Skid-Homework: An Open-Source AI Homework Solver

Ever stared at a tricky homework problem and wished you had a helpful nudge in the right direction? Or maybe you’re a developer curious about how AI can be applied to structured problem-solving? Skid-Homework is a new open-source project that tackles exactly that. It’s not about giving away answers, but about building a tool that can understand and work through academic problems step-by-step, powered by AI.

This project is interesting because it takes the raw reasoning capability of large language models (LLMs) and channels it into a focused, educational assistant. It’s a practical look at how we can interface with AI for more than just chat.

What It Does

Skid-Homework is an AI-powered application designed to solve homework problems. You feed it a question—think math, science, or other text-based problems—and it uses an LLM (configured to work with OpenAI or local models) to reason through the solution. The goal is to generate not just a final answer, but a logical breakdown of the steps involved.

It’s built with a simple web interface using Streamlit, making it accessible without needing to run commands in a terminal. Under the hood, it’s a Python project that handles prompt engineering, API communication, and presenting the results cleanly.

Why It’s Cool

The clever part here is in the setup and the potential. It’s architected to be more than a simple ChatGPT wrapper. By focusing on the “homework” domain, the prompts can be tuned to encourage step-by-step reasoning, which is crucial for learning. The project is structured to allow model flexibility, meaning you could potentially point it at a local, open-source LLM if you wanted to keep things private or experiment.

For developers, it’s a great reference for a few things:

• Building a simple AI web app: It’s a clear example of integrating an LLM API into a Streamlit frontend.
• Prompt structuring: You can see how to design prompts for specific types of tasks (problem-solving vs. creative writing).
• Open-source experimentation: Since the code is available, you can fork it, change the logic, or adapt it for your own niche problem-solving tasks, like generating code snippets or debugging.

It’s a focused tool that demonstrates a practical use case for AI, packaged in a way that’s easy to dissect and rebuild.

How to Try It

The quickest way to see it in action is to check out the live demo. The repository README usually has the latest link, but you can often find it deployed via Streamlit Community Cloud.

If you want to run it locally (and you’ll need your own API key for this), it’s a standard Python setup:

git clone https://github.com/cubewhy/skid-homework.git
cd skid-homework
pip install -r requirements.txt

Then, you’ll need to set your OpenAI API key as an environment variable:

export OPENAI_API_KEY='your-key-here'

Finally, run the Streamlit app:

streamlit run app.py

Head to localhost:8501 in your browser, and you’re set.

Final Thoughts

Skid-Homework is a neat, focused project. It’s useful for students looking for study aid concepts, but honestly, it’s even more useful for developers wanting a clean, minimal blueprint for an AI problem-solving app. It avoids unnecessary complexity and shows how a single good idea can be turned into a functional tool quickly.

The open-source nature means its real value might be what you build from it. Could you adapt it to explain error messages? To comment on code structure? The pattern is there. It’s a solid starting point for anyone wanting to tinker with applied AI.

Follow for more interesting projects: @githubprojects