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Du

Dub Any Podcast in Your Language Using Local Models

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Dub Any Podcast in Your Language Using Local Models

Worth mentioning that the pipeline uses all open-weight models, though for the demo I used Kimi K3 for the translation piece due to the Kimi founder being interviewed. Have a listen for yourself: https://www.youtube.com/watch?v=92BQg2oozBg The pipeline can be mostly run locally on an M-series Mac with at least 16 GB of RAM. The translation stage is the main quality constraint but local models are getting better and better. Could see a specialized 30B model be more than enough here. Did this on a whim so I could listen to the Kimi founder being interviewed. What stood out to me is how fast the software and even the models themselves seem to be getting commoditized. Far faster than I ever anticipated. Built using Kimi Code, thought the model needed some guidance, so not a 1-shot effort quite yet.

Visit productView on Hacker NewsJul 26, 2026
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1points
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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, model, models · Missing: agents, macos, agent
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 · Missing: supports, reddit linkedin, podcasting
75%75% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: pipe, io · Missing: https docs, excited, just released
66%66% 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 · Missing: mobile apps, ios, personal
38%38% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% 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
18%18% 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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