Akas: an app to share AI generated podcast

Hacker News

INVITE ONLY CODE: BVN55L Let's be real—AI is taking over everything, even podcasting. Platforms like NotebookLM and others are spitting out incredible AI-generated podcasts every day. But there's one problem: no one's figured out how to share them easily. That's where Akas comes in. What is Akas? Akas is the place to upload, share, and discover AI-generated podcasts. Got a mind-blowing AI-generated episode? Upload it to Akas and share it with your friends or the world. Wanna share your own voice? Record your raw, unfiltered thoughts right in the app. We're the bridge between AI creativity and your personal voice. Why Akas? Because sharing AI-generated content shouldn't be a hassle. Whether it's a 10-minute deep dive from NotebookLM or your late-night ramblings, Akas makes it seamless. No perfect setup. No overthinking. Just upload, share, and vibe. What Makes Akas Different? AI First: We're built for the AI generation. Your podcasts, your way. Your Voice Matters: Mix your recordings with AI content or keep it personal. For the Feels: Akas is about raw vibes, not over-polished production. How It Works 1. Download your favorite AI-generated podcast from platforms like NotebookLM. 2.Upload it to Akas in seconds. 3.Share it with your friends, the world, or keep it to yourself. I am figuring out ways where you don't have to download, and works with NotebookLM links. Also, the app is invite only for now. And, here is my invitation code: BVN55L

Visit product View on Hacker NewsDec 23, 2024

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

95%95% 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: recordings, code · Missing: mac, agents, macos

93%93% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

41%41% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

41%41% 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

16%16% 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

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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