Freelancers using Swift in NairobiFreelancers using Swift in Nairobi
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George Wandhe's avatar
George Wandhe
maxNairobi, Kenya
Full-stack software developer. | Replit Services
$50k+
Earned
61x
Hired
4.9
Rating
66
Followers
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Expert

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Hired

+1
Full-stack software developer. | Replit Services
Cover image for IOS Wellness Tracking App Development
IOS Wellness Tracking App Development
1
142
Cover image for Replit App UI/UX Optimization & Performance Enhancement
Replit App UI/UX Optimization & Performance Enhancement
1
133
Cover image for Finanalytics
Finanalytics
2
265
Cover image for QUESTIENT Games
QUESTIENT Games
0
36
Swift(1)
Teddy Omondi's avatar
Teddy Omondi
Nairobi, Kenya
Python & Django Backend Developer | API Developer
New to Contra
Python & Django Backend Developer | API Developer
Cover image for A cross-platform music application designed
A cross-platform music application designed to stream music and display synchronized lyrics across multiple platforms including Android and Windows. The app focuses on smooth playback, responsive UI, and integrated lyrics discovery for an improved listening experience. Built using modern development technologies for performance and usability.
0
15
Cover image for A property and rent management
A property and rent management web application developed to help landlords manage tenants, rent payments, property records, and financial tracking. The system includes tenant management, payment monitoring, authentication, and responsive administrative dashboards. Built with Python, Django, HTML, CSS, JavaScript, and database technologies.
0
7
Cover image for A web-based HR management system
A web-based HR management system designed to streamline employee management, attendance tracking, and administrative workflows. The system includes employee records management, authentication, role-based access, and responsive dashboards for efficient HR operations. Built using Python, Django, HTML, CSS, JavaScript, and SQL technologies.
0
9
Cover image for A Django-based machine learning web
A Django-based machine learning web application designed to identify students at risk of academic failure or dropout using performance analytics and prediction models. Built with Python, Django, scikit-learn, HTML, CSS, and JavaScript.
0
18
Swift(1)