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KlarifAi

Devpost

Inspiration

The idea for KlarifAi came from a problem we experienced ourselves while studying for standardized exams like the SAT. We frequently used ChatGPT to help solve math problems, but we found ourselves spending a lot of time manually describing equations, diagrams, graphs, and problem statements. For visual questions in particular, explaining the context accurately was often more work than solving the problem itself. We started asking a simple question: why should students have to translate what they see into text for an AI to understand it? What if an AI could see the problem directly and explain it the same way a tutor sitting next to you would? That question became the foundation for KlarifAi.

What it does

KlarifAi is a desktop AI assistant that analyzes what's on your screen and provides visual, step-by-step explanations for math problems in real time. Instead of copying and pasting questions or manually describing diagrams, users can simply have the problem visible on their screen. KlarifAi identifies the relevant mathematical content, understands the context, and generates guided explanations that break the solution into manageable steps. The goal is not just to provide answers, but to help students understand the reasoning behind them through visual and interactive guidance.

How we built it

At the core of KlarifAi is a multi-step AI workflow designed to replicate how a human tutor approaches a problem. First, the system captures and analyzes the user's screen, identifying the relevant mathematical content and surrounding context. That information is stored and managed through a memory layer that preserves important details throughout the interaction. Next, KlarifAi classifies the user's intent—whether they need a full solution, a hint, conceptual clarification, or a step-by-step walkthrough. Based on that intent, the system performs targeted research and gathers additional context that may help explain the underlying concepts. Finally, all of this information—the screen analysis, conversation memory, user intent, research results, and visual context—is combined into a single reasoning request. The final AI call synthesizes everything into a structured explanation that is then rendered visually on screen for the user. Rather than relying on a single prompt, KlarifAi uses an orchestrated pipeline that separates observation, memory management, research, reasoning, and presentation into a unified workflow.

Challenges we ran into

One of our biggest challenges was reliably understanding complex mathematical content from screenshots. SAT-style problems often include graphs, geometric diagrams, tables, and formatting that can be difficult for AI systems to interpret correctly. Another challenge was balancing accuracy with usability. We wanted explanations that were detailed enough to teach concepts without overwhelming students with unnecessary information. We also spent significant time optimizing the user experience so that assistance felt immediate and seamless rather than requiring multiple manual steps.

Accomplishments we're proud of

We're proud that we transformed a frustration we personally experienced into a working product that feels natural to use. Instead of forcing students to rewrite questions for an AI, KlarifAi allows them to get help directly from what they already have on screen. We're especially proud of the quality of the visual step-by-step explanations and how quickly users can move from confusion to understanding. Most importantly, we built a tool that focuses on learning and comprehension rather than simply generating answers.

What we learned

Building KlarifAi taught us that the biggest barriers to learning are often not the concepts themselves, but the friction involved in accessing help. We learned how important visual context is for educational AI applications and how much information can be lost when students are forced to convert diagrams and equations into plain text. On the technical side, we gained experience integrating computer vision, multimodal AI systems, desktop application development, and user-centered design into a single product.

What's next

Our vision is to expand KlarifAi into a universal learning companion that can assist students across a wide range of subjects, not just mathematics. Future plans include: Support for science, physics, and engineering problems. More interactive visual explanations and tutoring experiences. Personalized learning paths based on student strengths and weaknesses. Improved real-time screen understanding and context awareness. Collaboration features for study groups and classrooms. Ultimately, we want KlarifAi to make high-quality, personalized tutoring accessible to anyone with a computer, turning every screen into an opportunity to learn.

Analysis

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Technology

Found in codeClaimed only
  • AnthropicIn code
  • CSSIn code
  • HTMLIn code
  • JavaScriptIn code
  • ReactIn code
  • TypeScriptIn code
  • RedisClaimed

6 of 7 appear in the indexed code. 1 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

  • CursorCommits

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

709 KB

Source files

147

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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