HN

HN Follow – Follow Your Friends on HN

Hacker News

HN Follow – Follow Your Friends on HN

HN Follow lets you follow authors on Hacker News, and get email notifications when they post. It was inspired by alerthn.com and hnreplies.com. The app was built in an experimental style on Val Town. We’re trying to create a new web primitive that you can: 1. write like a function 2. run like a script 3. fork like a repo 4. install like an app This is our 5th iteration of this same “HN Follow” app. We launched the 3rd version here on Hacker News six months ago[1], but it was very kindly removed from the front page by dang in favor of us launching Val Town itself first, which we did in January[2]. We’re trying to strike the right balance between something you can use and install with one click, and something you can infinitely customize. For example, you could fork `@rodrigoTello.hnFollowApp`[3] and change the input parameter from authors to a generic query, like I do here[4] to get notifications whenever “val town” is mentioned on HN. In addition to emailing myself (via `console.email`), I also send a message to our team’s Discord. The possibilities are endless, but it can also be overwhelming. We’re trying to find the balance where we help you navigate the space of possible integrations, without limiting you the way a no-code tool would. We would really appreciate your guys’ feedback and suggestions! [1] - HN Follow, first launch: https://news.ycombinator.com/item?id=33533830 [2] - Val Town launch: https://news.ycombinator.com/item?id=34343122 [3] - `@rodrigotello.hnFollowApp`: https://www.val.town/v/rodrigotello.hnFollowApp [4] - My fork of hnFollow: https://www.val.town/v/stevekrouse.hnValTown

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Actual performance

133points
81comments
Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: para · Missing: supports, reddit linkedin, podcasting
78%78% 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: hacker news, io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: month, way, para · Missing: mobile apps, ios, personal
55%55% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: new, email, code · Missing: mac, agents, macos
47%47% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
28%28% 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
18%18% 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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