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Create ePub Out of URL

Hacker News

Create ePub Out of URL

With the purchase of reMarkable 2, I have this need to easily send web articles to my reMarkable 2 from my phone, while officially they only provided a Chrome extension, which can only be used on desktops. As a result I wrote some go code ( https://github.com/fishy/url2epub ) for the past 2 days, to generate ePub from URL. I also implemented reMarkable API to send them to reMarkable tablets directly. The current UI for it is implemented as a Telegram bot ( https://t.me/url2rM_bot?start=1 ), running on AppEngine (code: https://github.com/fishy/url2epub/tree/main/appengine ). I initially considered making an Android app for the UI, but decided that Telegram bot is less work for me, and works good enough for this use case (sorry for people who don't use Telegram, but this also means that people on iOS, desktop, etc. will be able to use it). For the future, I might do: - Expand the URLs supported (currently it only supports URLs with an AMP version provided, and the AMP version does have article tag inside) - (if there's enough demand for this) Remove the reMarkable restriction, so if you don't have a reMarkable account linked, it sends the ePub file back to you instead. - Provide REST API on AppEngine - Create an Android app

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Actual performance

3points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, ios · Missing: reddit linkedin, podcasting, created
77%77% 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: code · Missing: mac, agents, macos
58%58% 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: ide, io · Missing: https docs, excited, just released
48%48% 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: ios · Missing: mobile apps, personal, entrepreneurs
34%34% 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
27%27% 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
17%17% 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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