Aaron VanSledright

Category: Technology

  • Using Strands to build an AWS Cost Analysis Agent

    Taking a break from Fantasy Football today to talk about a quick weekend project I put together.

    A friend of mine was chatting about how their AWS costs are getting out of control and they aren’t sure where to start when it comes to cleaning up the account. This prompted me with an idea to utilize AI to build an Agent that can interact with your AWS account to review resources, provide cost analysis and give you clear CLI commands or console instructions to help clean up the account.

    In order to do this, I wanted to incur as little cost as possible. So, I built a Docker image in order to run it locally. First, there is a shell script that will build an IAM User in your account that provides read-only access to the account, Cost Explorer access and access to Bedrock (to communicate with an AI model).

    The Docker image runs and builds an Agent that interacts with whichever model you want to utilize. I picked Amazon’s Nova model just to keep the costs down. The container then presents a web interface where the account’s bill break down will be displayed:

    It will also display some common costly resources and their counts:

    The next block is where things get very helpful. The AI will present to you suggestions as to how to save some money as well as some risk calculations. Because I ran this against my real account I had to blur out some information but you get the idea:

    So, now you have some actionable activities to work through to help you save money on your AWS bill. But what if you have more questions? I also included a simple chat box to help you work with the bot to come up with other explanations or find other ways to save cost.

    So I asked the AI to find the largest instance in my account and then determine the right size for it. Here is the response:

    Why would this be important? Well, if you had the AI review all of the instances in your account you could identify EC2 instances that are oversized and have them be changed accordingly. After I implemented a few of the changes that the AI recommended (and verified they didn’t break anything), my account billing decreased by about $100.

    If this is something you are interested in running on your own account feel free to reach out! I’d be happy to help you setup up the container on your machine and make suggestions as to how to save some money!

  • Fantasy Football and AI – Week 5

    It feels good to win. Week 5 locks up the 3rd win for our AI managed fantasy football team. It was also the first week where players could be on a “bye” and it handled that with out issue! We had great performances from a bunch of players and most players were fairly close to their projections. I will say, our opponent did start a player who did not play at all but the point differential overall I don’t think would have helped him win.

    Colts defense was a great suggested pickup and Sam Darnold played a HUGE game and ultimately still lost… Poor guy. The AI suggested picking up Dalton Kincaid and boy was that a home run pick.

    Now that I am back home and able to work on the code again I have a few things to fix including QB’s on waivers. For some reason the AI is not able to retrieve them. I also want to continue working on the speed that it returns information. I think implementing MCP into the architecture will help. So as my life gets back to normal I will look into how to integrate these new features!

    As always, thank you for following along. Hopefully week 6 is another victory!

  • Fantasy Football & AI – Week 3

    Week 3 is here. I started late yesterday by getting a full analysis of the team and any targets for free agency and waivers.

    Most important was to get a better Tight End as the predictions for last weekend were incredibly wrong. This is my fault and reinforces the idea that giving bad data to AI will just result in bad output.

    Here is the updated selection to get Juwan Johnson. This makes a lot more sense than picking Hunter Henry last week.

    The second thing I looked into was getting a good defensive matchup. Currently I have the Rams on the roster but they play the Eagles this week and that matchup is not going to favor the Rams.

    Here is some output from our friendly AI Dan Campbell:

    Alright, coach! Here's what I'm seeing for Week 3 defenses that can help you bite some kneecaps and put up points:

TOP WEEK 3 DEFENSIVE MATCHUPS:

1. Green Bay Packers DST (7.6 projected points)
- Only 45% owned, so there's a good chance they're available
- This is their highest projected week of the season!
- They're facing a matchup that should generate turnovers and sacks

2. Buffalo Bills DST (7.5 projected points)
- 65% owned but worth checking
- Solid Week 3 projection and they're consistent all season

3. Kansas City Chiefs DST (7.5 projected points)
- Only 12% owned - this is a STEAL if available!
- Same 7.5 projection as Buffalo but way less owned
- Championship defense with a great matchup

Now, looking at your current situation - you've got the LA Rams DST starting right now. Let me tell you something, coach - any of these top 3 options would be a significant upgrade for Week 3!

