HN Follow – email alerts from your friends on Hacker News

Hacker News

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. Instead of being a normal app where we store all your data, it is just a wrapper over a series of JavaScript snippets that we guide you through installing, and which you can then customize 100%. The scripts call out to Agolia’s wonderful HN Search API. You can read more about the app’s architecture and it’s motivation here[1]. HN Follow runs on Val Town [2], an online scripting tool my team and I are building. Part of why we built hnfollow.com is to show off the kind of things you can do with the primitives Val Town provides:emailing yourself (`console.email`), persistent state (`@me.foo = "bar"`), persistent cron jobs (`setInterval`), etc. Here’s a demo[3], the docs[4], and Discord[5] for Val Town. We hope hnfollow.com is useful to you, and you have fun customizing the code directly on Val Town! Let us know if you have questions or feedback [1] - https://www.notion.so/End-programmer-Programming-a749beb4a9b... [2] - https://val.town [3] - https://www.loom.com/share/878294970d8e48919c819f35d0cd0da4 [4] - https://www.notion.so/Val-Town-Docs-01c8eb9c534b4899802f3a9e... [5] - https://discord.gg/dHv45uN5RY

Visit product View on Hacker NewsNov 9, 2022

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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

71%71% 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, email, code · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

45%45% 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.

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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