Linkcast – Convert Articles into 1-Minute Audio Summaries

Hacker News

I built Linkcast to solve a personal pain: I was drowning in articles each week and struggling to figure out which ones were worth my time. I wanted a fast way to get the gist of each article, without spending hours reading. Linkcast turns any article into a 1-minute audio summary, so you can listen while commuting, working out, or doing chores. Features: - Converts any article into a short, spoken summary (~1 minute) - No sign-up or credit card needed to get started - Costs ~1¢ per article (you get $1 in free credits to try it out) Tech stack: - Python + Django + HTMX - Groq's Llama3 for summarization - Google TTS for audio - UI help via Claude 3.7 Sonnet :) Would love your feedback, bug reports, and ideas for improvement!

Visit product View on Hacker NewsApr 18, 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

87%87% 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: claude, google · Missing: mac, agents, macos

63%63% 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: llama, ide, io · Missing: https docs, excited, just released

39%39% 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 · Missing: plus, platform, intuitive

33%33% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

24%24% 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

13%13% 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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