MY RECOMMENDATION:
If the Kansas City Chiefs DST is available, that's your move right there! Only 12% owned with a 7.5 projection - that's championship-level value sitting on waivers. If not them, grab the Packers at 7.6 points projected.

The Atlanta Falcons (7.1 projected, only 1.3% owned) are also a sneaky good play if you want to get really aggressive on the waiver wire.

These defenses are gonna feast in Week 3, and you need every advantage you can get to dominate your league! Go get 'em, coach!

    I made a claim for the Packers. I would prefer not to pick them because of my love for the Detroit Lions…

    One other waiver selection that I made was at the kicker position. Here is AI Dan Campbell’s suggestions:

    Alright, let me tell you what - you've got Chase McLaughlin right now, and he's been solid, but let me break down the kicker landscape for Week 3 like we're game planning for the playoffs!

Top Week 3 Kicker Options:

Brandon McManus (GB) - 8.2 projected points
- Only 14% owned, so he's sitting there waiting for you!
- Green Bay's offense has been moving the ball well
- This guy's got ice in his veins and a leg like a cannon

Tyler Loop (BAL) - 8.0 projected points
- Baltimore's offense creates plenty of scoring opportunities
- 65% owned though, so he might be taken in your league

Parker Romo (ATL) - 7.8 projected points
- Only 1.4% owned - this is a diamond in the rough!
- Atlanta's been getting into the red zone consistently

Jason Myers (SEA) - 7.9 projected points
- Seattle's offense with Sam Darnold (who you've got as your OP!) creates opportunities
- Only 10% owned

Now here's the deal - Chase McLaughlin has been putting up decent numbers for Tampa Bay, but if you want to maximize your Week 3 scoring, I'd be looking hard at Brandon McManus. The Packers offense is humming, and McManus has that veteran reliability. Plus at only 14% ownership, there's a good chance he's available.

Parker Romo is my sleeper pick - less than 2% owned but projected for nearly 8 points. Atlanta's been moving the ball, and sometimes you gotta take a swing on the unknown soldier!

You comfortable rolling with McLaughlin, or you want to make a move? In this league, every point matters, and kickers can be the difference between victory and going home empty-handed!

    I picked up Parker Romo for the team. I thought it would be fun to test out the “sleeper” pick.

    And finally, our starting lineup, barring any injuries or set backs during the practice week:

    I think we’ve got a good matchup this week. Most of our opponent ranks are pretty low. Barring injuries we should maybe, hopefully, get our first win of the year.

    Tune in next week for our results!

  • Week 2 – AI Plays Fantasy Football

    We lost again. AI is 0-2.

    For the most part I agreed with the picks that AI made last week. Except for one. The tight end. It felt a little weird picking Hunter Henry up off waiver wire and boy did I found out why. More on that later. Here are the results from the week:

    There are definitely some misses on wide receiver but that is always such a hit or miss position. What shocked me the most was Josh Allen only get 11 points. Also, J.J. McCarthy was injured in his game. Not a single player hit their projection except for the Rams’ defense. If we take a look at the bench there are definitely some better picks we could have made strictly from looking at points for the game. However, even if we had selected all the better preforming players the team would have still lost as our opponent had 164 points this week.

    Anyway, that’s enough football for this post. Let’s talk about some tech. I mentioned earlier that I was questioning the waiver acquisition of Hunter Henry and I wish I had done some more debugging as to why that pick was selected.

    I added a chat feature to the overall application so that we can identify waiver picks and other things faster rather than having to get a full weekly analysis every time. In that code there is a function that generates a recommendation score. I’ll admit, I vibe coded some of this with some prompting of how this should be determined.

    # Penalty for high ownership
        #ownership_penalty = float(waiver_player['percent_owned']) / 10  # Convert to float
        #score -= ownership_penalty

    You can see here that we lowing our recommendation score because of “high ownership” this is because the reverse engineered ESPN Fantasy API doesn’t identify if a player is on a teams roster or not when I pull the players. So a player with a 100% ownership would likely never be recommended. So a player like Zach Ertz, who had 18 points this week was not suggested as he had approximately 80% ownership across ALL of ESPN’s fantasy platform. Well… this problem has been fixed.

