RSS Feed Generator

Hacker News

RSS Feed Generator

I built a typescript app that will take a directory of mp3 files, rip the metadata and cover art, and turn them into a podcast feed. I had been self-hosting a podcast feed for a couple friends and wanted to build something better than I had found previously, at least for my specific use case. I wouldn't be happy turning this in at work = it's not perfect, definitely not optimized, and docs need work. I'm sure it breaks a lot of rules for clean code, good abstractions, and proper nodejs usage, but it's my first time using ts/js for backend and I haven't always had the time for personal coding projects like I've wanted to, so I'm just glad it's working. Just swapped out the docker image on my homelab and was excited to start seeing per-episode cover art show up in my feed.

Visit product View on Hacker NewsOct 20, 2025

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

80%80% 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: dock, using, coding · Missing: mac, agents, macos

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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.

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

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

38%38% 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

15%15% 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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