Aaron VanSledright

Category: Cloud Architecting

  • Fantasy Football and AI – Week 7

    Well, our win streak was too good to be true. Unfortunately we lost a close one in week 6. It came down to the Monday night games and Sam Darnold just wasn’t able to get it going over the Texans even though the Seahawks still pulled out the win.

    Our running back group also did not preform well outside of Josh Jacobs. The loss was by a difference of about 7 points so if anyone had put up another touchdown we could have won.

    Anyway, on to week week 8. A few byes to contend with but otherwise most of are starts will be playing. The AI suggested grabbing the Colts defense and kicker as they are playing Tennessee. Breece Hall is currently questionable to play so we will have to keep an eye on that but he has a favorable matchup against the Bengals. The current roster is below.

    I promised to work on MCP this week but have only made a little bit of progress. I’ve been doing a lot of research on doing it in a cost effective manner as this project makes ZERO dollars and so I can’t afford to setup a bunch of expensive infrastructure. SO – this week I worked on combining the waiver table and the stats table into one table so that we can minimize DynamoDB calls throughout the application. The other thing I did was setup DynamoDB streams which are then converted into text files for each player and placed into an S3 bucket. This is what I think will be the first step in setting up a RAG pipeline so that a model can begin to be more “aware” of current NFL and Fantasy Football landscape.

    Here is an updated architecture diagram. You’ll notice the S3 bucket on the right side. This is the eventual start of our knowledgebase.

    You’ll also notice the waiver table removed. The new player structure looks like this:

    {
 "player_id": "George Kittle#TE",
 "espn_player_id": 3040151,
 "player_name": "George Kittle",
 "position": "TE",
 "seasons": {
  "2024": {
   "season_totals": {
    "MISC_FL": 0,
    "MISC_FPTS": 158.6,
    "MISC_FPTS/G": 10.6,
    "MISC_G": 15,
    "MISC_ROST": "99.4%",
    "Player": "George Kittle",
    "Rank": 1,
    "RECEIVING_20+": 21,
    "RECEIVING_LG": 43,
    "RECEIVING_REC": 78,
    "RECEIVING_TD": 8,
    "RECEIVING_TGT": 94,
    "RECEIVING_Y/R": 14.2,
    "RECEIVING_YDS": 1106,
    "RUSHING_ATT": 0,
    "RUSHING_TD": 0,
    "RUSHING_YDS": 0
   },
   "weekly_stats": {
    "1": {
     "fantasy_points": 4,
     "opponent": "NYJ"
    },
    "2": {
     "fantasy_points": 13.6,
     "opponent": "MIN"
    },
    "4": {
     "fantasy_points": 10.5,
     "opponent": "NE"
    },
    "5": {
     "fantasy_points": 12.4,
     "opponent": "ARI"
    },
    "6": {
     "fantasy_points": 17.8,
     "opponent": "SEA"
    },
    "7": {
     "fantasy_points": 9.2,
     "opponent": "KC"
    },
    "8": {
     "fantasy_points": 18.8,
     "opponent": "DAL"
    },
    "10": {
     "fantasy_points": 11.7,
     "opponent": "TB"
    },
    "12": {
     "fantasy_points": 14.2,
     "opponent": "GB"
    },
    "13": {
     "fantasy_points": 0.7,
     "opponent": "BUF"
    },
    "14": {
     "fantasy_points": 15.1,
     "opponent": "CHI"
    },
    "15": {
     "fantasy_points": 6.1,
     "opponent": "LA"
    },
    "16": {
     "fantasy_points": 10.6,
     "opponent": "MIA"
    },
    "17": {
     "fantasy_points": 11.2,
     "opponent": "DET"
    },
    "18": {
     "fantasy_points": 2.7,
     "opponent": "ARI"
    }
   }
  },
  "2025": {
   "injury_status": "ACTIVE",
   "jersey_number": "85",
   "percent_owned": 98.97,
   "pro_team_id": 25,
   "season_projections": {
    "MISC_FL": 0.5,
    "MISC_FPTS": 147.6,
    "RECEIVING_REC": 76,
    "RECEIVING_TD": 7.5,
    "RECEIVING_YDS": 1036.9
   },
   "team": "SF",
   "weekly_outlooks": {
    "1": "George Kittle is healthy and wealthy for the 49ers' Week 1 matchup against Seattle after signing a big four-year contract extension in the offseason. Kittle's role as a pass catcher should be intensified early on with WR Brandon Aiyuk (ACL) on the PUP list to begin the campaign and Jauan Jennings (calf, contract) uncertain to suit up against the Seahawks. Kittle is coming off a 78-catch, 1,106-yard, eight-TD 2024 campaign, further cementing his place as one of the NFL's elite producers at tight end. The Seahawks were middle-of-the-pack against the position last year, giving up an average of 51.5 receiving yards per game.",
    "2": "George Kittle won't play in San Francisco's Week 2 matchup against New Orleans due to a hamstring injury that landed him on IR. Luke Farrell and Jake Tonges, who caught a TD in Kittle's absence last week against the Seahawks, will be asked to step in at tight end for the 49ers.",
    "3": "George Kittle will miss his second straight game for the 49ers in Week 3 against the Cardinals while he remains on IR due to a hamstring injury. Jake Tonges and Luke Farrell should continue to hold down the fort at TE for Kittle until the latter is able to return. Kittle won't be eligible to suit up again until Week 6."
   },
   "weekly_projections": {
    "5": 12.7,
    "6": 13.1,
    "7": 13.4,
    "8": 11.7,
    "10": 14.6,
    "12": 11.6,
    "13": 13.2,
    "14": 13.4,
    "15": 14.1,
    "16": 14.1,
    "17": 14.5
   },
   "weekly_stats": {
    "1": {
     "fantasy_points": 12.5,
     "opponent": "SEA",
     "team": "SF",
     "updated_at": "2025-10-15T17:40:58.625370"
    },
    "2": {
     "fantasy_points": 12.5,
     "opponent": "NO",
     "team": "SF",
     "updated_at": "2025-09-16T17:08:05.179797"
    },
    "3": {
     "fantasy_points": 12.5,
     "opponent": "ARI",
     "team": "SF",
     "updated_at": "2025-09-23T15:00:13.907272"
    },
    "4": {
     "fantasy_points": 12.5,
     "opponent": "JAX",
     "team": "SF",
     "updated_at": "2025-09-30T15:00:14.035733"
    },
    "5": {
     "fantasy_points": 12.5,
     "opponent": "LAR",
     "team": "SF",
     "updated_at": "2025-10-07T15:00:13.665217"
    },
    "6": {
     "fantasy_points": 12.5,
     "opponent": "TB",
     "team": "SF",
     "updated_at": "2025-10-14T15:00:14.748804"
    }
   }
  }
 },
 "updated_at": "2025-10-22T18:11:05.039158"
}

