Aaron VanSledright

Category: Technology

  • We’re BACK – Fantasy Football 2026-27

    Back by literally no demand will be my Fantasy Football series! New team, new draft agent, new tech to support the team. If you are new here, last year I did a whole series about using AI to manage my Fantasy Football team. I was even . interviewed about it.

    During the NFL offseason I retooled the drafting agent. Some highlights:

    1. New data backbone using https://gridirondata.com that has historical season context, player news, and season actuals (once the season starts)
    2. Newer AI models – Last year I used Anthropic’s Haiku 3.5 model. This year, we have Haiku 4.5. The model reasoning is now more roster aware in the sense that it wants to add starters first and then focus on bench seats.
    3. A real GUI – last years draft was done all via CLI. This year I have a full web interface for drafting, seeing the AI recommendations as well as “best available” players.

    I’m still retooling the “Coach” but don’t worry, I will still force the AI to adopt a Dan Campbell persona.

    So, I present to you for the first time… The Hallucination Hail Mary’s!

    If you think we are going to win the league this year comment below. Otherwise just let me know what you think!

  • An agentic Kanban Workflow

    An agentic Kanban Workflow

    I had this idea the other day and started building it into a project I’m working on. We all hate Jira but, the idea of Kanban is still a useful way to track projects.

    As we think about this in the AI era we could easily integrate the Jira MCP into a workflow but, once again we hate Jira. So that led me down a path of reinventing the wheel, mostly for my own purposes. I came up with this simple diagram:

    We will forever want to keep a human in the loop so a web interface is still likely necessary. However, a text based interface could also be cool…. Maybe in the future!

    What we end up with is a system of three agents:

    1. Developer agent – this agent writes and builds code in a sandboxed container based on specs written either by a human or by bugs found by the QA agent
    2. Build Agent – This agent monitors our build pipelines and if there is a failure it diagnosis why and opens a bug accordingly for the developer agent to fix
    3. QA Agent – Arguably the most important agent. This one will execute testing as close to simulating a human interaction with the software as possible. Upon finding bugs it would be able to log them back into the Kanban for the developer agent.

    Now we have a full DevOps life cycle with three agents. If I build this out, I become the user who is simply entering specs as features or bugs for the developer agent to work through. The code is still stored inside of some git based repository, build failures can utilize my already coded Build Failure Agent. Claude Code or Codex could function as the developer agent or we could run the whole thing on AWS Bedrock.

    Proof of concept coming some day when I have time!

    Don’t miss an update

  • What I’ve Built in 2026

    What I’ve Built in 2026

    Q1 is done. Feels like time is flying by this year already. We’ve already hit that gross humid stage in Chicago. I’m sure all the AI data centers aren’t contributing to climate change.

    Anyway, I’ve been hard at work both at the day job as well as hammering out personal projects. I’ve been spending a lot more time setting up systems for 45Squared, my web development company that I started back in 2016 crazy to think it will be turning 10 years old this year!

    I built a custom CRM for monitoring and managing incoming leads. The frontend lives in my home lab but reaches out to a DynamoDB table where I have leads being scraped using Google Places through an automated Lambda. Some other features:

    • Email sending and templating
    • Email tracking and unsubscribe
    • Incorporated SEOScoreAPI so I can get quick scores
    • Gemma4 Integration for suggesting which leads to contact
    • Custom scraper for enriching leads with phone numbers, addresses publicly available emails
    • Apollo.io for email validation
    • Hunter.io for email searching
    • AWS Cost agent integrated
    • Stripe and Quickbooks Revenue being tracked

    So far i’ve gotten 3 unsubscribes and sent over 100 emails! Success!

    SEOScoreAPI is continuing to slowly grow with over 1000 audits. I built in ADA compliance checking after reading about so many frivolous lawsuits on Reddit. I also added GEO scoring so that you can gauge how well your site is readable by LLM’s.

    My Fantasy Football pipeline is working and I think it’s ready for the next season! I’ll still probably lose but at least this year I will be able to blame technology again.

    The build remediation pipeline is working swimmingly. I’ve been integrating it into every pipeline I setup so that I can continue to build data for training a true DevOps AI agent on at some point.

    I’m still active on UpWork and taking on new work there as it comes to me. Bidding on jobs with UpWork is an absolute nightmare. My last engagement was handling background noise with AWS Lex.

    My AWS Cost Agent now supports multiple accounts as well as tracking AI/Bedrock usage costs. I will be uploading a video about how this works soon.