    Anyway, I also added chat. And I programmed it to respond only as Dan Campbell. Here is some output when I asked “What do you think of my team?”

    pounds table

Well I'll be damned, you got yourself a HELL of a roster there, chief! This team's got some serious bite to it - let me break it down for ya.

Your Strengths - These are STUDS:
- Josh Allen - That's your franchise quarterback right there. Guy's a warrior and puts up points like it's going out of style
- Saquon Barkley & Josh Jacobs - Holy smokes, that's a one-two punch at RB that'll knock people's teeth out! Both these guys are workhorses
- Breece Hall in the FLEX - Are you kidding me?! Most teams would KILL to have Hall as their RB1, and you got him as a flex play. That's championship-level depth
- DK Metcalf - Big body receiver who can take the top off any defense
- Your bench depth - Terry McLaurin, DJ Moore, Cooper Kupp, Keenan Allen?! That's four WR1/WR2 caliber guys just sitting there!

Areas that need some attention:
- George Kittle on IR - That hurts, but Hunter Henry's a solid fill-in for now
- J.J. McCarthy at OP - Kid's got potential but he's still unproven. Might want to consider streaming that spot based on matchups
- Marvin Harrison Jr. - Rookie receivers can be boom-or-bust, but the talent's there

The Bottom Line:
This roster is LOADED, especially at RB and WR. You've got championship potential written all over it. The depth is ridiculous - you could probably trade some of that bench talent for upgrades at TE or find a more consistent OP play.

You're sitting pretty, but don't get comfortable! Keep grinding, keep looking for those edges. That's how you win championships!

What specific position are you thinking about upgrading, or you want me to take a look at some waiver wire options?

    And here is an architecture diagram update to include the chat elements:

    So, I’ll be back tomorrow with a new lineup. Hopefully week three treats us better!

  • Week 2 – Fantasy Football and AI

    Week 2 – Fantasy Football and AI

    After a heartbreaking (lol) loss in week one, our agent is back with its picks for week two!

    But, before we start talking about rosters and picks and how I think AI is going to lose week two, let’s talk about the overall architecture of the application.

    Current Architecture diagram

    You may notice that after my post on Tuesday I have substantially reduced the data storage. I’m now using three DynamoDB tables to handle everything.

    1. Current Roster – This table is populated by an automated scraper that pulls the rosters for all the teams in the league.
    2. Player Data Table – This table populates all the historical data from the draft as well as projected stats for the 2025 season. It also holds the actual points received after the week has completed.
    3. Waiver Table – this is probably the most notable addition to the overall Agent. This table is populated by both ESPN and FantasyPros

    The waiver wire functionality is a massive addition to the Agent. It now has the ability to know what players are available for me to add to the team. If we combine that with the player stats in the Player Data Table we can get a clear picture as to how the player MIGHT preform on a week to week basis.

    The waiver table is populated by a lambda function that goes out and scrapes the ESPN Fantasy Platform. It is quite involved code as there is no API for ESPN. I’m still not sure why they don’t build one. It seems like an easy win for them especially as they get into more sports gambling. You can read the code here. This Lambda function runs on a CRON every day so that the Agent always has daily updated data.

    The other major addition is a web interface. I realized that accessing this via a terminal is great but, it would be way more interesting to have something to look at. Especially if I am away from the computer.

    The web interface consists of a display of the roster:

    Roster Screenshot

    There are a couple things I need to fix. You’ll notice that a few players “have points” this is a problem with the data in the Player Data Table from when I was merging all the sources. Ideally, this will display the points the player has received for the week. Eventually I would like to add some live streaming of the point totals.

    Bench Players

    It also displays the bench (notice the same data glitch). On my list of things to do is to make these boxes drag and drop and auto update the roster table so that we can move players around. I also want to add their projections to each block so I can see the projected points per week for each player.

    The BEST part (in my opinion) is the prediction functionality. There is a box that we can choose which week to get a prediction for and then it will return output from the agent.

    So, let’s take a look at week two!