    I hope to continue to refine this so that it can be used for future seasons. Then we can continue to use the bot into 2026’s season.

    Anyway, hopefully I can figure out MCP and the knowledgebase this week. Winter is coming so its time to hunker down and build AWS Architectures!

  • 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!

  • 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!

  • Fantasy Football and AI Week 1

    Fantasy Football and AI Week 1

    The last you heard from me I was building a drafting agent for Fantasy Football. Well, the draft has finished and my roster is set. Behold, the Fantasy Football AI drafted team for my 8 team, PPR league.

    STARTING LINEUP
    QB – Josh Allen (BUF)
    RB – Saquon Barkley (PHI)
    RB – Josh Jacobs (GB)
    WR – Terry McLaurin (WSH)
    WR – DJ Moore (CHI)
    TE – George Kittle (SF)
    FLEX – Breece Hall (NYJ)
    OP – Sam Darnold (SEA)
    D/ST – Lions (DET)
    K – Chase McLaughlin (TB)
    BENCH
    WR – DK Metcalf (PIT)
    WR – Marvin Harrison Jr. (ARI)
    TE – Evan Engram (DEN)
    RB – Aaron Jones Sr. (MIN)
    WR – Cooper Kupp (SEA)
    QB – J.J. McCarthy (MIN)
    WR – Keenan Allen (LAC)
    RB – Travis Etienne Jr. (JAX)

    Now, I have also added another feature to the overall solution which is to include a week to week manager I’m calling the “coach”. I built a database that contains the 2024 statistics for each player and who they played against. I’m also scraping FantasyPros.com as well for future projections still.

    I added a new Lambda function and API call to my architecture so that I can send a request to the AI Agent to build out my ideal weekly roster.

    The roster I posted above is what I will be starting for week 1. The agent also provides some context as to why it selected each player.

