there. This week was no different… The other interesting thing is that Saquon Barkley just isn’t the same back as he was last year. He is trending down.
In response to Josh Allen’s poor outing I added a deviation and historical performance analysis against and opponent to the data set so now we have a value like:
I then coded a new tool inside of the AI to reference this data set to assist with its final calculations.
So, when we ask the tool about Josh Allen’s performance against a team we get something like:
Found exactly what you're looking for in Josh Allen's performance data.
Josh Allen vs Kansas City (Week 9, 2025):
- 28.8 fantasy points - That's a STRONG performance!
- He was projected for 22.0 points but exceeded by 6.8 points (+30.9%)
- This was one of his better games this season
Historical Context (2024 vs KC):
- Week 11, 2024: 24.0 fantasy points vs Kansas City
Bottom Line: Josh Allen has performed WELL against Kansas City in recent matchups. In their two most recent meetings:
- 2025 (Week 9): 28.8 points
- 2024 (Week 11): 24.0 points
That's an average of 26.4 fantasy points against KC - well above his season average. Allen seems to rise to the occasion against elite competition like Kansas City. The Chiefs' defense can be vulnerable to mobile QBs who can extend plays, and Allen's dual-threat ability has historically given them problems.
The takeaway: When Allen faces KC, he typically delivers QB1 numbers. That 28.8-point performance this season shows he can still light up even the defending champs!
I need to backload some more historical data yet but that is a problem for a different day. Looking forward, I hope to build my own model based on this data and setup an automated workflow that will include data ingestion and automated model building so that I can consistently build predictions.
So, on to week 13. We have a bunch of injuries this week to contend with so this lineup will likely be changing once people are reporting. Here is the current lineup:
In other news I built and launched https://gridirondata.com which is an API to reference all of the data I have collected so far.
Unfortunately, its not free. But if you message me about it I’ll probably hook you up!
Well, week 10 has completed. And, WE WON AGAIN! It was a close one that came down to Monday Night Football. I had to buy a day pass to SlingTV so that I could watch the game due to the Youtube TV and Disney dispute. But, Josh Jacobs squared off against Saquon Barkley in the most BORING of football games. At any point in time, if AJ Brown had scored a touchdown I think we would have lost the matchup. Here are the final results.
For week 11 we dropped the Panther’s defense. Honestly, im not sure why the AI recommend them. I know the Saint’s are bad, but I think the Panther’s defense is worse. Anyway, we picked up the Bear’s for week 11. Badgley and the Colts have a bye this week so anyone playing against Jonathan Taylor can breathe a sigh of relief. The AI also suggested grabbing Riley Patterson and Zach Ertz. Here is our current lineup for week 11:
Marvin Harrison Jr. Is out with appendicitis which is a big bummer for his divisional matchup.
From a technical standpoint, I added in a data source and tool for the AI to check depth charts in order to assist in picking up players. This way it won’t suggest lower players. We can also research picks based on injury. In Marvin Harrison Jr.’s case we can see this:
Cardinals WR Depth Chart:
1. Marvin Harrison Jr. - OUT (your guy who's hurting)
2. Zay Jones - This cat becomes the clear WR1 with Harrison out. He's got experience and should see a big target bump
3. Simi Fehoko - Depth piece, might see some snaps but not a fantasy factor
4. Michael Wilson - Could be interesting as a deeper option, might get more looks
5. Greg Dortch - Slot guy who could see increased usage in 3-WR sets
6. Xavier Weaver - Rookie, probably not fantasy relevant yet
One thing to note is that it doesn’t know about other injury statuses yet. So I will be looking to add that in shortly.
Tied for 1st. Playoffs start soon. Here comes the big push to finish out this season!
