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About MemoMorph
Inspiration
MemoMorph was inspired by a common student experience: recording lectures and group discussions in pieces.
In practice, students rarely have a complete recording. Instead, they gather voice messages from classmates, partial recordings from their own phones, and clips captured at different times. When exam time arrives, the issue isn’t a lack of information; it’s that everything is scattered, overlapping, and inconsistent.
Most existing tools focus on transcription or summarization, but they assume the input is clean. MemoMorph starts from a different idea: academic audio is naturally messy, and tools should be built for that reality.
What I Learned
Building MemoMorph taught me that working with AI is less about generating answers and more about creating the right structure around uncertainty.
I learned:
- How to design AI-assisted workflows that reveal uncertainty instead of hiding it
- How to translate messy human behavior into clear interaction models
- How important pacing, visual clarity, and trust are when creating tools for students
- That a good demo is not about showing features, but about guiding understanding
Most importantly, I learned that clarity is a design choice, not just a result of using AI.
How I Built the Project
MemoMorph is built as a web-based application focused on rapid iteration and clear user experience:
- Frontend: Vite + React
- ASR: Whisper, served through a lightweight Express backend
- AI Reconstruction: Gemini API, called directly from the frontend
- Core Logic:
- Fragment-level processing
- Detection of overlaps, duplicates, and complementary content
- Context-aware reconstruction for lectures and group discussions
- Fragment-level processing
Instead of providing a single “final answer,” MemoMorph maintains a transparent pipeline:
- Original transcript
- Reconstructed structure
- Clear markers for inferred or conflicting content
This lets students understand how results are formed, not just consume them.
Challenges Faced
One of the biggest challenges was not over-automating.
It was tempting to hide uncertainty and produce something that appeared perfectly clean. However, doing so would break trust, especially in academic contexts.
Other challenges included:
- Designing merge logic that feels intuitive rather than like “AI magic”
- Maintaining a simple interface while managing complex underlying logic
- Fitting a meaningful story into a strict 3-minute demo format
Balancing power, clarity, and honesty was the hardest — and most valuable — part of the project.
Closing Thoughts
MemoMorph is not just a transcription tool. It seeks to rethink how students interact with imperfect information.
By treating fragmentation as a key issue rather than an edge case, MemoMorph helps turn messy academic audio into something students can genuinely study with.