    🏈 Fantasy Lineup for Team 1, Week 1
==================================================

🏆 STARTING LINEUP:
    QB: Josh Allen             (BUF) 23.4 pts
    RB: Saquon Barkley         (PHI) 19.9 pts
    RB: Josh Jacobs            (GB)  15.5 pts
    WR: Terry McLaurin         (WAS) 11.1 pts
    WR: DJ Moore               (CHI) 12.5 pts
    TE: George Kittle          (SF)  10.6 pts
  FLEX: Breece Hall            (NYJ) 11.5 pts
    OP: Sam Darnold            (SEA) 18.6 pts
     K: Chase McLaughlin       (TB)   8.5 pts
   DST: Lions                  (DET)  9.2 pts

  💯 TOTAL PROJECTED: 145.4 points

📋 BENCH (Top 5):
    WR: Keenan Allen            7.6 pts
    WR: DK Metcalf              8.6 pts
    WR: Marvin Harrison Jr.     9.5 pts
    WR: Cooper Kupp             9.0 pts
    RB: Aaron Jones Sr.        12.8 pts

💡 COACH ANALYSIS:
==================================================
Made one key adjustment to the computed lineup: replaced
  Keenan Allen with DJ Moore at WR2. While Allen showed
  decent recent form (9.325 avg last 4 games), DJ Moore is
  the higher-drafted talent with WR1 upside who should be
  prioritized in Week 1. Moore's lack of 2024 data likely
  indicates injury, but his talent level and role in
  Chicago's offense make him the better play. The rest of
  the lineup is solid: Allen/Darnold QB combo maximizes
  ceiling, Barkley/Jacobs/Hall provide strong RB production,
  McLaurin offers consistency at WR1, and Kittle remains a
  reliable TE1. Lions DST should perform well at home, and
  McLaughlin provides steady kicking in Tampa Bay's offense.

==================================================

    There are still some gaps I need to fill with the data set as D.J. Moore did play in 2024 so I’m likely missing some data sets. I also have plans to build a “general manager” who can scan available players and find maybe some hidden gems on a week to week basis.

    Finally, command line tools are fun but, I think the solution needs a web interface so watch for updates on that. The coach will inevitably automated and send me a report on the weeks performance as well as suggestions for the following week.

    If you like Fantasy Football and technology follow along to see how this team performs throughout the season!

    All the code is available here on GitHub: https://github.com/avansledright/fantasy-football-agent

  • 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!

  • Creating a List of API Gateway resources using Terraform

    For some reason when you utilize Terraform with AWS, specifically when you want to get a list of API Gateway resources, that data element simply does not exist. Below is a relatively quick solution that will create a comma separated list of API Gateways so that you can iterate through them.

    In order to execute this element you need to have the AWS CLI setup within your preferred deployment method. Personally, I love GitHub Actions so I needed to add another stage in my deployment to install the CLI.

    The way this work is to create a data element that you can trigger as needed to execute a simple shell script.

    data "external" "apis" {
   program = ["sh", "-c", "aws apigateway get-rest-apis    --query 'items[?starts_with(name,`${var.prefix}`)].name' --output json | jq -r '{\"names\": (. | join(\",\"))}'"]
}

    We also are creating a variable called “prefix” so that you can filter as required by your project. Personally, I used this to create Cloudwatch Dashboards so I can easily monitor my resources.

    If this is helpful for you, please share it on your social media!

  • Building out a reusable Terraform framework for Flask Applications

    I find myself utilizing the same architecture for deploying demo applications on the great Python library Flask. I’ve been using the same Terraform files over and over again to build out the infrastructure.

    Last weekend I decided it was time to build a reusable framework for deploying these applications. So, I began building out the repository. The purpose of this repository is to give myself a jumping off point to quickly deploy applications for demonstrations or live environments.

    Let’s take a look at the features:

    • Customizable Environments within Terraform for managing the infrastructure across your development and production environments
    • Modules for:
      • Application Load Balancer
      • Elastic Container registry
      • Elastic Container Service
      • VPC & Networking components
    • Dockerfile and Docker Compose file for launching and building the application
    • Demo code for the Flask application
    • Automated build and deploy for the container upon code changes

    This module is built for any developer who wants to get started quickly and deploy applications fast. Using this framework will allow you to speed up your development time by being able to focus solely on the application rather than the infrastructure.

    Upcoming features:

    • CI/CD features using either GitHub Actions or Amazon Web Services like CodePipeline and Codebuild
    • Custom Domain Name support for your application

    If there are other features you would like to see me add shoot me a message anytime!

    Check out the repository here:
    https://github.com/avansledright/terraform-flask-module