A Weekend Project: Snap a Problem, Get a Guided Solution

A Weekend Project: Snap a Problem, Get a Guided Solution

Some weekend projects start with a very small annoyance.

For me, the annoyance was input. A lot of AI study tools assume the student will type a clean question into a chat box. But real homework is often sitting on paper, in a textbook, or across a few messy photos. Equations are hard to type. Diagrams are harder. Multi-part questions are easy to lose when everything is converted into plain text too early.

So I tried building a small flow around a simpler starting point: take a photo first, then let the system turn that into a guided explanation.

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The Basic Idea

The prototype is intentionally straightforward:

• capture a homework problem with the camera
• extract the question from the image
• identify the subject and problem type
• send it through a reasoning workflow
• return an explanation that is easier to follow than a raw answer

That last point matters. A final answer is useful, but it is rarely enough. If the student cannot see why the answer works, the tool has only saved time in the short term.

The more interesting challenge is making the output feel like a guided solution rather than a magic result.

Image Input Is Messier Than It Looks

At first glance, "snap a problem" sounds like the easy part. In practice, it is where a lot of the important work begins.

A photo might contain printed text, handwriting, a diagram, margin notes, or several questions on the same page. Sometimes the useful information is not the largest text on the image. Sometimes the diagram carries half the meaning. Sometimes the student captures two pages because the context is split.

That pushed the workflow toward a few early steps:

• cleanly read text from the image
• preserve math notation where possible
• keep diagrams and layout in mind
• detect whether the problem is math, physics, chemistry, or another subject
• avoid treating uncertain extraction as certain

OCR is part of the stack, but it is not the whole stack. The model needs enough context to reason, not just a pile of recognized characters.

Routing Before Reasoning

One thing I wanted to avoid was sending every problem through the exact same prompt.

A geometry proof, a quadratic equation, and a physics word problem all need different handling. Even if an LLM can technically answer all three, the explanation is better when the system knows what kind of problem it is dealing with.

So the workflow includes a light routing step. The tool tries to understand the subject and shape of the problem before deciding how to ask for a solution.

For a student-facing app, this can make the difference between:

• "Here is the answer"
• "Here is the method that applies, and here is why"

The second version is the one I care about.

Why I Tried Multiple Solvers

Another experiment was to let more than one model or solver look at the same problem.

This is not because more models automatically mean a better product. It is because comparison can be useful in learning. If two solutions reach the same result using different paths, the student gets a wider view of the concept. If the solutions disagree, that disagreement is a signal to slow down and inspect the reasoning.

For example, one approach might solve an algebra problem by factoring. Another might use the quadratic formula. Both can be valid, but one may click faster for a particular learner.

In that sense, multiple answers are not just a reliability feature. They can become part of the learning interface.

Keeping The Output Useful

The biggest product temptation is to optimize for speed alone.

Fast answers are satisfying, but they can also flatten the learning process. I found myself coming back to a simple rule: the explanation should be the main object on the screen, not an afterthought under the answer.

That means the response should usually include:

• the key concept being used
• the reason for the first step
• intermediate steps with enough detail
• a short final answer
• notes about common mistakes when relevant

For simple problems, the explanation should not be bloated. For harder ones, it should not skip the part where students usually get stuck. That balance is harder than it sounds.

Multi-Photo Context

Multi-image support became more important than I expected.

Students do not always have one neat problem in one neat image. A worksheet can span several pages. A diagram can be on one page while the questions are on the next. A long word problem can have givens scattered across the page.

If each image is solved independently, the student has to reconnect the logic manually. A better approach is to treat multiple images as one context and let the reasoning step work across the full set.

This feels like a small UX feature, but it changes the quality of the solution. The system can keep variables consistent, use earlier information later, and explain the problem as one connected task.

What I Learned From The Weekend Build

The main thing I learned is that the hard part is not simply calling an AI model.

The hard part is building the path around it:

• a low-friction input
• enough structure before reasoning
• subject-aware routing
• clear explanations
• comparison when it helps
• restraint when a concise answer is better

That path is what turns a raw model response into something closer to a study tool.

Closing Note

I still think AI homework tools should be designed carefully. They can easily become answer machines. But they can also help students get unstuck, especially when the interface makes the reasoning visible.

For this weekend project, the most useful design principle was simple: start where the student starts.

Sometimes that is not a prompt box. Sometimes it is just a photo of the problem in front of them.