TL
TL;DWOL – Summarize videos or audio on your machine using AI
Hacker NewsTL;DWOL – Summarize videos or audio on your machine using AI
"TL;DWOL - Too Long; Didn't Watch or Listen" is a Python-based HTTP API that summarizes multimedia content. Currently, it supports YouTube, Apple Podcasts and direct audio/video file URLs. The API utilizes whisper.cpp for accurate audio-to-text transcription and llama.cpp for summarization. Besides solving a real-world problem, this project also serves as my playground for learning Python and exploring modern tools.
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Indie HackersFits the IH revenue-focused audience · Strong signals: supports · Missing: reddit linkedin, podcasting, created91%91% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: mac, apple, using · Missing: agents, macos, agent69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: llama, ide, io · Missing: https docs, excited, just released55%55% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue17%17% 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, crypto13%13% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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