Artemis, a Calm Web Reader

Hacker News

Earlier this year, I made a web reader that only showed a list of post titles, author domains, and links. The reader only updated once per day, so I wouldn't feel compelled to keep checking for new posts. I have been using the tool, which I called Artemis, for several months. Every morning, I looked forward to my "morning paper" of blogs I love reading. There are no notifications, read vs. unread states, counts of posts, etc. Only the last seven days of posts are available. The colour scheme is changeable. Dark mode is supported. All popular feed formats are supported. There is no reading interface to read blog posts; rather, the links take you to the authors' websites. Many of my favourite bloggers put a lot of effort into the design of their blogs and like to change things up; I wanted an experience that embraced that. The reader is now available for anyone to use (with invite code "hn").

Visit product View on Hacker NewsDec 20, 2024

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Actual performance

312points

67comments

Made the leaderboard

Launch Intel predictions

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

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.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

58%58% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: new, using, code · Missing: mac, agents, macos

54%54% 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

48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% 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

16%16% 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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