    Seems like there is more and I’m sure there is. If you want to hear more about any individual project feel free to reach out to me at anytime!

    Don’t miss an update

  • Evolution of my build failure agent

    Evolution of my build failure agent

    I’ve written in the past about troubleshooting build pipelines with AI. While all of this is a great step in speeding up your development and reducing the amount of troubleshooting the DevOps team needs to do in the enterprise, it is NOT the end goal.

    The end goal would be to have the AI fix the problem for you.

    I’m rebranding my Jenkins Sentinel to just be Sentinel. This workflow allows you to automate remediation for your pipelines while still retaining human in the loop security.

    The other primary feature is storing your build failures and remediations in a database that you can view, update, analyze for custom model training.

    Originally we had the dispatch layer that would notify us of build failures and possible resolutions. The new addition is the cluster of “workers”. Running on AWS Fargate, this team of developers works with the LLM on Bedrock to resolve the failure.

    1. The task spins up in the cluster
    2. The build logs identify the repository and branch
    3. The repository is cloned, and branch checked out
    4. The code fix is implemented
    5. The task generates its reasoning and updates the database accordingly
    6. Code is committed to a new branch and a pull request is opened.
    7. The task cleans up and shuts down

    Dispatch still remains the same and the developer is notified accordingly. I need to implement developer specific notifications so that channels are not flooded or email lists abused.

    The other major thing I wanted to see was the cost per fix.

    This screenshot is from the dashboard which shows the compute spend and the LLM spend. For this simple Terraform fix you can see the was a little around $0.02. Assuming your code bases are more complex this value could increase proportionally.

    I also included a stats page which shows the totals for the entire organization.

    This is all real data from my testing project. The build agent is successfully troubleshooting pipelines for:

    • Python
    • Terraform
    • Java
    • Typescript
    • Docker
    • Kubernetes
    • Go
    • Cloudformation

    I plan to continue to add more supported platforms and languages as time allows. The other major integration that I am working on is support for GitHub Actions. Once I complete that integration and put this into all of my pipelines I expect that my troubleshooting and development time will decrease rapidly.

    Other future plans include:

    • Ingestion of bugs through sources like Jira, ToDoist (my favorite), or another ticketing system.
    • Discord Dispatching
    • Teams Dispatching – although this is really hard to develop for without a paid account
    • Custom model – using the build failure data to train a model

    Anyway, this project has been super fun. If you want to implement it on your own infrastructure feel free to reach out!

    Don’t miss an update

    PS: the featured image was generated and setup through my Nano Banana WordPress plugin

  • Fantasy Football and AI – The next season

    Fantasy Football and AI – The next season

    If you aren’t familiar with my Fantasy Football series from last year, I built an AI assisted drafting agent based on data I collected from previous seasons. I then iterated over that setup to build an in season coach that determined my line up each week. AI and I did pretty well but ultimately we are still chasing the championship.

    So now what? How do I improve the system? Well, I started over.

    This off season I took all of the stats I could get from 2020-2025 seasons. I restructured my database and moved it all to my local home lab. The purpose of this was to be able to cost effectively train, re-train, throwaway, and train again multiple models using multiple strategies for each position until I could accurately predict the results of the 2025 season.

    The result is a CatBoost and LightGBM system of models that has the following features:

    • Rolling Stats – 3, 5, 8 week rolling average of fantasy points
    • Vegas lines – spread, over/under, team implied total, implied win probabilty
    • Next Gen Stats: snap counts, target share, CPOE, rush yards over expected
    • Play-by-play: EPA per play, weighted opportunity share, red zone target share, goal-line carry share
    • Defensive matchup: rolling 5-week EPA allowed by the opposing defense at each position
    • Injury status: game designation, practice participation, teammate injuries
    • Schedule context: home/away, dome/outdoor, rest days, primetime

    I tested XGBoost, LightGBM, CatBoost, NGBoost, LSTM, and Transformer architectures. CatBoost + LightGBM ensemble won — the gradient boosting models crushed the deep learning approaches on tabular data (MAE 2.95 vs 4.68 for the LSTM).

    Using this, I then build a brand new draft simulator that tests various selection strategies based on position in the draft, draft type, and number of teams.

    The infrastructure is setup for next season. I’ll be going into more detail on each aspect of this setup throughout the off season. If you are interested in using the model feel free to reach out!

