Digest – Create a personal newsletter with the content you consume

Hacker News

As a personal need I have been working on building this over the last year. Like others, I often found myself in the middle of work, but taking breaks to read Reddit, HN, watch YouTube videos, etc. Those short breaks often turned into doom scrolling, and before I knew it I had wasted hours throughout my day/week. I felt that if I just had the content that I was already consuming bundled up and delivered to me in a daily newsletter, I'd no longer have the need to keep checking these sites throughout the day. This eventually evolved into adding sources that I was not wasting time on but were also important to me, like my daily meetings from Google Calendar, or yesterdays revenue from my Stripe account. I'm still working on adding more sources, but would love to hear what you think.

Visit product View on Hacker NewsJan 24, 2024

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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: google, stripe, new · Missing: mac, agents, macos

76%76% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, video, google · Missing: mobile apps, ios, entrepreneurs

60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

54%54% 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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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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