The problem Mindify solves

Mindify solves the problem of inefficient and time-consuming brainstorming sessions. When inspiration strikes, people often struggle to capture their ideas quickly and effectively using traditional productivity tools. The process of typing out ideas can be slow and may hinder the natural flow of thoughts, leading to a loss of valuable ideas and connections.

Moreover, once the brainstorming session is complete, users are left with a disorganized collection of notes that require significant effort to structure and refine. This manual process of organizing ideas can be tedious, time-consuming, and may even discourage people from engaging in brainstorming activities altogether.

Mindify addresses these issues by providing a voice-powered brainstorming tool that allows users to speak their ideas freely without the constraints of typing. By simply speaking, users can quickly capture their thoughts and ideas as they occur, ensuring that no valuable insights are lost.

Furthermore, Mindify's intelligent organization feature automatically structures the spoken ideas, saving users the time and effort required to manually organize their notes. This allows users to focus on generating ideas and exploring new connections without worrying about the logistics of organization.

In summary, Mindify solves the problem of inefficient brainstorming by enabling users to capture ideas quickly through voice input and automatically organizing those ideas, streamlining the entire brainstorming process.

Challenges I ran into

1. Live Speech Chunking

Challenge: Extracting coherent ideas from live speech requires parsing overlapping intervals, creating disjoint sentence groups, and assessing similarity via NLP.

Potential Solutions:

Use techniques like sliding window chunking with overlap to capture context across chunk boundaries.
Apply specialized chunking methods that preserve the structure and hierarchy of the speech content, such as prosodic chunking.
Leverage state-of-the-art speech recognition models like Wav2Vec2 that enable fast, live inference on audio streams 

2. API Rate Limits

Challenge: OpenAI's rate limits necessitate more efficient audio processing and fewer API requests for keyword extraction and embeddings.

Potential Solutions:

Optimize the chunking strategy to minimize the number of API calls needed, e.g., by using larger chunk sizes with some overlap.
Investigate alternative or self-hosted NLP models and libraries that have higher or no rate limits.
Implement caching mechanisms to store and reuse results for repeated or similar requests.

3. Filler Sentences

Challenge: Not every sentence contains a distinct idea. Filler sentences without substance can clutter the idea graph.

Potential Solutions:

Train a classifier to identify and filter out low-information filler sentences.
Use part-of-speech tagging and chunking to extract only the most meaningful phrases.
Allow user control to manually prune or merge nodes in the idea graph.

4. Visualization

Challenge: Mimicking the premium force graph layout requires a better node placement system to declutter the graph.

Potential Solutions:

Investigate open-source alternatives to the React Flow force graph that provide similar functionality.
Develop a custom layout algorithm that optimizes node positioning based on semantic similarity and graph structure.
Provide user controls for manual layout adjustments, collapsing/expanding nodes, and focusing on subgraphs.