    Optimal Week 2 Lineup

    Here is a screenshot of the week two optimal lineup. This is the lineup I will be starting for week two unless one of my tight-ends becomes healthy. With George Kittle on injured reserve and Evan Engram also questionable, the AI suggested picking up Hunter Henry.

    The Agent will also provide up with some waiver targets as well as matchup analysis

    It will also output the full response for each week. Here is the full text of what the Agent has predicted for week two:

    LINEUP OPTIMIZATION FOR WEEK 2

    Starting Lineup Strategy:
    – Josh Allen (QB): Elite QB1 with 38.8 points in Week 1, facing NYJ defense that allowed significant passing yards. His 2024 average of 31.85 and strong 2025 projection make him a must-start.
    – Josh Jacobs (RB1): Top projected RB at 18.4 points, facing Washington at home. Green Bay’s new offensive system should benefit Jacobs significantly.
    – Saquon Barkley (RB2): Projected 18.1 points against Kansas City. His 2024 average of 15.93 and strong 2025 projection of 273.2 season points make him a core play.
    – DK Metcalf (WR1): Highest projected WR at 14.3 points. Pittsburgh vs Seattle should be a competitive game with passing volume.
    – Marvin Harrison Jr. (WR2): Rookie with 14.2 projection facing Carolina. His 154.5 season projection suggests breakout potential.
    – Hunter Henry (TE): Best available TE option at 9.3 projected points with Kittle on IR and Engram questionable.
    – DJ Moore (FLEX): Solid 13.9 projection against Detroit. Chicago’s passing game should see volume in what could be a high-scoring affair.
    – Breece Hall (OP): Critical OP selection at 14.9 projected points. Hall offers RB1 upside against Buffalo, and using an RB in the OP slot provides higher floor than WR options.

    CRITICAL WAIVER WIRE TARGETS:

    Roster analysis shows no critical positional needs. Your team has excellent depth across all positions with 19 total players and strong projections.

    TOP DEFENSE WAIVER PICKUP:
    No specific waiver targets identified in the data, but consider streaming defenses based on matchups. Look for teams facing backup QBs or offenses with high turnover rates.

    INJURY CONCERNS:
    – George Kittle: On Injury Reserve, unavailable for Week 2
    – Evan Engram: Listed as Questionable, monitor his status throughout the week

    MATCHUP ANALYSIS:
    – Josh Allen vs NYJ: Excellent matchup for Allen, Jets defense struggled in Week 1
    – Jacobs vs WSH: Home game advantage for Green Bay, Washington’s run defense showed vulnerabilities
    – Barkley vs KC: High-scoring game potential, Eagles should stay competitive requiring rushing attempts
    – DK Metcalf vs SEA: Revenge game narrative, Pittsburgh’s passing attack should be featured

    BEST DEFENSE MATCHUPS

    Detroit DST faces Chicago at home, which presents a solid matchup. Other potential streaming options to consider:
    – Teams facing rookie QBs or backup quarterbacks
    – Defenses at home against struggling offensive lines
    – Units facing teams with high turnover rates from Week 1

    WAIVER PRIORITY:
    1. Streaming Defense: Look for defenses with favorable Week 3 matchups
    2. Handcuff RBs: Target backup RBs for your elite runners (Jacobs, Barkley, Hall)
    3. High-Upside WRs: Monitor snap counts and target shares from Week 2 for emerging receivers

    Final Comments:
    Your roster is exceptionally deep and well-constructed. The OP position selection of Breece Hall provides excellent upside, as RBs typically offer higher floors than WRs in flex positions. Focus on matchup-based streaming for defense and monitor the waiver wire for breakout players rather than addressing positional needs. Your core lineup projects for strong Week 2 performance with multiple players having 15+ point upside.

    What’s Next?

    So we have a pretty solid week two prediction. Josh Allen and Saquon Barkley I have faith in. The rest of the team is hit or miss. Chicago faces Detroit at Ford Field (Go Lions!) and both teams lost week one. But Ben Johnson facing his old team for the first time has me nervous.

    This brings up a few of my to-dos for the overall program.

