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Nudges.fyi – simple, unmissable reminders via phone/text/email

Hacker News

Nudges.fyi – simple, unmissable reminders via phone/text/email

I built this app primarily for my wife, who has tried many mainstream todo-list apps (OmniFocus, Things, and Todoist come to mind) over the years with little success. She isn't particularly interested in setting up a productivity system and the administrivia that goes with it. Even having to remember to look at an app once a day was far from ideal for her. This app is an attempt at a solution for anyone that fits this description, with a focus on alerting over organization. Here's how it works: you create a nudge that's set to trigger at a given date and time, and the app phones you, texts you, or emails you (or all three) at the right moment. Nudges can trigger on a schedule, so something like "call me about monthly bills for the next month on the last day of every month" is quite easy to set up. It also works well (sample size 1, admittedly) as a supplement to a more robust GTD system. I use Things for almost everything, but my most important reminders are set up as nudges. I've worked on this on and off for the last month or so and I think it's ready for a Show HN. There's likely some rough edges in there so I wouldn't use it for anything _critical_ just yet (let me know if you see anything that looks buggy!). I cut a lot of scope in order to release an initial version quickly; here's a list of things I'm considering adding to the app in the near future: - Implement something analogous to Pagerduty: create nudges that repeatedly nag you (with something like an escalation policy) until you acknowledge them - More notification channels: get nudges on Telegram, WhatsApp, Slack, etc. - Families (or teams, possibly) share a namespace and can send nudges to each other - Nudges that collect a response: possibly for polls, a daily diary entry, or habit tracker - Incoming and outgoing webhooks - Snooze a nudge so it re-triggers in X minutes I work on distributed systems at my day job and haven't done frontend and CRUD things in a long while now, so building this out was a nice change of pace. If anyone's curious, the app is built with: Next.js (in static HTML mode) and Tailwind for the frontend, Go for the API server and background nudge loop, and SQLite (+Litestream) for persistence. In any case, I'm looking for feedback from the HN community here: is this something you would use? TL;DR: schedule reminders for yourself via phone call, text message, and/or email (PS: the free plan doesn't allow call/SMS nudges because I'm a bit wary of spam, but if you'd like to give this a shot and can't [or don't want to] subscribe to a paid plan at this point, send me an email at tim@nudges.fyi for a 1-month code)

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Indie HackersFits the IH revenue-focused audience · Strong signals: wife · Missing: supports, reddit linkedin, podcasting
84%84% 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: slack, apps, email · Missing: mac, agents, macos
82%82% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
65%65% 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: apps, month, monthly · Missing: mobile apps, ios, personal
58%58% 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
41%41% 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
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · 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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