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Summary Cat, a YouTube Video Summary Generator

Hacker News

Summary Cat, a YouTube Video Summary Generator

Hello HN! Please check out Summary Cat ( https://www.summarycat.com ). It uses OpenAI's GPT-3.5 to summarize YouTube transcripts. Please note that it only works for - *English* videos. - videos that are not too long in length. I'd appreciate any feedbacks, criticisms, or feature requests!! You can also find my contact info in my profile. Thank you in advance. ------------Technical Details--------------- Tech Stack - Frontend: HTML/CSS - Backend: Python/Flask APIs: - For grabbing YouTube's transcripts: I used youtube-transcript-api (https://pypi.org/project/youtube-transcript-api/) - For summarizing the transcripts: I used OpenAI's GPT-3.5-turbo-16k: https://platform.openai.com/docs/guides/gpt. - I used GPT-3.5 because GPT-4 is quite a lot more expensive (roughly 10X). My Prompt (Super Simple!) - "please summarize the following text into a few paragraphs:" + the full transcript. Thoughts about GPT-4 vs GPT-3.5-Turbo-16k for Summary Cat - GPT-4 was 20% better for "summary quality" - GPT-4 feels 50% faster - However, GPT-4 is about 10X as expensive as GPT-3.5 - Winner: GPT-3.5-Turbo-16k

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Actual performance

41points
49comments
Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
73%73% 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: openai, apis, open · Missing: mac, agents, macos
55%55% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video, para · Missing: mobile apps, ios, personal
47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide · Missing: https docs, excited, just released
34%34% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
20%20% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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