    1. Defensive matchups – I need to get data for the Defenses to find the best matchups week to week. Having a good defense play is an easy way to get an advantage every week.
    2. Add authentication – I added a really simple authentication method to the code just for the time being. But, it would be nice to have a Single Sign On or something a little bit more secure.
    3. Drag-n-drop interface – I need to add functionality to be able to modify the roster on the web interface. It would be nice if this could also update ESPN.
    4. Slow Output – I’m always looking for ways to optimize the Agent’s output. Currently it takes about 45 seconds to a minute to return the output.

    Thoughts? I hope this series is entertaining. If you have ideas for the Agent please comment below or shoot me a message somewhere!

  • AI Loses Its First Matchup – Fantasy Football Agentic AI

    Straight to the point. AI lost is week one matchup by 2.28 points. I watched as many of the games as I could so that I could give a slight bit of commentary.

    First a re-cap. If you haven’t been following along, I have built and am continuing to improve upon an Agentic AI solution for drafting and managing a Fantasy Football team for the 2025 season. The team is entirely AI selected and you can see its predictions for week 1 here.

    There was a couple of concerns that I had looking at the lineup. Most notably Sam Darnold in the superflex (OP) position as I thought some of the other players might have break out games and boy was I right!

    Here is the results from week 1

    Now, let’s comment on a few things. George Kittle left his game with an injury and is likely to miss a few weeks. AI can’t predict in game injuries, yet. DJ Moore was the final hope Monday night and he was either not targeted when he was open or Caleb Williams simply didn’t throw a good ball. AI, can’t predict in game performance, yet.

    Now, the Agent did hit on Josh Allen with his amazing performance against the Ravens. Breece Hall was also a great pick beating his projections.

    What’s Next?

    So we have some clear things to work out.

    1. Injuries – the AI Coach needs to understand that Kittle is likely out for a few weeks.
    2. Waivers – Now that we have an injury we need to replace a player. Engram is on the bench but is he the best tight end?

    With these clear needs in mind I am actively working on building out a waiver wire monitoring tool to grab available players from the ESPN Fantasy platform. Because ESPN doesn’t have a native API this has been particularly challenging. I added in a Lambda function that will run daily and update the other teams rosters in a DynamoDB table so that we could potentially compare lists of players from other sources. This would give us a subset of “available” players. I also will be adding in an injury parameter that will help assist the Agent in determining the next lineup. Finally, I am scraping out the fantasy points earned per team and storing them as another data set that the Agent can use to help make predictions.

    Current architecture diagram:

    I’m also looking heavily into how I can structure all the data more efficiently so there is less infrastructure to manage. Ideally, it would be nice to have a single table with the player as the primary key and all of the subsets of data underneath.

    I think the AI is close to dominating the rest of the league! I will be posting its predictions for next week sometime on Thursday before the game!

  • An AI Fantasy Football Draft Assistant

    Last year I attempted to program a Fantasy Football draft assistant which took live data from ESPN’s Fantasy Platform. Boy was that a mistake…

    First of all, shame on ESPN for not having an API for their Fantasy sports applications. The reverse engineered methods were not fast enough nor were they reliable. So, this year I took a new approach to building out a system for getting draft pick recommendations for my team.

    I also wanted to put to use the example architecture and code I wrote the other day for the Strands SDK to work so I utilized it to build an API which would utilize the AWS Bedrock platform to analyze data and and ultimately return the best possible picks.

    Here is a simple workflow of how the tool works:

    I generated this with Claude AI. It is pretty OK.

    The first problem I encountered was getting data. I needed two things:
    1. Historical data for players
    2. Projected fantasy data for the upcoming season

    The historical data provides information about the players past season and the projections are for the upcoming season, obviously. The projections are useful because of any incoming rookies.

    In the repository I link below I put a scripts to scrape FantasyPros for both the historical and projected data. It stores them in separate files in case you want to utilize them in a different way. There is also a script to combine them into one data source and ultimately load them into a DynamoDB table.

    The most important piece of the puzzle was actually simulating the draft. I needed to create a program that would be able to track the other team’s draft picks as well as give me the suggestions and track my teams picks. This is the heart of the repository and I will be using it to get suggestions and track the draft for this coming season.

