DEV Community: Scott Antwi

Building CredVex: a full-stack fintech system

Scott Antwi — Tue, 31 Mar 2026 17:55:59 +0000

I’ve been working on CredVex, a savings and lending platform designed to help users save, borrow, and manage money digitally in a simple way.

It’s built as a full backend + frontend system with a modular architecture covering real fintech use cases like:

💰 Wallet system (deposit, withdraw, transactions)
🏦 Savings plans (create, fund, break savings goals)
🤝 Group savings (ajo/susu-style contributions)
📊 Lending system (loan application, approval, repayment, credit score)
🎯 Challenges & gamification (badges, streaks, leaderboards)
🔔 Notifications + referrals system
💸 Payment processing layer (integration-ready for mobile money & disbursement flows)

The backend is structured as a versioned API (/api/v1) with clearly separated modules for authentication, wallet, savings, lending, and group finance.

This is still an ongoing build, but it’s already a complete end-to-end system in development, and I’m now refining it for real-world integrations like mobile money (MoMo) and production security layers.

📌 Tech focus: Full-stack fintech architecture, API design, system thinking

I’m building this step by step, not as a demo, but as a real product that can scale.

Open to feedbacks and ideas on scaling fintech systems in Africa.

#AfricaTech

🎓 ScholarFinder: Bridging the Opportunity Gap for Students in Crisis

Scott Antwi — Sat, 28 Feb 2026 00:56:14 +0000

An AI-powered platform helping students in underserved regions discover scholarships and global education opportunities in one place.

🌍 The Community I Serve

Growing up in northern Ghana, I saw firsthand how instability and limited resources can interrupt education. Many talented students are not held back by ability, but by access — access to scholarships, mentorship, and reliable information about global opportunities.

I built ScholarFinder for students across Africa and other underserved regions who are trying to navigate education systems without guidance or centralized resources. For many students, the challenge is not intelligence or ambition; it is simply finding the right opportunity at the right time.

❗ The Problem

Scholarship and academic opportunity information is scattered across hundreds of websites. Many platforms are difficult to navigate, hidden behind paywalls, or poorly optimized for low-bandwidth environments.

As a result, students often miss life-changing opportunities because:

information is fragmented,
deadlines are hard to track,
application requirements are confusing,
and reliable guidance is limited.

Opportunity exists — but access does not.

🚀 What I Built

ScholarFinder is a web platform that centralizes global academic opportunities into one accessible system designed for students in resource-constrained environments.

Key Features

📚 Opportunity Database: 485 scholarships, 184 universities, and 138 global programs
🤖 AI-assisted discovery to help students find relevant opportunities faster
✅ Application tracking support to help manage requirements and deadlines
🌍 Visa and test preparation guidance for exams like IELTS and SAT
💰 Cost-of-living comparisons across 51 cities to help students plan realistically

⚙️ Technical Execution

Backend: Python + Flask

Hosting: PythonAnywhere

Database: Structured datasets of scholarships, universities, and opportunities

Design Goal: Lightweight architecture optimized for mobile devices and low-bandwidth internet access.

The platform prioritizes simplicity, speed, and accessibility so students can use it even with unstable connectivity.

❤️ Why It Matters

ScholarFinder is built around a simple belief:

Opportunity should not depend on geography or privilege.

Students in many parts of the world spend more time searching for opportunities than preparing for them. By reducing information barriers, ScholarFinder helps students focus on learning, growth, and applying with confidence.

The goal is simple: make global education opportunities visible and reachable for communities that are often overlooked.

🎥 Demo

🌍https://scholarfinder.pythonanywhere.com/

GitHub:

https://github.com/ScottT2-spec/scholar-finder-web

🔮 What’s Next

Future improvements include:

personalized AI recommendations,
expanded scholarship datasets,
improved search intelligence,
and enhanced accessibility features.

Thank you to the DEV community for encouraging builders to create technology that serves real people and real needs.

devchallenge #weekendchallenge #webdev

I'm 17, I Have No Laptop, and I Just Built a Neural Network from Scratch

Scott Antwi — Mon, 09 Feb 2026 05:04:23 +0000

I Built a Neural Network from Scratch on My Phone — No Laptop, No Frameworks

I'm a high school student in Ghana. I don't have a laptop. Everything I code, I do on my phone using Google Colab.

Yesterday, I built a neural network from scratch — just Python and NumPy. No TensorFlow. No PyTorch. Every piece written by hand: forward pass, backpropagation, gradient descent. It recognizes handwritten digits with 96% accuracy.

Here's how.

The Setup

I opened Google Colab on my phone browser, loaded the MNIST dataset (60,000 handwritten digit images), and started coding.

The network has three layers:

Input: 784 neurons (each pixel of a 28x28 image)
Hidden: 64 neurons with sigmoid activation
Output: 10 neurons (one for each digit 0-9)

The Hard Part

Writing backpropagation by hand. When the network makes a wrong prediction, you need to figure out which weights caused the error and adjust them. The math isn't complicated — it's chain rule from calculus — but implementing it without a framework means you understand exactly what's happening.

No model.fit(). No magic. Just matrix multiplication and derivatives.

The Results

After 20 passes through the training data:

95.75% accuracy on test data
Correctly identifies most digits on the first try
Struggles most with 5s and 8s (they look similar even to humans)

I submitted my predictions to the Kaggle Digit Recognizer competition and got a score of 0.947.

What I Actually Learned

Before this project, I could tell you "neural networks learn from data." After building one from scratch, I can tell you exactly HOW:

Data goes in, gets multiplied by weights, passes through an activation function
The output gets compared to the correct answer
The error flows backward through the network (backpropagation)
Each weight gets adjusted slightly to reduce the error
Repeat 60,000 times

That's it. That's deep learning at its core. Frameworks hide this behind one line of code. Building it yourself means you actually get it.

Why I Did This

I'm applying to study AI at university. I wanted my GitHub to show that I understand the fundamentals — not just that I can call an API.

I also built a Telegram bot called ScholarFinder that helps students find fully-funded scholarships. 50+ scholarships searchable by level, field, and region. If you're a student looking for funding, try it.

The Code

Everything is on GitHub: mnist-neural-network

It's one Python file. No dependencies beyond NumPy. If you want to understand how neural networks work under the hood, read it.

What's Next

Adding ReLU activation to improve accuracy
Building a second hidden layer
Trying convolutional neural networks (still from scratch)
Improving my Kaggle ranking

If I can build this on a phone in Ghana with no laptop, you can build it too. Stop watching tutorials. Open a notebook and start.

Find me on GitHub or Kaggle.