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Quietone – search audio and video by transcript

Hacker News

Quietone – search audio and video by transcript

Hey. This is my first electron app. It is called Quietone and I just thought it would be cool to navigate and manipulate videos by their transcript because searching through videos for the things I was looking for was very slow otherwise. I used whisper.cpp to do the transcribing. It has held up really well. The tiny model (the fastest one) can transcribe 2 hours of video in a couple of minutes on my m2 mac mini and the accurate of even the tiny model is pretty good. I used Electron Forge to package the app for different operating systems and distribution channels. There are no guides on the internet on how to do that properly so that took quite a bit of time to figure out. At multiple stages I had to look through source code of Electron Forge's dependencies to figure out how I was supposed to use it to correctly sign the app for the mac app store. I made this app using the tools I like to use, which is not very typical. I use straight javascript, no transpiling, and I wrote every single library myself from the ORM https://github.com/thebinarysearchtree/flyweight to the front-end framework, which is a thin wrapper over web components. Oh yeah, Electron recently started supporting esm, but Electron Forge doesn't fully support it so I had to use esbuild to compile to cjs just for the packaging step. I use events to make everything change in real-time in the UI. Umm... oh yeah I did include yt-dlp features but had to remove that for the store versions. It is available on the mac store, and soon on the windows store. There is also a trial you can download. The windows store experience is not very polished compared to the mac store. I was amazed that there are humans reviewers looking through everything I upload. hah. Anyway, I have to go find a job now and hope my username doesn't check out. Bye. Thanks for reading my blog.

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, user · Missing: agents, macos, agent
90%90% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
54%54% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: video, way · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: soon · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
10%10% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: audio · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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