    Through the application, when you issue the “next” command the application will send a request to the API with the current situation of the draft. The payload looks like this:

    payload = {
            "team_needs": team_needs,
            "your_roster": your_roster,
            "already_drafted": all_drafted_players,
            "scoring_format": self.session.scoring_format if self.session else "ppr",
            "league_size": self.session.league_size if self.session else 12
        }

    The “team_needs” key represents the current number of players remaining for each position. The “your_roster” position is all of the current players on my team. The other important key is “already_drafted”. This key sends all of the drafted players to the AI agent so it knows who NOT to recommend.

    The application goes through all of the picks and you are able to manually enter each of the other teams picks until the draft is complete.

    I’ll post an update after my draft on August 24th with the team I end up with! I still will probably lose in my league but this was fun to build. I hope to add in some sort of week-to-week management of my team as well as a trade analysis tool in the future. It would also be cool to add in some sort of analysis that could send updates to my Slack or Discord.

    If you have other ideas message me on any platform you can find me on!

    GitHub: https://github.com/avansledright/fantasy-football-agent

  • Deploying a Strands Agent on AWS Lambda using Terraform

    Recently I’ve been exploring the AI space a lot more as I’m sure a lot of you are doing as well. I’ve been looking at the Strands Agent SDK. I see this SDK as being very helpful in building out agents in the future (follow the blog to see what I come up with!).

    One thing that is not included in the SDK is the ability to deploy with Terraform. The SDK includes examples of how to package and deploy with Amazon Web Services CDK so I adapted that to utilize Terraform.

    I took my adaptation a step further and added an API Gateway layer so that you have the beginnings of a very simple AI agent deployed with the Strands SDK.

    Check out the code here: https://github.com/avansledright/terraform-strands-agent-api

    The code in the repository is fairly simple and includes everything you need to build an API Gateway, Lambda function, and some other useful resources just to help out.

    The key to all of this is packaging the required dependencies inside of the Lambda Layer. Without this the function will not work.

    File structure:
    terraform-strands-agent-api/
    └── lambda_code/
    │ ├── lambda_function.py # Your Strands agent logic
    │ └── requirements.txt # strands-agents + dependencies
    ├── api_gateway.tf # API Gateway configuration
    ├── iam.tf # IAM roles and policies
    ├── lambda.tf # Lambda function setup
    ├── locals.tf # Environment variables
    ├── logs.tf # CloudWatch logging
    ├── s3.tf # Deployment artifacts
    ├── variables.tf # Configurable inputs
    └── outputs.tf # API endpoints and resource IDs

    You shouldn’t have to change much in any of these files until you want to fully start customizing the actual functionality of the agent.

    To get started follow the instructions below!

    git clone https://github.com/avansledright/terraform-strands-agent-api
cd terraform-strands-agent-api

# Configure your settings. Add other values as needed
echo 'aws_region = "us-west-2"' > terraform.tfvars

# Deploy everything
terraform init
terraform plan
terraform apply

    If everything goes as planned you should see the output of a curl command which will give you the ability to test the demo code.

    If you run into any issues feel free to let me know! I’d be happy to help you get this up and running.

    Github

    If this has helped you in any way, please share it on your social media and with any of your friends!

  • Automating Proper Terraform Formatting using Git Pre-Hooks

    I’ve noticed lately that a lot of Terraform is formatted differently. Some developers utilize two indents, others one indent. As long as the Terraform as functional most people overlook the formatting of their infrastructure as code files.

    Personally I don’t think we should ever push messy code into our repositories. How could we solve this problem? Well, Terraform has a built in formatter the terraform fmt command will automatically format your code.

    #!/usr/bin/env bash

# Initialize variables
EXIT_CODE=0
AFFECTED_FILES=()

# Detect OS for cross-platform compatibility
OS=$(uname -s)
IS_WINDOWS=false
if [[ "$OS" == MINGW* ]] || [[ "$OS" == CYGWIN* ]] || [[ "$OS" == MSYS* ]]; then
    IS_WINDOWS=true
fi

# Find all .tf files - cross-platform compatible method
if [ "$IS_WINDOWS" = true ]; then
    # For Windows using Git Bash
    TF_FILES=$(find . -type f -name "*.tf" -not -path "*/\\.*" | sed 's/\\/\//g')
else
    # For Linux/Mac
    TF_FILES=$(find . -type f -name "*.tf" -not -path "*/\.*")
fi

