Month: January 2025

Note: This app was never published.

For one of my Master’s of Organizational Leadership courses, we were tasked with evaluating our groups performance as a team.

Evaluating the chat

During the course, we discussed using different methods and metrics to evaluate how well a team is working together. We were also tasked with creating our own set of team evaluation criteria to self evaluate our group. At the beginning of the class we established group communication expectations. One of those expectations was responding to each other’s queries within 48 hours. Because of this, I thought it would be an interesting experiment to create a program to analyze our groups compliance with our established 48 hour norm and have a Large Language Model (LLM) analyze our groups’ collaboration. Because most of our team interaction took place on a Microsoft Team’s chat, I decided to use the Team’s chat log as the content that would be used to evaluate our group.

Extracting the chat and metadata

The most difficult part of this task was actually getting the Team’s chat log into a ChatMessage object format that I could analyze. Microsoft Teams typically only allows an organization admin to export chat logs. To get around this, I developed a macOS app that could take in screenshots of our chat and use Optical Character Recognition (OCR) to extract the text content with high accuracy. Once the text was extracted, I looked for patterns in the extracted text to identify what parts were the message’s content, the date and time of the message, the message’s author, whether an individual was mentioned and whether or not the author was replying to another user. The function I wrote to analyze the extracted String for this information was extremely complex but in the end I was able to extract the metadata from each chat message and put it into a better form for further analysis.

Analyzing the Chat

Now that I had the chat in a format I could work with. I wrote code to determine how many messages were sent by each group member, the average response time between a chat member being mentioned and their next message (a response), and how many times they failed to respond within our group’s established communication expectation of 48 hours. This seemed to work quite well and after manually review, the accuracy of the app determining whether someone failed to respond in 48 hours was quite high.

When it came to analyzing the chat with an LLM. I utilized LLAMA 3, an open-source LLM created by Meta running on a MacBook Pro through Ollama. At first, I realized that the LLM had trouble with keeping up with the contest and learned that their was a ceiling for the amount of tokens (sort of like words) the LLM could accept at once and not lose track of context. To get around this, I ended up feeding the LLM each person’s chat history one at a time and was able to get summaries for each of their contributions. However, I was unable to get summaries of their interactions with others within the chat context as providing everything at once was not possible with this LLM.

Conclusion

This experiment was very unique and is definitely worth revisiting in the future especially as Large Language Models continue to improve.

The CU Athlete app allows for Colorado NCAA Athletes to easily log their fueling station attendance, check their Gold Card balances, RSVP and check in to department events and more! The app is used by every single Colorado student-athlete on every team daily.

Learn about how the app was created to version 3.0 (The current version is version 6) (Previously known as the “CU Buffs Fueling Station App”) (using Archive.org because when learning how to self-host I accidentally deleted my previous WordPress site).

Core Features: Athlete

The app allowed athletes to do the following:

-Check in for meals with one button press.
-RSVP for athletic department events with a single button press.
-Check into athletic department events using a rotating QR code resistant to spoofing (or sharing with friends).
-See their Gold Card (Flatiron Meal Plan) balance.
-Play a Ralphie run mini minigame.
-See the Fueling Station meal times.
-Receive announcements.
-Set a personal CU branded countdown widget for their events.
-Reliable even in situations with unstable or no internet.

Core Features: Staff

The CU Athlete Admin iPad app allowed staff to do the following:

-Create accounts for athletes (including via importing spreadsheets).
-See Fueling Station checkins in real-time and export data to spreadsheets.
-Allows staff to scan athlete’s QR codes for checking into events.
-Schedule athletic department events and see RSVP’s and checkins (and export those to spreadsheets).
-Set the meal times for the fueling station.

The Christmas Trivia TV app was created by me in 2019 and has been updated on an annual basis. The app is an automatic trivia game that cycles through trivia questions relating to Christmas with beautiful backgrounds to go along with. The app is designed for iPad, iPhone, Apple TV, and Mac. The app is best experienced on a large TV and is perfect to throw on for Christmas parties or family gatherings.

The app utilizes a Node.js REST server I self host that has the trivia questions stored in it (students in ATLAS Web are able to query this database server for their Javascript unit).

The app also has an in-app purchase called fireplace mode that puts a virtual fireplace (a looped recording of my own fireplace) on in the background.

Click here to download the app

Made mostly obsolete by the release of Apple Intelligence but was helpful beforehand

ML Image Sorter was an app I designed in 2024 at the request of one of my former High School art teachers. He needed a way to sort through thousands of images in Apple Photos and place them into specific albums using very unique criteria such as the style of the image.

To assist him with his request, I thought that allowing him to train his own Machine Learning models on the specific sorting criteria he desired could help him.

Writing the app

Because he works in Apple Photos and already has a very detailed photo organization strategy, I decided to make a macOS app that could take a custom Image Classification model made in CreateML, find images that match the ML model’s criteria, and copy those photos into new Photos app albums named after the model’s classification. I also made the app compatible with sorting photos into folders if he were to decide to use it outside of the Apple Photos app.

Results

The app itself worked flawlessly. However, it was limited to the performance of the Image Classification Model that the user would manually create in Apple’s CreateML application (included with Xcode). I included instructions for my former teacher on how to create an accurate model by providing good model specimen. However, since he could only include so many examples, some classifications were difficult (especially models that attempted to classify images by very obscure differences). Since my primary audience was only one person, I could have done a better job of making it more visually appealing. That said, I got the app in a good enough state to be published on the Mac App Store.

Since publishing this, the Apple Photos app received some pretty hefty upgrades in the form of natural language searching support which allows for a user to search for more specific inquires. This largely makes my software obsolete. However, at the present moment it is still available on the Mac App Store.

App Store Link: https://apps.apple.com/us/app/ml-image-sorter/id6503262847?mt=12