  • Why I built my own WordPress Platform

    Why I built my own WordPress Platform

    I’ve been building websites for a long time. I remember learning HTML and Microsoft FrontPage as my first website builder. It was such a fun time to be creating horrific looking websites back in the early 2000s. As the internet progressed so did my skills and back in 2016 I formed my company 45Squared to build websites for small businesses. My whole goal is to be your trusted resource when it comes to being online.

    When I started the company I built WordPress websites of various shapes and sizes but they always ran on AWS. This helped me expand my AWS skills as well as provide robust infrastructure for my client’s websites to live on. I managed the website and the underlying infrastructure for a small monthly cost that beat the competition. The result, a bunch of paying customers a decent side hustle.

    As time went on, selling became harder and the race to zero for cost was apparent. So, as the AI boom is on, I decided that it was time to automate the site building process.

    I started documenting out how I would want this to work. Fully automated website deployments, design, content, custom domains, good SEO base and deployed FAST!

    Enter https://ai.45sq.net. This platform is fully automated. The customer can provide inputs and descriptions of what they want as well as photos or other graphical content. The workflow takes all of the inputs and builds a fully functional WordPress website hosted on AWS. The user can easily point their own domain to the server and setup automatic payments. They then get full administrative access to their website so they can expand and add features just like any other WordPress site.

    So why did I build this?

    If you contact a web designer now you will have to pay them to build up the initial design, work with their timelines, end up with something that needs revisions and your time to live will be in the weeks not minutes.

    The platform I built for 45Squared eliminates the need for the initial design fees and focuses on getting you online quickly. Its great for small businesses who are just getting started.

    So now when I get a request to build a site I can tell the customer that I have two options. First, fully custom. I’m still willing to sit with you and build out the picture perfect website. Or, two, you can launch your own and I will still support the website and help you with your online presence.

    So that’s it. An easy to use WordPress website launcher. Running on enterprise grade cloud. With content, design, layout and all the rest handled by the magic of Claude Opus.

    Try it out: https://ai.45sq.net. No contracts. No weird fees. Get online today.

  • I built a WordPress Plugin For Generating Images With Nano Banana

    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.

    1. Type your prompt
    2. Choose your model
    3. Hit generate
    4. 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!

    Don’t miss an update

  • Building in Public – The Automated WordPress Deployment Platform Part 2

    Building in Public – The Automated WordPress Deployment Platform Part 2

    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!

    Don’t miss an update

  • How I’m Using AI Agents to Run My Side Projects

    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!

    Don’t miss an update

  • Letting OpenClaw control an AWS EC2 Instance

    If you are even remotely interested in the AI space you know about OpenClaw. If you aren’t familiar, OpenClaw is an AI Agent that runs on your computer. It can access various tools and you can communicate with it via a multitude of channels like WhatsApp or Slack.

    It was notorious for either deleting peoples information or being highly insecure.

    Anyway, as someone who is highly skeptical about all things new and AI, I watched closely but did not engage. Until now!

    I carefully installed OpenClaw in an isolated virtual machine on my home lab. I set it up so that I can engage with it via Slack. It has its own channel where we engage and it sends updates about what its doing.

    I gave it system prompt that explained who it was and what its goal was. I then created a new EC2 instance inside of a new untouched AWS account. No IAM role. No security group rules except to allow inbound SSH and outbound HTTPS.

    The goal? OpenClaw needs to reach a monthly recurring revenue of $100. It has explicit instructions that if it needs anything to achieve this goal it has to show me its plan and accounting.

    So what did it do? It toiled around with some ideas for a while about building different API’s or data scrapers. I told it to find a niche that it could really work with. It landed on building an SEO scoring tool that is API based. OpenClaw then requested I buy it a domain which I did, https://seoscoreapi.com. The domain is pointed at its EC2 instance where the API lives.

    So now what? OpenClaw built a fully functional API. It works great. You can use it for free. But how does this make money? Since I left to go to sleep last night, OpenClaw has been quietly emailing Web Developers and SEO Agencies while sending them their scores. It has created an entire scoreboard of site rankings based on its own API.

    It has also been adding itself to SEO tool lists on Github. Opening pull requests on its own.

    So, it’s been 24 hours since OpenClaw launched https://seoscoreapi.com. How much revenue has it created?

    None.

    How much have I spent in Claude Opus API Credits? $90.56.

    Was this experiment worth it? Absolutely. The power of AI is real. Configured properly and monitored it can build some really cool things.