Articyl – save anything, consume it anywhere (articles, podcasts, RSS)

Hacker News

I've been iterating on an app idea for about 6 months now. I kept emailing articles to my Kindle as messy PDFs, so I built a tool to batch them into clean EPUBs and deliver them on a schedule. Six months later, it's grown into a "consume it later" app for articles, podcasts, notes, and RSS feeds. Some of its key features... - AI narration, listen to any article - Speed reading mode - Scheduled Kindle/e-reader delivery (PDF & EPUB) - Cross-device position sync for articles, podcasts, and narrations - RSS feed & podcast subscriptions - Full-text search and tagging It's at a point where I'm using it daily and getting a lot of value from it, I'd now love to get some feedback from a wider audience.

Visit product View on Hacker NewsMar 25, 2026

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Actual performance

3points

4comments

Did not reach leaderboard

Launch Intel predictions

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

89%89% 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: email, using, notes · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

39%39% 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

36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: arr, subscription · Missing: mrr, revenue, profit

23%23% 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.

Correct prediction on native model

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