GitHub repository through a Podcast

Hacker News

Understanding any GitHub repository is tough, especially a big one - this project aims to make it a tad bit easier by giving the listener a primer + some details on the specific repository. For some people (like me) - audio/visual medium is more easily understandable. So, I hope this project is helpful to some of them. This project was inspired by https://gitdiagram.com - so check it out as well, and maybe use them together for better understanding. Future plans: 1. To add more voices and let users choose most natural one (decide between OpenAI/Microsoft Speech) 2. Custom instructions to the SSML creator. Self hosting: 1. For speech - You can use Microsoft Speech (they do have free credits I think) 2. For SSML text - You can also use Gemini flash 2.0 exp - with 15 free credits (I am using OpenAI GPT 4o)

Visit product View on Hacker NewsDec 31, 2024

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, visual, openai · Missing: mac, agents, macos

87%87% 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.

AppSumoStrong fit for a featured deal · Strong signals: host, users · Missing: plus, platform, intuitive

62%62% predicted probability of success on AppSumo, 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

61%61% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: users · Missing: mobile apps, ios, personal

50%50% 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, revenue

17%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, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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