Hi, I'm Franklin Smith, a software engineer working at a fruit company. I love creating software built on iOS/macOS frameworks and technologies, and the objective-c/swift languages.
Software Engineering
Snowboarding
Contact
(971)-267-9409
Fast Model Sports
Fast Model Sports, Chicago Illinois
Software Engineering Intern
Created, supported, modified, and tested internal company web application. Application provided critical internal
company information on carousel style dashboard to users. This application was directly supervised by company CTO Anthony Schaller and CEO Ross Comerford . Technologies used: MySQL Workbench, Maven, Gradle,
Postman, Restful API procedures, AWS Environment, Google Auth, Localytics API, Tableau, JSON, Tomcat Apache Server,
Jira, Jenkins CI Server, Bit Bucket, Google Visualizations library, HTML, Java 8, Javascript.
Created and engineered all software for iOS application Snowpack in Swift, currently available on apple iTunes app store.
The most user-friendly app that provides real time snowfall conditions, weather reports, and resort updates for skier and
snowboarders, for all U.S resorts within seconds. Ratings and Reviews 5.0 out of 5
Knuckleheads, Portland Oregon
iOS Software Engineering Intern
Designed and built advanced app features for the iOS platform,
Independently crafted project solutions by applying solid Object-Oriented-Design principles
Worked in a team of talented iOS engineers developing amazing native apps.
Worked closely with product management & UX to execute an idea from concept to delivery using excellent software
design, coding, & processes.
Continuously discovered, evaluated, and implemented new technologies to maximize development efficiency.
Co-Programmed with my wonderful classmate Kaitlyn Wright
software for our iOS application Friend Maps in Swift, currently available on the apple iTunes app store. My direct role
in this project included creating, maintaining, and deploying the Firebase Google Cloud Developers Real Time Database Infrastructure. It was my responsiblity to implement the iOS features of user accounts, creating accounts with a profile photo, adding friends,
and sorting this data with 500 Millisecond request response time from the user interface. Kaitlyn played a principal role in the project by programming all of the Map Kit features
and the ability to search and sort for friends/users in the data base from a text input field user request. These features include adding and getting users
location coordinates on the app, connecting with different users. GPS Algorithm design with mapkit geolocation to search and identify businesses. Custom profile,
user, and business annotations. Also integrating the Lyft widget feature which provides users with live ridesharing
price quotes to their midpoint or selected friend/business location destination using the API.
Friend Maps allows you to add your friends to your map network, select them on your map, and choose fun meetup points, locations, and establishments around you, them, or the halfway point between the two of you. Friend Maps provides maps and business directories to users. We packaged together the entire process of ride sharing. Headed to the airport? Date night? Or, uh, fishing? Our built in Lyft api can get you a ride in minutes, wherever you’re headed, with the click of a button.
Arduino Sonar Sensor
Automating Dev Ops with Docker Application Technology Shell Scripts
With an emerging rise of Dev Ops technology like Docker and other application containers comes an underlying challenge that has been plaguing the computer industry for years, how to efficiently learn and use the technology in a timely manner. Most users are tired of long and meaningless online tutorials and videos which shove irrelevant information down the throat of the consumer. I have solved this problem by programming a shell script that automates the dev ops process with docker while allowing the user to interact and choose where, what, and how they would like to learn about the technology. With a computer execution run time of 2-3 minutes, one can now learn to: set up their docker environment; build an image and run as one container; scale their application to run multiple containers; distribute their application across a cluster; stack their services by adding a back end database; and deploy their application to production. Available for download here.
Research Paper
User Installation Guide
iOS Machine Learning Photo Prediction
Created and engineered all software for iOS application Photo Prediction in Swift, currently available on apple iTunes app store.
Photo Prediction is an iOS application the uses ResNet-50 which is a deep residual network, to predict what is being seen by the camera.
Project Title
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