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Bite Size Notes

· 2 min read
Philip Chung
Philip Chung
AI Enthusiast | Software Developer

This fun personal project i started with provided me tons of benefits, I call it Bite-Size Notes! 🍪

It started off as an end user, my initial thoughts was how can i maximize my time as i absorb knowledge from tutorial videos or live conferences without missing the important details?

The solution was a personal notetaker that can listen alongside, take notes and summarize it for me. Whether i am participating at a live event or live only stream, the assistant is readily available to note everything down.

I proceeded to blueprint and spec the architecture with my rocket book on what i need to achieve this product.

Data is very important to me so i took the local-first approach, but of course model provider agnostic option being available. Windows first Mac later.

The full python only stack: Summarization Model - Qwen3-4b-GGUF (Local) Transcription Model - Whisper GGUF (Local) PySide6 - GUI Faster-Whisper - Transcribe engine Llama-cpp - LLM engine Huggingface - Model downloading json - Session files stored in computer

The MVP in a nutshell

  1. Start a new session
  2. Start recording
  3. Whisper captures transcripts live while you take notes on the side
  4. Qwen captures array of transcript and your notes then applies a system prompt to get a deterministic output tailored for meetings
  5. Export notes as txt, profit.
  6. Retains with json files stored in your local appdata/roaming to represent sessions
  7. Ability to download the transcripts if you don't like your local summarization model

First customer was my girlfriend and bias or not she loved it, as she can use it for school or work meetings to focus on what matters most, the lectures and dialogs.

No scale needed, the app sits in your computer but is limited to your hardware specs of course, unless you decide to refactor to use open ai spec.

Anyway i solved my problem with automating notetaking at conferences and tutorials; it was a fun learning experience. Shipping as a executable is the next step!

Reference project: https://lnkd.in/gz9S9vka

hashtag#Learning hashtag#SpeechToText hashtag#Summarization hashtag#LocalFirst hashtag#AutomateMyLife hashtag#AI