Stenobird, podcast transcription service for your agent

Hacker News

Hello HN - As much as I love podcasts, there are some episodes where I want to know about what the show is about before listening. Some shows post transcripts, but this is not the norm. That's why I built Stenobird. Stenobird is a very fast way for your agent to get a hold of any podcast transcript as long as the bare audio file is accessible somewhere on the public internet. I'm using the Parakeet 0.6B V3 model on NVIDIA 4090s and A5000s serverless via Runpod. It's able to tackle about 1 hour of audio in 1 minute while remaining fairly accurate. Now, I can point my OpenClaw at Stenobird and ask it to tell me about what the latest episode of Linux Unplugged was about and get a nice summary. I'm happy to answer any questions and would appreciate any feedback from humans or agents. Try it out: https://stenobird.com

Visit product View on Hacker NewsApr 23, 2026

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor

93%93% 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: para · Missing: supports, reddit linkedin, podcasting

80%80% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: 000, io · Missing: https docs, excited, just released

44%44% 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: way, para · Missing: mobile apps, ios, personal

43%43% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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