# Check each file individually for better reporting
for file in $TF_FILES; do
    # Get the directory of the file
    dir=$(dirname "$file")
    filename=$(basename "$file")
    
    # Run terraform fmt check on the specific file - handle both OS formats
    terraform -chdir="$dir" fmt -check "$filename" >/dev/null 2>&1
    
    # If format check fails, record the file
    if [ $? -ne 0 ]; then
        AFFECTED_FILES+=("$file")
        EXIT_CODE=1
    fi
done

# If any files need formatting, list them and exit with error
if [ $EXIT_CODE -ne 0 ]; then
    echo "Error: The following Terraform files need formatting:"
    for file in "${AFFECTED_FILES[@]}"; do
        echo " - $file"
    done
    echo ""
    echo "Please run the following command to format these files:"
    echo "terraform fmt -recursive"
    exit 1
fi

echo "All Terraform files are properly formatted"
exit 0

    Put this code inside your “.git/hooks/” directory so that it automatically runs when someone does a push. If there is badly formatted Terraform you should see something like:

    Running Terraform format check...
Error: The following Terraform files need formatting:
  - ./main.tf

Please run the following command to format these files:
terraform fmt -recursive
error: failed to push some refs to 'github.com:avansledright/terraform-fmt-pre-hook.git'

    After running the <code>terraform fmt -recursive</code> it should push successfully!

    If this was helpful to your or your team please share it across your social media!

    YouTube video of this script in action

  • Building a Python Script to Export WordPress Posts: A Step-by-Step Database to CSV Guide

    Today, I want to share a Python script I’ve been using to extract blog posts from WordPress databases. Whether you’re planning to migrate your content, create backups, or analyze your blog posts, this tool makes it straightforward to pull your content into a CSV file.

    I originally created this script when I needed to analyze my blog’s content patterns, but it’s proven useful for various other purposes. Let’s dive into how you can use it yourself.

    Prerequisites

    Before we start, you’ll need a few things set up on your system:

    • Python 3.x installed on your machine
    • Access to your WordPress database credentials
    • Basic familiarity with running Python scripts

    Setting Up Your Environment

    First, you’ll need to install the required Python packages. Open your terminal and run:

    pip install mysql-connector-python pandas python-dotenv

    Next, create a file named .env in your project directory. This will store your database credentials securely:

    DB_HOST=your_database_host
DB_USERNAME=your_database_username
DB_PASS=your_database_password
DB_NAME=your_database_name
DB_PREFIX=wp  # Usually 'wp' unless you changed it during installation

    The Script in Action

    The script is pretty straightforward – it connects to your WordPress database, fetches all published posts, and saves them to a CSV file. Here’s what happens under the hood:

    • Loads environment variables from your .env file
    • Establishes a secure connection to your WordPress database
    • Executes a SQL query to fetch all published posts
    • Converts the results to a pandas DataFrame
    • Saves everything to a CSV file named ‘wordpress_blog_posts.csv’

    Running the script is as simple as:

    python main.py

    Security Considerations

    A quick but important note about security: never commit your .env file to version control. I’ve made this mistake before, and trust me, you don’t want your database credentials floating around in your Git history. Add .env to your .gitignore file right away.

    Potential Use Cases

    I wrote this script to feed my posts to AI to help with SEO optimization and also help with writing content for my other businesses. Here are some other ways I’ve found this script useful:

    • Creating offline backups of blog content
    • Analyzing post patterns and content strategy
    • Preparing content for migration to other platforms
    • Generating content reports

    Room for Improvement

    The script is intentionally simple, but there’s plenty of room for enhancement. You might want to add:

    • Support for extracting post meta data
    • Category and tag information
    • Featured image URLs
    • Comment data

    Wrapping Up

    This tool has saved me countless hours of manual work, and I hope it can do the same for you. Feel free to grab the code from my GitHub repository and adapt it to your needs. If you run into any issues or have ideas for improvements, drop a comment below.

    Happy coding!

    Get the code on GitHub